mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-18 13:45:44 +02:00
Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Eugene YurtsevandHunter Lovell
|
d1d4abf70e |
+2
-2
@@ -13,10 +13,10 @@ build-prebuilt:
|
||||
uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
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set +x; \
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fi
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uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md
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uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
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build-docs: build-prebuilt
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TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict
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uv run python -m mkdocs build --clean -f mkdocs.yml --strict
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llms-text:
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uv run python -m _scripts.generate_llms_text docs/llms-full.txt
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@@ -4,11 +4,9 @@ import argparse
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import requests
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from langchain_anthropic import ChatAnthropic
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from textwrap import dedent
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# Load reference TypeScript snippets
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URL = "https://gist.githubusercontent.com/dqbd/b35d49e2ceec80e654fe1c5ab61ec477/raw/f4768aeedb67628190a4e06d063a938afc8e7672/snippets.md"
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URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
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response = requests.get(URL)
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response.raise_for_status()
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reference_snippets = response.text
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@@ -16,80 +14,6 @@ reference_snippets = response.text
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# Initialize model
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model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
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FLUENT_INTERFACE_PROMPT = (
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"CRITICAL: Always use method chaining (fluent interface) for StateGraph operations in TypeScript. "
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"Never create separate variables for the graph builder or call methods individually. "
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"The fluent interface provides better type safety and is the preferred pattern.\n\n"
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"CORRECT examples with fluent interface:\n"
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+ dedent(
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"""
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```typescript
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const graph = new StateGraph(MyState)
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.addNode('node1', node1)
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.addNode('node2', node2)
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.addEdge(START, 'node1')
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.addEdge('node1', 'node2')
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.addEdge('node2', END)
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.compile()
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```
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```typescript
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const graph = new StateGraph(MyState)
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.addNode('chatbot', chatbot)
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.addEdge(START, 'chatbot')
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.addEdge('chatbot', END)
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.compile()
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```
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|
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```typescript
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const graph = new StateGraph(MyState)
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.addNode('chatbot', chatbot)
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.addEdge(START, 'chatbot')
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.addEdge('chatbot', END)
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.compile()
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```
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||||
"""
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)
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+ "\n"
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+ "INCORRECT examples to avoid:\n"
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+ dedent(
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"""
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```typescript
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// WRONG: Creating separate builder variable
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const graphBuilder = new StateGraph(MyState)
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graphBuilder.addNode('node1', node1)
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graphBuilder.addEdge(START, 'node1')
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const graph = graphBuilder.compile()
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```
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|
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```typescript
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// WRONG: Using Python-style method names
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const workflow = new StateGraph(MyState)
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workflow.add_node('node1', node1)
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workflow.add_edge(START, 'node1')
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const graph = workflow.compile()
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```
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```typescript
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// WRONG: Calling methods individually
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const graphBuilder = new StateGraph(MyState)
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graphBuilder.addNode('chatbot', chatbot)
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graphBuilder.addEdge(START, 'chatbot')
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graphBuilder.addEdge('chatbot', END)
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const graph = graphBuilder.compile()
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```
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"""
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)
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+ "\n"
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+ "Key rules:\n"
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+ "- Always chain methods directly on the StateGraph constructor\n"
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+ "- Use camelCase method names (addNode, addEdge, not add_node, add_edge)\n"
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+ "- Always end with .compile()\n"
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+ "- Never store the builder in a separate variable\n"
|
||||
)
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||||
|
||||
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TRANSLATION_PROMPT = (
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||||
"You are a helpful assistant that translates Python-based technical "
|
||||
"documentation written in Markdown to equivalent TypeScript-based documentation. "
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@@ -108,12 +32,6 @@ TRANSLATION_PROMPT = (
|
||||
"the translation. "
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||||
"Use the reference TypeScript snippets as guidance whenever possible to "
|
||||
"maintain alignment with existing conventions.\n\n"
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"IMPORTANT REQUIREMENTS:\n"
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"- Use Zod for state definition for StateGraph. Avoid using Annotation since it will be deprecated in the future.\n"
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||||
"- ALWAYS use fluent interface (method chaining) for StateGraph operations - this is CRITICAL\n"
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||||
"- Never create separate variables for graph builders\n"
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"- Always chain methods directly on the StateGraph constructor and end with .compile()\n\n"
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f"{FLUENT_INTERFACE_PROMPT}\n\n"
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f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
|
||||
)
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||||
|
||||
@@ -1,181 +0,0 @@
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||||
"""Logic to identify and transform cross-reference links in markdown files.
|
||||
|
||||
This module allows supporting custom markdown syntax for "autolinks". These are links
|
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that will be transformed based on the current scope context, such as "global", "python",
|
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or "js" into an appropriate markdown link format.
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||||
|
||||
For example,
|
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|
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```markdown
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@[StateGraph]
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```
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May be transformed into:
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||||
|
||||
```markdown
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[StateGraph](some_path/api-reference/state-graph.md)
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||||
```
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||||
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||||
The transformation value depends on the scope in which the link is used.
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||||
"""
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||||
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import logging
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import re
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from typing import Optional
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from _scripts.link_map import SCOPE_LINK_MAPS
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logger = logging.getLogger(__name__)
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||||
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def _transform_link(
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link_name: str, scope: str, file_path: str, line_number: int, custom_title: Optional[str] = None
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) -> Optional[str]:
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||||
"""Transform a cross-reference link based on the current scope.
|
||||
|
||||
Args:
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link_name: The name of the link to transform (e.g., "StateGraph").
|
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scope: The current scope context ("global", "python", "js", etc.).
|
||||
file_path: The file path for error reporting.
|
||||
line_number: The line number for error reporting.
|
||||
custom_title: Optional custom title for the link. If None, uses link_name.
|
||||
|
||||
Returns:
|
||||
A formatted markdown link if the link is found in the scope mapping,
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||||
None otherwise.
|
||||
|
||||
Example:
|
||||
>>> _transform_link("StateGraph", "python", "file.md", 5)
|
||||
"[StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
|
||||
|
||||
>>> _transform_link("StateGraph", "python", "file.md", 5, "Custom Title")
|
||||
"[Custom Title](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
|
||||
|
||||
>>> _transform_link("unknown-link", "python", "file.md", 5)
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||||
None
|
||||
"""
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||||
if scope == "global":
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# Special scope that is composed of both Python and JS links
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||||
# For now, we will substitute in the python scope!
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||||
# But we need to add support for handling both scopes.
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||||
scope = "python"
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||||
logger.error(
|
||||
"Encountered unhandled 'global' scope. Defaulting to 'python'."
|
||||
"In file: %s, line %d, link_name: %s",
|
||||
file_path,
|
||||
line_number,
|
||||
link_name,
|
||||
)
|
||||
link_map = SCOPE_LINK_MAPS.get(scope, {})
|
||||
url = link_map.get(link_name)
|
||||
|
||||
if url:
|
||||
title = custom_title if custom_title is not None else link_name
|
||||
return f"[{title}]({url})"
|
||||
else:
|
||||
# Log error with file location information
|
||||
logger.info(
|
||||
# Using %s
|
||||
"Link '%s' not found in scope '%s'. "
|
||||
"In file: %s, line %d. Available links in scope: %s",
|
||||
link_name,
|
||||
scope,
|
||||
file_path,
|
||||
line_number,
|
||||
list(link_map.keys() if link_map else []),
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
CONDITIONAL_FENCE_PATTERN = re.compile(
|
||||
r"""
|
||||
^ # Start of line
|
||||
(?P<indent>[ \t]*) # Optional indentation (spaces or tabs)
|
||||
::: # Literal fence marker
|
||||
(?P<language>\w+)? # Optional language identifier (named group: language)
|
||||
\s* # Optional trailing whitespace
|
||||
$ # End of line
|
||||
""",
|
||||
re.VERBOSE,
|
||||
)
|
||||
CROSS_REFERENCE_PATTERN = re.compile(
|
||||
r"""
|
||||
(?: # Non-capturing group for two possible formats:
|
||||
@\[ # @ symbol followed by opening bracket for title
|
||||
(?P<title>[^\]]+) # Custom title - one or more non-bracket characters
|
||||
\] # Closing bracket for title
|
||||
\[ # Opening bracket for link name
|
||||
(?P<link_name_with_title>[^\]]+) # Link name - one or more non-bracket characters
|
||||
\] # Closing bracket for link name
|
||||
| # OR
|
||||
@\[ # @ symbol followed by opening bracket
|
||||
(?P<link_name>[^\]]+) # Link name - one or more non-bracket characters
|
||||
\] # Closing bracket
|
||||
)
|
||||
""",
|
||||
re.VERBOSE,
|
||||
)
|
||||
|
||||
|
||||
def _replace_autolinks(markdown: str, file_path: str, *, default_scope: str = "python") -> str:
|
||||
"""Preprocess markdown lines to handle @[links] with conditional fence scopes.
|
||||
|
||||
This function processes markdown content to transform @[link_name] references
|
||||
based on the current conditional fence scope. Conditional fences use the
|
||||
syntax :::language to define scope boundaries.
|
||||
|
||||
Args:
|
||||
markdown: The markdown content to process.
|
||||
file_path: The file path for error reporting.
|
||||
default_scope: The default scope to use if no scope is matched.
|
||||
|
||||
Returns:
|
||||
Processed markdown content with @[references] transformed to proper
|
||||
markdown links or left unchanged if not found.
|
||||
|
||||
Example:
|
||||
Input:
|
||||
"@[StateGraph]\\n:::python\\n@[Command]\\n:::\\n"
|
||||
Output:
|
||||
"[StateGraph](url)\\n:::python\\n[Command](url)\\n:::\\n"
|
||||
"""
|
||||
# Track the current scope context
|
||||
current_scope = default_scope
|
||||
lines = markdown.splitlines(keepends=True)
|
||||
processed_lines = []
|
||||
|
||||
for line_number, line in enumerate(lines, 1):
|
||||
line_stripped = line.strip()
|
||||
|
||||
# Check if this line defines a new conditional fence scope
|
||||
fence_match = CONDITIONAL_FENCE_PATTERN.match(line_stripped)
|
||||
if fence_match:
|
||||
language = fence_match.group("language")
|
||||
# Set scope to the specified language, or reset to global if no language
|
||||
current_scope = language.lower() if language else default_scope
|
||||
processed_lines.append(line)
|
||||
continue
|
||||
|
||||
# Transform all @[link_name] references in this line based on current scope
|
||||
def replace_cross_reference(match: re.Match[str]) -> str:
|
||||
"""Replace a single @[link_name] with the scoped equivalent."""
|
||||
# Check if this is the @[title][ref] format or @[ref] format
|
||||
title = match.group("title")
|
||||
if title is not None:
|
||||
# This is @[title][ref] format
|
||||
link_name = match.group("link_name_with_title")
|
||||
custom_title = title
|
||||
else:
|
||||
# This is @[ref] format
|
||||
link_name = match.group("link_name")
|
||||
custom_title = None
|
||||
|
||||
transformed = _transform_link(
|
||||
link_name, current_scope, file_path, line_number, custom_title
|
||||
)
|
||||
return transformed if transformed is not None else match.group(0)
|
||||
|
||||
transformed_line = CROSS_REFERENCE_PATTERN.sub(replace_cross_reference, line)
|
||||
processed_lines.append(transformed_line)
|
||||
|
||||
return "".join(processed_lines)
|
||||
@@ -1,6 +0,0 @@
|
||||
.prettierrc
|
||||
.eslint.config.mjs
|
||||
package.json
|
||||
README.md
|
||||
tsconfig.json
|
||||
yarn.lock
|
||||
@@ -1,19 +0,0 @@
|
||||
{
|
||||
"$schema": "https://json.schemastore.org/prettierrc",
|
||||
"printWidth": 80,
|
||||
"tabWidth": 2,
|
||||
"useTabs": false,
|
||||
"semi": true,
|
||||
"singleQuote": false,
|
||||
"quoteProps": "as-needed",
|
||||
"jsxSingleQuote": false,
|
||||
"trailingComma": "es5",
|
||||
"bracketSpacing": true,
|
||||
"arrowParens": "always",
|
||||
"requirePragma": false,
|
||||
"insertPragma": false,
|
||||
"proseWrap": "preserve",
|
||||
"htmlWhitespaceSensitivity": "css",
|
||||
"vueIndentScriptAndStyle": false,
|
||||
"endOfLine": "lf"
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
# \_codeblocks
|
||||
@@ -1,14 +0,0 @@
|
||||
import js from "@eslint/js";
|
||||
import globals from "globals";
|
||||
import tseslint from "typescript-eslint";
|
||||
import { defineConfig } from "eslint/config";
|
||||
|
||||
export default defineConfig([
|
||||
{
|
||||
files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
|
||||
plugins: { js },
|
||||
extends: ["js/recommended"],
|
||||
languageOptions: { globals: globals.browser },
|
||||
},
|
||||
tseslint.configs.recommended,
|
||||
]);
|
||||
@@ -1,27 +0,0 @@
|
||||
{
|
||||
"name": "_codeblocks",
|
||||
"packageManager": "yarn@4.6.0",
|
||||
"scripts": {
|
||||
"lint": "eslint .",
|
||||
"lint:fix": "eslint . --fix",
|
||||
"format": "prettier --write .",
|
||||
"format:fix": "prettier --write . --fix"
|
||||
},
|
||||
"dependencies": {
|
||||
"@langchain/anthropic": "^0.3.24",
|
||||
"@langchain/core": "^0.3.66",
|
||||
"@langchain/langgraph": "^0.3.11",
|
||||
"@langchain/langgraph-api": "^0.0.52",
|
||||
"@langchain/langgraph-sdk": "^0.0.102",
|
||||
"@langchain/openai": "^0.6.3",
|
||||
"zod": "^4.0.10"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@eslint/js": "^9.32.0",
|
||||
"eslint": "^9.32.0",
|
||||
"globals": "^16.3.0",
|
||||
"jiti": "^2.5.1",
|
||||
"typescript": "^5.8.3",
|
||||
"typescript-eslint": "^8.38.0"
|
||||
}
|
||||
}
|
||||
@@ -1,114 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
/* Visit https://aka.ms/tsconfig to read more about this file */
|
||||
|
||||
/* Projects */
|
||||
// "incremental": true, /* Save .tsbuildinfo files to allow for incremental compilation of projects. */
|
||||
// "composite": true, /* Enable constraints that allow a TypeScript project to be used with project references. */
|
||||
// "tsBuildInfoFile": "./.tsbuildinfo", /* Specify the path to .tsbuildinfo incremental compilation file. */
|
||||
// "disableSourceOfProjectReferenceRedirect": true, /* Disable preferring source files instead of declaration files when referencing composite projects. */
|
||||
// "disableSolutionSearching": true, /* Opt a project out of multi-project reference checking when editing. */
|
||||
// "disableReferencedProjectLoad": true, /* Reduce the number of projects loaded automatically by TypeScript. */
|
||||
|
||||
/* Language and Environment */
|
||||
"target": "esnext", /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */
|
||||
// "lib": [], /* Specify a set of bundled library declaration files that describe the target runtime environment. */
|
||||
// "jsx": "preserve", /* Specify what JSX code is generated. */
|
||||
// "libReplacement": true, /* Enable lib replacement. */
|
||||
// "experimentalDecorators": true, /* Enable experimental support for legacy experimental decorators. */
|
||||
// "emitDecoratorMetadata": true, /* Emit design-type metadata for decorated declarations in source files. */
|
||||
// "jsxFactory": "", /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */
|
||||
// "jsxFragmentFactory": "", /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */
|
||||
// "jsxImportSource": "", /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */
|
||||
// "reactNamespace": "", /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */
|
||||
// "noLib": true, /* Disable including any library files, including the default lib.d.ts. */
|
||||
// "useDefineForClassFields": true, /* Emit ECMAScript-standard-compliant class fields. */
|
||||
// "moduleDetection": "auto", /* Control what method is used to detect module-format JS files. */
|
||||
|
||||
/* Modules */
|
||||
"module": "nodenext", /* Specify what module code is generated. */
|
||||
// "rootDir": "./", /* Specify the root folder within your source files. */
|
||||
"moduleResolution": "nodenext", /* Specify how TypeScript looks up a file from a given module specifier. */
|
||||
// "baseUrl": "./", /* Specify the base directory to resolve non-relative module names. */
|
||||
// "paths": {}, /* Specify a set of entries that re-map imports to additional lookup locations. */
|
||||
// "rootDirs": [], /* Allow multiple folders to be treated as one when resolving modules. */
|
||||
// "typeRoots": [], /* Specify multiple folders that act like './node_modules/@types'. */
|
||||
// "types": [], /* Specify type package names to be included without being referenced in a source file. */
|
||||
// "allowUmdGlobalAccess": true, /* Allow accessing UMD globals from modules. */
|
||||
// "moduleSuffixes": [], /* List of file name suffixes to search when resolving a module. */
|
||||
// "allowImportingTsExtensions": true, /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */
|
||||
// "rewriteRelativeImportExtensions": true, /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */
|
||||
// "resolvePackageJsonExports": true, /* Use the package.json 'exports' field when resolving package imports. */
|
||||
// "resolvePackageJsonImports": true, /* Use the package.json 'imports' field when resolving imports. */
|
||||
// "customConditions": [], /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */
|
||||
// "noUncheckedSideEffectImports": true, /* Check side effect imports. */
|
||||
// "resolveJsonModule": true, /* Enable importing .json files. */
|
||||
// "allowArbitraryExtensions": true, /* Enable importing files with any extension, provided a declaration file is present. */
|
||||
// "noResolve": true, /* Disallow 'import's, 'require's or '<reference>'s from expanding the number of files TypeScript should add to a project. */
|
||||
|
||||
/* JavaScript Support */
|
||||
// "allowJs": true, /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */
|
||||
// "checkJs": true, /* Enable error reporting in type-checked JavaScript files. */
|
||||
// "maxNodeModuleJsDepth": 1, /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */
|
||||
|
||||
/* Emit */
|
||||
// "declaration": true, /* Generate .d.ts files from TypeScript and JavaScript files in your project. */
|
||||
// "declarationMap": true, /* Create sourcemaps for d.ts files. */
|
||||
// "emitDeclarationOnly": true, /* Only output d.ts files and not JavaScript files. */
|
||||
// "sourceMap": true, /* Create source map files for emitted JavaScript files. */
|
||||
// "inlineSourceMap": true, /* Include sourcemap files inside the emitted JavaScript. */
|
||||
// "noEmit": true, /* Disable emitting files from a compilation. */
|
||||
// "outFile": "./", /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */
|
||||
// "outDir": "./", /* Specify an output folder for all emitted files. */
|
||||
// "removeComments": true, /* Disable emitting comments. */
|
||||
// "importHelpers": true, /* Allow importing helper functions from tslib once per project, instead of including them per-file. */
|
||||
// "downlevelIteration": true, /* Emit more compliant, but verbose and less performant JavaScript for iteration. */
|
||||
// "sourceRoot": "", /* Specify the root path for debuggers to find the reference source code. */
|
||||
// "mapRoot": "", /* Specify the location where debugger should locate map files instead of generated locations. */
|
||||
// "inlineSources": true, /* Include source code in the sourcemaps inside the emitted JavaScript. */
|
||||
// "emitBOM": true, /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */
|
||||
// "newLine": "crlf", /* Set the newline character for emitting files. */
|
||||
// "stripInternal": true, /* Disable emitting declarations that have '@internal' in their JSDoc comments. */
|
||||
// "noEmitHelpers": true, /* Disable generating custom helper functions like '__extends' in compiled output. */
|
||||
// "noEmitOnError": true, /* Disable emitting files if any type checking errors are reported. */
|
||||
// "preserveConstEnums": true, /* Disable erasing 'const enum' declarations in generated code. */
|
||||
// "declarationDir": "./", /* Specify the output directory for generated declaration files. */
|
||||
|
||||
/* Interop Constraints */
|
||||
// "isolatedModules": true, /* Ensure that each file can be safely transpiled without relying on other imports. */
|
||||
// "verbatimModuleSyntax": true, /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */
|
||||
// "isolatedDeclarations": true, /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */
|
||||
// "erasableSyntaxOnly": true, /* Do not allow runtime constructs that are not part of ECMAScript. */
|
||||
// "allowSyntheticDefaultImports": true, /* Allow 'import x from y' when a module doesn't have a default export. */
|
||||
"esModuleInterop": true, /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */
|
||||
// "preserveSymlinks": true, /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */
|
||||
"forceConsistentCasingInFileNames": true, /* Ensure that casing is correct in imports. */
|
||||
|
||||
/* Type Checking */
|
||||
"strict": false, /* Enable all strict type-checking options. */
|
||||
// "noImplicitAny": true, /* Enable error reporting for expressions and declarations with an implied 'any' type. */
|
||||
// "strictNullChecks": true, /* When type checking, take into account 'null' and 'undefined'. */
|
||||
// "strictFunctionTypes": true, /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */
|
||||
// "strictBindCallApply": true, /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */
|
||||
// "strictPropertyInitialization": true, /* Check for class properties that are declared but not set in the constructor. */
|
||||
// "strictBuiltinIteratorReturn": true, /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */
|
||||
// "noImplicitThis": true, /* Enable error reporting when 'this' is given the type 'any'. */
|
||||
// "useUnknownInCatchVariables": true, /* Default catch clause variables as 'unknown' instead of 'any'. */
|
||||
// "alwaysStrict": true, /* Ensure 'use strict' is always emitted. */
|
||||
// "noUnusedLocals": true, /* Enable error reporting when local variables aren't read. */
|
||||
// "noUnusedParameters": true, /* Raise an error when a function parameter isn't read. */
|
||||
// "exactOptionalPropertyTypes": true, /* Interpret optional property types as written, rather than adding 'undefined'. */
|
||||
// "noImplicitReturns": true, /* Enable error reporting for codepaths that do not explicitly return in a function. */
|
||||
// "noFallthroughCasesInSwitch": true, /* Enable error reporting for fallthrough cases in switch statements. */
|
||||
// "noUncheckedIndexedAccess": true, /* Add 'undefined' to a type when accessed using an index. */
|
||||
// "noImplicitOverride": true, /* Ensure overriding members in derived classes are marked with an override modifier. */
|
||||
// "noPropertyAccessFromIndexSignature": true, /* Enforces using indexed accessors for keys declared using an indexed type. */
|
||||
// "allowUnusedLabels": true, /* Disable error reporting for unused labels. */
|
||||
// "allowUnreachableCode": true, /* Disable error reporting for unreachable code. */
|
||||
|
||||
/* Completeness */
|
||||
// "skipDefaultLibCheck": true, /* Skip type checking .d.ts files that are included with TypeScript. */
|
||||
"skipLibCheck": true /* Skip type checking all .d.ts files. */
|
||||
""
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,150 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
"""Extracts typescript code blocks from a markdown file."""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import re
|
||||
import os
|
||||
from typing import List, TypedDict, Literal
|
||||
|
||||
|
||||
class CodeBlock(TypedDict):
|
||||
"""A code block extracted from a markdown file."""
|
||||
starting_line: int
|
||||
"""The line number where the code block starts in the source file"""
|
||||
ending_line: int
|
||||
"""The line number where the code block ends in the source file"""
|
||||
indentation: int
|
||||
"""Number of spaces/tabs used for indentation of the code block"""
|
||||
source_file: str
|
||||
"""Path to the markdown file containing this code block"""
|
||||
frontmatter: str
|
||||
"""Any metadata or frontmatter specified after the opening code fence"""
|
||||
code: str
|
||||
"""The actual code content within the code block"""
|
||||
language: str
|
||||
"""The language of the code block (e.g. typescript, javascript)"""
|
||||
|
||||
|
||||
def extract_code_blocks(markdown_content: str, source_file: str) -> List[CodeBlock]:
|
||||
"""Extracts code blocks from a markdown file.
|
||||
|
||||
Args:
|
||||
markdown_content: The content of the markdown file.
|
||||
source_file: The path to the markdown file.
|
||||
|
||||
Returns:
|
||||
A list of TypedDicts, where each dict represents a code block.
|
||||
"""
|
||||
# Regex to find code blocks with specified languages, capturing indentation
|
||||
# and frontmatter.
|
||||
pattern = re.compile(
|
||||
r"^(?P<indentation>\s*)```(?P<language>typescript|javascript|ts|js)(?P<frontmatter>[^\n]*)\n(?P<code>.*?)\n^(?P=indentation)```\s*$",
|
||||
re.DOTALL | re.MULTILINE,
|
||||
)
|
||||
|
||||
code_blocks: List[CodeBlock] = []
|
||||
for match in pattern.finditer(markdown_content):
|
||||
start_pos = match.start()
|
||||
|
||||
# Calculate line numbers
|
||||
starting_line = markdown_content.count("\n", 0, start_pos) + 1
|
||||
ending_line = starting_line + match.group(0).count("\n")
|
||||
|
||||
indentation_str = match.group("indentation")
|
||||
|
||||
code_block: CodeBlock = {
|
||||
"starting_line": starting_line,
|
||||
"ending_line": ending_line,
|
||||
"indentation": len(indentation_str),
|
||||
"source_file": source_file,
|
||||
"frontmatter": match.group("frontmatter").strip(),
|
||||
"code": match.group("code"),
|
||||
"language": match.group("language"),
|
||||
}
|
||||
code_blocks.append(code_block)
|
||||
|
||||
return code_blocks
|
||||
|
||||
def dump_code_blocks(input_file: str, output_file: str, format: Literal["json", "inline"]) -> None:
|
||||
"""Function to extract and save code blocks from a markdown file.
|
||||
|
||||
Args:
|
||||
input_file: Path to the input markdown file.
|
||||
output_file: Path to the output JSON file for the extracted code blocks.
|
||||
format: Output format - either "json" or "inline"
|
||||
"""
|
||||
with open(input_file, "r", encoding="utf-8") as f:
|
||||
markdown_content = f.read()
|
||||
|
||||
extracted_code = extract_code_blocks(markdown_content, input_file)
|
||||
|
||||
if len(extracted_code) == 0:
|
||||
print(f"No code blocks found in {input_file}")
|
||||
return
|
||||
|
||||
if format == "json":
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
json.dump(extracted_code, f, indent=2)
|
||||
elif format == "inline":
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
for code_block in extracted_code:
|
||||
f.write(f"// {json.dumps({k:v for k,v in code_block.items() if k != 'code'})}\n")
|
||||
f.write("\n")
|
||||
f.write(code_block["code"])
|
||||
f.write("\n")
|
||||
print(f"Extracted {len(extracted_code)} code blocks from {input_file} to {output_file}")
|
||||
|
||||
def main(input_path: str, output_path: str, format: Literal["json", "inline"]) -> None:
|
||||
"""Main function to extract code blocks from a markdown file.
|
||||
|
||||
Args:
|
||||
input_file: Path to the input markdown file.
|
||||
output_file: Path to the output JSON file for the extracted code blocks.
|
||||
format: Output format - either "json" or "inline"
|
||||
"""
|
||||
# Check if input path is a directory
|
||||
if os.path.isdir(input_path):
|
||||
if os.path.isfile(output_path):
|
||||
raise ValueError("If input_path is a directory, output_path must also be a directory")
|
||||
if not os.path.isdir(output_path):
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
|
||||
# Process each markdown file in the directory recursively
|
||||
for root, _, files in os.walk(input_path):
|
||||
for filename in files:
|
||||
if filename.endswith(".md"):
|
||||
# Get relative path to maintain directory structure
|
||||
rel_path = os.path.relpath(root, input_path)
|
||||
input_file = os.path.join(root, filename)
|
||||
# Create output directory if it doesn't exist
|
||||
output_dir = os.path.join(output_path, rel_path)
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
output_file = os.path.join(output_dir, filename.replace(".md", ".ts"))
|
||||
dump_code_blocks(input_file, output_file, format)
|
||||
else:
|
||||
# Process single file
|
||||
dump_code_blocks(input_path, output_path, format)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Extract typescript code blocks from a markdown file."
|
||||
)
|
||||
parser.add_argument(
|
||||
"input_file",
|
||||
help="Path to the input markdown file.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"output_file",
|
||||
help="Path to the output JSON file for the extracted code blocks.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--format",
|
||||
choices=["json", "inline"],
|
||||
default="json",
|
||||
help="Output format - either 'json' or 'inline'",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args.input_file, args.output_file, args.format)
|
||||
+3
-141
@@ -1,143 +1,5 @@
|
||||
"""Link mapping for cross-reference resolution across different scopes.
|
||||
|
||||
This module provides link mappings for different language/framework scopes
|
||||
to resolve @[link_name] references to actual URLs.
|
||||
"""
|
||||
|
||||
# Python-specific link mappings
|
||||
# Python-specific link mappings
|
||||
PYTHON_LINK_MAP = {
|
||||
"StateGraph": "reference/graphs/#langgraph.graph.StateGraph",
|
||||
"add_conditional_edges": "reference/graphs/#langgraph.graph.StateGraph.add_conditional_edges",
|
||||
"add_edge": "reference/graphs/#langgraph.graph.StateGraph.add_edge",
|
||||
"add_node": "reference/graphs/#langgraph.graph.StateGraph.add_node",
|
||||
"add_messages": "reference/messages/#langgraph.graph.message.add_messages",
|
||||
"ToolNode": "reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode",
|
||||
"CompiledStateGraph.astream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.astream",
|
||||
"Pregel.astream": "reference/graphs/#langgraph.pregel.Pregel.astream",
|
||||
"AsyncPostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.aio.AsyncPostgresSaver",
|
||||
"AsyncSqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver",
|
||||
"BaseCheckpointSaver": "reference/checkpoints/#langgraph.checkpoint.base.BaseCheckpointSaver",
|
||||
"BaseStore": "reference/stores/#langgraph.store.base.BaseStore",
|
||||
"BaseStore.put": "reference/stores/#langgraph.store.base.BaseStore.put",
|
||||
"BinaryOperatorAggregate": "reference/channels/#langgraph.channels.BinaryOperatorAggregate",
|
||||
"CipherProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.CipherProtocol",
|
||||
"client.runs.stream": "reference/client/#langgraph_sdk.client.RunsClient.stream",
|
||||
"client.runs.wait": "reference/client/#langgraph_sdk.client.RunsClient.wait",
|
||||
"client.threads.get_history": "reference/client/#langgraph_sdk.client.ThreadsClient.get_history",
|
||||
"client.threads.update_state": "reference/client/#langgraph_sdk.client.ThreadsClient.update_state",
|
||||
"Command": "reference/types/#langgraph.types.Command",
|
||||
"CompiledStateGraph": "reference/graphs/#langgraph.graph.state.CompiledStateGraph",
|
||||
"create_react_agent": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
|
||||
"create_supervisor": "reference/supervisor/#langgraph_supervisor.supervisor.create_supervisor",
|
||||
"EncryptedSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer",
|
||||
"entrypoint.final": "reference/functions/#langgraph.func.entrypoint.final",
|
||||
"entrypoint": "reference/functions/#langgraph.func.entrypoint",
|
||||
"from_pycryptodome_aes": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes",
|
||||
# "getContextVariable": "<insert-ref>",
|
||||
"get_state_history": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.get_state_history",
|
||||
"get_stream_writer": "reference/config/#langgraph.config.get_stream_writer",
|
||||
"HumanInterrupt": "reference/prebuilt/#langgraph.prebuilt.interrupt.HumanInterrupt",
|
||||
"InjectedState": "reference/prebuilt/#langgraph.prebuilt.InjectedState",
|
||||
"InMemorySaver": "reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver",
|
||||
"interrupt": "reference/graphs/#langgraph.graph.interrupt",
|
||||
"CompiledStateGraph.invoke": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.invoke",
|
||||
"JsonPlusSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer",
|
||||
"langgraph.json": "reference/configuration/#configuration-file",
|
||||
"LastValue": "reference/channels/#langgraph.channels.LastValue",
|
||||
# "MemorySaver": "<insert-ref>",
|
||||
# "messagesStateReducer": "<insert-ref>",
|
||||
"PostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.PostgresSaver",
|
||||
"Pregel": "reference/graphs/#langgraph.pregel.Pregel",
|
||||
"Pregel.stream": "reference/graphs/#langgraph.pregel.Pregel.stream",
|
||||
"pre_model_hook": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
|
||||
"protocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
|
||||
"Send": "reference/types/#langgraph.types.Send",
|
||||
"SerializerProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
|
||||
"SqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.SqliteSaver",
|
||||
"START": "reference/constants/#langgraph.constants.START",
|
||||
"CompiledStateGraph.stream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.stream",
|
||||
"task": "reference/functions/#langgraph.func.task",
|
||||
"Topic": "reference/channels/#langgraph.channels.Topic",
|
||||
"update_state": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.update_state",
|
||||
}
|
||||
|
||||
|
||||
# JavaScript-specific link mappings
|
||||
JS_LINK_MAP = {
|
||||
"Auth": "reference/classes/sdk_auth.Auth.html",
|
||||
"StateGraph": "reference/classes/langgraph.StateGraph.html",
|
||||
"add_conditional_edges": "reference/functions/langgraph_StateGraph.addConditionalEdges.html",
|
||||
"add_edge": "reference/functions/langgraph_StateGraph.addEdge.html",
|
||||
"add_node": "reference/functions/langgraph_StateGraph.addNode.html",
|
||||
"add_messages": "reference/functions/langgraph_message.addMessages.html",
|
||||
"ToolNode": "reference/classes/langgraph_prebuilt.ToolNode.html",
|
||||
"CompiledStateGraph.astream()": "reference/functions/langgraph_CompiledStateGraph.astream.html",
|
||||
"Pregel.astream": "reference/functions/langgraph_Pregel.astream.html",
|
||||
"AsyncPostgresSaver": "reference/classes/langgraph_checkpoint_postgres_aio.AsyncPostgresSaver.html",
|
||||
"AsyncSqliteSaver": "reference/classes/langgraph_checkpoint_sqlite_aio.AsyncSqliteSaver.html",
|
||||
"BaseCheckpointSaver": "reference/classes/langgraph_checkpoint_base.BaseCheckpointSaver.html",
|
||||
"BaseStore": "reference/classes/langgraph_store_base.BaseStore.html",
|
||||
"BaseStore.put": "reference/functions/langgraph_store_base.BaseStore.put.html",
|
||||
"BinaryOperatorAggregate": "reference/classes/langgraph_channels.BinaryOperatorAggregate.html",
|
||||
"CipherProtocol": "reference/classes/langgraph_checkpoint_serde_base.CipherProtocol.html",
|
||||
"client.runs.stream": "reference/functions/langgraph_sdk_client.RunsClient.stream.html",
|
||||
"client.runs.wait": "reference/functions/langgraph_sdk_client.RunsClient.wait.html",
|
||||
"client.threads.get_history": "reference/functions/langgraph_sdk_client.ThreadsClient.getHistory.html",
|
||||
"client.threads.update_state": "reference/functions/langgraph_sdk_client.ThreadsClient.updateState.html",
|
||||
"Command": "reference/classes/langgraph.Command.html",
|
||||
"CompiledStateGraph": "reference/classes/langgraph.CompiledStateGraph.html",
|
||||
"create_react_agent": "reference/functions/langgraph_prebuilt.createReactAgent.html",
|
||||
"create_supervisor": "reference/functions/langgraph_supervisor.createSupervisor.html",
|
||||
"EncryptedSerializer": "reference/classes/langgraph_checkpoint_serde_encrypted.EncryptedSerializer.html",
|
||||
"entrypoint.final": "reference/functions/langgraph_func.entrypoint.final.html",
|
||||
"entrypoint": "reference/functions/langgraph_func.entrypoint.html",
|
||||
"from_pycryptodome_aes": "reference/functions/langgraph_checkpoint_serde_encrypted.EncryptedSerializer.fromPycryptodomeAes.html",
|
||||
"getContextVariable": "https://v03.api.js.langchain.com/functions/_langchain_core.context.getContextVariable.html",
|
||||
"get_state_history": "reference/functions/langgraph_CompiledStateGraph.getStateHistory.html",
|
||||
"get_stream_writer": "reference/functions/langgraph_config.getStreamWriter.html",
|
||||
"HumanInterrupt": "reference/classes/langgraph_prebuilt.HumanInterrupt.html",
|
||||
"InjectedState": "reference/classes/langgraph_prebuilt.InjectedState.html",
|
||||
"InMemorySaver": "reference/classes/langgraph_checkpoint_memory.InMemorySaver.html",
|
||||
"interrupt": "reference/functions/langgraph.interrupt-2.html",
|
||||
"CompiledStateGraph.invoke": "reference/functions/langgraph_CompiledStateGraph.invoke.html",
|
||||
"JsonPlusSerializer": "reference/classes/langgraph_checkpoint_serde_jsonplus.JsonPlusSerializer.html",
|
||||
"langgraph.json": "reference/configuration.html",
|
||||
"LastValue": "reference/classes/langgraph_channels.LastValue.html",
|
||||
"MemorySaver": "reference/classes/checkpoint.MemorySaver.html",
|
||||
"messagesStateReducer": "reference/functions/langgraph.messagesStateReducer.html",
|
||||
"PostgresSaver": "reference/classes/langgraph_checkpoint_postgres.PostgresSaver.html",
|
||||
"Pregel": "reference/classes/langgraph.Pregel.html",
|
||||
"Pregel.stream": "reference/functions/langgraph_Pregel.stream.html",
|
||||
"pre_model_hook": "reference/functions/langgraph_prebuilt.createReactAgent.html",
|
||||
"protocol": "reference/classes/langgraph_checkpoint_serde_base.SerializerProtocol.html",
|
||||
"Send": "reference/classes/langgraph.Send.html",
|
||||
"SerializerProtocol": "reference/classes/langgraph_checkpoint_serde_base.SerializerProtocol.html",
|
||||
"SqliteSaver": "reference/classes/langgraph_checkpoint_sqlite.SqliteSaver.html",
|
||||
"START": "reference/constants.html#START",
|
||||
"CompiledStateGraph.stream": "reference/functions/langgraph_CompiledStateGraph.stream.html",
|
||||
"task": "reference/functions/langgraph_func.task.html",
|
||||
"Topic": "reference/classes/langgraph_channels.Topic.html",
|
||||
"update_state": "reference/functions/langgraph_CompiledStateGraph.updateState.html",
|
||||
}
|
||||
|
||||
# TODO: Allow updating these to localhost for local development
|
||||
PY_REFERENCE_HOST = "https://langchain-ai.github.io/langgraph/"
|
||||
JS_REFERENCE_HOST = "https://langchain-ai.github.io/langgraphjs/"
|
||||
|
||||
for key, value in PYTHON_LINK_MAP.items():
|
||||
# Ensure the link is absolute
|
||||
if not value.startswith("http"):
|
||||
PYTHON_LINK_MAP[key] = f"{PY_REFERENCE_HOST}{value}"
|
||||
|
||||
for key, value in JS_LINK_MAP.items():
|
||||
# Ensure the link is absolute
|
||||
if not value.startswith("http"):
|
||||
JS_LINK_MAP[key] = f"{JS_REFERENCE_HOST}{value}"
|
||||
|
||||
# Global scope is assembled from the Python and JS mappings
|
||||
# Combined mapping by scope
|
||||
SCOPE_LINK_MAPS = {
|
||||
"python": PYTHON_LINK_MAP,
|
||||
"js": JS_LINK_MAP,
|
||||
"langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
|
||||
"create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
|
||||
"langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
|
||||
}
|
||||
|
||||
@@ -16,7 +16,7 @@ from mkdocs.structure.files import Files, File
|
||||
from mkdocs.structure.pages import Page
|
||||
|
||||
from _scripts.generate_api_reference_links import update_markdown_with_imports
|
||||
from _scripts.handle_auto_links import _replace_autolinks
|
||||
from _scripts.link_map import JS_LINK_MAP
|
||||
from _scripts.notebook_convert import convert_notebook
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -127,30 +127,6 @@ REDIRECT_MAP = {
|
||||
"how-tos/human_in_the_loop/breakpoints.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
|
||||
"cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
|
||||
"how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
|
||||
# LGP migration-related redirects - once LG is also migrated, we can add a redirect for the whole site
|
||||
"concepts/langgraph_platform.md": "https://docs.langchain.com/langgraph-platform",
|
||||
"concepts/langgraph_components.md": "https://docs.langchain.com/langgraph-platform/components",
|
||||
"concepts/langgraph_server.md": "https://docs.langchain.com/langgraph-platform/langgraph-server",
|
||||
"concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
|
||||
"cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
|
||||
"concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
|
||||
"cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
|
||||
"concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/sdk",
|
||||
"concepts/auth.md": "https://docs.langchain.com/langgraph-platform/auth",
|
||||
"concepts/assistants.md": "https://docs.langchain.com/langgraph-platform/assistants",
|
||||
"concepts/deployment_options.md": "https://docs.langchain.com/langgraph-platform/deployment-options",
|
||||
"cloud/quick_start.md": "https://docs.langchain.com/langgraph-platform/deployment-quickstart",
|
||||
"cloud/deployment/setup.md": "https://docs.langchain.com/langgraph-platform/setup-app-requirements-txt",
|
||||
"concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-data-plane",
|
||||
"concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted-control-plane",
|
||||
"concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/standalone-container",
|
||||
"cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-data-plane",
|
||||
"cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-control-plane",
|
||||
"cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-standalone-container",
|
||||
"concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/server-mcp",
|
||||
"cloud/reference/cli.md": "https://docs.langchain.com/langgraph-platform/cli",
|
||||
"cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langgraph-platform/use-stream-react",
|
||||
"cloud/how-tos/generative-ui-react.md": "https://docs.langchain.com/langgraph-platform/generative-ui-react",
|
||||
}
|
||||
|
||||
|
||||
@@ -200,7 +176,31 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
|
||||
return code_block_pattern.sub(replace_code_block_header, markdown)
|
||||
|
||||
|
||||
# Compiled regex patterns for better performance and readability
|
||||
def _resolve_cross_references(md_text: str, link_map: dict[str, str]) -> str:
|
||||
"""Replace [title][identifier] with [title](url) using language-specific link_map.
|
||||
|
||||
Args:
|
||||
md_text: The markdown text to process.
|
||||
link_map: mapping of identifier to URL.
|
||||
|
||||
Returns:
|
||||
The processed markdown text with cross-references resolved.
|
||||
"""
|
||||
# Pattern to match [title][identifier]
|
||||
pattern = re.compile(r"\[([^\]]+)\]\[([^\]]+)\]")
|
||||
|
||||
def replace_reference(match: re.Match) -> str:
|
||||
"""Replace the matched reference with the corresponding URL."""
|
||||
title, identifier = match.group(1), match.group(2)
|
||||
url = link_map.get(identifier)
|
||||
|
||||
if url:
|
||||
return f"[{title}]({url})"
|
||||
else:
|
||||
# Leave it unchanged if not found
|
||||
return match.group(0)
|
||||
|
||||
return pattern.sub(replace_reference, md_text)
|
||||
|
||||
|
||||
def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
|
||||
@@ -210,7 +210,7 @@ def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
|
||||
pattern = re.compile(
|
||||
r"(?P<indent>[ \t]*):::(?P<language>\w+)\s*\n"
|
||||
r"(?P<content>((?:.*\n)*?))" # Capture the content inside the block
|
||||
r"(?P=indent)[ \t]*:::" # Match closing with the same indentation + any additional whitespace
|
||||
r"(?P=indent):::" # Match closing with the same indentation
|
||||
)
|
||||
|
||||
def replace_conditional_blocks(match: re.Match) -> str:
|
||||
@@ -295,7 +295,7 @@ def _highlight_code_blocks(markdown: str) -> str:
|
||||
opening_fence += f" {attributes}"
|
||||
|
||||
if highlighted_lines:
|
||||
opening_fence += f' hl_lines="{" ".join(highlighted_lines)}"'
|
||||
opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
|
||||
|
||||
return (
|
||||
# The indent and opening fence
|
||||
@@ -310,19 +310,10 @@ def _highlight_code_blocks(markdown: str) -> str:
|
||||
return markdown
|
||||
|
||||
|
||||
def _save_page_output(markdown: str, output_path: str):
|
||||
"""Save markdown content to a file, creating parent directories if needed.
|
||||
TARGET_LANGUAGE = os.environ.get("TARGET_LANGUAGE", "python")
|
||||
|
||||
Args:
|
||||
markdown: The markdown content to save
|
||||
output_path: The file path to save to
|
||||
"""
|
||||
# Create parent directories recursively if they don't exist
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
|
||||
# Write the markdown content to the file
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
f.write(markdown)
|
||||
if TARGET_LANGUAGE not in {"python", "js"}:
|
||||
raise ValueError(f"TARGET_LANGUAGE must be 'python' or 'js', got {TARGET_LANGUAGE}")
|
||||
|
||||
|
||||
def _on_page_markdown_with_config(
|
||||
@@ -340,14 +331,6 @@ def _on_page_markdown_with_config(
|
||||
# logger.info("Processing Jupyter notebook: %s", page.file.src_path)
|
||||
markdown = convert_notebook(page.file.abs_src_path)
|
||||
|
||||
target_language = kwargs.get(
|
||||
"target_language",
|
||||
os.environ.get("TARGET_LANGUAGE", "python")
|
||||
)
|
||||
|
||||
# Apply cross-reference preprocessing to all markdown content
|
||||
markdown = _replace_autolinks(markdown, page.file.src_path, default_scope=target_language)
|
||||
|
||||
# Append API reference links to code blocks
|
||||
if add_api_references:
|
||||
markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
|
||||
@@ -355,7 +338,17 @@ def _on_page_markdown_with_config(
|
||||
markdown = _highlight_code_blocks(markdown)
|
||||
|
||||
# Apply conditional rendering for code blocks
|
||||
markdown = _apply_conditional_rendering(markdown, target_language)
|
||||
markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
|
||||
if TARGET_LANGUAGE == "js":
|
||||
markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
|
||||
elif TARGET_LANGUAGE == "python":
|
||||
# Via a dedicated plugin
|
||||
pass
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported target language: {TARGET_LANGUAGE}. "
|
||||
"Supported languages are 'python' and 'js'."
|
||||
)
|
||||
|
||||
# Add file path as an attribute to code blocks that are executable.
|
||||
# This file path is used to associate fixtures with the executable code
|
||||
@@ -370,19 +363,15 @@ def _on_page_markdown_with_config(
|
||||
|
||||
|
||||
def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
|
||||
finalized_markdown = _on_page_markdown_with_config(
|
||||
markdown,
|
||||
page,
|
||||
add_api_references=True,
|
||||
**kwargs,
|
||||
finalized_markdown = (
|
||||
_on_page_markdown_with_config(
|
||||
markdown,
|
||||
page,
|
||||
add_api_references=True,
|
||||
**kwargs,
|
||||
)
|
||||
)
|
||||
page.meta["original_markdown"] = finalized_markdown
|
||||
|
||||
output_path = os.environ.get("MD_OUTPUT_PATH")
|
||||
if output_path:
|
||||
file_path = os.path.join(output_path, page.file.src_path)
|
||||
_save_page_output(finalized_markdown, file_path)
|
||||
|
||||
return finalized_markdown
|
||||
|
||||
|
||||
@@ -453,7 +442,6 @@ height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
|
||||
else:
|
||||
return html # fallback if no <body> found
|
||||
|
||||
|
||||
def _inject_markdown_into_html(html: str, page: Page) -> str:
|
||||
"""Inject the original markdown content into the HTML page as JSON."""
|
||||
original_markdown = page.meta.get("original_markdown", "")
|
||||
@@ -486,7 +474,6 @@ def _inject_markdown_into_html(html: str, page: Page) -> str:
|
||||
)
|
||||
return html.replace("</head>", f"{script_content}</head>")
|
||||
|
||||
|
||||
def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
|
||||
"""Inject Google Tag Manager noscript tag immediately after <body>.
|
||||
|
||||
@@ -501,7 +488,6 @@ def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
|
||||
html = _inject_markdown_into_html(html, page)
|
||||
return _inject_gtm(html)
|
||||
|
||||
|
||||
# Create HTML files for redirects after site dir has been built
|
||||
def on_post_build(config):
|
||||
use_directory_urls = config.get("use_directory_urls")
|
||||
|
||||
@@ -15,10 +15,9 @@ If you’re looking for other prebuilt libraries, explore the community-built op
|
||||
below. These libraries can extend LangGraph's functionality in various ways.
|
||||
|
||||
## 📚 Available Libraries
|
||||
|
||||
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
|
||||
|
||||
:::python
|
||||
{python_library_list}
|
||||
{library_list}
|
||||
|
||||
## ✨ Contributing Your Library
|
||||
|
||||
@@ -29,39 +28,16 @@ To share your project, simply open a Pull Request adding an entry for your packa
|
||||
|
||||
**Guidelines**
|
||||
|
||||
- Your repo must be distributed as an installable package on PyPI 📦
|
||||
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
|
||||
for JavaScript/TypeScript, etc.) 📦
|
||||
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
|
||||
the Functional API (exposing an `entrypoint`).
|
||||
- The package must include documentation (e.g., a `README.md` or docs site)
|
||||
explaining how to use it.
|
||||
|
||||
|
||||
We'll review your contribution and merge it in!
|
||||
|
||||
Thanks for contributing! 🚀
|
||||
:::
|
||||
|
||||
:::js
|
||||
{js_library_list}
|
||||
|
||||
## ✨ Contributing Your Library
|
||||
|
||||
Have you built an awesome open-source library using LangGraph? We'd love to feature
|
||||
your project on the official LangGraph documentation pages! 🏆
|
||||
|
||||
To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
|
||||
|
||||
**Guidelines**
|
||||
|
||||
- Your repo must be distributed as an installable package on npm 📦
|
||||
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
|
||||
the Functional API (exposing an `entrypoint`).
|
||||
- The package must include documentation (e.g., a `README.md` or docs site)
|
||||
explaining how to use it.
|
||||
|
||||
We'll review your contribution and merge it in!
|
||||
|
||||
Thanks for contributing! 🚀
|
||||
:::
|
||||
"""
|
||||
|
||||
|
||||
@@ -70,18 +46,36 @@ class ResolvedPackage(TypedDict):
|
||||
"""The name of the package."""
|
||||
repo: str
|
||||
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
|
||||
monorepo_path: str | None
|
||||
"""Optional: The path to the package in the monorepo. Must be relative to the root of the monorepo."""
|
||||
language: str
|
||||
"""The language of the package. (either 'python' or 'js')"""
|
||||
weekly_downloads: int | None
|
||||
"""The weekly download count of the package."""
|
||||
description: str
|
||||
"""A brief description of what the package does."""
|
||||
|
||||
def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
|
||||
"""Generate the package table for the third party page.
|
||||
|
||||
def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
|
||||
"""Generate the markdown content for the third party page.
|
||||
|
||||
Args:
|
||||
resolved_packages: A list of resolved package information.
|
||||
language: str
|
||||
|
||||
Returns:
|
||||
The markdown content as a string.
|
||||
"""
|
||||
# Update the URL to the actual file once the initial version is merged
|
||||
if language == "python":
|
||||
langgraph_url = (
|
||||
"https://github.com/langchain-ai/langgraph/blob/main/docs"
|
||||
"/_scripts/third_party_page/packages.yml"
|
||||
)
|
||||
elif language == "js":
|
||||
langgraph_url = (
|
||||
"https://github.com/langchain-ai/langgraphjs/blob/main/docs"
|
||||
"/_scripts/third_party/packages.yml"
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
|
||||
|
||||
sorted_packages = sorted(
|
||||
resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
|
||||
)
|
||||
@@ -91,15 +85,7 @@ def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
|
||||
]
|
||||
for package in sorted_packages:
|
||||
name = f"**{package['name']}**"
|
||||
|
||||
monorepo_path = package.get("monorepo_path", "")
|
||||
if monorepo_path:
|
||||
monorepo_path = monorepo_path[1:] if monorepo_path.startswith('/') else monorepo_path
|
||||
repo_url_suffix = f"/tree/main/{monorepo_path}"
|
||||
else:
|
||||
repo_url_suffix = ""
|
||||
repo_url = f"https://github.com/{package['repo']}{repo_url_suffix}"
|
||||
|
||||
repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
|
||||
stars_badge = (
|
||||
f"https://img.shields.io/github/stars/{package['repo']}?style=social"
|
||||
)
|
||||
@@ -107,39 +93,13 @@ def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
|
||||
downloads = package["weekly_downloads"] or "-"
|
||||
row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
|
||||
rows.append(row)
|
||||
return "\n".join(rows)
|
||||
|
||||
def generate_markdown(resolved_packages: List[ResolvedPackage]) -> str:
|
||||
"""Generate the markdown content for the third party page.
|
||||
|
||||
Args:
|
||||
resolved_packages: A list of resolved package information.
|
||||
|
||||
Returns:
|
||||
The markdown content as a string.
|
||||
"""
|
||||
# Update the URL to the actual file once the initial version is merged
|
||||
langgraph_url = (
|
||||
"https://github.com/langchain-ai/langgraph/blob/main/docs"
|
||||
"/_scripts/third_party_page/packages.yml"
|
||||
)
|
||||
|
||||
python_library_list = generate_package_table(
|
||||
[p for p in resolved_packages if p["language"] == "python"]
|
||||
)
|
||||
js_library_list = generate_package_table(
|
||||
[p for p in resolved_packages if p["language"] == "js"]
|
||||
)
|
||||
|
||||
markdown_content = MARKDOWN.format(
|
||||
python_library_list=python_library_list,
|
||||
js_library_list=js_library_list,
|
||||
langgraph_url=langgraph_url,
|
||||
library_list="\n".join(rows), langgraph_url=langgraph_url
|
||||
)
|
||||
return markdown_content
|
||||
|
||||
|
||||
def main(input_file: str, output_file: str) -> None:
|
||||
def main(input_file: str, output_file: str, language: str) -> None:
|
||||
"""Main function to create the third party page.
|
||||
|
||||
Args:
|
||||
@@ -151,7 +111,7 @@ def main(input_file: str, output_file: str) -> None:
|
||||
with open(input_file, "r") as f:
|
||||
resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
|
||||
|
||||
markdown_content = generate_markdown(resolved_packages)
|
||||
markdown_content = generate_markdown(resolved_packages, language)
|
||||
|
||||
# Write the markdown content to the output file
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
@@ -167,6 +127,12 @@ if __name__ == "__main__":
|
||||
parser.add_argument(
|
||||
"output_file", help="Path to the output file for the third party page."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--language",
|
||||
choices=["python", "js"],
|
||||
default="python",
|
||||
help="The language for which to generate the third party page. Defaults to 'python'.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
main(args.input_file, args.output_file)
|
||||
main(args.input_file, args.output_file, args.language)
|
||||
|
||||
@@ -11,146 +11,101 @@ import yaml
|
||||
|
||||
|
||||
class Package(TypedDict):
|
||||
"""A TypedDict representing a package"""
|
||||
|
||||
name: str
|
||||
"""The name of the package."""
|
||||
repo: str
|
||||
"""Repository ID within github. Format is: [orgname]/[repo_name]."""
|
||||
monorepo_path: str | None
|
||||
"""The path to the package in the monorepo. Only used for JS packages."""
|
||||
description: str
|
||||
"""A brief description of what the package does."""
|
||||
|
||||
|
||||
class ResolvedPackage(Package):
|
||||
weekly_downloads: int | None
|
||||
"""The weekly download count of the package."""
|
||||
language: str
|
||||
"""The language of the package. (either 'python' or 'js')"""
|
||||
|
||||
|
||||
HERE = pathlib.Path(__file__).parent
|
||||
PACKAGES_FILE = HERE / "packages.yml"
|
||||
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())["packages"]
|
||||
PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
|
||||
|
||||
def _get_pypi_downloads(package: Package) -> int:
|
||||
"""Retrieve the weekly download count for a package from PyPIStats."""
|
||||
|
||||
# First check if package exists on PyPI
|
||||
pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
|
||||
try:
|
||||
pypi_response = requests.get(pypi_url)
|
||||
pypi_response.raise_for_status()
|
||||
except requests.exceptions.HTTPError:
|
||||
raise AssertionError(f"Package {package['name']} does not exist on PyPI")
|
||||
|
||||
# Get first release date
|
||||
pypi_data = pypi_response.json()
|
||||
releases = pypi_data["releases"]
|
||||
first_release_date = None
|
||||
for version_releases in releases.values():
|
||||
if version_releases: # Some versions may be empty lists
|
||||
upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
|
||||
if first_release_date is None or upload_time < first_release_date:
|
||||
first_release_date = upload_time
|
||||
|
||||
if first_release_date is None:
|
||||
raise AssertionError(f"Package {package['name']} has no releases yet")
|
||||
|
||||
# If package was published in last 48 hours, skip download stats
|
||||
if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
|
||||
url = f"https://pypistats.org/api/packages/{package['name']}/overall"
|
||||
|
||||
response = requests.get(url)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
sorted_data = sorted(
|
||||
data["data"],
|
||||
key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
# Sum the last 7 days of downloads
|
||||
return sum(entry["downloads"] for entry in sorted_data[:7])
|
||||
else:
|
||||
return None
|
||||
|
||||
def _get_npm_downloads(package: Package) -> int:
|
||||
"""Retrieve the weekly download count for a package on the npm registry."""
|
||||
|
||||
# Check if package exists on the npm registry
|
||||
npm_url = f"https://registry.npmjs.org/{package['name']}"
|
||||
try:
|
||||
npm_response = requests.get(npm_url)
|
||||
npm_response.raise_for_status()
|
||||
except requests.exceptions.HTTPError:
|
||||
raise AssertionError(f"Package {package['name']} does not exist on npm registry")
|
||||
|
||||
npm_data = npm_response.json()
|
||||
|
||||
# Retrieve the first publish date using the 'created' timestamp from the 'time' field.
|
||||
created_str = npm_data.get("time", {}).get("created")
|
||||
if created_str is None:
|
||||
raise AssertionError(f"Package {package['name']} has no creation time in registry data")
|
||||
# Remove the trailing 'Z' if present and parse the ISO format timestamp
|
||||
first_publish_date = datetime.fromisoformat(created_str.rstrip("Z"))
|
||||
|
||||
# If package was published more than 48 hours ago, fetch download stats.
|
||||
if (datetime.now() - first_publish_date).total_seconds() >= 48 * 3600:
|
||||
stats_url = f"https://api.npmjs.org/downloads/point/last-week/{package['name']}"
|
||||
stats_response = requests.get(stats_url)
|
||||
stats_response.raise_for_status()
|
||||
stats_data = stats_response.json()
|
||||
return stats_data.get("downloads", None)
|
||||
else:
|
||||
return None
|
||||
|
||||
def _get_weekly_downloads(packages: dict[str, list[Package]], fake: bool) -> list[ResolvedPackage]:
|
||||
"""Retrieve the weekly download count for a dictionary of python or js packages."""
|
||||
def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
|
||||
"""Retrieve the monthly download count for a list of packages from PyPIStats."""
|
||||
resolved_packages: list[ResolvedPackage] = []
|
||||
|
||||
if fake:
|
||||
# To avoid making network requests during testing, return fake download counts
|
||||
for language, package_list in packages.items():
|
||||
for package in package_list:
|
||||
resolved_packages.append(
|
||||
{
|
||||
"name": package["name"],
|
||||
"repo": package["repo"],
|
||||
"monorepo_path": package.get("monorepo_path", None),
|
||||
"language": language,
|
||||
"description": package["description"],
|
||||
"weekly_downloads": -12345,
|
||||
}
|
||||
)
|
||||
return resolved_packages
|
||||
|
||||
for language, package_list in packages.items():
|
||||
for package in package_list:
|
||||
if language == "python":
|
||||
num_downloads = _get_pypi_downloads(package)
|
||||
elif language == "js":
|
||||
num_downloads = _get_npm_downloads(package)
|
||||
else:
|
||||
num_downloads = None
|
||||
|
||||
for package in packages:
|
||||
resolved_packages.append(
|
||||
{
|
||||
"name": package["name"],
|
||||
"repo": package["repo"],
|
||||
"monorepo_path": package.get("monorepo_path", None),
|
||||
"language": language,
|
||||
"weekly_downloads": -12345,
|
||||
"description": package["description"],
|
||||
"weekly_downloads": num_downloads,
|
||||
}
|
||||
)
|
||||
return resolved_packages
|
||||
|
||||
for package in packages:
|
||||
# First check if package exists on PyPI
|
||||
pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
|
||||
try:
|
||||
pypi_response = requests.get(pypi_url)
|
||||
pypi_response.raise_for_status()
|
||||
except requests.exceptions.HTTPError:
|
||||
raise AssertionError(f"Package {package['name']} does not exist on PyPI")
|
||||
|
||||
# Get first release date
|
||||
pypi_data = pypi_response.json()
|
||||
releases = pypi_data["releases"]
|
||||
first_release_date = None
|
||||
for version_releases in releases.values():
|
||||
if version_releases: # Some versions may be empty lists
|
||||
upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
|
||||
if first_release_date is None or upload_time < first_release_date:
|
||||
first_release_date = upload_time
|
||||
|
||||
if first_release_date is None:
|
||||
raise AssertionError(f"Package {package['name']} has no releases yet")
|
||||
|
||||
# If package was published in last 48 hours, skip download stats
|
||||
if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
|
||||
url = f"https://pypistats.org/api/packages/{package['name']}/overall"
|
||||
|
||||
response = requests.get(url)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
sorted_data = sorted(
|
||||
data["data"],
|
||||
key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
# Sum the last 7 days of downloads
|
||||
num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
|
||||
else:
|
||||
num_downloads = None
|
||||
|
||||
resolved_packages.append(
|
||||
{
|
||||
"name": package["name"],
|
||||
"repo": package["repo"],
|
||||
"weekly_downloads": num_downloads,
|
||||
"description": package["description"],
|
||||
}
|
||||
)
|
||||
|
||||
return resolved_packages
|
||||
|
||||
|
||||
|
||||
def main(output_file: str, fake: bool) -> None:
|
||||
"""Main function to generate package download information.
|
||||
|
||||
Args:
|
||||
output_file: Path to the output YAML file.
|
||||
fake: If True, use fake download counts for testing purposes.
|
||||
"""
|
||||
resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
|
||||
|
||||
|
||||
@@ -1,58 +1,41 @@
|
||||
#A list of third-party packages to surface on the third-party page.
|
||||
packages:
|
||||
python:
|
||||
- name: "trustcall"
|
||||
repo: "hinthornw/trustcall"
|
||||
description: "Tenacious tool calling built on LangGraph."
|
||||
- name: "breeze-agent"
|
||||
repo: "andrestorres123/breeze-agent"
|
||||
description: "A streamlined research system built inspired on STORM and built on LangGraph."
|
||||
- name: "langgraph-supervisor"
|
||||
repo: "langchain-ai/langgraph-supervisor-py"
|
||||
description: "Build supervisor multi-agent systems with LangGraph."
|
||||
- name: "langmem"
|
||||
repo: "langchain-ai/langmem"
|
||||
description: "Build agents that learn and adapt from interactions over time."
|
||||
- name: "langchain-mcp-adapters"
|
||||
repo: "langchain-ai/langchain-mcp-adapters"
|
||||
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
|
||||
- name: "open-deep-research"
|
||||
repo: "langchain-ai/open_deep_research"
|
||||
description: "Open source assistant for iterative web research and report writing."
|
||||
- name: "langgraph-swarm"
|
||||
repo: "langchain-ai/langgraph-swarm-py"
|
||||
description: "Build swarm-style multi-agent systems using LangGraph."
|
||||
- name: "delve-taxonomy-generator"
|
||||
repo: "andrestorres123/delve"
|
||||
description: "A taxonomy generator for unstructured data"
|
||||
- name: "nodeology"
|
||||
repo: "xyin-anl/Nodeology"
|
||||
description: "Enable researcher to build scientific workflows easily with simplified interface."
|
||||
- name: "langgraph-bigtool"
|
||||
repo: "langchain-ai/langgraph-bigtool"
|
||||
description: "Build LangGraph agents with large numbers of tools."
|
||||
- name: "ai-data-science-team"
|
||||
repo: "business-science/ai-data-science-team"
|
||||
description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
|
||||
- name: "langgraph-reflection"
|
||||
repo: "langchain-ai/langgraph-reflection"
|
||||
description: "LangGraph agent that runs a reflection step."
|
||||
- name: "langgraph-codeact"
|
||||
repo: "langchain-ai/langgraph-codeact"
|
||||
description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
|
||||
js:
|
||||
- name: "@langchain/mcp-adapters"
|
||||
repo: "langchain-ai/langchainjs"
|
||||
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
|
||||
- name: "@langchain/langgraph-supervisor"
|
||||
repo: "langchain-ai/langgraphjs"
|
||||
monorepo_path: "libs/langgraph-supervisor"
|
||||
description: "Build supervisor multi-agent systems with LangGraph"
|
||||
- name: "@langchain/langgraph-swarm"
|
||||
repo: "langchain-ai/langgraphjs"
|
||||
monorepo_path: "libs/langgraph-swarm"
|
||||
description: "Build multi-agent swarms with LangGraph"
|
||||
- name: "@langchain/langgraph-cua"
|
||||
repo: "langchain-ai/langgraphjs"
|
||||
monorepo_path: "libs/langgraph-cua"
|
||||
description: "Build computer use agents with LangGraph"
|
||||
- name: "trustcall"
|
||||
repo: "hinthornw/trustcall"
|
||||
description: "Tenacious tool calling built on LangGraph."
|
||||
- name: "breeze-agent"
|
||||
repo: "andrestorres123/breeze-agent"
|
||||
description: "A streamlined research system built inspired on STORM and built on LangGraph."
|
||||
- name: "langgraph-supervisor"
|
||||
repo: "langchain-ai/langgraph-supervisor-py"
|
||||
description: "Build supervisor multi-agent systems with LangGraph."
|
||||
- name: "langmem"
|
||||
repo: "langchain-ai/langmem"
|
||||
description: "Build agents that learn and adapt from interactions over time."
|
||||
- name: "langchain-mcp-adapters"
|
||||
repo: "langchain-ai/langchain-mcp-adapters"
|
||||
description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
|
||||
- name: "open-deep-research"
|
||||
repo: "langchain-ai/open_deep_research"
|
||||
description: "Open source assistant for iterative web research and report writing."
|
||||
- name: "langgraph-swarm"
|
||||
repo: "langchain-ai/langgraph-swarm-py"
|
||||
description: "Build swarm-style multi-agent systems using LangGraph."
|
||||
- name: "delve-taxonomy-generator"
|
||||
repo: "andrestorres123/delve"
|
||||
description: "A taxonomy generator for unstructured data"
|
||||
- name: "nodeology"
|
||||
repo: "xyin-anl/Nodeology"
|
||||
description: "Enable researcher to build scientific workflows easily with simplified interface."
|
||||
- name: "langgraph-bigtool"
|
||||
repo: "langchain-ai/langgraph-bigtool"
|
||||
description: "Build LangGraph agents with large numbers of tools."
|
||||
- name: "ai-data-science-team"
|
||||
repo: "business-science/ai-data-science-team"
|
||||
description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
|
||||
- name: "langgraph-reflection"
|
||||
repo: "langchain-ai/langgraph-reflection"
|
||||
description: "LangGraph agent that runs a reflection step."
|
||||
- name: "langgraph-codeact"
|
||||
repo: "langchain-ai/langgraph-codeact"
|
||||
description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
|
||||
|
||||
+8
-232
@@ -15,40 +15,23 @@ This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable*
|
||||
|
||||
Before you start this tutorial, ensure you have the following:
|
||||
|
||||
- An [Anthropic](https://console.anthropic.com/settings/keys) API key
|
||||
- An [Anthropic](https://console.anthropic.com/settings/keys) API key
|
||||
|
||||
## 1. Install dependencies
|
||||
|
||||
If you haven't already, install LangGraph and LangChain:
|
||||
|
||||
:::python
|
||||
|
||||
```
|
||||
pip install -U langgraph "langchain[anthropic]"
|
||||
```
|
||||
|
||||
!!! info
|
||||
!!! info
|
||||
|
||||
LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```bash
|
||||
npm install @langchain/langgraph @langchain/core @langchain/anthropic
|
||||
```
|
||||
|
||||
!!! info
|
||||
|
||||
LangChain is installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).
|
||||
|
||||
:::
|
||||
|
||||
## 2. Create an agent
|
||||
|
||||
:::python
|
||||
To create an agent, use @[`create_react_agent`][create_react_agent]:
|
||||
To create an agent, use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
@@ -73,52 +56,9 @@ agent.invoke(
|
||||
2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
|
||||
3. Provide a list of tools for the model to use.
|
||||
4. Provide a system prompt (instructions) to the language model used by the agent.
|
||||
:::
|
||||
|
||||
:::js
|
||||
To create an agent, use [`createReactAgent`](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html):
|
||||
|
||||
```typescript
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { tool } from "@langchain/core/tools";
|
||||
import { z } from "zod";
|
||||
|
||||
const getWeather = tool(
|
||||
// (1)!
|
||||
async ({ city }) => {
|
||||
return `It's always sunny in ${city}!`;
|
||||
},
|
||||
{
|
||||
name: "get_weather",
|
||||
description: "Get weather for a given city.",
|
||||
schema: z.object({
|
||||
city: z.string().describe("The city to get weather for"),
|
||||
}),
|
||||
}
|
||||
);
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }), // (2)!
|
||||
tools: [getWeather], // (3)!
|
||||
stateModifier: "You are a helpful assistant", // (4)!
|
||||
});
|
||||
|
||||
// Run the agent
|
||||
await agent.invoke({
|
||||
messages: [{ role: "user", content: "what is the weather in sf" }],
|
||||
});
|
||||
```
|
||||
|
||||
1. Define a tool for the agent to use. Tools can be defined using the `tool` function. For more advanced tool usage and customization, check the [tools](./tools.md) page.
|
||||
2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
|
||||
3. Provide a list of tools for the model to use.
|
||||
4. Provide a system prompt (instructions) to the language model used by the agent.
|
||||
:::
|
||||
|
||||
## 3. Configure an LLM
|
||||
|
||||
:::python
|
||||
To configure an LLM with specific parameters, such as temperature, use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):
|
||||
|
||||
```python
|
||||
@@ -139,45 +79,19 @@ agent = create_react_agent(
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
To configure an LLM with specific parameters, such as temperature, use a model instance:
|
||||
|
||||
```typescript
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
// highlight-next-line
|
||||
const model = new ChatAnthropic({
|
||||
model: "claude-3-5-sonnet-latest",
|
||||
// highlight-next-line
|
||||
temperature: 0,
|
||||
});
|
||||
|
||||
const agent = createReactAgent({
|
||||
// highlight-next-line
|
||||
llm: model,
|
||||
tools: [getWeather],
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
For more information on how to configure LLMs, see [Models](./models.md).
|
||||
|
||||
## 4. Add a custom prompt
|
||||
|
||||
Prompts instruct the LLM how to behave. Add one of the following types of prompts:
|
||||
|
||||
- **Static**: A string is interpreted as a **system message**.
|
||||
- **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
|
||||
* **Static**: A string is interpreted as a **system message**.
|
||||
* **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
|
||||
|
||||
=== "Static prompt"
|
||||
|
||||
Define a fixed prompt string or list of messages:
|
||||
|
||||
:::python
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
|
||||
@@ -193,30 +107,9 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
|
||||
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
|
||||
)
|
||||
```
|
||||
:::
|
||||
|
||||
:::js
|
||||
```typescript
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
|
||||
tools: [getWeather],
|
||||
// A static prompt that never changes
|
||||
// highlight-next-line
|
||||
stateModifier: "Never answer questions about the weather."
|
||||
});
|
||||
|
||||
await agent.invoke({
|
||||
messages: [{ role: "user", content: "what is the weather in sf" }]
|
||||
});
|
||||
```
|
||||
:::
|
||||
|
||||
=== "Dynamic prompt"
|
||||
|
||||
:::python
|
||||
Define a function that returns a message list based on the agent's state and configuration:
|
||||
|
||||
```python
|
||||
@@ -251,52 +144,12 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
|
||||
- Internal agent state updated during a multi-step reasoning process (using `state`).
|
||||
|
||||
Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
|
||||
:::
|
||||
|
||||
:::js
|
||||
Define a function that returns messages based on the agent's state and configuration:
|
||||
|
||||
```typescript
|
||||
import { type BaseMessageLike } from "@langchain/core/messages";
|
||||
import { type RunnableConfig } from "@langchain/core/runnables";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
// highlight-next-line
|
||||
const dynamicPrompt = (state: { messages: BaseMessageLike[] }, config: RunnableConfig): BaseMessageLike[] => { // (1)!
|
||||
const userName = config.configurable?.user_name;
|
||||
const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
|
||||
return [{ role: "system", content: systemMsg }, ...state.messages];
|
||||
};
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: "anthropic:claude-3-5-sonnet-latest",
|
||||
tools: [getWeather],
|
||||
// highlight-next-line
|
||||
stateModifier: dynamicPrompt
|
||||
});
|
||||
|
||||
await agent.invoke(
|
||||
{ messages: [{ role: "user", content: "what is the weather in sf" }] },
|
||||
// highlight-next-line
|
||||
{ configurable: { user_name: "John Smith" } }
|
||||
);
|
||||
```
|
||||
|
||||
1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
|
||||
|
||||
- Information passed at runtime, like a `user_id` or API credentials (using `config`).
|
||||
- Internal agent state updated during a multi-step reasoning process (using `state`).
|
||||
|
||||
Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
|
||||
:::
|
||||
|
||||
For more information, see [Context](./context.md).
|
||||
|
||||
## 5. Add memory
|
||||
|
||||
To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a checkpointer when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
|
||||
|
||||
:::python
|
||||
To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
@@ -329,50 +182,8 @@ ny_response = agent.invoke(
|
||||
|
||||
1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
|
||||
2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { MemorySaver } from "@langchain/langgraph";
|
||||
|
||||
// highlight-next-line
|
||||
const checkpointer = new MemorySaver();
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: "anthropic:claude-3-5-sonnet-latest",
|
||||
tools: [getWeather],
|
||||
// highlight-next-line
|
||||
checkpointSaver: checkpointer, // (1)!
|
||||
});
|
||||
|
||||
// Run the agent
|
||||
// highlight-next-line
|
||||
const config = { configurable: { thread_id: "1" } };
|
||||
const sfResponse = await agent.invoke(
|
||||
{ messages: [{ role: "user", content: "what is the weather in sf" }] },
|
||||
// highlight-next-line
|
||||
config // (2)!
|
||||
);
|
||||
const nyResponse = await agent.invoke(
|
||||
{ messages: [{ role: "user", content: "what about new york?" }] },
|
||||
// highlight-next-line
|
||||
config
|
||||
);
|
||||
```
|
||||
|
||||
1. `checkpointSaver` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
|
||||
2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
|
||||
:::
|
||||
|
||||
:::python
|
||||
When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
|
||||
:::
|
||||
|
||||
:::js
|
||||
When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `MemorySaver`).
|
||||
:::
|
||||
|
||||
Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.
|
||||
|
||||
@@ -380,7 +191,6 @@ For more information, see [Memory](../how-tos/memory/add-memory.md).
|
||||
|
||||
## 6. Configure structured output
|
||||
|
||||
:::python
|
||||
To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.
|
||||
|
||||
```python
|
||||
@@ -405,43 +215,9 @@ response = agent.invoke(
|
||||
response["structured_response"]
|
||||
```
|
||||
|
||||
1. When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
|
||||
1. When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
|
||||
|
||||
To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
To produce structured responses conforming to a schema, use the `responseFormat` parameter. The schema can be defined with a `Zod` schema. The result will be accessible via the `structuredResponse` field.
|
||||
|
||||
```typescript
|
||||
import { z } from "zod";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const WeatherResponse = z.object({
|
||||
conditions: z.string(),
|
||||
});
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: "anthropic:claude-3-5-sonnet-latest",
|
||||
tools: [getWeather],
|
||||
// highlight-next-line
|
||||
responseFormat: WeatherResponse, // (1)!
|
||||
});
|
||||
|
||||
const response = await agent.invoke({
|
||||
messages: [{ role: "user", content: "what is the weather in sf" }],
|
||||
});
|
||||
|
||||
// highlight-next-line
|
||||
response.structuredResponse;
|
||||
```
|
||||
|
||||
1. When `responseFormat` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
|
||||
|
||||
To provide a system prompt to this LLM, use an object `{ prompt, schema }`, e.g., `responseFormat: { prompt, schema: WeatherResponse }`.
|
||||
|
||||
:::
|
||||
To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
|
||||
|
||||
!!! Note "LLM post-processing"
|
||||
|
||||
|
||||
+33
-140
@@ -1,42 +1,40 @@
|
||||
# Context
|
||||
|
||||
**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that an AI application can accomplish a task. Context can be characterized along two key dimensions:
|
||||
**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
|
||||
|
||||
1. By **mutability**:
|
||||
- **Static context**: Immutable data that doesn't change during execution (e.g., user metadata, database connections, tools)
|
||||
- **Dynamic context**: Mutable data that evolves as the application runs (e.g., conversation history, intermediate results, tool call observations)
|
||||
2. By **lifetime**:
|
||||
- **Runtime context**: Data scoped to a single run or invocation
|
||||
- **Cross-conversation context**: Data that persists across multiple conversations or sessions
|
||||
Context includes *any* data outside the message list that can shape behavior. This can be:
|
||||
|
||||
!!! tip "Runtime context vs LLM context"
|
||||
- Information passed at runtime, like a `user_id` or API credentials.
|
||||
- Internal state updated during a multi-step reasoning process.
|
||||
- Persistent memory or facts from previous interactions.
|
||||
|
||||
Runtime context refers to local context: data and dependencies your code needs to run. It does **not** refer to:
|
||||
LangGraph provides **three** primary ways to manage context:
|
||||
|
||||
| Type | Description | Mutable? | Lifetime |
|
||||
|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
|
||||
| [**Runtime Context**](#runtime-context) | data passed at the start of a run | ❌ | per run |
|
||||
| [**Short-term memory (State)**](#short-term-memory-mutable-context) | dynamic data that can change during execution | ✅ | per run or conversation |
|
||||
| [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅ | across conversations |
|
||||
|
||||
### Runtime Context
|
||||
|
||||
Runtime context is for immutable data like user metadata, tools, db connections, etc. Use this when you have values that don't change mid-run.
|
||||
|
||||
!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces `config['configurable']`"
|
||||
|
||||
The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
|
||||
|
||||
!!! note
|
||||
|
||||
Runtime context refers to local context: data and dependencies your code needs to run. It does not refer to:
|
||||
|
||||
* The LLM context, which is the data passed into the LLM's prompt.
|
||||
* The "context window", which is the maximum number of tokens that can be passed to the LLM.
|
||||
|
||||
Runtime context can be used to optimize the LLM context. For example, you can use user metadata
|
||||
in the runtime context to fetch user preferences and feed them into the context window.
|
||||
You likely want to use the local context to optimize the LLM's context window. For example, you
|
||||
could use a user id to fetch a user's name and information from a database to populate the context window with relevant memories.
|
||||
|
||||
LangGraph provides three ways to manage context, which combines the mutability and lifetime dimensions:
|
||||
|
||||
:::python
|
||||
|
||||
| Context type | Description | Mutability | Lifetime | Access method |
|
||||
| ------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------- | ------------------ | --------------------------------------- |
|
||||
| [**Static runtime context**](#static-runtime-context) | User metadata, tools, db connections passed at startup | Static | Single run | `context` argument to `invoke`/`stream` |
|
||||
| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state) | Mutable data that evolves during a single run | Dynamic | Single run | LangGraph state object |
|
||||
| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations | Dynamic | Cross-conversation | LangGraph store |
|
||||
|
||||
## Static runtime context
|
||||
|
||||
**Static runtime context** represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the `context` argument to `invoke`/`stream`. This data does not change during execution.
|
||||
|
||||
!!! version-added "New in LangGraph v0.6: `context` replaces `config['configurable']`"
|
||||
|
||||
Runtime context is now passed to the `context` argument of `invoke`/`stream`,
|
||||
which replaces the previous pattern of passing application configuration to `config['configurable']`.
|
||||
Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
@@ -114,41 +112,9 @@ graph.invoke( # (1)!
|
||||
|
||||
See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
|
||||
|
||||
!!! tip
|
||||
### Short-term memory (mutable context)
|
||||
|
||||
The `Runtime` object can be used to access static context and other utilities like the active store and stream writer.
|
||||
See the [Runtime][langgraph.runtime.Runtime] documentation for details.
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
| Context type | Description | Mutability | Lifetime |
|
||||
| ------------------------------------------------------------------------------------------- | --------------------------------------------- | ---------- | ------------------ |
|
||||
| [**Config**](#config-static-context) | data passed at the start of a run | Static | Single run |
|
||||
| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state) | Mutable data that evolves during a single run | Dynamic | Single run |
|
||||
| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations | Dynamic | Cross-conversation |
|
||||
|
||||
## Config (static context)
|
||||
|
||||
Config is for immutable data like user metadata or API keys. Use this when you have values that don't change mid-run.
|
||||
|
||||
Specify configuration using a key called **"configurable"** which is reserved for this purpose.
|
||||
|
||||
```typescript
|
||||
await graph.invoke(
|
||||
// (1)!
|
||||
{ messages: [{ role: "user", content: "hi!" }] }, // (2)!
|
||||
// highlight-next-line
|
||||
{ configurable: { user_id: "user_123" } } // (3)!
|
||||
);
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Dynamic runtime context (state)
|
||||
|
||||
**Dynamic runtime context** represents mutable data that can evolve during a single run and is managed through the LangGraph state object. This includes conversation history, intermediate results, and values derived from tools or LLM outputs. In LangGraph, the state object acts as [short-term memory](../concepts/memory.md) during a run.
|
||||
State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
|
||||
|
||||
=== "In an agent"
|
||||
|
||||
@@ -156,7 +122,6 @@ await graph.invoke(
|
||||
|
||||
State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
|
||||
|
||||
:::python
|
||||
```python
|
||||
from langchain_core.messages import AnyMessage
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
@@ -191,51 +156,10 @@ await graph.invoke(
|
||||
|
||||
1. Define a custom state schema that extends `AgentState` or `MessagesState`.
|
||||
2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
|
||||
:::
|
||||
|
||||
:::js
|
||||
```typescript
|
||||
import type { BaseMessage } from "@langchain/core/messages";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { MessagesZodState } from "@langchain/langgraph";
|
||||
import { z } from "zod";
|
||||
|
||||
// highlight-next-line
|
||||
const CustomState = z.object({ // (1)!
|
||||
messages: MessagesZodState.shape.messages,
|
||||
userName: z.string(),
|
||||
});
|
||||
|
||||
const prompt = (
|
||||
// highlight-next-line
|
||||
state: z.infer<typeof CustomState>
|
||||
): BaseMessage[] => {
|
||||
const userName = state.userName;
|
||||
const systemMsg = `You are a helpful assistant. User's name is ${userName}`;
|
||||
return [{ role: "system", content: systemMsg }, ...state.messages];
|
||||
};
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: model,
|
||||
tools: [...],
|
||||
// highlight-next-line
|
||||
stateSchema: CustomState, // (2)!
|
||||
stateModifier: prompt,
|
||||
});
|
||||
|
||||
await agent.invoke({
|
||||
messages: [{ role: "user", content: "hi!" }],
|
||||
userName: "John Smith",
|
||||
});
|
||||
```
|
||||
|
||||
1. Define a custom state schema that extends `MessagesZodState` or creates a new schema.
|
||||
2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
|
||||
:::
|
||||
|
||||
=== "In a workflow"
|
||||
|
||||
:::python
|
||||
```python
|
||||
from typing_extensions import TypedDict
|
||||
from langchain_core.messages import AnyMessage
|
||||
@@ -260,49 +184,18 @@ await graph.invoke(
|
||||
builder.set_entry_point("node")
|
||||
graph = builder.compile()
|
||||
```
|
||||
|
||||
|
||||
1. Define a custom state
|
||||
2. Access the state in any node or tool
|
||||
3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
|
||||
:::
|
||||
|
||||
:::js
|
||||
```typescript
|
||||
import type { BaseMessage } from "@langchain/core/messages";
|
||||
import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
|
||||
import { z } from "zod";
|
||||
|
||||
// highlight-next-line
|
||||
const CustomState = z.object({ // (1)!
|
||||
messages: MessagesZodState.shape.messages,
|
||||
extraField: z.number(),
|
||||
});
|
||||
|
||||
const builder = new StateGraph(CustomState)
|
||||
.addNode("node", async (state) => { // (2)!
|
||||
const messages = state.messages;
|
||||
// ...
|
||||
return { // (3)!
|
||||
// highlight-next-line
|
||||
extraField: state.extraField + 1,
|
||||
};
|
||||
})
|
||||
.addEdge(START, "node");
|
||||
|
||||
const graph = builder.compile();
|
||||
```
|
||||
|
||||
1. Define a custom state
|
||||
2. Access the state in any node or tool
|
||||
3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
|
||||
:::
|
||||
|
||||
!!! tip "Turning on memory"
|
||||
|
||||
Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
|
||||
|
||||
## Dynamic cross-conversation context (store)
|
||||
### Long-term memory (cross-conversation context)
|
||||
|
||||
**Dynamic cross-conversation context** represents persistent, mutable data that spans across multiple conversations or sessions and is managed through the LangGraph store. This includes user profiles, preferences, and historical interactions. The LangGraph store acts as [long-term memory](../concepts/memory.md#long-term-memory) across multiple runs. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).
|
||||
For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).
|
||||
|
||||
For more information, see the [Memory guide](../how-tos/memory/add-memory.md).
|
||||
For more information, see the [Memory guide](../how-tos/memory/add-memory.md).
|
||||
+2
-139
@@ -11,8 +11,6 @@ hide:
|
||||
|
||||
To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:
|
||||
|
||||
:::python
|
||||
|
||||
```python
|
||||
def evaluator(*, outputs: dict, reference_outputs: dict):
|
||||
# compare agent outputs against reference outputs
|
||||
@@ -22,51 +20,16 @@ def evaluator(*, outputs: dict, reference_outputs: dict):
|
||||
return {"key": "evaluator_score", "score": score}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
type EvaluatorParams = {
|
||||
outputs: Record<string, any>;
|
||||
referenceOutputs: Record<string, any>;
|
||||
};
|
||||
|
||||
function evaluator({ outputs, referenceOutputs }: EvaluatorParams) {
|
||||
// compare agent outputs against reference outputs
|
||||
const outputMessages = outputs.messages;
|
||||
const referenceMessages = referenceOutputs.messages;
|
||||
const score = compareMessages(outputMessages, referenceMessages);
|
||||
return { key: "evaluator_score", score: score };
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
To get started, you can use prebuilt evaluators from `AgentEvals` package:
|
||||
|
||||
:::python
|
||||
|
||||
```bash
|
||||
pip install -U agentevals
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```bash
|
||||
npm install agentevals
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Create evaluator
|
||||
|
||||
A common way to evaluate agent performance is by comparing its trajectory (the order in which it calls its tools) against a reference trajectory:
|
||||
|
||||
:::python
|
||||
|
||||
```python
|
||||
import json
|
||||
# highlight-next-line
|
||||
@@ -117,72 +80,15 @@ result = evaluator(
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
|
||||
|
||||
const outputs = [
|
||||
{
|
||||
role: "assistant",
|
||||
tool_calls: [
|
||||
{
|
||||
function: {
|
||||
name: "get_weather",
|
||||
arguments: JSON.stringify({ city: "san francisco" }),
|
||||
},
|
||||
},
|
||||
{
|
||||
function: {
|
||||
name: "get_directions",
|
||||
arguments: JSON.stringify({ destination: "presidio" }),
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
];
|
||||
|
||||
const referenceOutputs = [
|
||||
{
|
||||
role: "assistant",
|
||||
tool_calls: [
|
||||
{
|
||||
function: {
|
||||
name: "get_weather",
|
||||
arguments: JSON.stringify({ city: "san francisco" }),
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
];
|
||||
|
||||
// Create the evaluator
|
||||
const evaluator = createTrajectoryMatchEvaluator({
|
||||
// Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: strict, unordered and subset
|
||||
trajectoryMatchMode: "superset", // (1)!
|
||||
});
|
||||
|
||||
// Run the evaluator
|
||||
const result = evaluator({
|
||||
outputs: outputs,
|
||||
referenceOutputs: referenceOutputs,
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)
|
||||
|
||||
|
||||
As a next step, learn more about how to [customize trajectory match evaluator](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#agent-trajectory-match).
|
||||
|
||||
### LLM-as-a-judge
|
||||
|
||||
You can use LLM-as-a-judge evaluator that uses an LLM to compare the trajectory against the reference outputs and output a score:
|
||||
|
||||
:::python
|
||||
|
||||
```python
|
||||
import json
|
||||
from agentevals.trajectory.llm import (
|
||||
@@ -197,24 +103,6 @@ evaluator = create_trajectory_llm_as_judge(
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import {
|
||||
createTrajectoryLlmAsJudge,
|
||||
TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
|
||||
} from "agentevals/trajectory/llm";
|
||||
|
||||
const evaluator = createTrajectoryLlmAsJudge({
|
||||
prompt: TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
|
||||
model: "openai:o3-mini",
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Run evaluator
|
||||
|
||||
To run an evaluator, you will first need to create a [LangSmith dataset](https://docs.smith.langchain.com/evaluation/concepts#datasets). To use the prebuilt AgentEvals evaluators, you will need a dataset with the following schema:
|
||||
@@ -222,8 +110,6 @@ To run an evaluator, you will first need to create a [LangSmith dataset](https:/
|
||||
- **input**: `{"messages": [...]}` input messages to call the agent with.
|
||||
- **output**: `{"messages": [...]}` expected message history in the agent output. For trajectory evaluation, you can choose to keep only assistant messages.
|
||||
|
||||
:::python
|
||||
|
||||
```python
|
||||
from langsmith import Client
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
@@ -239,27 +125,4 @@ experiment_results = client.evaluate(
|
||||
data="<Name of your dataset>",
|
||||
evaluators=[evaluator]
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import { Client } from "langsmith";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
|
||||
|
||||
const client = new Client();
|
||||
const agent = createReactAgent({...});
|
||||
const evaluator = createTrajectoryMatchEvaluator({...});
|
||||
|
||||
const experimentResults = await client.evaluate(
|
||||
(inputs) => agent.invoke(inputs),
|
||||
// replace with your dataset name
|
||||
{ data: "<Name of your dataset>" },
|
||||
{ evaluators: [evaluator] }
|
||||
);
|
||||
```
|
||||
|
||||
:::
|
||||
```
|
||||
+1
-340
@@ -9,31 +9,10 @@ hide:
|
||||
|
||||
# Use MCP
|
||||
|
||||
[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
|
||||
|
||||
|
||||
|
||||
:::python
|
||||
Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
|
||||
|
||||
```bash
|
||||
pip install langchain-mcp-adapters
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
Install the `@langchain/mcp-adapters` library to use MCP tools in LangGraph:
|
||||
|
||||
```bash
|
||||
npm install langchain-mcp-adapters
|
||||
```
|
||||
|
||||
:::
|
||||
The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
|
||||
|
||||
## Use MCP tools
|
||||
|
||||
:::python
|
||||
The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
|
||||
|
||||
=== "In an agent"
|
||||
@@ -146,111 +125,10 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
The `@langchain/mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
|
||||
|
||||
=== "In an agent"
|
||||
|
||||
```typescript title="Agent using tools defined on MCP servers"
|
||||
// highlight-next-line
|
||||
import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
|
||||
import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
// highlight-next-line
|
||||
const client = new MultiServerMCPClient({
|
||||
math: {
|
||||
command: "node",
|
||||
// Replace with absolute path to your math_server.js file
|
||||
args: ["/path/to/math_server.js"],
|
||||
transport: "stdio",
|
||||
},
|
||||
weather: {
|
||||
// Ensure you start your weather server on port 8000
|
||||
url: "http://localhost:8000/mcp",
|
||||
transport: "streamable_http",
|
||||
},
|
||||
});
|
||||
|
||||
// highlight-next-line
|
||||
const tools = await client.getTools();
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "claude-3-7-sonnet-latest" }),
|
||||
// highlight-next-line
|
||||
tools,
|
||||
});
|
||||
|
||||
const mathResponse = await agent.invoke({
|
||||
messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
|
||||
});
|
||||
|
||||
const weatherResponse = await agent.invoke({
|
||||
messages: [{ role: "user", content: "what is the weather in nyc?" }],
|
||||
});
|
||||
```
|
||||
|
||||
=== "In a workflow"
|
||||
|
||||
```typescript
|
||||
import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
|
||||
import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
|
||||
import { ToolNode } from "@langchain/langgraph/prebuilt";
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
import { AIMessage } from "@langchain/core/messages";
|
||||
import { z } from "zod";
|
||||
|
||||
const model = new ChatOpenAI({ model: "gpt-4" });
|
||||
|
||||
const client = new MultiServerMCPClient({
|
||||
math: {
|
||||
command: "node",
|
||||
// Make sure to update to the full absolute path to your math_server.js file
|
||||
args: ["./examples/math_server.js"],
|
||||
transport: "stdio",
|
||||
},
|
||||
weather: {
|
||||
// make sure you start your weather server on port 8000
|
||||
url: "http://localhost:8000/mcp/",
|
||||
transport: "streamable_http",
|
||||
},
|
||||
});
|
||||
|
||||
const tools = await client.getTools();
|
||||
|
||||
const builder = new StateGraph(MessagesZodState)
|
||||
.addNode("callModel", async (state) => {
|
||||
const response = await model.bindTools(tools).invoke(state.messages);
|
||||
return { messages: [response] };
|
||||
})
|
||||
.addNode("tools", new ToolNode(tools))
|
||||
.addEdge(START, "callModel")
|
||||
.addConditionalEdges("callModel", (state) => {
|
||||
const lastMessage = state.messages.at(-1) as AIMessage | undefined;
|
||||
if (!lastMessage?.tool_calls?.length) {
|
||||
return "__end__";
|
||||
}
|
||||
return "tools";
|
||||
})
|
||||
.addEdge("tools", "callModel");
|
||||
|
||||
const graph = builder.compile();
|
||||
|
||||
const mathResponse = await graph.invoke({
|
||||
messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
|
||||
});
|
||||
|
||||
const weatherResponse = await graph.invoke({
|
||||
messages: [{ role: "user", content: "what is the weather in nyc?" }],
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Custom MCP servers
|
||||
|
||||
:::python
|
||||
To create your own MCP servers, you can use the `mcp` library. This library provides a simple way to define tools and run them as servers.
|
||||
|
||||
Install the MCP library:
|
||||
@@ -258,24 +136,8 @@ Install the MCP library:
|
||||
```bash
|
||||
pip install mcp
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
To create your own MCP servers, you can use the `@modelcontextprotocol/sdk` library. This library provides a simple way to define tools and run them as servers.
|
||||
|
||||
Install the MCP SDK:
|
||||
|
||||
```bash
|
||||
npm install @modelcontextprotocol/sdk
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
Use the following reference implementations to test your agent with MCP tool servers.
|
||||
|
||||
:::python
|
||||
|
||||
```python title="Example Math Server (stdio transport)"
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
|
||||
@@ -295,115 +157,6 @@ if __name__ == "__main__":
|
||||
mcp.run(transport="stdio")
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript title="Example Math Server (stdio transport)"
|
||||
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
|
||||
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
|
||||
import {
|
||||
CallToolRequestSchema,
|
||||
ListToolsRequestSchema,
|
||||
} from "@modelcontextprotocol/sdk/types.js";
|
||||
|
||||
const server = new Server(
|
||||
{
|
||||
name: "math-server",
|
||||
version: "0.1.0",
|
||||
},
|
||||
{
|
||||
capabilities: {
|
||||
tools: {},
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
server.setRequestHandler(ListToolsRequestSchema, async () => {
|
||||
return {
|
||||
tools: [
|
||||
{
|
||||
name: "add",
|
||||
description: "Add two numbers",
|
||||
inputSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "First number",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "Second number",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
},
|
||||
},
|
||||
{
|
||||
name: "multiply",
|
||||
description: "Multiply two numbers",
|
||||
inputSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "First number",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "Second number",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
});
|
||||
|
||||
server.setRequestHandler(CallToolRequestSchema, async (request) => {
|
||||
switch (request.params.name) {
|
||||
case "add": {
|
||||
const { a, b } = request.params.arguments as { a: number; b: number };
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: String(a + b),
|
||||
},
|
||||
],
|
||||
};
|
||||
}
|
||||
case "multiply": {
|
||||
const { a, b } = request.params.arguments as { a: number; b: number };
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: String(a * b),
|
||||
},
|
||||
],
|
||||
};
|
||||
}
|
||||
default:
|
||||
throw new Error(`Unknown tool: ${request.params.name}`);
|
||||
}
|
||||
});
|
||||
|
||||
async function main() {
|
||||
const transport = new StdioServerTransport();
|
||||
await server.connect(transport);
|
||||
console.error("Math MCP server running on stdio");
|
||||
}
|
||||
|
||||
main();
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::python
|
||||
|
||||
```python title="Example Weather Server (Streamable HTTP transport)"
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
|
||||
@@ -418,100 +171,8 @@ if __name__ == "__main__":
|
||||
mcp.run(transport="streamable-http")
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript title="Example Weather Server (HTTP transport)"
|
||||
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
|
||||
import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";
|
||||
import {
|
||||
CallToolRequestSchema,
|
||||
ListToolsRequestSchema,
|
||||
} from "@modelcontextprotocol/sdk/types.js";
|
||||
import express from "express";
|
||||
|
||||
const app = express();
|
||||
app.use(express.json());
|
||||
|
||||
const server = new Server(
|
||||
{
|
||||
name: "weather-server",
|
||||
version: "0.1.0",
|
||||
},
|
||||
{
|
||||
capabilities: {
|
||||
tools: {},
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
server.setRequestHandler(ListToolsRequestSchema, async () => {
|
||||
return {
|
||||
tools: [
|
||||
{
|
||||
name: "get_weather",
|
||||
description: "Get weather for location",
|
||||
inputSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
location: {
|
||||
type: "string",
|
||||
description: "Location to get weather for",
|
||||
},
|
||||
},
|
||||
required: ["location"],
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
});
|
||||
|
||||
server.setRequestHandler(CallToolRequestSchema, async (request) => {
|
||||
switch (request.params.name) {
|
||||
case "get_weather": {
|
||||
const { location } = request.params.arguments as { location: string };
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: `It's always sunny in ${location}`,
|
||||
},
|
||||
],
|
||||
};
|
||||
}
|
||||
default:
|
||||
throw new Error(`Unknown tool: ${request.params.name}`);
|
||||
}
|
||||
});
|
||||
|
||||
app.post("/mcp", async (req, res) => {
|
||||
const transport = new SSEServerTransport("/mcp", res);
|
||||
await server.connect(transport);
|
||||
});
|
||||
|
||||
const PORT = process.env.PORT || 8000;
|
||||
app.listen(PORT, () => {
|
||||
console.log(`Weather MCP server running on port ${PORT}`);
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::python
|
||||
|
||||
## Additional resources
|
||||
|
||||
- [MCP documentation](https://modelcontextprotocol.io/introduction)
|
||||
- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
|
||||
- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
## Additional resources
|
||||
|
||||
- [MCP documentation](https://modelcontextprotocol.io/introduction)
|
||||
- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
|
||||
- [`@langchain/mcp-adapters`](https://npmjs.com/package/@langchain/mcp-adapters)
|
||||
:::
|
||||
|
||||
+6
-141
@@ -2,70 +2,18 @@
|
||||
|
||||
LangGraph provides built-in support for [LLMs (language models)](https://python.langchain.com/docs/concepts/chat_models/) via the LangChain library. This makes it easy to integrate various LLMs into your agents and workflows.
|
||||
|
||||
|
||||
## Initialize a model
|
||||
|
||||
:::python
|
||||
Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
|
||||
|
||||
{% include-markdown "../../snippets/chat_model_tabs.md" %}
|
||||
:::
|
||||
|
||||
:::js
|
||||
Use model provider classes to initialize models:
|
||||
|
||||
=== "OpenAI"
|
||||
|
||||
```typescript
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
|
||||
const model = new ChatOpenAI({
|
||||
model: "gpt-4o",
|
||||
temperature: 0,
|
||||
});
|
||||
```
|
||||
|
||||
=== "Anthropic"
|
||||
|
||||
```typescript
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
|
||||
const model = new ChatAnthropic({
|
||||
model: "claude-3-5-sonnet-20240620",
|
||||
temperature: 0,
|
||||
maxTokens: 2048,
|
||||
});
|
||||
```
|
||||
|
||||
=== "Google"
|
||||
|
||||
```typescript
|
||||
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
|
||||
|
||||
const model = new ChatGoogleGenerativeAI({
|
||||
model: "gemini-1.5-pro",
|
||||
temperature: 0,
|
||||
});
|
||||
```
|
||||
|
||||
=== "Groq"
|
||||
|
||||
```typescript
|
||||
import { ChatGroq } from "@langchain/groq";
|
||||
|
||||
const model = new ChatGroq({
|
||||
model: "llama-3.1-70b-versatile",
|
||||
temperature: 0,
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::python
|
||||
|
||||
### Instantiate a model directly
|
||||
|
||||
If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:
|
||||
|
||||
|
||||
```python
|
||||
# Anthropic is already supported by `init_chat_model`,
|
||||
# but you can also instantiate it directly.
|
||||
@@ -78,20 +26,19 @@ model = ChatAnthropic(
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
!!! important "Tool calling support"
|
||||
|
||||
If you are building an agent or workflow that requires the model to call external tools, ensure that the underlying
|
||||
language model supports [tool calling](../concepts/tools.md). Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
|
||||
|
||||
|
||||
## Use in an agent
|
||||
|
||||
:::python
|
||||
When using `create_react_agent` you can specify the model by its name string, which is a shorthand for initializing the model using `init_chat_model`. This allows you to use the model without needing to import or instantiate it directly.
|
||||
|
||||
=== "model name"
|
||||
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
|
||||
@@ -123,33 +70,10 @@ When using `create_react_agent` you can specify the model by its name string, wh
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
When using `createReactAgent` you can pass the model instance directly:
|
||||
|
||||
```typescript
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const model = new ChatOpenAI({
|
||||
model: "gpt-4o",
|
||||
temperature: 0,
|
||||
});
|
||||
|
||||
const agent = createReactAgent({
|
||||
llm: model,
|
||||
tools: tools,
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Advanced model configuration
|
||||
|
||||
### Disable streaming
|
||||
|
||||
:::python
|
||||
To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
|
||||
|
||||
=== "`init_chat_model`"
|
||||
@@ -177,25 +101,9 @@ To disable streaming of the individual LLM tokens, set `disable_streaming=True`
|
||||
```
|
||||
|
||||
Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
|
||||
:::
|
||||
|
||||
:::js
|
||||
To disable streaming of the individual LLM tokens, set `streaming: false` when initializing the model:
|
||||
|
||||
```typescript
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
|
||||
const model = new ChatOpenAI({
|
||||
model: "gpt-4o",
|
||||
streaming: false,
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
### Add model fallbacks
|
||||
|
||||
:::python
|
||||
You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
|
||||
|
||||
=== "`init_chat_model`"
|
||||
@@ -228,28 +136,6 @@ You can add a fallback to a different model or a different LLM provider using `m
|
||||
```
|
||||
|
||||
See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
|
||||
:::
|
||||
|
||||
:::js
|
||||
You can add a fallback to a different model or a different LLM provider using `model.withFallbacks([...])`:
|
||||
|
||||
```typescript
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
|
||||
const modelWithFallbacks = new ChatOpenAI({
|
||||
model: "gpt-4o",
|
||||
}).withFallbacks([
|
||||
new ChatAnthropic({
|
||||
model: "claude-3-5-sonnet-20240620",
|
||||
}),
|
||||
]);
|
||||
```
|
||||
|
||||
See this [guide](https://js.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
|
||||
:::
|
||||
|
||||
:::python
|
||||
|
||||
### Use the built-in rate limiter
|
||||
|
||||
@@ -266,49 +152,28 @@ rate_limiter = InMemoryRateLimiter(
|
||||
)
|
||||
|
||||
model = ChatAnthropic(
|
||||
model_name="claude-3-opus-20240229",
|
||||
model_name="claude-3-opus-20240229",
|
||||
rate_limiter=rate_limiter
|
||||
)
|
||||
```
|
||||
|
||||
See the LangChain docs for more information on how to [handle rate limiting](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/).
|
||||
:::
|
||||
|
||||
## Bring your own model
|
||||
|
||||
If your desired LLM isn't officially supported by LangChain, consider these options:
|
||||
|
||||
:::python
|
||||
|
||||
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://js.langchain.com/docs/how_to/custom_chat/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
|
||||
:::
|
||||
|
||||
2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
|
||||
Refer to the [custom streaming documentation](../how-tos/streaming.md#use-with-any-llm) for guidance. This approach suits custom workflows where prebuilt agent integration is not necessary.
|
||||
|
||||
|
||||
## Additional resources
|
||||
|
||||
:::python
|
||||
|
||||
- [Multimodal inputs](https://python.langchain.com/docs/how_to/multimodal_inputs/)
|
||||
- [Structured outputs](https://python.langchain.com/docs/how_to/structured_output/)
|
||||
- [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
|
||||
- [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
|
||||
- [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
|
||||
- [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
- [Multimodal inputs](https://js.langchain.com/docs/how_to/multimodal_inputs/)
|
||||
- [Structured outputs](https://js.langchain.com/docs/how_to/structured_output/)
|
||||
- [Model integration directory](https://js.langchain.com/docs/integrations/chat/)
|
||||
- [Force model to call a specific tool](https://js.langchain.com/docs/how_to/tool_choice/)
|
||||
- [All chat model how-to guides](https://js.langchain.com/docs/how_to/#chat-models)
|
||||
- [Chat model integrations](https://js.langchain.com/docs/integrations/chat/)
|
||||
:::
|
||||
|
||||
@@ -22,7 +22,6 @@ Two of the most popular multi-agent architectures are:
|
||||
|
||||
|
||||
|
||||
:::python
|
||||
Use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent system:
|
||||
|
||||
```bash
|
||||
@@ -83,76 +82,10 @@ for chunk in supervisor.stream(
|
||||
print("\n")
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
Use [`@langchain/langgraph-supervisor`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor) library to create a supervisor multi-agent system:
|
||||
|
||||
```bash
|
||||
npm install @langchain/langgraph-supervisor
|
||||
```
|
||||
|
||||
```typescript
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
// highlight-next-line
|
||||
import { createSupervisor } from "langgraph-supervisor";
|
||||
|
||||
function bookHotel(hotelName: string) {
|
||||
/**Book a hotel*/
|
||||
return `Successfully booked a stay at ${hotelName}.`;
|
||||
}
|
||||
|
||||
function bookFlight(fromAirport: string, toAirport: string) {
|
||||
/**Book a flight*/
|
||||
return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
|
||||
}
|
||||
|
||||
const flightAssistant = createReactAgent({
|
||||
llm: "openai:gpt-4o",
|
||||
tools: [bookFlight],
|
||||
stateModifier: "You are a flight booking assistant",
|
||||
// highlight-next-line
|
||||
name: "flight_assistant",
|
||||
});
|
||||
|
||||
const hotelAssistant = createReactAgent({
|
||||
llm: "openai:gpt-4o",
|
||||
tools: [bookHotel],
|
||||
stateModifier: "You are a hotel booking assistant",
|
||||
// highlight-next-line
|
||||
name: "hotel_assistant",
|
||||
});
|
||||
|
||||
// highlight-next-line
|
||||
const supervisor = createSupervisor({
|
||||
agents: [flightAssistant, hotelAssistant],
|
||||
llm: new ChatOpenAI({ model: "gpt-4o" }),
|
||||
systemPrompt:
|
||||
"You manage a hotel booking assistant and a " +
|
||||
"flight booking assistant. Assign work to them.",
|
||||
});
|
||||
|
||||
for await (const chunk of supervisor.stream({
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
|
||||
},
|
||||
],
|
||||
})) {
|
||||
console.log(chunk);
|
||||
console.log("\n");
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Swarm
|
||||
|
||||
|
||||
|
||||
:::python
|
||||
Use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent system:
|
||||
|
||||
```bash
|
||||
@@ -210,82 +143,18 @@ for chunk in swarm.stream(
|
||||
print("\n")
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
Use [`@langchain/langgraph-swarm`](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm) library to create a swarm multi-agent system:
|
||||
|
||||
```bash
|
||||
npm install @langchain/langgraph-swarm
|
||||
```
|
||||
|
||||
```typescript
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
// highlight-next-line
|
||||
import { createSwarm, createHandoffTool } from "@langchain/langgraph-swarm";
|
||||
|
||||
const transferToHotelAssistant = createHandoffTool({
|
||||
agentName: "hotel_assistant",
|
||||
description: "Transfer user to the hotel-booking assistant.",
|
||||
});
|
||||
|
||||
const transferToFlightAssistant = createHandoffTool({
|
||||
agentName: "flight_assistant",
|
||||
description: "Transfer user to the flight-booking assistant.",
|
||||
});
|
||||
|
||||
const flightAssistant = createReactAgent({
|
||||
llm: "anthropic:claude-3-5-sonnet-latest",
|
||||
// highlight-next-line
|
||||
tools: [bookFlight, transferToHotelAssistant],
|
||||
stateModifier: "You are a flight booking assistant",
|
||||
// highlight-next-line
|
||||
name: "flight_assistant",
|
||||
});
|
||||
|
||||
const hotelAssistant = createReactAgent({
|
||||
llm: "anthropic:claude-3-5-sonnet-latest",
|
||||
// highlight-next-line
|
||||
tools: [bookHotel, transferToFlightAssistant],
|
||||
stateModifier: "You are a hotel booking assistant",
|
||||
// highlight-next-line
|
||||
name: "hotel_assistant",
|
||||
});
|
||||
|
||||
// highlight-next-line
|
||||
const swarm = createSwarm({
|
||||
agents: [flightAssistant, hotelAssistant],
|
||||
defaultActiveAgent: "flight_assistant",
|
||||
});
|
||||
|
||||
for await (const chunk of swarm.stream({
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
|
||||
},
|
||||
],
|
||||
})) {
|
||||
console.log(chunk);
|
||||
console.log("\n");
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Handoffs
|
||||
|
||||
A common pattern in multi-agent interactions is **handoffs**, where one agent _hands off_ control to another. Handoffs allow you to specify:
|
||||
A common pattern in multi-agent interactions is **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:
|
||||
|
||||
- **destination**: target agent to navigate to
|
||||
- **payload**: information to pass to that agent
|
||||
|
||||
:::python
|
||||
This is used both by `langgraph-supervisor` (supervisor hands off to individual agents) and `langgraph-swarm` (an individual agent can hand off to other agents).
|
||||
|
||||
To implement handoffs with `create_react_agent`, you need to:
|
||||
|
||||
1. Create a special tool that can transfer control to a different agent
|
||||
1. Create a special tool that can transfer control to a different agent
|
||||
|
||||
```python
|
||||
def transfer_to_bob():
|
||||
@@ -304,7 +173,7 @@ To implement handoffs with `create_react_agent`, you need to:
|
||||
)
|
||||
```
|
||||
|
||||
2. Create individual agents that have access to handoff tools:
|
||||
1. Create individual agents that have access to handoff tools:
|
||||
|
||||
```python
|
||||
flight_assistant = create_react_agent(
|
||||
@@ -315,7 +184,7 @@ To implement handoffs with `create_react_agent`, you need to:
|
||||
)
|
||||
```
|
||||
|
||||
3. Define a parent graph that contains individual agents as nodes:
|
||||
1. Define a parent graph that contains individual agents as nodes:
|
||||
|
||||
```python
|
||||
from langgraph.graph import StateGraph, MessagesState
|
||||
@@ -327,60 +196,8 @@ To implement handoffs with `create_react_agent`, you need to:
|
||||
)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
This is used both by `@langchain/langgraph-supervisor` (supervisor hands off to individual agents) and `@langchain/langgraph-swarm` (an individual agent can hand off to other agents).
|
||||
|
||||
To implement handoffs with `createReactAgent`, you need to:
|
||||
|
||||
1. Create a special tool that can transfer control to a different agent
|
||||
|
||||
```typescript
|
||||
function transferToBob() {
|
||||
/**Transfer to bob.*/
|
||||
return new Command({
|
||||
// name of the agent (node) to go to
|
||||
// highlight-next-line
|
||||
goto: "bob",
|
||||
// data to send to the agent
|
||||
// highlight-next-line
|
||||
update: { messages: [...] },
|
||||
// indicate to LangGraph that we need to navigate to
|
||||
// agent node in a parent graph
|
||||
// highlight-next-line
|
||||
graph: Command.PARENT,
|
||||
});
|
||||
}
|
||||
```
|
||||
|
||||
2. Create individual agents that have access to handoff tools:
|
||||
|
||||
```typescript
|
||||
const flightAssistant = createReactAgent({
|
||||
..., tools: [bookFlight, transferToHotelAssistant]
|
||||
});
|
||||
const hotelAssistant = createReactAgent({
|
||||
..., tools: [bookHotel, transferToFlightAssistant]
|
||||
});
|
||||
```
|
||||
|
||||
3. Define a parent graph that contains individual agents as nodes:
|
||||
|
||||
```typescript
|
||||
import { StateGraph, MessagesZodState } from "@langchain/langgraph";
|
||||
const multiAgentGraph = new StateGraph(MessagesZodState)
|
||||
.addNode("flight_assistant", flightAssistant)
|
||||
.addNode("hotel_assistant", hotelAssistant)
|
||||
// ...
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
Putting this together, here is how you can implement a simple multi-agent system with two agents — a flight booking assistant and a hotel booking assistant:
|
||||
|
||||
:::python
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
from langchain_core.tools import tool, InjectedToolCallId
|
||||
@@ -481,157 +298,11 @@ for chunk in multi_agent_graph.stream(
|
||||
3. Name of the agent or node to hand off to.
|
||||
4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
|
||||
5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import { tool } from "@langchain/core/tools";
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import {
|
||||
StateGraph,
|
||||
START,
|
||||
MessagesZodState,
|
||||
Command,
|
||||
} from "@langchain/langgraph";
|
||||
import { z } from "zod";
|
||||
|
||||
function createHandoffTool({
|
||||
agentName,
|
||||
description,
|
||||
}: {
|
||||
agentName: string;
|
||||
description?: string;
|
||||
}) {
|
||||
const name = `transfer_to_${agentName}`;
|
||||
const toolDescription = description || `Transfer to ${agentName}`;
|
||||
|
||||
return tool(
|
||||
async (_, config) => {
|
||||
const toolMessage = {
|
||||
role: "tool" as const,
|
||||
content: `Successfully transferred to ${agentName}`,
|
||||
name: name,
|
||||
tool_call_id: config.toolCall?.id!,
|
||||
};
|
||||
return new Command({
|
||||
// (2)!
|
||||
// highlight-next-line
|
||||
goto: agentName, // (3)!
|
||||
// highlight-next-line
|
||||
update: { messages: [toolMessage] }, // (4)!
|
||||
// highlight-next-line
|
||||
graph: Command.PARENT, // (5)!
|
||||
});
|
||||
},
|
||||
{
|
||||
name,
|
||||
description: toolDescription,
|
||||
schema: z.object({}),
|
||||
}
|
||||
);
|
||||
}
|
||||
|
||||
// Handoffs
|
||||
const transferToHotelAssistant = createHandoffTool({
|
||||
agentName: "hotel_assistant",
|
||||
description: "Transfer user to the hotel-booking assistant.",
|
||||
});
|
||||
|
||||
const transferToFlightAssistant = createHandoffTool({
|
||||
agentName: "flight_assistant",
|
||||
description: "Transfer user to the flight-booking assistant.",
|
||||
});
|
||||
|
||||
// Simple agent tools
|
||||
const bookHotel = tool(
|
||||
async ({ hotelName }) => {
|
||||
/**Book a hotel*/
|
||||
return `Successfully booked a stay at ${hotelName}.`;
|
||||
},
|
||||
{
|
||||
name: "book_hotel",
|
||||
description: "Book a hotel",
|
||||
schema: z.object({
|
||||
hotelName: z.string().describe("Name of the hotel to book"),
|
||||
}),
|
||||
}
|
||||
);
|
||||
|
||||
const bookFlight = tool(
|
||||
async ({ fromAirport, toAirport }) => {
|
||||
/**Book a flight*/
|
||||
return `Successfully booked a flight from ${fromAirport} to ${toAirport}.`;
|
||||
},
|
||||
{
|
||||
name: "book_flight",
|
||||
description: "Book a flight",
|
||||
schema: z.object({
|
||||
fromAirport: z.string().describe("Departure airport code"),
|
||||
toAirport: z.string().describe("Arrival airport code"),
|
||||
}),
|
||||
}
|
||||
);
|
||||
|
||||
// Define agents
|
||||
const flightAssistant = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
|
||||
// highlight-next-line
|
||||
tools: [bookFlight, transferToHotelAssistant],
|
||||
stateModifier: "You are a flight booking assistant",
|
||||
// highlight-next-line
|
||||
name: "flight_assistant",
|
||||
});
|
||||
|
||||
const hotelAssistant = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
|
||||
// highlight-next-line
|
||||
tools: [bookHotel, transferToFlightAssistant],
|
||||
stateModifier: "You are a hotel booking assistant",
|
||||
// highlight-next-line
|
||||
name: "hotel_assistant",
|
||||
});
|
||||
|
||||
// Define multi-agent graph
|
||||
const multiAgentGraph = new StateGraph(MessagesZodState)
|
||||
.addNode("flight_assistant", flightAssistant)
|
||||
.addNode("hotel_assistant", hotelAssistant)
|
||||
.addEdge(START, "flight_assistant")
|
||||
.compile();
|
||||
|
||||
// Run the multi-agent graph
|
||||
for await (const chunk of multiAgentGraph.stream({
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: "book a flight from BOS to JFK and a stay at McKittrick Hotel",
|
||||
},
|
||||
],
|
||||
})) {
|
||||
console.log(chunk);
|
||||
console.log("\n");
|
||||
}
|
||||
```
|
||||
|
||||
1. Access agent's state
|
||||
2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
|
||||
3. Name of the agent or node to hand off to.
|
||||
4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
|
||||
5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
|
||||
:::
|
||||
|
||||
!!! Note
|
||||
|
||||
This handoff implementation assumes that:
|
||||
|
||||
- each agent receives overall message history (across all agents) in the multi-agent system as its input
|
||||
- each agent outputs its internal messages history to the overall message history of the multi-agent system
|
||||
|
||||
:::python
|
||||
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.
|
||||
:::
|
||||
|
||||
:::js
|
||||
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm#customizing-handoff-tools) documentation to learn how to customize handoffs.
|
||||
:::
|
||||
Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.
|
||||
+21
-177
@@ -14,7 +14,7 @@ LangGraph provides both low-level primitives and high-level prebuilt components
|
||||
|
||||
## What is an agent?
|
||||
|
||||
An _agent_ consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
|
||||
An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
|
||||
|
||||
The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
|
||||
|
||||
@@ -27,12 +27,12 @@ The LLM operates in a loop. In each iteration, it selects a tool to invoke, prov
|
||||
|
||||
LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
|
||||
|
||||
- [**Memory integration**](../how-tos/memory/add-memory.md): Native support for _short-term_ (session-based) and _long-term_ (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
|
||||
- [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause _indefinitely_ to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
|
||||
- [**Memory integration**](../how-tos/memory/add-memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
|
||||
- [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
|
||||
- [**Streaming support**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
|
||||
- [**Deployment tooling**](../tutorials/langgraph-platform/local-server.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
|
||||
- **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
|
||||
- Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.
|
||||
- **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
|
||||
- Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.
|
||||
|
||||
## High-level building blocks
|
||||
|
||||
@@ -40,32 +40,30 @@ LangGraph comes with a set of prebuilt components that implement common agent be
|
||||
|
||||
Using LangGraph for agent development allows you to focus on your application's logic and behavior, instead of building and maintaining the supporting infrastructure for state, memory, and human feedback.
|
||||
|
||||
:::python
|
||||
|
||||
## Package ecosystem
|
||||
|
||||
The high-level components are organized into several packages, each with a specific focus.
|
||||
|
||||
| Package | Description | Installation |
|
||||
| ------------------------------------------ | ---------------------------------------------------------------------------------------- | --------------------------------------- |
|
||||
| `langgraph-prebuilt` (part of `langgraph`) | Prebuilt components to [**create agents**](./agents.md) | `pip install -U langgraph langchain` |
|
||||
| `langgraph-supervisor` | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents | `pip install -U langgraph-supervisor` |
|
||||
| `langgraph-swarm` | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm` |
|
||||
| `langchain-mcp-adapters` | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
|
||||
| `langmem` | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md) | `pip install -U langmem` |
|
||||
| `agentevals` | Utilities to [**evaluate agent performance**](./evals.md) | `pip install -U agentevals` |
|
||||
| Package | Description | Installation |
|
||||
|--------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------|
|
||||
| `langgraph-prebuilt` (part of `langgraph`) | Prebuilt components to [**create agents**](./agents.md) | `pip install -U langgraph langchain` |
|
||||
| `langgraph-supervisor` | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents | `pip install -U langgraph-supervisor` |
|
||||
| `langgraph-swarm` | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm` |
|
||||
| `langchain-mcp-adapters` | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
|
||||
| `langmem` | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md) | `pip install -U langmem` |
|
||||
| `agentevals` | Utilities to [**evaluate agent performance**](./evals.md) | `pip install -U agentevals` |
|
||||
|
||||
## Visualize an agent graph
|
||||
|
||||
Use the following tool to visualize the graph generated by
|
||||
@[`create_react_agent`][create_react_agent]
|
||||
[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
|
||||
and to view an outline of the corresponding code.
|
||||
It allows you to explore the infrastructure of the agent as defined by the presence of:
|
||||
|
||||
- [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
|
||||
- [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
|
||||
- `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
|
||||
- [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
|
||||
* [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
|
||||
* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
|
||||
* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
|
||||
* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
|
||||
|
||||
<div class="agent-layout">
|
||||
<div class="agent-graph-features-container">
|
||||
@@ -84,13 +82,15 @@ It allows you to explore the infrastructure of the agent as defined by the prese
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
The following code snippet shows how to create the above agent (and underlying graph) with
|
||||
@[`create_react_agent`][create_react_agent]:
|
||||
[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
|
||||
|
||||
<div class="language-python">
|
||||
<pre><code id="agent-code" class="language-python"></code></pre>
|
||||
</div>
|
||||
|
||||
|
||||
<script>
|
||||
function getCheckedValue(id) {
|
||||
return document.getElementById(id).checked ? "1" : "0";
|
||||
@@ -189,159 +189,3 @@ function initializeWidget() {
|
||||
window.addEventListener("DOMContentLoaded", initializeWidget);
|
||||
document$.subscribe(initializeWidget);
|
||||
</script>
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
## Package ecosystem
|
||||
|
||||
The high-level components are organized into several packages, each with a specific focus.
|
||||
|
||||
| Package | Description | Installation |
|
||||
| ------------------------ | --------------------------------------------------------------------------- | -------------------------------------------------- |
|
||||
| `langgraph` | Prebuilt components to [**create agents**](./agents.md) | `npm install @langchain/langgraph @langchain/core` |
|
||||
| `langgraph-supervisor` | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents | `npm install @langchain/langgraph-supervisor` |
|
||||
| `langgraph-swarm` | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `npm install @langchain/langgraph-swarm` |
|
||||
| `langchain-mcp-adapters` | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `npm install @langchain/mcp-adapters` |
|
||||
| `agentevals` | Utilities to [**evaluate agent performance**](./evals.md) | `npm install agentevals` |
|
||||
|
||||
## Visualize an agent graph
|
||||
|
||||
Use the following tool to visualize the graph generated by @[`createReactAgent`][create_react_agent] and to view an outline of the corresponding code. It allows you to explore the infrastructure of the agent as defined by the presence of:
|
||||
|
||||
- [`tools`](./tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
|
||||
- `preModelHook`: A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
|
||||
- `postModelHook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
|
||||
- [`responseFormat`](./agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output (via Zod schemas).
|
||||
|
||||
<div class="agent-layout">
|
||||
<div class="agent-graph-features-container">
|
||||
<div class="agent-graph-features">
|
||||
<h3 class="agent-section-title">Features</h3>
|
||||
<label><input type="checkbox" id="tools" checked> <code>tools</code></label>
|
||||
<label><input type="checkbox" id="preModelHook"> <code>preModelHook</code></label>
|
||||
<label><input type="checkbox" id="postModelHook"> <code>postModelHook</code></label>
|
||||
<label><input type="checkbox" id="responseFormat"> <code>responseFormat</code></label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="agent-graph-container">
|
||||
<h3 class="agent-section-title">Graph</h3>
|
||||
<img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
The following code snippet shows how to create the above agent (and underlying graph) with @[`createReactAgent`][create_react_agent]:
|
||||
|
||||
<div class="language-typescript">
|
||||
<pre><code id="agent-code" class="language-typescript"></code></pre>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
function getCheckedValue(id) {
|
||||
return document.getElementById(id).checked ? "1" : "0";
|
||||
}
|
||||
|
||||
function getKey() {
|
||||
return [
|
||||
getCheckedValue("responseFormat"),
|
||||
getCheckedValue("postModelHook"),
|
||||
getCheckedValue("preModelHook"),
|
||||
getCheckedValue("tools")
|
||||
].join("");
|
||||
}
|
||||
|
||||
function dedent(strings, ...values) {
|
||||
const str = String.raw({ raw: strings }, ...values)
|
||||
const [space] = str.split("\n").filter(Boolean).at(0).match(/^(\s*)/)
|
||||
const spaceLen = space.length
|
||||
return str.split("\n").map(line => line.slice(spaceLen)).join("\n").trim()
|
||||
}
|
||||
|
||||
Object.assign(dedent, {
|
||||
offset: (size) => (strings, ...values) => {
|
||||
return dedent(strings, ...values).split("\n").map(line => " ".repeat(size) + line).join("\n")
|
||||
}
|
||||
})
|
||||
|
||||
|
||||
|
||||
|
||||
function generateCodeSnippet({ tools, pre, post, response }) {
|
||||
const lines = []
|
||||
|
||||
lines.push(dedent`
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
`)
|
||||
|
||||
if (tools) lines.push(`import { tool } from "@langchain/core/tools";`);
|
||||
if (response || tools) lines.push(`import { z } from "zod";`);
|
||||
|
||||
lines.push("", dedent`
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatOpenAI({ model: "o4-mini" }),
|
||||
`)
|
||||
|
||||
if (tools) {
|
||||
lines.push(dedent.offset(2)`
|
||||
tools: [
|
||||
tool(() => "Sample tool output", {
|
||||
name: "sampleTool",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
],
|
||||
`)
|
||||
}
|
||||
|
||||
if (pre) {
|
||||
lines.push(dedent.offset(2)`
|
||||
preModelHook: (state) => ({ llmInputMessages: state.messages }),
|
||||
`)
|
||||
}
|
||||
|
||||
if (post) {
|
||||
lines.push(dedent.offset(2)`
|
||||
postModelHook: (state) => state,
|
||||
`)
|
||||
}
|
||||
|
||||
if (response) {
|
||||
lines.push(dedent.offset(2)`
|
||||
responseFormat: z.object({ result: z.string() }),
|
||||
`)
|
||||
}
|
||||
|
||||
lines.push(`});`);
|
||||
|
||||
return lines.join("\n");
|
||||
}
|
||||
|
||||
function render() {
|
||||
const key = getKey();
|
||||
document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
|
||||
|
||||
const state = {
|
||||
tools: document.getElementById("tools").checked,
|
||||
pre: document.getElementById("preModelHook").checked,
|
||||
post: document.getElementById("postModelHook").checked,
|
||||
response: document.getElementById("responseFormat").checked
|
||||
};
|
||||
|
||||
document.getElementById("agent-code").textContent = generateCodeSnippet(state);
|
||||
}
|
||||
|
||||
function initializeWidget() {
|
||||
render(); // no need for `await` here
|
||||
document.querySelectorAll(".agent-graph-features input").forEach((input) => {
|
||||
input.addEventListener("change", render);
|
||||
});
|
||||
}
|
||||
|
||||
// Init for both full reload and SPA nav (used by MkDocs Material)
|
||||
window.addEventListener("DOMContentLoaded", initializeWidget);
|
||||
document$.subscribe(initializeWidget);
|
||||
</script>
|
||||
|
||||
:::
|
||||
|
||||
@@ -5,24 +5,23 @@ If you’re looking for other prebuilt libraries, explore the community-built op
|
||||
below. These libraries can extend LangGraph's functionality in various ways.
|
||||
|
||||
## 📚 Available Libraries
|
||||
|
||||
[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
|
||||
|
||||
:::python
|
||||
| Name | GitHub URL | Description | Weekly Downloads | Stars |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **trustcall** | https://github.com/hinthornw/trustcall | Tenacious tool calling built on LangGraph. | -12345 | 
|
||||
| **breeze-agent** | https://github.com/andrestorres123/breeze-agent | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | 
|
||||
| **langgraph-supervisor** | https://github.com/langchain-ai/langgraph-supervisor-py | Build supervisor multi-agent systems with LangGraph. | -12345 | 
|
||||
| **langmem** | https://github.com/langchain-ai/langmem | Build agents that learn and adapt from interactions over time. | -12345 | 
|
||||
| **langchain-mcp-adapters** | https://github.com/langchain-ai/langchain-mcp-adapters | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | 
|
||||
| **open-deep-research** | https://github.com/langchain-ai/open_deep_research | Open source assistant for iterative web research and report writing. | -12345 | 
|
||||
| **langgraph-swarm** | https://github.com/langchain-ai/langgraph-swarm-py | Build swarm-style multi-agent systems using LangGraph. | -12345 | 
|
||||
| **delve-taxonomy-generator** | https://github.com/andrestorres123/delve | A taxonomy generator for unstructured data | -12345 | 
|
||||
| **nodeology** | https://github.com/xyin-anl/Nodeology | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | 
|
||||
| **langgraph-bigtool** | https://github.com/langchain-ai/langgraph-bigtool | Build LangGraph agents with large numbers of tools. | -12345 | 
|
||||
| **ai-data-science-team** | https://github.com/business-science/ai-data-science-team | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | 
|
||||
| **langgraph-reflection** | https://github.com/langchain-ai/langgraph-reflection | LangGraph agent that runs a reflection step. | -12345 | 
|
||||
| **langgraph-codeact** | https://github.com/langchain-ai/langgraph-codeact | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | 
|
||||
| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph. | -12345 | 
|
||||
| **breeze-agent** | [andrestorres123/breeze-agent](https://github.com/andrestorres123/breeze-agent) | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | 
|
||||
| **langgraph-supervisor** | [langchain-ai/langgraph-supervisor-py](https://github.com/langchain-ai/langgraph-supervisor-py) | Build supervisor multi-agent systems with LangGraph. | -12345 | 
|
||||
| **langmem** | [langchain-ai/langmem](https://github.com/langchain-ai/langmem) | Build agents that learn and adapt from interactions over time. | -12345 | 
|
||||
| **langchain-mcp-adapters** | [langchain-ai/langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | 
|
||||
| **open-deep-research** | [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research) | Open source assistant for iterative web research and report writing. | -12345 | 
|
||||
| **langgraph-swarm** | [langchain-ai/langgraph-swarm-py](https://github.com/langchain-ai/langgraph-swarm-py) | Build swarm-style multi-agent systems using LangGraph. | -12345 | 
|
||||
| **delve-taxonomy-generator** | [andrestorres123/delve](https://github.com/andrestorres123/delve) | A taxonomy generator for unstructured data | -12345 | 
|
||||
| **nodeology** | [xyin-anl/Nodeology](https://github.com/xyin-anl/Nodeology) | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | 
|
||||
| **langgraph-bigtool** | [langchain-ai/langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool) | Build LangGraph agents with large numbers of tools. | -12345 | 
|
||||
| **ai-data-science-team** | [business-science/ai-data-science-team](https://github.com/business-science/ai-data-science-team) | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | 
|
||||
| **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | 
|
||||
| **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | 
|
||||
|
||||
## ✨ Contributing Your Library
|
||||
|
||||
@@ -33,41 +32,13 @@ To share your project, simply open a Pull Request adding an entry for your packa
|
||||
|
||||
**Guidelines**
|
||||
|
||||
- Your repo must be distributed as an installable package on PyPI 📦
|
||||
- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm
|
||||
for JavaScript/TypeScript, etc.) 📦
|
||||
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
|
||||
the Functional API (exposing an `entrypoint`).
|
||||
- The package must include documentation (e.g., a `README.md` or docs site)
|
||||
explaining how to use it.
|
||||
|
||||
|
||||
We'll review your contribution and merge it in!
|
||||
|
||||
Thanks for contributing! 🚀
|
||||
:::
|
||||
|
||||
:::js
|
||||
| Name | GitHub URL | Description | Weekly Downloads | Stars |
|
||||
| --- | --- | --- | --- | --- |
|
||||
| **@langchain/mcp-adapters** | https://github.com/langchain-ai/langchainjs | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | 
|
||||
| **@langchain/langgraph-supervisor** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-supervisor | Build supervisor multi-agent systems with LangGraph | -12345 | 
|
||||
| **@langchain/langgraph-swarm** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-swarm | Build multi-agent swarms with LangGraph | -12345 | 
|
||||
| **@langchain/langgraph-cua** | https://github.com/langchain-ai/langgraphjs/tree/main/libs/langgraph-cua | Build computer use agents with LangGraph | -12345 | 
|
||||
|
||||
## ✨ Contributing Your Library
|
||||
|
||||
Have you built an awesome open-source library using LangGraph? We'd love to feature
|
||||
your project on the official LangGraph documentation pages! 🏆
|
||||
|
||||
To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
|
||||
|
||||
**Guidelines**
|
||||
|
||||
- Your repo must be distributed as an installable package on npm 📦
|
||||
- The repo should either use the Graph API (exposing a `StateGraph` instance) or
|
||||
the Functional API (exposing an `entrypoint`).
|
||||
- The package must include documentation (e.g., a `README.md` or docs site)
|
||||
explaining how to use it.
|
||||
|
||||
We'll review your contribution and merge it in!
|
||||
|
||||
Thanks for contributing! 🚀
|
||||
:::
|
||||
|
||||
@@ -9,27 +9,18 @@ hide:
|
||||
|
||||
# Running agents
|
||||
|
||||
|
||||
Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .ainvoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](../how-tos/streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
|
||||
|
||||
|
||||
## Basic usage
|
||||
|
||||
Agents can be executed in two primary modes:
|
||||
|
||||
:::python
|
||||
|
||||
- **Synchronous** using `.invoke()` or `.stream()`
|
||||
- **Asynchronous** using `await .ainvoke()` or `async for` with `.astream()`
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
- **Synchronous** using `.invoke()` or `.stream()`
|
||||
- **Asynchronous** using `await .invoke()` or `for await` with `.stream()`
|
||||
:::
|
||||
|
||||
:::python
|
||||
=== "Sync invocation"
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
|
||||
@@ -40,7 +31,6 @@ Agents can be executed in two primary modes:
|
||||
```
|
||||
|
||||
=== "Async invocation"
|
||||
|
||||
```python
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
|
||||
@@ -49,24 +39,6 @@ Agents can be executed in two primary modes:
|
||||
response = await agent.ainvoke({"messages": [{"role": "user", "content": "what is the weather in sf"}]})
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const agent = createReactAgent(...);
|
||||
// highlight-next-line
|
||||
const response = await agent.invoke({
|
||||
"messages": [
|
||||
{ "role": "user", "content": "what is the weather in sf" }
|
||||
]
|
||||
});
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
## Inputs and outputs
|
||||
|
||||
Agents use a language model that expects a list of `messages` as an input. Therefore, agent inputs and outputs are stored as a list of `messages` under the `messages` key in the agent [state](../concepts/low_level.md#working-with-messages-in-graph-state).
|
||||
@@ -75,73 +47,33 @@ Agents use a language model that expects a list of `messages` as an input. There
|
||||
|
||||
Agent input must be a dictionary with a `messages` key. Supported formats are:
|
||||
|
||||
:::python
|
||||
| Format | Example |
|
||||
| Format | Example |
|
||||
|--------------------|-------------------------------------------------------------------------------------------------------------------------------|
|
||||
| String | `{"messages": "Hello"}` — Interpreted as a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage) |
|
||||
| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}` |
|
||||
| List of messages | `{"messages": [{"role": "user", "content": "Hello"}]}` |
|
||||
| With custom state | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom `state_schema` |
|
||||
:::
|
||||
| String | `{"messages": "Hello"}` — Interpreted as a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage) |
|
||||
| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}` |
|
||||
| List of messages | `{"messages": [{"role": "user", "content": "Hello"}]}` |
|
||||
| With custom state | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom `state_schema` |
|
||||
|
||||
:::js
|
||||
| Format | Example |
|
||||
|--------------------|-------------------------------------------------------------------------------------------------------------------------------|
|
||||
| String | `{"messages": "Hello"}` — Interpreted as a [HumanMessage](https://js.langchain.com/docs/concepts/messages/#humanmessage) |
|
||||
| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}` |
|
||||
| List of messages | `{"messages": [{"role": "user", "content": "Hello"}]}` |
|
||||
| With custom state | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom state definition |
|
||||
:::
|
||||
|
||||
:::python
|
||||
Messages are automatically converted into LangChain's internal message format. You can read
|
||||
more about [LangChain messages](https://python.langchain.com/docs/concepts/messages/#langchain-messages) in the LangChain documentation.
|
||||
:::
|
||||
|
||||
:::js
|
||||
Messages are automatically converted into LangChain's internal message format. You can read
|
||||
more about [LangChain messages](https://js.langchain.com/docs/concepts/messages/#langchain-messages) in the LangChain documentation.
|
||||
:::
|
||||
|
||||
!!! tip "Using custom agent state"
|
||||
|
||||
:::python
|
||||
You can provide additional fields defined in your agent's state schema directly in the input dictionary. This allows dynamic behavior based on runtime data or prior tool outputs.
|
||||
You can provide additional fields defined in your agent’s state schema directly in the input dictionary. This allows dynamic behavior based on runtime data or prior tool outputs.
|
||||
See the [context guide](./context.md) for full details.
|
||||
:::
|
||||
|
||||
:::js
|
||||
You can provide additional fields defined in your agent's state directly in the state definition. This allows dynamic behavior based on runtime data or prior tool outputs.
|
||||
See the [context guide](./context.md) for full details.
|
||||
:::
|
||||
|
||||
!!! note
|
||||
|
||||
:::python
|
||||
A string input for `messages` is converted to a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage). This behavior differs from the `prompt` parameter in `create_react_agent`, which is interpreted as a [SystemMessage](https://python.langchain.com/docs/concepts/messages/#systemmessage) when passed as a string.
|
||||
:::
|
||||
|
||||
:::js
|
||||
A string input for `messages` is converted to a [HumanMessage](https://js.langchain.com/docs/concepts/messages/#humanmessage). This behavior differs from the `prompt` parameter in `createReactAgent`, which is interpreted as a [SystemMessage](https://js.langchain.com/docs/concepts/messages/#systemmessage) when passed as a string.
|
||||
:::
|
||||
|
||||
## Output format
|
||||
|
||||
:::python
|
||||
Agent output is a dictionary containing:
|
||||
|
||||
- `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
|
||||
- Optionally, `structured_response` if [structured output](./agents.md#6-configure-structured-output) is configured.
|
||||
- If using a custom `state_schema`, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
|
||||
:::
|
||||
|
||||
:::js
|
||||
Agent output is a dictionary containing:
|
||||
|
||||
- `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
|
||||
- Optionally, `structuredResponse` if [structured output](./agents.md#6-configure-structured-output) is configured.
|
||||
- If using a custom state definition, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
|
||||
:::
|
||||
|
||||
See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.
|
||||
|
||||
@@ -155,7 +87,6 @@ Agents support streaming responses for more responsive applications. This includ
|
||||
|
||||
Streaming is available in both sync and async modes:
|
||||
|
||||
:::python
|
||||
=== "Sync streaming"
|
||||
|
||||
```python
|
||||
@@ -176,36 +107,14 @@ Streaming is available in both sync and async modes:
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
|
||||
```typescript
|
||||
for await (const chunk of agent.stream(
|
||||
{ messages: [{ role: "user", content: "what is the weather in sf" }] },
|
||||
{ streamMode: "updates" }
|
||||
)) {
|
||||
console.log(chunk);
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
!!! tip
|
||||
|
||||
For full details, see the [streaming guide](../how-tos/streaming.md).
|
||||
|
||||
## Max iterations
|
||||
|
||||
:::python
|
||||
To control agent execution and avoid infinite loops, set a recursion limit. This defines the maximum number of steps the agent can take before raising a `GraphRecursionError`. You can configure `recursion_limit` at runtime or when defining agent via `.with_config()`:
|
||||
:::
|
||||
|
||||
:::js
|
||||
To control agent execution and avoid infinite loops, set a recursion limit. This defines the maximum number of steps the agent can take before raising a `GraphRecursionError`. You can configure `recursionLimit` at runtime or when defining agent via `.withConfig()`:
|
||||
:::
|
||||
|
||||
:::python
|
||||
=== "Runtime"
|
||||
|
||||
```python
|
||||
@@ -254,70 +163,6 @@ To control agent execution and avoid infinite loops, set a recursion limit. This
|
||||
print("Agent stopped due to max iterations.")
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::js
|
||||
=== "Runtime"
|
||||
|
||||
```typescript
|
||||
import { GraphRecursionError } from "@langchain/langgraph";
|
||||
import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const maxIterations = 3;
|
||||
// highlight-next-line
|
||||
const recursionLimit = 2 * maxIterations + 1;
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
|
||||
tools: [getWeather]
|
||||
});
|
||||
|
||||
try {
|
||||
const response = await agent.invoke(
|
||||
{"messages": [{"role": "user", "content": "what's the weather in sf"}]},
|
||||
// highlight-next-line
|
||||
{ recursionLimit }
|
||||
);
|
||||
} catch (error) {
|
||||
if (error instanceof GraphRecursionError) {
|
||||
console.log("Agent stopped due to max iterations.");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
=== "`.withConfig()`"
|
||||
|
||||
```typescript
|
||||
import { GraphRecursionError } from "@langchain/langgraph";
|
||||
import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
|
||||
import { createReactAgent } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const maxIterations = 3;
|
||||
// highlight-next-line
|
||||
const recursionLimit = 2 * maxIterations + 1;
|
||||
const agent = createReactAgent({
|
||||
llm: new ChatAnthropic({ model: "claude-3-5-haiku-latest" }),
|
||||
tools: [getWeather]
|
||||
});
|
||||
// highlight-next-line
|
||||
const agentWithRecursionLimit = agent.withConfig({ recursionLimit });
|
||||
|
||||
try {
|
||||
const response = await agentWithRecursionLimit.invoke(
|
||||
{"messages": [{"role": "user", "content": "what's the weather in sf"}]},
|
||||
);
|
||||
} catch (error) {
|
||||
if (error instanceof GraphRecursionError) {
|
||||
console.log("Agent stopped due to max iterations.");
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
:::
|
||||
|
||||
:::python
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)
|
||||
:::
|
||||
* [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)
|
||||
|
||||
@@ -31,7 +31,7 @@ Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_
|
||||
|
||||
!!! Important
|
||||
|
||||
Agent Chat UI works best if your LangGraph agent interrupts using the @[`HumanInterrupt` schema][HumanInterrupt]. If you do not use that schema, the Agent Chat UI will be able to render the input passed to the `interrupt` function, but it will not have full support for resuming your graph.
|
||||
Agent Chat UI works best if your LangGraph agent interrupts using the [`HumanInterrupt` schema][langgraph.prebuilt.interrupt.HumanInterrupt]. If you do not use that schema, the Agent Chat UI will be able to render the input passed to the `interrupt` function, but it will not have full support for resuming your graph.
|
||||
|
||||
## Generative UI
|
||||
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
## Cron jobs
|
||||
|
||||
There are many situations in which it is useful to run an assistant on a schedule.
|
||||
|
||||
For example, say that you're building an assistant that runs daily and sends an email summary
|
||||
of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
|
||||
|
||||
LangGraph Platform supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server will:
|
||||
|
||||
- Create a new thread with the specified assistant
|
||||
- Send the specified input to that thread
|
||||
|
||||
Note that this sends the same input to the thread every time. See the [how-to guide](../../cloud/how-tos/cron_jobs.md) for creating cron jobs.
|
||||
|
||||
The LangGraph Platform API provides several endpoints for creating and managing cron jobs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
|
||||
@@ -0,0 +1,54 @@
|
||||
# Data Storage and Privacy
|
||||
|
||||
This document describes how data is processed in the LangGraph CLI and the LangGraph Server for both the in-memory server (`langgraph dev`) and the local Docker server (`langgraph up`). It also describes what data is tracked when interacting with the hosted LangGraph Studio frontend.
|
||||
|
||||
## CLI
|
||||
|
||||
LangGraph **CLI** is the command-line interface for building and running LangGraph applications; see the [CLI guide](../../concepts/langgraph_cli.md) to learn more.
|
||||
|
||||
By default, calls to most CLI commands log a single analytics event upon invocation. This helps us better prioritize improvements to the CLI experience. Each telemetry event contains the calling process's OS, OS version, Python version, the CLI version, the command name (`dev`, `up`, `run`, etc.), and booleans representing whether a flag was passed to the command. You can see the full analytics logic [here](https://github.com/langchain-ai/langgraph/blob/main/libs/cli/langgraph_cli/analytics.py).
|
||||
|
||||
You can disable all CLI telemetry by setting `LANGGRAPH_CLI_NO_ANALYTICS=1`.
|
||||
|
||||
## LangGraph Server (in-memory & docker)
|
||||
|
||||
The [LangGraph Server](../../concepts/langgraph_server.md) provides a durable execution runtime that relies on persisting checkpoints of your application state, long-term memories, thread metadata, assistants, and similar resources to the local file system or a database. Unless you have deliberately customized the storage location, this information is either written to local disk (for `langgraph dev`) or a PostgreSQL database (for `langgraph up` and in all deployments).
|
||||
|
||||
### LangSmith Tracing
|
||||
|
||||
When running the LangGraph server (either in-memory or in Docker), LangSmith tracing may be enabled to facilitate faster debugging and offer observability of graph state and LLM prompts in production. You can always disable tracing by setting `LANGSMITH_TRACING=false` in your server's runtime environment.
|
||||
|
||||
### In-memory development server (`langgraph dev`)
|
||||
|
||||
`langgraph dev` runs an [in-memory development server](../../tutorials/langgraph-platform/local-server.md) as a single Python process, designed for quick development and testing. It saves all checkpointing and memory data to disk within a `.langgraph_api` directory in the current working directory. Apart from the telemetry data described in the [CLI](#cli) section, no data leaves the machine unless you have enabled tracing or your graph code explicitly contacts an external service.
|
||||
|
||||
### Standalone Container (`langgraph up`)
|
||||
|
||||
`langgraph up` builds your local package into a Docker image and runs the server as a [standalone container](../../concepts/deployment_options.md#standalone-container) consisting of three containers: the API server, a PostgreSQL container, and a Redis container. All persistent data (checkpoints, assistants, etc.) are stored in the PostgreSQL database. Redis is used as a pubsub connection for real-time streaming of events. You can encrypt all checkpoints before saving to the database by setting a valid `LANGGRAPH_AES_KEY` environment variable. You can also specify [TTLs](../../how-tos/ttl/configure_ttl.md) for checkpoints and cross-thread memories in `langgraph.json` to control how long data is stored. All persisted threads, memories, and other data can be deleted via the relevant API endpoints.
|
||||
|
||||
Additional API calls are made to confirm that the server has a valid license and to track the number of executed runs and tasks. Periodically, the API server validates the provided license key (or API key).
|
||||
|
||||
If you've disabled [tracing](#langsmith-tracing), no user data is persisted externally unless your graph code explicitly contacts an external service.
|
||||
|
||||
## Studio
|
||||
|
||||
[LangGraph Studio](../../concepts/langgraph_studio.md) is a graphical interface for interacting with your LangGraph server. It does not persist any private data (the data you send to your server is not sent to LangSmith). Though the studio interface is served at [smith.langchain.com](https://smith.langchain.com), it is run in your browser and connects directly to your local LangGraph server so that no data needs to be sent to LangSmith.
|
||||
|
||||
If you are logged in, LangSmith does collect some usage analytics to help improve studio's user experience. This includes:
|
||||
|
||||
- Page visits and navigation patterns
|
||||
- User actions (button clicks)
|
||||
- Browser type and version
|
||||
- Screen resolution and viewport size
|
||||
|
||||
Importantly, no application data or code (or other sensitive configuration details) are collected. All of that is stored in the persistence layer of your LangGraph server. When using Studio anonymously, no account creation is required and usage analytics are not collected.
|
||||
|
||||
## Quick reference
|
||||
|
||||
In summary, you can opt-out of server-side telemetry by turning off CLI analytics and disabling tracing.
|
||||
|
||||
| Variable | Purpose | Default |
|
||||
| ------------------------------ | ------------------------- | -------------------------------- |
|
||||
| `LANGGRAPH_CLI_NO_ANALYTICS=1` | Disable CLI analytics | Analytics enabled |
|
||||
| `LANGSMITH_API_KEY` | Enable LangSmith tracing | Tracing disabled |
|
||||
| `LANGSMITH_TRACING=false` | Disable LangSmith tracing | Depends on environment |
|
||||
@@ -0,0 +1,7 @@
|
||||
# Webhooks
|
||||
|
||||
Webhooks enable event-driven communication from your LangGraph Platform application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Platform has finished running.
|
||||
|
||||
Many LangGraph Platform endpoints accept a `webhook` parameter. If this parameter is specified by an endpoint that can accept POST requests, LangGraph Platform will send a request at the completion of a run.
|
||||
|
||||
See the corresponding [how-to guide](../../cloud/how-tos/webhooks.md) for more detail.
|
||||
@@ -0,0 +1,128 @@
|
||||
# How to Deploy to Cloud SaaS
|
||||
|
||||
Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. LangGraph Platform applications are deployed from GitHub repositories. Configure and upload a LangGraph Platform application to a GitHub repository in order to deploy it to LangGraph Platform.
|
||||
1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Platform will fail as well.
|
||||
|
||||
## Create New Deployment
|
||||
|
||||
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
|
||||
|
||||
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
|
||||
1. In the top-right corner, select `+ New Deployment` to create a new deployment.
|
||||
1. In the `Create New Deployment` panel, fill out the required fields.
|
||||
1. `Deployment details`
|
||||
1. Select `Import from GitHub` and follow the GitHub OAuth workflow to install and authorize LangChain's `hosted-langserve` GitHub app to access the selected repositories. After installation is complete, return to the `Create New Deployment` panel and select the GitHub repository to deploy from the dropdown menu. **Note**: The GitHub user installing LangChain's `hosted-langserve` GitHub app must be an [owner](https://docs.github.com/en/organizations/managing-peoples-access-to-your-organization-with-roles/roles-in-an-organization#organization-owners) of the organization or account.
|
||||
1. Specify a name for the deployment.
|
||||
1. Specify the desired `Git Branch`. A deployment is linked to a branch. When a new revision is created, code for the linked branch will be deployed. The branch can be updated later in the [Deployment Settings](#deployment-settings).
|
||||
1. Specify the full path to the [LangGraph API config file](../reference/cli.md#configuration-file) including the file name. For example, if the file `langgraph.json` is in the root of the repository, simply specify `langgraph.json`.
|
||||
1. Check/uncheck checkbox to `Automatically update deployment on push to branch`. If checked, the deployment will automatically be updated when changes are pushed to the specified `Git Branch`. This setting can be enabled/disabled later in the [Deployment Settings](#deployment-settings).
|
||||
1. Select the desired `Deployment Type`.
|
||||
1. `Development` deployments are meant for non-production use cases and are provisioned with minimal resources.
|
||||
1. `Production` deployments can serve up to 500 requests/second and are provisioned with highly available storage with automatic backups.
|
||||
1. Determine if the deployment should be `Shareable through LangGraph Studio`.
|
||||
1. If unchecked, the deployment will only be accessible with a valid LangSmith API key for the workspace.
|
||||
1. If checked, the deployment will be accessible through LangGraph Studio to any LangSmith user. A direct URL to LangGraph Studio for the deployment will be provided to share with other LangSmith users.
|
||||
1. Specify `Environment Variables` and secrets. See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for the deployment.
|
||||
1. Sensitive values such as API keys (e.g. `OPENAI_API_KEY`) should be specified as secrets.
|
||||
1. Additional non-secret environment variables can be specified as well.
|
||||
1. A new LangSmith `Tracing Project` is automatically created with the same name as the deployment.
|
||||
1. In the top-right corner, select `Submit`. After a few seconds, the `Deployment` view appears and the new deployment will be queued for provisioning.
|
||||
|
||||
## Create New Revision
|
||||
|
||||
When [creating a new deployment](#create-new-deployment), a new revision is created by default. Subsequent revisions can be created to deploy new code changes.
|
||||
|
||||
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
|
||||
|
||||
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
|
||||
1. Select an existing deployment to create a new revision for.
|
||||
1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
|
||||
1. In the `New Revision` modal, fill out the required fields.
|
||||
1. Specify the full path to the [LangGraph API config file](../reference/cli.md#configuration-file) including the file name. For example, if the file `langgraph.json` is in the root of the repository, simply specify `langgraph.json`.
|
||||
1. Determine if the deployment should be `Shareable through LangGraph Studio`.
|
||||
1. If unchecked, the deployment will only be accessible with a valid LangSmith API key for the workspace.
|
||||
1. If checked, the deployment will be accessible through LangGraph Studio to any LangSmith user. A direct URL to LangGraph Studio for the deployment will be provided to share with other LangSmith users.
|
||||
1. Specify `Environment Variables` and secrets. Existing secrets and environment variables are prepopulated. See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for the revision.
|
||||
1. Add new secrets or environment variables.
|
||||
1. Remove existing secrets or environment variables.
|
||||
1. Update the value of existing secrets or environment variables.
|
||||
1. Select `Submit`. After a few seconds, the `New Revision` modal will close and the new revision will be queued for deployment.
|
||||
|
||||
## View Build and Server Logs
|
||||
|
||||
Build and server logs are available for each revision.
|
||||
|
||||
Starting from the `LangGraph Platform` view...
|
||||
|
||||
1. Select the desired revision from the `Revisions` table. A panel slides open from the right-hand side and the `Build` tab is selected by default, which displays build logs for the revision.
|
||||
1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
|
||||
1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
|
||||
|
||||
## View Deployment Metrics
|
||||
|
||||
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
|
||||
|
||||
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
|
||||
1. Select an existing deployment to monitor.
|
||||
1. Select the `Monitoring` tab to view the deployment metrics. See a list of [all available metrics](../../concepts/langgraph_control_plane.md#monitoring).
|
||||
1. Within the `Monitoring` tab, use the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
|
||||
|
||||
## Interrupt Revision
|
||||
|
||||
Interrupting a revision will stop deployment of the revision.
|
||||
|
||||
!!! warning "Undefined Behavior"
|
||||
Interrupted revisions have undefined behavior. This is only useful if you need to deploy a new revision and you already have a revision "stuck" in progress. In the future, this feature may be removed.
|
||||
|
||||
Starting from the `LangGraph Platform` view...
|
||||
|
||||
1. Select the menu icon (three dots) on the right-hand side of the row for the desired revision from the `Revisions` table.
|
||||
1. Select `Interrupt` from the menu.
|
||||
1. A modal will appear. Review the confirmation message. Select `Interrupt revision`.
|
||||
|
||||
## Delete Deployment
|
||||
|
||||
Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
|
||||
|
||||
1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
|
||||
1. Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select `Delete`.
|
||||
1. A `Confirmation` modal will appear. Select `Delete`.
|
||||
|
||||
## Deployment Settings
|
||||
|
||||
Starting from the `LangGraph Platform` view...
|
||||
|
||||
1. In the top-right corner, select the gear icon (`Deployment Settings`).
|
||||
1. Update the `Git Branch` to the desired branch.
|
||||
1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
|
||||
1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
|
||||
1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.
|
||||
|
||||
## Add or Remove GitHub Repositories
|
||||
|
||||
After installing and authorizing LangChain's `hosted-langserve` GitHub app, repository access for the app can be modified to add new repositories or remove existing repositories. If a new repository is created, it may need to be added explicitly.
|
||||
|
||||
1. From the GitHub profile, navigate to `Settings` > `Applications` > `hosted-langserve` > click `Configure`.
|
||||
1. Under `Repository access`, select `All repositories` or `Only select repositories`. If `Only select repositories` is selected, new repositories must be explicitly added.
|
||||
1. Click `Save`.
|
||||
1. When creating a new deployment, the list of GitHub repositories in the dropdown menu will be updated to reflect the repository access changes.
|
||||
|
||||
## Whitelisting IP Addresses
|
||||
|
||||
All traffic from `LangGraph Platform` deployments created after January 6th 2025 will come through a NAT gateway.
|
||||
This NAT gateway will have several static ip addresses depending on the region you are deploying in. Refer to the table below for the list of IP addresses to whitelist:
|
||||
|
||||
| US | EU |
|
||||
|----------------|-----------------|
|
||||
| 35.197.29.146 | 34.90.213.236 |
|
||||
| 34.145.102.123 | 34.13.244.114 |
|
||||
| 34.169.45.153 | 34.32.180.189 |
|
||||
| 34.82.222.17 | 34.34.69.108 |
|
||||
| 35.227.171.135 | 34.32.145.240 |
|
||||
| 34.169.88.30 | 34.90.157.44 |
|
||||
| 34.19.93.202 | 34.141.242.180 |
|
||||
| 34.19.34.50 | 34.32.141.108 |
|
||||
@@ -0,0 +1,19 @@
|
||||
# How to customize Dockerfile
|
||||
|
||||
Users can add an array of additional lines to add to the Dockerfile following the import from the parent LangGraph image. In order to do this, you simply need to modify your `langgraph.json` file by passing in the commands you want run to the `dockerfile_lines` key. For example, if we wanted to use `Pillow` in our graph you would need to add the following dependencies:
|
||||
|
||||
```
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"openai_agent": "./openai_agent.py:agent",
|
||||
},
|
||||
"env": "./.env",
|
||||
"dockerfile_lines": [
|
||||
"RUN apt-get update && apt-get install -y libjpeg-dev zlib1g-dev libpng-dev",
|
||||
"RUN pip install Pillow"
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
This would install the system packages required to use Pillow if we were working with `jpeg` or `png` image formats.
|
||||
@@ -0,0 +1,119 @@
|
||||
# Egress for Subscription Metrics and Operational Metadata
|
||||
|
||||
> **Important: Self Hosted Only**
|
||||
> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
|
||||
> This does not apply to SaaS or Hybrid deployments.
|
||||
|
||||
Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
|
||||
|
||||
In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
|
||||
|
||||
> **Warning**
|
||||
> **This will require egress to `https://beacon.langchain.com` from your network.**
|
||||
> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
|
||||
|
||||
Generally, data that we send to Beacon can be categorized as follows:
|
||||
|
||||
- **Subscription Metrics**
|
||||
- Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
|
||||
- Nodes Executed
|
||||
- Runs Executed
|
||||
- License Key Verification
|
||||
- **Operational Metadata**
|
||||
- This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
|
||||
|
||||
## Example Payloads
|
||||
|
||||
In an effort to maximize transparency, we provide sample payloads here:
|
||||
|
||||
### License Verification (If using an Enterprise License)
|
||||
|
||||
**Endpoint:**
|
||||
|
||||
`POST beacon.langchain.com/v1/beacon/verify`
|
||||
|
||||
**Request:**
|
||||
|
||||
```json
|
||||
{
|
||||
"license": "<YOUR_LICENSE_KEY>"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
|
||||
}
|
||||
```
|
||||
|
||||
### Api Key Verification (If using a LangSmith API Key)
|
||||
|
||||
**Endpoint:**
|
||||
`POST api.smith.langchain.com/auth`
|
||||
|
||||
**Request:**
|
||||
|
||||
```json
|
||||
"Headers": {
|
||||
X-Api-Key: <YOUR_API_KEY>
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
{
|
||||
"org_config": {
|
||||
"org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
|
||||
... // Additional organization details
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Usage Reporting
|
||||
|
||||
**Endpoint:**
|
||||
|
||||
`POST beacon.langchain.com/v1/metadata/submit`
|
||||
|
||||
**Request:**
|
||||
|
||||
```json
|
||||
{
|
||||
"license": "<YOUR_LICENSE_KEY>",
|
||||
"from_timestamp": "2025-01-06T09:00:00Z",
|
||||
"to_timestamp": "2025-01-06T10:00:00Z",
|
||||
"tags": {
|
||||
"langgraph.python.version": "0.1.0",
|
||||
"langgraph_api.version": "0.2.0",
|
||||
"langgraph.platform.revision": "abc123",
|
||||
"langgraph.platform.variant": "standard",
|
||||
"langgraph.platform.host": "host-1",
|
||||
"langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
|
||||
"langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
|
||||
"langgraph.platform.plan": "enterprise",
|
||||
"user_app.uses_indexing": "true",
|
||||
"user_app.uses_custom_app": "false",
|
||||
"user_app.uses_custom_auth": "true",
|
||||
"user_app.uses_thread_ttl": "true",
|
||||
"user_app.uses_store_ttl": "false"
|
||||
},
|
||||
"measures": {
|
||||
"langgraph.platform.runs": 150,
|
||||
"langgraph.platform.nodes": 450
|
||||
},
|
||||
"logs": []
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
|
||||
```json
|
||||
"204 No Content"
|
||||
```
|
||||
|
||||
## Our Commitment
|
||||
|
||||
LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.
|
||||
@@ -0,0 +1,147 @@
|
||||
# Rebuild Graph at Runtime
|
||||
|
||||
You might need to rebuild your graph with a different configuration for a new run. For example, you might need to use a different graph state or graph structure depending on the config. This guide shows how you can do this.
|
||||
|
||||
!!! note "Note"
|
||||
In most cases, customizing behavior based on the config should be handled by a single graph where each node can read a config and change its behavior based on it
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Make sure to check out [this how-to guide](./setup.md) on setting up your app for deployment first.
|
||||
|
||||
## Define graphs
|
||||
|
||||
Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:
|
||||
|
||||
```
|
||||
my-app/
|
||||
|-- requirements.txt
|
||||
|-- .env
|
||||
|-- openai_agent.py # code for your graph
|
||||
```
|
||||
|
||||
where the graph is defined in `openai_agent.py`.
|
||||
|
||||
### No rebuild
|
||||
|
||||
In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of `openai_agent.py`, which looks like the following:
|
||||
|
||||
```python
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.graph import END, START, MessageGraph
|
||||
|
||||
model = ChatOpenAI(temperature=0)
|
||||
|
||||
graph_workflow = MessageGraph()
|
||||
|
||||
graph_workflow.add_node("agent", model)
|
||||
graph_workflow.add_edge("agent", END)
|
||||
graph_workflow.add_edge(START, "agent")
|
||||
|
||||
agent = graph_workflow.compile()
|
||||
```
|
||||
|
||||
To make the server aware of your graph, you need to specify a path to the variable that contains the `CompiledStateGraph` instance in your LangGraph API configuration (`langgraph.json`), e.g.:
|
||||
|
||||
```
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"openai_agent": "./openai_agent.py:agent",
|
||||
},
|
||||
"env": "./.env"
|
||||
}
|
||||
```
|
||||
|
||||
### Rebuild
|
||||
|
||||
To make your graph rebuild on each new run with custom configuration, you need to rewrite `openai_agent.py` to instead provide a _function_ that takes a config and returns a graph (or compiled graph) instance. Let's say we want to return our existing graph for user ID '1', and a tool-calling agent for other users. We can modify `openai_agent.py` as follows:
|
||||
|
||||
```python
|
||||
from typing import Annotated
|
||||
from typing_extensions import TypedDict
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.graph import END, START, MessageGraph
|
||||
from langgraph.graph.state import StateGraph
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.prebuilt import ToolNode
|
||||
from langchain_core.tools import tool
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
|
||||
|
||||
class State(TypedDict):
|
||||
messages: Annotated[list[BaseMessage], add_messages]
|
||||
|
||||
|
||||
model = ChatOpenAI(temperature=0)
|
||||
|
||||
def make_default_graph():
|
||||
"""Make a simple LLM agent"""
|
||||
graph_workflow = StateGraph(State)
|
||||
def call_model(state):
|
||||
return {"messages": [model.invoke(state["messages"])]}
|
||||
|
||||
graph_workflow.add_node("agent", call_model)
|
||||
graph_workflow.add_edge("agent", END)
|
||||
graph_workflow.add_edge(START, "agent")
|
||||
|
||||
agent = graph_workflow.compile()
|
||||
return agent
|
||||
|
||||
|
||||
def make_alternative_graph():
|
||||
"""Make a tool-calling agent"""
|
||||
|
||||
@tool
|
||||
def add(a: float, b: float):
|
||||
"""Adds two numbers."""
|
||||
return a + b
|
||||
|
||||
tool_node = ToolNode([add])
|
||||
model_with_tools = model.bind_tools([add])
|
||||
def call_model(state):
|
||||
return {"messages": [model_with_tools.invoke(state["messages"])]}
|
||||
|
||||
def should_continue(state: State):
|
||||
if state["messages"][-1].tool_calls:
|
||||
return "tools"
|
||||
else:
|
||||
return END
|
||||
|
||||
graph_workflow = StateGraph(State)
|
||||
|
||||
graph_workflow.add_node("agent", call_model)
|
||||
graph_workflow.add_node("tools", tool_node)
|
||||
graph_workflow.add_edge("tools", "agent")
|
||||
graph_workflow.add_edge(START, "agent")
|
||||
graph_workflow.add_conditional_edges("agent", should_continue)
|
||||
|
||||
agent = graph_workflow.compile()
|
||||
return agent
|
||||
|
||||
|
||||
# this is the graph making function that will decide which graph to
|
||||
# build based on the provided config
|
||||
def make_graph(config: RunnableConfig):
|
||||
user_id = config.get("configurable", {}).get("user_id")
|
||||
# route to different graph state / structure based on the user ID
|
||||
if user_id == "1":
|
||||
return make_default_graph()
|
||||
else:
|
||||
return make_alternative_graph()
|
||||
```
|
||||
|
||||
Finally, you need to specify the path to your graph-making function (`make_graph`) in `langgraph.json`:
|
||||
|
||||
```
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"openai_agent": "./openai_agent.py:make_graph",
|
||||
},
|
||||
"env": "./.env"
|
||||
}
|
||||
```
|
||||
|
||||
See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
|
||||
Binary file not shown.
|
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|
After Width: | Height: | Size: 514 KiB |
@@ -0,0 +1,53 @@
|
||||
# How to Deploy Self-Hosted Control Plane
|
||||
|
||||
Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
|
||||
|
||||
!!! info "Important"
|
||||
The Self-Hosted Control Plane deployment option requires an [Enterprise](../../concepts/plans.md) plan.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. You are using Kubernetes.
|
||||
1. You have self-hosted LangSmith deployed.
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster has access to.
|
||||
1. `KEDA` is installed on your cluster.
|
||||
|
||||
helm repo add kedacore https://kedacore.github.io/charts
|
||||
helm install keda kedacore/keda --namespace keda --create-namespace
|
||||
1. Ingress Configuration
|
||||
1. You must set up an ingress for your LangSmith instance. All agents will be deployed as Kubernetes services behind this ingress.
|
||||
1. You can use this guide to [set up an ingress](https://docs.smith.langchain.com/self_hosting/configuration/ingress) for your instance.
|
||||
1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
|
||||
1. A valid Dynamic PV provisioner or PVs available on your cluster. You can verify this by running:
|
||||
|
||||
kubectl get storageclass
|
||||
|
||||
1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
|
||||
|
||||
## Setup
|
||||
|
||||
1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
|
||||
1. `listener`: This is a service that listens to the [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
|
||||
1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
|
||||
1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
|
||||
1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
|
||||
1. Two additional images will be used by the chart. Use the images that are specified in the latest release.
|
||||
|
||||
hostBackendImage:
|
||||
repository: "docker.io/langchain/hosted-langserve-backend"
|
||||
pullPolicy: IfNotPresent
|
||||
operatorImage:
|
||||
repository: "docker.io/langchain/langgraph-operator"
|
||||
pullPolicy: IfNotPresent
|
||||
|
||||
1. In your config file for langsmith (usually `langsmith_config.yaml`, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
|
||||
|
||||
config:
|
||||
langgraphPlatform:
|
||||
enabled: true
|
||||
langgraphPlatformLicenseKey: "YOUR_LANGGRAPH_PLATFORM_LICENSE_KEY"
|
||||
1. In your `values.yaml` file, configure the `hostBackendImage` and `operatorImage` options (if you need to mirror images)
|
||||
|
||||
1. You can also configure base templates for your agents by overriding the base templates [here](https://github.com/langchain-ai/helm/blob/main/charts/langsmith/values.yaml#L898).
|
||||
1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
|
||||
@@ -0,0 +1,59 @@
|
||||
# How to Deploy Self-Hosted Data Plane
|
||||
|
||||
Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
|
||||
|
||||
!!! info "Important"
|
||||
The Self-Hosted Data Plane deployment option requires an [Enterprise](../../concepts/plans.md) plan.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster or Amazon ECS cluster has access to.
|
||||
|
||||
## Kubernetes
|
||||
|
||||
### Prerequisites
|
||||
1. `KEDA` is installed on your cluster.
|
||||
|
||||
helm repo add kedacore https://kedacore.github.io/charts
|
||||
helm install keda kedacore/keda --namespace keda --create-namespace
|
||||
|
||||
1. A valid `Ingress` controller is installed on your cluster.
|
||||
1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
|
||||
1. You will need to enable egress to two control plane URLs. The listener polls these endpoints for deployments:
|
||||
|
||||
https://api.host.langchain.com
|
||||
https://api.smith.langchain.com
|
||||
|
||||
### Setup
|
||||
|
||||
1. You give us your LangSmith organization ID. We will enable the Self-Hosted Data Plane for your organization.
|
||||
1. We provide you a [Helm chart](https://github.com/langchain-ai/helm/tree/main/charts/langgraph-dataplane) which you run to setup your Kubernetes cluster. This chart contains a few important components.
|
||||
1. `langgraph-listener`: This is a service that listens to LangChain's [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
|
||||
1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph Platform deployment.
|
||||
1. `langgraph-platform-operator`: This operator handles changes to your LangGraph Platform CRDs.
|
||||
1. Configure your `langgraph-dataplane-values.yaml` file.
|
||||
|
||||
config:
|
||||
langsmithApiKey: "" # API Key of your Workspace
|
||||
langsmithWorkspaceId: "" # Workspace ID
|
||||
hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU
|
||||
smithBackendUrl: "https://api.smith.langchain.com" # Only override this if on EU
|
||||
|
||||
1. Deploy `langgraph-dataplane` Helm chart.
|
||||
|
||||
helm repo add langchain https://langchain-ai.github.io/helm/
|
||||
helm repo update
|
||||
helm upgrade -i langgraph-dataplane langchain/langgraph-dataplane --values langgraph-dataplane-values.yaml
|
||||
|
||||
1. If successful, you will see two services start up in your namespace.
|
||||
|
||||
NAME READY STATUS RESTARTS AGE
|
||||
langgraph-dataplane-listener-7fccd788-wn2dx 0/1 Running 0 9s
|
||||
langgraph-dataplane-redis-0 0/1 ContainerCreating 0 9s
|
||||
|
||||
1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
|
||||
|
||||
## Amazon ECS
|
||||
|
||||
Coming soon!
|
||||
@@ -0,0 +1,123 @@
|
||||
# How to add semantic search to your LangGraph deployment
|
||||
|
||||
This guide explains how to add semantic search to your LangGraph deployment's cross-thread [store](../../concepts/persistence.md#memory-store), so that your agent can search for memories and other documents by semantic similarity.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- A LangGraph deployment (see [how to deploy](setup_pyproject.md))
|
||||
- API keys for your embedding provider (in this case, OpenAI)
|
||||
- `langchain >= 0.3.8` (if you specify using the string format below)
|
||||
|
||||
## Steps
|
||||
|
||||
1. Update your `langgraph.json` configuration file to include the store configuration:
|
||||
|
||||
```json
|
||||
{
|
||||
...
|
||||
"store": {
|
||||
"index": {
|
||||
"embed": "openai:text-embedding-3-small",
|
||||
"dims": 1536,
|
||||
"fields": ["$"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This configuration:
|
||||
|
||||
- Uses OpenAI's text-embedding-3-small model for generating embeddings
|
||||
- Sets the embedding dimension to 1536 (matching the model's output)
|
||||
- Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
|
||||
|
||||
2. To use the string embedding format above, make sure your dependencies include `langchain >= 0.3.8`:
|
||||
|
||||
```toml
|
||||
# In pyproject.toml
|
||||
[project]
|
||||
dependencies = [
|
||||
"langchain>=0.3.8"
|
||||
]
|
||||
```
|
||||
|
||||
Or if using requirements.txt:
|
||||
|
||||
```
|
||||
langchain>=0.3.8
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
Once configured, you can use semantic search in your LangGraph nodes. The store requires a namespace tuple to organize memories:
|
||||
|
||||
```python
|
||||
def search_memory(state: State, *, store: BaseStore):
|
||||
# Search the store using semantic similarity
|
||||
# The namespace tuple helps organize different types of memories
|
||||
# e.g., ("user_facts", "preferences") or ("conversation", "summaries")
|
||||
results = store.search(
|
||||
namespace=("memory", "facts"), # Organize memories by type
|
||||
query="your search query",
|
||||
limit=3 # number of results to return
|
||||
)
|
||||
return results
|
||||
```
|
||||
|
||||
## Custom Embeddings
|
||||
|
||||
If you want to use custom embeddings, you can pass a path to a custom embedding function:
|
||||
|
||||
```json
|
||||
{
|
||||
...
|
||||
"store": {
|
||||
"index": {
|
||||
"embed": "path/to/embedding_function.py:embed",
|
||||
"dims": 1536,
|
||||
"fields": ["$"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The deployment will look for the function in the specified path. The function must be async and accept a list of strings:
|
||||
|
||||
```python
|
||||
# path/to/embedding_function.py
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
client = AsyncOpenAI()
|
||||
|
||||
async def aembed_texts(texts: list[str]) -> list[list[float]]:
|
||||
"""Custom embedding function that must:
|
||||
1. Be async
|
||||
2. Accept a list of strings
|
||||
3. Return a list of float arrays (embeddings)
|
||||
"""
|
||||
response = await client.embeddings.create(
|
||||
model="text-embedding-3-small",
|
||||
input=texts
|
||||
)
|
||||
return [e.embedding for e in response.data]
|
||||
```
|
||||
|
||||
## Querying via the API
|
||||
|
||||
You can also query the store using the LangGraph SDK. Since the SDK uses async operations:
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
async def search_store():
|
||||
client = get_client()
|
||||
results = await client.store.search_items(
|
||||
("memory", "facts"),
|
||||
query="your search query",
|
||||
limit=3 # number of results to return
|
||||
)
|
||||
return results
|
||||
|
||||
# Use in an async context
|
||||
results = await search_store()
|
||||
```
|
||||
@@ -0,0 +1,188 @@
|
||||
# How to Set Up a LangGraph Application with requirements.txt
|
||||
|
||||
A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.
|
||||
|
||||
This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
|
||||
|
||||
!!! tip "Setup with pyproject.toml"
|
||||
If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Platform.
|
||||
|
||||
!!! tip "Setup with a Monorepo"
|
||||
If you are interested in deploying a graph located inside a monorepo, take a look at [this repository](https://github.com/langchain-ai/langgraph-example-monorepo) for an example of how to do so.
|
||||
|
||||
The final repository structure will look something like this:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── requirements.txt # package dependencies
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
├── .env # environment variables
|
||||
└── langgraph.json # configuration file for LangGraph
|
||||
```
|
||||
|
||||
After each step, an example file directory is provided to demonstrate how code can be organized.
|
||||
|
||||
## Specify Dependencies
|
||||
|
||||
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
|
||||
|
||||
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
|
||||
|
||||
```
|
||||
langgraph>=0.3.27
|
||||
langgraph-sdk>=0.1.66
|
||||
langgraph-checkpoint>=2.0.23
|
||||
langchain-core>=0.2.38
|
||||
langsmith>=0.1.63
|
||||
orjson>=3.9.7,<3.10.17
|
||||
httpx>=0.25.0
|
||||
tenacity>=8.0.0
|
||||
uvicorn>=0.26.0
|
||||
sse-starlette>=2.1.0,<2.2.0
|
||||
uvloop>=0.18.0
|
||||
httptools>=0.5.0
|
||||
jsonschema-rs>=0.20.0
|
||||
structlog>=24.1.0
|
||||
cloudpickle>=3.0.0
|
||||
```
|
||||
|
||||
Example `requirements.txt` file:
|
||||
|
||||
```
|
||||
langgraph
|
||||
langchain_anthropic
|
||||
tavily-python
|
||||
langchain_community
|
||||
langchain_openai
|
||||
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ └── requirements.txt # package dependencies
|
||||
```
|
||||
|
||||
## Specify Environment Variables
|
||||
|
||||
Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for a deployment.
|
||||
|
||||
Example `.env` file:
|
||||
|
||||
```
|
||||
MY_ENV_VAR_1=foo
|
||||
MY_ENV_VAR_2=bar
|
||||
OPENAI_API_KEY=key
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ └── requirements.txt # package dependencies
|
||||
└── .env # environment variables
|
||||
```
|
||||
|
||||
## Define Graphs
|
||||
|
||||
Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
|
||||
|
||||
Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):
|
||||
|
||||
```python
|
||||
# my_agent/agent.py
|
||||
from typing import Literal
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.graph import StateGraph, END, START
|
||||
from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
|
||||
from my_agent.utils.state import AgentState # import state
|
||||
|
||||
# Define the runtime context
|
||||
class GraphContext(TypedDict):
|
||||
model_name: Literal["anthropic", "openai"]
|
||||
|
||||
workflow = StateGraph(AgentState, context_schema=GraphContext)
|
||||
workflow.add_node("agent", call_model)
|
||||
workflow.add_node("action", tool_node)
|
||||
workflow.add_edge(START, "agent")
|
||||
workflow.add_conditional_edges(
|
||||
"agent",
|
||||
should_continue,
|
||||
{
|
||||
"continue": "action",
|
||||
"end": END,
|
||||
},
|
||||
)
|
||||
workflow.add_edge("action", "agent")
|
||||
|
||||
graph = workflow.compile()
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── requirements.txt # package dependencies
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
└── .env # environment variables
|
||||
```
|
||||
|
||||
## Create LangGraph Configuration File
|
||||
|
||||
Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
|
||||
|
||||
Example `langgraph.json` file:
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["./my_agent"],
|
||||
"graphs": {
|
||||
"agent": "./my_agent/agent.py:graph"
|
||||
},
|
||||
"env": ".env"
|
||||
}
|
||||
```
|
||||
|
||||
Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).
|
||||
|
||||
!!! warning "Configuration File Location"
|
||||
The LangGraph configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── requirements.txt # package dependencies
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
├── .env # environment variables
|
||||
└── langgraph.json # configuration file for LangGraph
|
||||
```
|
||||
|
||||
## Next
|
||||
|
||||
After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
|
||||
@@ -0,0 +1,199 @@
|
||||
# How to Set Up a LangGraph.js Application
|
||||
|
||||
A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using `package.json` to specify project dependencies.
|
||||
|
||||
This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraphjs-studio-starter), which you can play around with to learn more about how to setup your LangGraph application for deployment.
|
||||
|
||||
The final repository structure will look something like this:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── src # all project code lies within here
|
||||
│ ├── utils # optional utilities for your graph
|
||||
│ │ ├── tools.ts # tools for your graph
|
||||
│ │ ├── nodes.ts # node functions for you graph
|
||||
│ │ └── state.ts # state definition of your graph
|
||||
│ └── agent.ts # code for constructing your graph
|
||||
├── package.json # package dependencies
|
||||
├── .env # environment variables
|
||||
└── langgraph.json # configuration file for LangGraph
|
||||
```
|
||||
|
||||
After each step, an example file directory is provided to demonstrate how code can be organized.
|
||||
|
||||
## Specify Dependencies
|
||||
|
||||
Dependencies can be specified in a `package.json`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).
|
||||
|
||||
Example `package.json` file:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "langgraphjs-studio-starter",
|
||||
"packageManager": "yarn@1.22.22",
|
||||
"dependencies": {
|
||||
"@langchain/community": "^0.2.31",
|
||||
"@langchain/core": "^0.2.31",
|
||||
"@langchain/langgraph": "^0.2.0",
|
||||
"@langchain/openai": "^0.2.8"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
When deploying your app, the dependencies will be installed using the package manager of your choice, provided they adhere to the compatible version ranges listed below:
|
||||
|
||||
```
|
||||
"@langchain/core": "^0.3.42",
|
||||
"@langchain/langgraph": "^0.2.57",
|
||||
"@langchain/langgraph-checkpoint": "~0.0.16",
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
└── package.json # package dependencies
|
||||
```
|
||||
|
||||
## Specify Environment Variables
|
||||
|
||||
Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for a deployment.
|
||||
|
||||
Example `.env` file:
|
||||
|
||||
```
|
||||
MY_ENV_VAR_1=foo
|
||||
MY_ENV_VAR_2=bar
|
||||
OPENAI_API_KEY=key
|
||||
TAVILY_API_KEY=key_2
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── package.json
|
||||
└── .env # environment variables
|
||||
```
|
||||
|
||||
## Define Graphs
|
||||
|
||||
Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each compiled graph to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
|
||||
|
||||
Here is an example `agent.ts`:
|
||||
|
||||
```ts
|
||||
import type { AIMessage } from "@langchain/core/messages";
|
||||
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
|
||||
import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";
|
||||
import { ToolNode } from "@langchain/langgraph/prebuilt";
|
||||
|
||||
const tools = [new TavilySearchResults({ maxResults: 3 })];
|
||||
|
||||
// Define the function that calls the model
|
||||
async function callModel(state: typeof MessagesAnnotation.State) {
|
||||
/**
|
||||
* Call the LLM powering our agent.
|
||||
* Feel free to customize the prompt, model, and other logic!
|
||||
*/
|
||||
const model = new ChatOpenAI({
|
||||
model: "gpt-4o",
|
||||
}).bindTools(tools);
|
||||
|
||||
const response = await model.invoke([
|
||||
{
|
||||
role: "system",
|
||||
content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`,
|
||||
},
|
||||
...state.messages,
|
||||
]);
|
||||
|
||||
// MessagesAnnotation supports returning a single message or array of messages
|
||||
return { messages: response };
|
||||
}
|
||||
|
||||
// Define the function that determines whether to continue or not
|
||||
function routeModelOutput(state: typeof MessagesAnnotation.State) {
|
||||
const messages = state.messages;
|
||||
const lastMessage: AIMessage = messages[messages.length - 1];
|
||||
// If the LLM is invoking tools, route there.
|
||||
if ((lastMessage?.tool_calls?.length ?? 0) > 0) {
|
||||
return "tools";
|
||||
}
|
||||
// Otherwise end the graph.
|
||||
return "__end__";
|
||||
}
|
||||
|
||||
// Define a new graph.
|
||||
// See https://langchain-ai.github.io/langgraphjs/how-tos/define-state/#getting-started for
|
||||
// more on defining custom graph states.
|
||||
const workflow = new StateGraph(MessagesAnnotation)
|
||||
// Define the two nodes we will cycle between
|
||||
.addNode("callModel", callModel)
|
||||
.addNode("tools", new ToolNode(tools))
|
||||
// Set the entrypoint as `callModel`
|
||||
// This means that this node is the first one called
|
||||
.addEdge("__start__", "callModel")
|
||||
.addConditionalEdges(
|
||||
// First, we define the edges' source node. We use `callModel`.
|
||||
// This means these are the edges taken after the `callModel` node is called.
|
||||
"callModel",
|
||||
// Next, we pass in the function that will determine the sink node(s), which
|
||||
// will be called after the source node is called.
|
||||
routeModelOutput,
|
||||
// List of the possible destinations the conditional edge can route to.
|
||||
// Required for conditional edges to properly render the graph in Studio
|
||||
["tools", "__end__"]
|
||||
)
|
||||
// This means that after `tools` is called, `callModel` node is called next.
|
||||
.addEdge("tools", "callModel");
|
||||
|
||||
// Finally, we compile it!
|
||||
// This compiles it into a graph you can invoke and deploy.
|
||||
export const graph = workflow.compile();
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── src # all project code lies within here
|
||||
│ ├── utils # optional utilities for your graph
|
||||
│ │ ├── tools.ts # tools for your graph
|
||||
│ │ ├── nodes.ts # node functions for you graph
|
||||
│ │ └── state.ts # state definition of your graph
|
||||
│ └── agent.ts # code for constructing your graph
|
||||
├── package.json # package dependencies
|
||||
├── .env # environment variables
|
||||
└── langgraph.json # configuration file for LangGraph
|
||||
```
|
||||
|
||||
## Create LangGraph API Config
|
||||
|
||||
Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
|
||||
|
||||
Example `langgraph.json` file:
|
||||
|
||||
```json
|
||||
{
|
||||
"node_version": "20",
|
||||
"dockerfile_lines": [],
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"agent": "./src/agent.ts:graph"
|
||||
},
|
||||
"env": ".env"
|
||||
}
|
||||
```
|
||||
|
||||
Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).
|
||||
|
||||
!!! info "Configuration Location"
|
||||
|
||||
The LangGraph configuration file must be placed in a directory that is at the same level or higher than the TypeScript files that contain compiled graphs and associated dependencies.
|
||||
|
||||
## Next
|
||||
|
||||
After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
|
||||
@@ -0,0 +1,201 @@
|
||||
# How to Set Up a LangGraph Application with pyproject.toml
|
||||
|
||||
A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies.
|
||||
|
||||
This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example-pyproject), which you can play around with to learn more about how to setup your LangGraph application for deployment.
|
||||
|
||||
!!! tip "Setup with requirements.txt"
|
||||
If you prefer using `requirements.txt` for dependency management, check out [this how-to guide](./setup.md).
|
||||
|
||||
!!! tip "Setup with a Monorepo"
|
||||
If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.
|
||||
|
||||
The final repository structure will look something like this:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
├── .env # environment variables
|
||||
├── langgraph.json # configuration file for LangGraph
|
||||
└── pyproject.toml # dependencies for your project
|
||||
```
|
||||
|
||||
After each step, an example file directory is provided to demonstrate how code can be organized.
|
||||
|
||||
## Specify Dependencies
|
||||
|
||||
Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
|
||||
|
||||
The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
|
||||
|
||||
```
|
||||
langgraph>=0.3.27
|
||||
langgraph-sdk>=0.1.66
|
||||
langgraph-checkpoint>=2.0.23
|
||||
langchain-core>=0.2.38
|
||||
langsmith>=0.1.63
|
||||
orjson>=3.9.7,<3.10.17
|
||||
httpx>=0.25.0
|
||||
tenacity>=8.0.0
|
||||
uvicorn>=0.26.0
|
||||
sse-starlette>=2.1.0,<2.2.0
|
||||
uvloop>=0.18.0
|
||||
httptools>=0.5.0
|
||||
jsonschema-rs>=0.20.0
|
||||
structlog>=24.1.0
|
||||
cloudpickle>=3.0.0
|
||||
```
|
||||
|
||||
Example `pyproject.toml` file:
|
||||
|
||||
```toml
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[project]
|
||||
name = "my-agent"
|
||||
version = "0.0.1"
|
||||
description = "An excellent agent build for LangGraph Platform."
|
||||
authors = [
|
||||
{name = "Polly the parrot", email = "1223+polly@users.noreply.github.com"}
|
||||
]
|
||||
license = {text = "MIT"}
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.9"
|
||||
dependencies = [
|
||||
"langgraph>=0.2.0",
|
||||
"langchain-fireworks>=0.1.3"
|
||||
]
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["my_agent"]
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
└── pyproject.toml # Python packages required for your graph
|
||||
```
|
||||
|
||||
## Specify Environment Variables
|
||||
|
||||
Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for a deployment.
|
||||
|
||||
Example `.env` file:
|
||||
|
||||
```
|
||||
MY_ENV_VAR_1=foo
|
||||
MY_ENV_VAR_2=bar
|
||||
FIREWORKS_API_KEY=key
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── .env # file with environment variables
|
||||
└── pyproject.toml
|
||||
```
|
||||
|
||||
## Define Graphs
|
||||
|
||||
Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
|
||||
|
||||
Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):
|
||||
|
||||
```python
|
||||
# my_agent/agent.py
|
||||
from typing import Literal
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langgraph.graph import StateGraph, END, START
|
||||
from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
|
||||
from my_agent.utils.state import AgentState # import state
|
||||
|
||||
# Define the runtime context
|
||||
class GraphContext(TypedDict):
|
||||
model_name: Literal["anthropic", "openai"]
|
||||
|
||||
workflow = StateGraph(AgentState, context_schema=GraphContext)
|
||||
workflow.add_node("agent", call_model)
|
||||
workflow.add_node("action", tool_node)
|
||||
workflow.add_edge(START, "agent")
|
||||
workflow.add_conditional_edges(
|
||||
"agent",
|
||||
should_continue,
|
||||
{
|
||||
"continue": "action",
|
||||
"end": END,
|
||||
},
|
||||
)
|
||||
workflow.add_edge("action", "agent")
|
||||
|
||||
graph = workflow.compile()
|
||||
```
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
├── .env
|
||||
└── pyproject.toml
|
||||
```
|
||||
|
||||
## Create LangGraph Configuration File
|
||||
|
||||
Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
|
||||
|
||||
Example `langgraph.json` file:
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"agent": "./my_agent/agent.py:graph"
|
||||
},
|
||||
"env": ".env"
|
||||
}
|
||||
```
|
||||
|
||||
Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).
|
||||
|
||||
!!! warning "Configuration File Location"
|
||||
The LangGraph configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
|
||||
|
||||
Example file directory:
|
||||
|
||||
```bash
|
||||
my-app/
|
||||
├── my_agent # all project code lies within here
|
||||
│ ├── utils # utilities for your graph
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── tools.py # tools for your graph
|
||||
│ │ ├── nodes.py # node functions for you graph
|
||||
│ │ └── state.py # state definition of your graph
|
||||
│ ├── __init__.py
|
||||
│ └── agent.py # code for constructing your graph
|
||||
├── .env # environment variables
|
||||
├── langgraph.json # configuration file for LangGraph
|
||||
└── pyproject.toml # dependencies for your project
|
||||
```
|
||||
|
||||
## Next
|
||||
|
||||
After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
|
||||
@@ -0,0 +1,111 @@
|
||||
# How to Deploy a Standalone Container
|
||||
|
||||
Before deploying, review the [conceptual guide for the Standalone Container](../../concepts/langgraph_standalone_container.md) deployment option.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
|
||||
1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`).
|
||||
1. The following environment variables are needed for a standalone container deployment.
|
||||
1. `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs. The value of `REDIS_URI` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
|
||||
|
||||
!!! Note "Shared Redis Instance"
|
||||
Multiple self-hosted deployments can share the same Redis instance. For example, for `Deployment A`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/1` and for `Deployment B`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/2`.
|
||||
|
||||
`1` and `2` are different database numbers within the same instance, but `<hostname_1>` is shared. **The same database number cannot be used for separate deployments**.
|
||||
|
||||
1. `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics. The value of `DATABASE_URI` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
|
||||
|
||||
!!! Note "Shared Postgres Instance"
|
||||
Multiple self-hosted deployments can share the same Postgres instance. For example, for `Deployment A`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_1>?host=<hostname_1>` and for `Deployment B`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_2>?host=<hostname_1>`.
|
||||
|
||||
`<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
|
||||
|
||||
1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
|
||||
1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
|
||||
1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
|
||||
1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
|
||||
|
||||
## Kubernetes (Helm)
|
||||
|
||||
Use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md) to deploy a LangGraph Server to a Kubernetes cluster.
|
||||
|
||||
## Docker
|
||||
|
||||
Run the following `docker` command:
|
||||
```shell
|
||||
docker run \
|
||||
--env-file .env \
|
||||
-p 8123:8000 \
|
||||
-e REDIS_URI="foo" \
|
||||
-e DATABASE_URI="bar" \
|
||||
-e LANGSMITH_API_KEY="baz" \
|
||||
my-image
|
||||
```
|
||||
|
||||
!!! note
|
||||
|
||||
* You need to replace `my-image` with the name of the image you built in the prerequisite steps (from `langgraph build`)
|
||||
and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
|
||||
* If your application requires additional environment variables, you can pass them in a similar way.
|
||||
|
||||
## Docker Compose
|
||||
|
||||
Docker Compose YAML file:
|
||||
```yml
|
||||
volumes:
|
||||
langgraph-data:
|
||||
driver: local
|
||||
services:
|
||||
langgraph-redis:
|
||||
image: redis:6
|
||||
healthcheck:
|
||||
test: redis-cli ping
|
||||
interval: 5s
|
||||
timeout: 1s
|
||||
retries: 5
|
||||
langgraph-postgres:
|
||||
image: postgres:16
|
||||
ports:
|
||||
- "5433:5432"
|
||||
environment:
|
||||
POSTGRES_DB: postgres
|
||||
POSTGRES_USER: postgres
|
||||
POSTGRES_PASSWORD: postgres
|
||||
volumes:
|
||||
- langgraph-data:/var/lib/postgresql/data
|
||||
healthcheck:
|
||||
test: pg_isready -U postgres
|
||||
start_period: 10s
|
||||
timeout: 1s
|
||||
retries: 5
|
||||
interval: 5s
|
||||
langgraph-api:
|
||||
image: ${IMAGE_NAME}
|
||||
ports:
|
||||
- "8123:8000"
|
||||
depends_on:
|
||||
langgraph-redis:
|
||||
condition: service_healthy
|
||||
langgraph-postgres:
|
||||
condition: service_healthy
|
||||
env_file:
|
||||
- .env
|
||||
environment:
|
||||
REDIS_URI: redis://langgraph-redis:6379
|
||||
LANGSMITH_API_KEY: ${LANGSMITH_API_KEY}
|
||||
POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable
|
||||
```
|
||||
|
||||
You can run the command `docker compose up` with this Docker Compose file in the same folder.
|
||||
|
||||
This will launch a LangGraph Server on port `8123` (if you want to change this, you can change this by changing the ports in the `langgraph-api` volume). You can test if the application is healthy by running:
|
||||
|
||||
```shell
|
||||
curl --request GET --url 0.0.0.0:8123/ok
|
||||
```
|
||||
Assuming everything is running correctly, you should see a response like:
|
||||
|
||||
```shell
|
||||
{"ok":true}
|
||||
```
|
||||
@@ -0,0 +1,486 @@
|
||||
# Human-in-the-loop using Server API
|
||||
|
||||
To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
|
||||
|
||||
## Dynamic interrupts
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
# highlight-next-line
|
||||
from langgraph_sdk.schema import Command
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# Run the graph until the interrupt is hit.
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"some_text": "original text"} # (1)!
|
||||
)
|
||||
|
||||
print(result['__interrupt__']) # (2)!
|
||||
# > [
|
||||
# > {
|
||||
# > 'value': {'text_to_revise': 'original text'},
|
||||
# > 'id': '...',
|
||||
# > }
|
||||
# > ]
|
||||
|
||||
|
||||
# Resume the graph
|
||||
print(await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
# highlight-next-line
|
||||
command=Command(resume="Edited text") # (3)!
|
||||
))
|
||||
# > {'some_text': 'Edited text'}
|
||||
```
|
||||
|
||||
1. The graph is invoked with some initial state.
|
||||
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
|
||||
3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// Run the graph until the interrupt is hit.
|
||||
const result = await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{ input: { "some_text": "original text" } } // (1)!
|
||||
);
|
||||
|
||||
console.log(result['__interrupt__']); // (2)!
|
||||
// > [
|
||||
// > {
|
||||
// > 'value': {'text_to_revise': 'original text'},
|
||||
// > 'resumable': True,
|
||||
// > 'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
|
||||
// > 'when': 'during'
|
||||
// > }
|
||||
// > ]
|
||||
|
||||
// Resume the graph
|
||||
console.log(await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
// highlight-next-line
|
||||
{ command: { resume: "Edited text" }} // (3)!
|
||||
));
|
||||
// > {'some_text': 'Edited text'}
|
||||
```
|
||||
|
||||
1. The graph is invoked with some initial state.
|
||||
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
|
||||
3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Run the graph until the interrupt is hit.:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"some_text\": \"original text\"}
|
||||
}"
|
||||
```
|
||||
|
||||
Resume the graph:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"command\": {
|
||||
\"resume\": \"Edited text\"
|
||||
}
|
||||
}"
|
||||
```
|
||||
|
||||
??? example "Extended example: using `interrupt`"
|
||||
|
||||
This is an example graph you can run in the LangGraph API server.
|
||||
See [LangGraph Platform quickstart](../quick_start.md) for more details.
|
||||
|
||||
```python
|
||||
from typing import TypedDict
|
||||
import uuid
|
||||
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
from langgraph.constants import START
|
||||
from langgraph.graph import StateGraph
|
||||
# highlight-next-line
|
||||
from langgraph.types import interrupt, Command
|
||||
|
||||
class State(TypedDict):
|
||||
some_text: str
|
||||
|
||||
def human_node(state: State):
|
||||
# highlight-next-line
|
||||
value = interrupt( # (1)!
|
||||
{
|
||||
"text_to_revise": state["some_text"] # (2)!
|
||||
}
|
||||
)
|
||||
return {
|
||||
"some_text": value # (3)!
|
||||
}
|
||||
|
||||
|
||||
# Build the graph
|
||||
graph_builder = StateGraph(State)
|
||||
graph_builder.add_node("human_node", human_node)
|
||||
graph_builder.add_edge(START, "human_node")
|
||||
|
||||
graph = graph_builder.compile()
|
||||
```
|
||||
|
||||
1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
|
||||
2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
|
||||
3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
|
||||
|
||||
Once you have a running LangGraph API server, you can interact with it using
|
||||
[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
# highlight-next-line
|
||||
from langgraph_sdk.schema import Command
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# Run the graph until the interrupt is hit.
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"some_text": "original text"} # (1)!
|
||||
)
|
||||
|
||||
print(result['__interrupt__']) # (2)!
|
||||
# > [
|
||||
# > {
|
||||
# > 'value': {'text_to_revise': 'original text'},
|
||||
# > 'id': '...',
|
||||
# > }
|
||||
# > ]
|
||||
|
||||
|
||||
# Resume the graph
|
||||
print(await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
# highlight-next-line
|
||||
command=Command(resume="Edited text") # (3)!
|
||||
))
|
||||
# > {'some_text': 'Edited text'}
|
||||
```
|
||||
|
||||
1. The graph is invoked with some initial state.
|
||||
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
|
||||
3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// Run the graph until the interrupt is hit.
|
||||
const result = await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{ input: { "some_text": "original text" } } // (1)!
|
||||
);
|
||||
|
||||
console.log(result['__interrupt__']); // (2)!
|
||||
// > [
|
||||
// > {
|
||||
// > 'value': {'text_to_revise': 'original text'},
|
||||
// > 'resumable': True,
|
||||
// > 'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
|
||||
// > 'when': 'during'
|
||||
// > }
|
||||
// > ]
|
||||
|
||||
// Resume the graph
|
||||
console.log(await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
// highlight-next-line
|
||||
{ command: { resume: "Edited text" }} // (3)!
|
||||
));
|
||||
// > {'some_text': 'Edited text'}
|
||||
```
|
||||
|
||||
1. The graph is invoked with some initial state.
|
||||
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
|
||||
3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Run the graph until the interrupt is hit:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"some_text\": \"original text\"}
|
||||
}"
|
||||
```
|
||||
|
||||
Resume the graph:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"command\": {
|
||||
\"resume\": \"Edited text\"
|
||||
}
|
||||
}"
|
||||
```
|
||||
|
||||
## Static interrupts
|
||||
|
||||
Static interrupts (also known as static breakpoints) are triggered either before or after a node executes.
|
||||
|
||||
!!! warning
|
||||
|
||||
Static interrupts are **not** recommended for human-in-the-loop workflows. They are best used for debugging and testing.
|
||||
|
||||
You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time:
|
||||
|
||||
```python
|
||||
# highlight-next-line
|
||||
graph = graph_builder.compile( # (1)!
|
||||
# highlight-next-line
|
||||
interrupt_before=["node_a"], # (2)!
|
||||
# highlight-next-line
|
||||
interrupt_after=["node_b", "node_c"], # (3)!
|
||||
)
|
||||
```
|
||||
|
||||
1. The breakpoints are set during `compile` time.
|
||||
2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
|
||||
3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
|
||||
|
||||
Alternatively, you can set static interrupts at run time:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# highlight-next-line
|
||||
await client.runs.wait( # (1)!
|
||||
thread_id,
|
||||
assistant_id,
|
||||
inputs=inputs,
|
||||
# highlight-next-line
|
||||
interrupt_before=["node_a"], # (2)!
|
||||
# highlight-next-line
|
||||
interrupt_after=["node_b", "node_c"] # (3)!
|
||||
)
|
||||
```
|
||||
|
||||
1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
|
||||
2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
|
||||
3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
// highlight-next-line
|
||||
await client.runs.wait( // (1)!
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: input,
|
||||
// highlight-next-line
|
||||
interruptBefore: ["node_a"], // (2)!
|
||||
// highlight-next-line
|
||||
interruptAfter: ["node_b", "node_c"] // (3)!
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
|
||||
2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
|
||||
3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"interrupt_before\": [\"node_a\"],
|
||||
\"interrupt_after\": [\"node_b\", \"node_c\"],
|
||||
\"input\": <INPUT>
|
||||
}"
|
||||
```
|
||||
|
||||
The following example shows how to add static interrupts:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# Run the graph until the breakpoint
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input=inputs # (1)!
|
||||
)
|
||||
|
||||
# Resume the graph
|
||||
await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input=None # (2)!
|
||||
)
|
||||
```
|
||||
|
||||
1. The graph is run until the first breakpoint is hit.
|
||||
2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// Run the graph until the breakpoint
|
||||
const result = await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{ input: input } // (1)!
|
||||
);
|
||||
|
||||
// Resume the graph
|
||||
await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{ input: null } // (2)!
|
||||
);
|
||||
```
|
||||
|
||||
1. The graph is run until the first breakpoint is hit.
|
||||
2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Run the graph until the breakpoint:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": <INPUT>
|
||||
}"
|
||||
```
|
||||
|
||||
Resume the graph:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\"
|
||||
}"
|
||||
```
|
||||
|
||||
|
||||
## Learn more
|
||||
|
||||
- [Human-in-the-loop conceptual guide](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features.
|
||||
- [Common patterns](../../how-tos/human_in_the_loop/add-human-in-the-loop.md#common-patterns): learn how to implement patterns like approving/rejecting actions, requesting user input, tool call review, and validating human input.
|
||||
@@ -0,0 +1,451 @@
|
||||
# How to kick off background runs
|
||||
|
||||
|
||||
This guide covers how to kick off background runs for your agent.
|
||||
This can be useful for long running jobs.
|
||||
|
||||
## Setup
|
||||
|
||||
First let's set up our client and thread:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
# create thread
|
||||
thread = await client.threads.create()
|
||||
print(thread)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
// create thread
|
||||
const thread = await client.threads.create();
|
||||
console.log(thread);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
'thread_id': '5cb1e8a1-34b3-4a61-a34e-71a9799bd00d',
|
||||
'created_at': '2024-08-30T20:35:52.062934+00:00',
|
||||
'updated_at': '2024-08-30T20:35:52.062934+00:00',
|
||||
'metadata': {},
|
||||
'status': 'idle',
|
||||
'config': {},
|
||||
'values': None
|
||||
}
|
||||
|
||||
## Check runs on thread
|
||||
|
||||
If we list the current runs on this thread, we will see that it's empty:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
runs = await client.runs.list(thread["thread_id"])
|
||||
print(runs)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let runs = await client.runs.list(thread['thread_id']);
|
||||
console.log(runs);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
[]
|
||||
|
||||
## Start runs on thread
|
||||
|
||||
Now let's kick off a run:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
|
||||
run = await client.runs.create(thread["thread_id"], assistant_id, input=input)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]};
|
||||
let run = await client.runs.create(thread["thread_id"], assistantID, { input });
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <ASSISTANT_ID>
|
||||
}'
|
||||
```
|
||||
|
||||
The first time we poll it, we can see `status=pending`:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(await client.runs.get(thread["thread_id"], run["run_id"]))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log(await client.runs.get(thread["thread_id"], run["run_id"]));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
|
||||
"thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
|
||||
"created_at": "2024-09-04T01:46:47.244887+00:00",
|
||||
"updated_at": "2024-09-04T01:46:47.244887+00:00",
|
||||
"metadata": {},
|
||||
"status": "pending",
|
||||
"kwargs": {
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "what's the weather in sf"
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {
|
||||
"metadata": {
|
||||
"created_by": "system"
|
||||
},
|
||||
"configurable": {
|
||||
"run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
|
||||
"user_id": "",
|
||||
"graph_id": "agent",
|
||||
"thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
|
||||
"checkpoint_id": null
|
||||
}
|
||||
},
|
||||
"webhook": null,
|
||||
"temporary": false,
|
||||
"stream_mode": [
|
||||
"values"
|
||||
],
|
||||
"feedback_keys": null,
|
||||
"interrupt_after": null,
|
||||
"interrupt_before": null
|
||||
},
|
||||
"multitask_strategy": "reject"
|
||||
}
|
||||
|
||||
|
||||
|
||||
Now we can join the run, wait for it to finish and check that status again:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
await client.runs.join(thread["thread_id"], run["run_id"])
|
||||
print(await client.runs.get(thread["thread_id"], run["run_id"]))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.runs.join(thread["thread_id"], run["run_id"]);
|
||||
console.log(await client.runs.get(thread["thread_id"], run["run_id"]));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join &&
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
|
||||
"thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
|
||||
"created_at": "2024-09-04T01:46:47.244887+00:00",
|
||||
"updated_at": "2024-09-04T01:46:47.244887+00:00",
|
||||
"metadata": {},
|
||||
"status": "success",
|
||||
"kwargs": {
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "what's the weather in sf"
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {
|
||||
"metadata": {
|
||||
"created_by": "system"
|
||||
},
|
||||
"configurable": {
|
||||
"run_id": "1ef6a5f8-bd86-6763-bbd6-bff042db7b1b",
|
||||
"user_id": "",
|
||||
"graph_id": "agent",
|
||||
"thread_id": "7885f0cf-94ad-4040-91d7-73f7ba007c8a",
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
|
||||
"checkpoint_id": null
|
||||
}
|
||||
},
|
||||
"webhook": null,
|
||||
"temporary": false,
|
||||
"stream_mode": [
|
||||
"values"
|
||||
],
|
||||
"feedback_keys": null,
|
||||
"interrupt_after": null,
|
||||
"interrupt_before": null
|
||||
},
|
||||
"multitask_strategy": "reject"
|
||||
}
|
||||
|
||||
|
||||
Perfect! The run succeeded as we would expect. We can double check that the run worked as expected by printing out the final state:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
final_result = await client.threads.get_state(thread["thread_id"])
|
||||
print(final_result)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let finalResult = await client.threads.getState(thread["thread_id"]);
|
||||
console.log(finalResult);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"values": {
|
||||
"messages": [
|
||||
{
|
||||
"content": "what's the weather in sf",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "human",
|
||||
"name": null,
|
||||
"id": "beba31bf-320d-4125-9c37-cadf526ac47a",
|
||||
"example": false
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
|
||||
"input": {},
|
||||
"name": "tavily_search_results_json",
|
||||
"type": "tool_use",
|
||||
"index": 0,
|
||||
"partial_json": "{\"query\": \"weather in san francisco\"}"
|
||||
}
|
||||
],
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {
|
||||
"stop_reason": "tool_use",
|
||||
"stop_sequence": null
|
||||
},
|
||||
"type": "ai",
|
||||
"name": null,
|
||||
"id": "run-f220faf8-1d27-4f73-ad91-6bb3f47e8639",
|
||||
"example": false,
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "tavily_search_results_json",
|
||||
"args": {
|
||||
"query": "weather in san francisco"
|
||||
},
|
||||
"id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
|
||||
"type": "tool_call"
|
||||
}
|
||||
],
|
||||
"invalid_tool_calls": [],
|
||||
"usage_metadata": {
|
||||
"input_tokens": 273,
|
||||
"output_tokens": 61,
|
||||
"total_tokens": 334
|
||||
}
|
||||
},
|
||||
{
|
||||
"content": "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1725052131, 'localtime': '2024-08-30 14:08'}, 'current': {'last_updated_epoch': 1725051600, 'last_updated': '2024-08-30 14:00', 'temp_c': 21.1, 'temp_f': 70.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 11.9, 'wind_kph': 19.1, 'wind_degree': 290, 'wind_dir': 'WNW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 59, 'cloud': 25, 'feelslike_c': 21.1, 'feelslike_f': 70.0, 'windchill_c': 18.6, 'windchill_f': 65.5, 'heatindex_c': 18.6, 'heatindex_f': 65.5, 'dewpoint_c': 12.2, 'dewpoint_f': 54.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 15.0, 'gust_kph': 24.2}}\"}]",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "tool",
|
||||
"name": "tavily_search_results_json",
|
||||
"id": "686b2487-f332-4e58-9508-89b3a814cd81",
|
||||
"tool_call_id": "toolu_01AaNPSPzqia21v7aAKwbKYm",
|
||||
"artifact": {
|
||||
"query": "weather in san francisco",
|
||||
"follow_up_questions": null,
|
||||
"answer": null,
|
||||
"images": [],
|
||||
"results": [
|
||||
{
|
||||
"title": "Weather in San Francisco",
|
||||
"url": "https://www.weatherapi.com/",
|
||||
"content": "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1725052131, 'localtime': '2024-08-30 14:08'}, 'current': {'last_updated_epoch': 1725051600, 'last_updated': '2024-08-30 14:00', 'temp_c': 21.1, 'temp_f': 70.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 11.9, 'wind_kph': 19.1, 'wind_degree': 290, 'wind_dir': 'WNW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 59, 'cloud': 25, 'feelslike_c': 21.1, 'feelslike_f': 70.0, 'windchill_c': 18.6, 'windchill_f': 65.5, 'heatindex_c': 18.6, 'heatindex_f': 65.5, 'dewpoint_c': 12.2, 'dewpoint_f': 54.0, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 5.0, 'gust_mph': 15.0, 'gust_kph': 24.2}}",
|
||||
"score": 0.976148,
|
||||
"raw_content": null
|
||||
}
|
||||
],
|
||||
"response_time": 3.07
|
||||
},
|
||||
"status": "success"
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"text": "\n\nThe search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70\u00b0F (21.1\u00b0C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.",
|
||||
"type": "text",
|
||||
"index": 0
|
||||
}
|
||||
],
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {
|
||||
"stop_reason": "end_turn",
|
||||
"stop_sequence": null
|
||||
},
|
||||
"type": "ai",
|
||||
"name": null,
|
||||
"id": "run-8fecc61d-3d9f-4e16-8e8a-92f702be498a",
|
||||
"example": false,
|
||||
"tool_calls": [],
|
||||
"invalid_tool_calls": [],
|
||||
"usage_metadata": {
|
||||
"input_tokens": 837,
|
||||
"output_tokens": 124,
|
||||
"total_tokens": 961
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"next": [],
|
||||
"tasks": [],
|
||||
"metadata": {
|
||||
"step": 3,
|
||||
"run_id": "1ef67140-eb23-684b-8253-91d4c90bb05e",
|
||||
"source": "loop",
|
||||
"writes": {
|
||||
"agent": {
|
||||
"messages": [
|
||||
{
|
||||
"id": "run-8fecc61d-3d9f-4e16-8e8a-92f702be498a",
|
||||
"name": null,
|
||||
"type": "ai",
|
||||
"content": [
|
||||
{
|
||||
"text": "\n\nThe search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70\u00b0F (21.1\u00b0C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.",
|
||||
"type": "text",
|
||||
"index": 0
|
||||
}
|
||||
],
|
||||
"example": false,
|
||||
"tool_calls": [],
|
||||
"usage_metadata": {
|
||||
"input_tokens": 837,
|
||||
"total_tokens": 961,
|
||||
"output_tokens": 124
|
||||
},
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {
|
||||
"stop_reason": "end_turn",
|
||||
"stop_sequence": null
|
||||
},
|
||||
"invalid_tool_calls": []
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"user_id": "",
|
||||
"graph_id": "agent",
|
||||
"thread_id": "5cb1e8a1-34b3-4a61-a34e-71a9799bd00d",
|
||||
"created_by": "system",
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca"
|
||||
},
|
||||
"created_at": "2024-08-30T21:09:00.079909+00:00",
|
||||
"checkpoint_id": "1ef67141-3ca2-6fae-8003-fe96832e57d6",
|
||||
"parent_checkpoint_id": "1ef67141-2129-6b37-8002-61fc3bf69cb5"
|
||||
}
|
||||
|
||||
We can also just print the content of the last AIMessage:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(final_result['values']['messages'][-1]['content'][0]['text'])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log(finalResult['values']['messages'][finalResult['values']['messages'].length-1]['content'][0]['text']);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | jq -r '.values.messages[-1].content.[0].text'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
The search results provide the current weather conditions in San Francisco. According to the data, as of 2:00 PM on August 30, 2024, the temperature in San Francisco is 70°F (21.1°C) with partly cloudy skies. The wind is blowing from the west-northwest at around 12 mph (19 km/h). The humidity is 59% and visibility is 9 miles (16 km). Overall, it looks like a nice late summer day in San Francisco with comfortable temperatures and partly sunny conditions.
|
||||
@@ -0,0 +1,36 @@
|
||||
# Debug LangSmith traces
|
||||
|
||||
This guide explains how to open LangSmith traces in LangGraph Studio for interactive investigation and debugging.
|
||||
|
||||
## Open deployed threads
|
||||
|
||||
1. Open the LangSmith trace, selecting the root run.
|
||||
2. Click "Run in Studio".
|
||||
|
||||
This will open LangGraph Studio connected to the associated LangGraph Platform deployment with the trace's parent thread selected.
|
||||
|
||||
## Testing local agents with remote traces
|
||||
|
||||
This section explains how to test a local agent against remote traces from LangSmith. This enables you to use production traces as input for local testing, allowing you to debug and verify agent modifications in your development environment.
|
||||
|
||||
### Requirements
|
||||
|
||||
- A LangSmith traced thread
|
||||
- A locally running agent. See [here](../how-tos/studio/quick_start.md#local-development-server) for setup
|
||||
instructions.
|
||||
|
||||
!!! info "Local agent requirements"
|
||||
|
||||
- langgraph>=0.3.18
|
||||
- langgraph-api>=0.0.32
|
||||
- Contains the same set of nodes present in the remote trace
|
||||
|
||||
### Cloning Thread
|
||||
|
||||
1. Open the LangSmith trace, selecting the root run.
|
||||
2. Click the dropdown next to "Run in Studio".
|
||||
3. Enter your local agent's URL.
|
||||
4. Select "Clone thread locally".
|
||||
5. If multiple graphs exist, select the target graph.
|
||||
|
||||
A new thread will be created in your local agent with the thread history inferred and copied from the remote thread, and you will be navigated to LangGraph Studio for your locally running application.
|
||||
@@ -0,0 +1,84 @@
|
||||
# Configurable Headers
|
||||
|
||||
LangGraph allows runtime configuration to modify agent behavior and permissions dynamically. When using the [LangGraph Platform](../quick_start.md), you can pass this configuration in the request body (`config`) or specific request headers. This enables adjustments based on user identity or other request data.
|
||||
|
||||
For privacy, control which headers are passed to the runtime configuration via the `http.configurable_headers` section in your `langgraph.json` file.
|
||||
|
||||
Here's how to customize the included and excluded headers:
|
||||
|
||||
```json
|
||||
{
|
||||
"http": {
|
||||
"configurable_headers": {
|
||||
"include": ["x-user-id", "x-organization-id", "my-prefix-*"],
|
||||
"exclude": ["authorization", "x-api-key"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
The `include` and `exclude` lists accept exact header names or patterns using `*` to match any number of characters. For your security, no other regex patterns are supported.
|
||||
|
||||
## Using within your graph
|
||||
|
||||
You can access the included headers in your graph using the `config` argument of any node.
|
||||
|
||||
```python
|
||||
def my_node(state, config):
|
||||
organization_id = config["configurable"].get("x-organization-id")
|
||||
...
|
||||
```
|
||||
|
||||
Or by fetching from context (useful in tools and or within other nested functions).
|
||||
|
||||
```python
|
||||
from langgraph.config import get_config
|
||||
|
||||
def search_everything(query: str):
|
||||
organization_id = get_config()["configurable"].get("x-organization-id")
|
||||
...
|
||||
```
|
||||
|
||||
|
||||
You can even use this to dynamically compile the graph.
|
||||
|
||||
```python
|
||||
# my_graph.py.
|
||||
import contextlib
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def generate_agent(config):
|
||||
organization_id = config["configurable"].get("x-organization-id")
|
||||
if organization_id == "org1":
|
||||
graph = ...
|
||||
yield graph
|
||||
else:
|
||||
graph = ...
|
||||
yield graph
|
||||
|
||||
```
|
||||
|
||||
```json
|
||||
{
|
||||
"graphs": {"agent": "my_grph.py:generate_agent"}
|
||||
}
|
||||
```
|
||||
|
||||
### Opt-out of configurable headers
|
||||
|
||||
If you'd like to opt-out of configurable headers, you can simply set a wildcard pattern in the `exclude` list:
|
||||
|
||||
```json
|
||||
{
|
||||
"http": {
|
||||
"configurable_headers": {
|
||||
"exclude": ["*"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This will exclude all headers from being added to your run's configuration.
|
||||
|
||||
Note that exclusions take precedence over inclusions.
|
||||
@@ -0,0 +1,330 @@
|
||||
# Manage assistants
|
||||
|
||||
In this guide we will show how to create, configure, and manage an [assistant](../../concepts/assistants.md).
|
||||
|
||||
First, as a brief refresher on the concept of runtime context, consider the following simple `call_model` node and context schema. Observe that this node tries to read and use the `model_provider` as defined by the `Runtime` object's `context` property.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class ContextSchema:
|
||||
llm_provider: str = "anthropic"
|
||||
|
||||
builder = StateGraph(AgentState, context_schema=ContextSchema)
|
||||
|
||||
def call_model(state, runtime: Runtime[ContextSchema]):
|
||||
messages = state["messages"]
|
||||
model = _get_model(runtime.context.llm_provider)
|
||||
response = model.invoke(messages)
|
||||
# We return a list, because this will get added to the existing list
|
||||
return {"messages": [response]}
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Annotation } from "@langchain/langgraph";
|
||||
|
||||
const ConfigSchema = Annotation.Root({
|
||||
model_name: Annotation<string>,
|
||||
system_prompt:
|
||||
});
|
||||
|
||||
const builder = new StateGraph(AgentState, ConfigSchema)
|
||||
|
||||
function callModel(state: State, config: RunnableConfig) {
|
||||
const messages = state.messages;
|
||||
const modelName = config.configurable?.model_name ?? "anthropic";
|
||||
const model = _getModel(modelName);
|
||||
const response = model.invoke(messages);
|
||||
// We return a list, because this will get added to the existing list
|
||||
return { messages: [response] };
|
||||
}
|
||||
```
|
||||
|
||||
For more information on runtime context, [see here](../../concepts/low_level.md#runtime-context).
|
||||
|
||||
## Create an assistant
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
To create an assistant, use the [LangGraph SDK](../../concepts/sdk.md) `create` method. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.AssistantsClient.create) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#create) SDK reference docs for more information.
|
||||
|
||||
This example uses the same configuration schema as above, and creates an assistant with `model_name` set to `openai`.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
openai_assistant = await client.assistants.create(
|
||||
# "agent" is the name of a graph we deployed
|
||||
"agent", config={"configurable": {"model_name": "openai"}}, name="Open AI Assistant"
|
||||
)
|
||||
|
||||
print(openai_assistant)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
const openAIAssistant = await client.assistants.create({
|
||||
graphId: 'agent',
|
||||
name: "Open AI Assistant",
|
||||
config: { "configurable": { "model_name": "openai" } },
|
||||
});
|
||||
|
||||
console.log(openAIAssistant);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{"graph_id":"agent", "config":{"configurable":{"model_name":"openai"}}, "name": "Open AI Assistant"}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
|
||||
"graph_id": "agent",
|
||||
"name": "Open AI Assistant"
|
||||
"config": {
|
||||
"configurable": {
|
||||
"model_name": "openai"
|
||||
}
|
||||
},
|
||||
"metadata": {}
|
||||
"created_at": "2024-08-31T03:09:10.230718+00:00",
|
||||
"updated_at": "2024-08-31T03:09:10.230718+00:00",
|
||||
}
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
You can also create assistants from the LangGraph Platform UI.
|
||||
|
||||
Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.
|
||||
|
||||
To create a new assistant, select the "+ New assistant" button. This will open a form where you can specify the graph this assistant is for, as well as provide a name, description, and the desired configuration for the assistant based on the configuration schema for that graph.
|
||||
|
||||
To confirm, click "Create assistant". This will take you to [LangGraph Studio](../../concepts/langgraph_studio.md) where you can test the assistant. If you go back to the "Assistants" tab in the deployment, you will see the newly created assistant in the table.
|
||||
|
||||
## Use an assistant
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
We have now created an assistant called "Open AI Assistant" that has `model_name` defined as `openai`. We can now use this assistant with this configuration:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
thread = await client.threads.create()
|
||||
input = {"messages": [{"role": "user", "content": "who made you?"}]}
|
||||
async for event in client.runs.stream(
|
||||
thread["thread_id"],
|
||||
# this is where we specify the assistant id to use
|
||||
openai_assistant["assistant_id"],
|
||||
input=input,
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving event of type: {event.event}")
|
||||
print(event.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const thread = await client.threads.create();
|
||||
const input = { "messages": [{ "role": "user", "content": "who made you?" }] };
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
thread["thread_id"],
|
||||
// this is where we specify the assistant id to use
|
||||
openAIAssistant["assistant_id"],
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
|
||||
for await (const event of streamResponse) {
|
||||
console.log(`Receiving event of type: ${event.event}`);
|
||||
console.log(event.data);
|
||||
console.log("\n\n");
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
thread_id=$(curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}' | jq -r '.thread_id') && \
|
||||
curl --request POST \
|
||||
--url "<DEPLOYMENT_URL>/threads/${thread_id}/runs/stream" \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <OPENAI_ASSISTANT_ID>,
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "who made you?"
|
||||
}
|
||||
]
|
||||
},
|
||||
"stream_mode": [
|
||||
"updates"
|
||||
]
|
||||
}' | \
|
||||
sed 's/\r$//' | \
|
||||
awk '
|
||||
/^event:/ {
|
||||
if (data_content != "") {
|
||||
print data_content "\n"
|
||||
}
|
||||
sub(/^event: /, "Receiving event of type: ", $0)
|
||||
printf "%s...\n", $0
|
||||
data_content = ""
|
||||
}
|
||||
/^data:/ {
|
||||
sub(/^data: /, "", $0)
|
||||
data_content = $0
|
||||
}
|
||||
END {
|
||||
if (data_content != "") {
|
||||
print data_content "\n\n"
|
||||
}
|
||||
}
|
||||
'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
Receiving event of type: metadata
|
||||
{'run_id': '1ef6746e-5893-67b1-978a-0f1cd4060e16'}
|
||||
|
||||
|
||||
|
||||
Receiving event of type: updates
|
||||
{'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-e1a6b25c-8416-41f2-9981-f9cfe043f414', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
|
||||
```
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
Inside your deployment, select the "Assistants" tab. For the assistant you would like to use, click the "Studio" button. This will open LangGraph Studio with the selected assistant. When you submit an input (either in Graph or Chat mode), the selected assistant and its configuration will be used.
|
||||
|
||||
## Create a new version for your assistant
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.AssistantsClient.update) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#update) SDK reference docs for more information.
|
||||
|
||||
!!! note "Note"
|
||||
|
||||
You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previous versions.
|
||||
|
||||
For example, to update your assistant's system prompt:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
openai_assistant_v2 = await client.assistants.update(
|
||||
openai_assistant["assistant_id"],
|
||||
config={
|
||||
"configurable": {
|
||||
"model_name": "openai",
|
||||
"system_prompt": "You are an unhelpful assistant!",
|
||||
}
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const openaiAssistantV2 = await client.assistants.update(
|
||||
openai_assistant["assistant_id"],
|
||||
{
|
||||
config: {
|
||||
configurable: {
|
||||
model_name: 'openai',
|
||||
system_prompt: 'You are an unhelpful assistant!',
|
||||
},
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request PATCH \
|
||||
--url <DEPOLYMENT_URL>/assistants/<ASSISTANT_ID> \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"config": {"model_name": "openai", "system_prompt": "You are an unhelpful assistant!"}
|
||||
}'
|
||||
```
|
||||
|
||||
This will create a new version of the assistant with the updated parameters and set this as the active version of your assistant. If you now run your graph and pass in this assistant id, it will use this latest version.
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
You can also edit assistants from the LangGraph Platform UI.
|
||||
|
||||
Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.
|
||||
|
||||
To edit an existing assistant, select the "Edit" button for the specified assistant. This will open a form where you can edit the assistant's name, description, and configuration.
|
||||
|
||||
Additionally, if using LangGraph Studio, you can edit the assistants and create new versions via the "Manage Assistants" button.
|
||||
|
||||
## Use a previous assistant version
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
You can also change the active version of your assistant. To do so, use the `setLatest` method.
|
||||
|
||||
In the example above, to rollback to the first version of the assistant:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
await client.assistants.set_latest(openai_assistant['assistant_id'], 1)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.assistants.setLatest(openaiAssistant['assistant_id'], 1);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/latest \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"version": 1
|
||||
}'
|
||||
```
|
||||
|
||||
If you now run your graph and pass in this assistant id, it will use the first version of the assistant.
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
If using LangGraph Studio, to set the active version of your assistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.
|
||||
|
||||
!!! warning "Deleting Assistants"
|
||||
Deleting as assistant will delete ALL of its versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.
|
||||
@@ -0,0 +1,184 @@
|
||||
# Use cron jobs
|
||||
|
||||
Sometimes you don't want to run your graph based on user interaction, but rather you would like to schedule your graph to run on a schedule - for example if you wish for your graph to compose and send out a weekly email of to-dos for your team. LangGraph Platform allows you to do this without having to write your own script by using the `Crons` client. To schedule a graph job, you need to pass a [cron expression](https://crontab.cronhub.io/) to inform the client when you want to run the graph. `Cron` jobs are run in the background and do not interfere with normal invocations of the graph.
|
||||
|
||||
## Setup
|
||||
|
||||
First, let's set up our SDK client, assistant, and thread:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
# create thread
|
||||
thread = await client.threads.create()
|
||||
print(thread)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
// create thread
|
||||
const thread = await client.threads.create();
|
||||
console.log(thread);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"limit": 10,
|
||||
"offset": 0
|
||||
}' | jq -c 'map(select(.config == null or .config == {})) | .[0].graph_id' && \
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
'thread_id': '9dde5490-2b67-47c8-aa14-4bfec88af217',
|
||||
'created_at': '2024-08-30T23:07:38.242730+00:00',
|
||||
'updated_at': '2024-08-30T23:07:38.242730+00:00',
|
||||
'metadata': {},
|
||||
'status': 'idle',
|
||||
'config': {},
|
||||
'values': None
|
||||
}
|
||||
|
||||
## Cron job on a thread
|
||||
|
||||
To create a cron job associated with a specific thread, you can write:
|
||||
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# This schedules a job to run at 15:27 (3:27PM) every day
|
||||
cron_job = await client.crons.create_for_thread(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
schedule="27 15 * * *",
|
||||
input={"messages": [{"role": "user", "content": "What time is it?"}]},
|
||||
)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
// This schedules a job to run at 15:27 (3:27PM) every day
|
||||
const cronJob = await client.crons.create_for_thread(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
{
|
||||
schedule: "27 15 * * *",
|
||||
input: { messages: [{ role: "user", content: "What time is it?" }] }
|
||||
}
|
||||
);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/crons \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <ASSISTANT_ID>,
|
||||
}'
|
||||
```
|
||||
|
||||
Note that it is **very** important to delete `Cron` jobs that are no longer useful. Otherwise you could rack up unwanted API charges to the LLM! You can delete a `Cron` job using the following code:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
await client.crons.delete(cron_job["cron_id"])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.crons.delete(cronJob["cron_id"]);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request DELETE \
|
||||
--url <DEPLOYMENT_URL>/runs/crons/<CRON_ID>
|
||||
```
|
||||
|
||||
## Cron job stateless
|
||||
|
||||
You can also create stateless cron jobs by using the following code:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# This schedules a job to run at 15:27 (3:27PM) every day
|
||||
cron_job_stateless = await client.crons.create(
|
||||
assistant_id,
|
||||
schedule="27 15 * * *",
|
||||
input={"messages": [{"role": "user", "content": "What time is it?"}]},
|
||||
)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
// This schedules a job to run at 15:27 (3:27PM) every day
|
||||
const cronJobStateless = await client.crons.create(
|
||||
assistantId,
|
||||
{
|
||||
schedule: "27 15 * * *",
|
||||
input: { messages: [{ role: "user", content: "What time is it?" }] }
|
||||
}
|
||||
);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/runs/crons \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <ASSISTANT_ID>,
|
||||
}'
|
||||
```
|
||||
|
||||
Again, remember to delete your job once you are done with it!
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
await client.crons.delete(cron_job_stateless["cron_id"])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.crons.delete(cronJobStateless["cron_id"]);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request DELETE \
|
||||
--url <DEPLOYMENT_URL>/runs/crons/<CRON_ID>
|
||||
```
|
||||
@@ -0,0 +1,12 @@
|
||||
# Add node to dataset
|
||||
|
||||
This guide shows how to add examples to [LangSmith datasets](https://docs.smith.langchain.com/evaluation/how_to_guides#dataset-management) from nodes in the thread log. This is useful to evaluate individual steps of the agent.
|
||||
|
||||
1. Select a thread.
|
||||
2. Click on the `Add to Dataset` button.
|
||||
3. Select nodes whose input/output you want to add to a dataset.
|
||||
4. For each selected node, select the target dataset to create the example in. By default a dataset for the specific assistant and node will be selected. If this dataset does not yet exist, it will be created.
|
||||
5. Edit the example's input/output as needed before adding it to the dataset.
|
||||
6. Select "Add to dataset" at the bottom of the page to add all selected nodes to their respective datasets.
|
||||
|
||||
See [Evaluating intermediate steps](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph#evaluating-intermediate-steps) for more details on how to evaluate intermediate steps.
|
||||
@@ -0,0 +1,255 @@
|
||||
# Enqueue
|
||||
|
||||
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
|
||||
|
||||
The guide covers the `enqueue` option for double texting, which adds the interruptions to a queue and executes them in the order they are received by the client. Below is a quick example of using the `enqueue` option.
|
||||
|
||||
## Setup
|
||||
|
||||
First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
function prettyPrint(m) {
|
||||
const padded = " " + m['type'] + " ";
|
||||
const sepLen = Math.floor((80 - padded.length) / 2);
|
||||
const sep = "=".repeat(sepLen);
|
||||
const secondSep = sep + (padded.length % 2 ? "=" : "");
|
||||
|
||||
console.log(`${sep}${padded}${secondSep}`);
|
||||
console.log("\n\n");
|
||||
console.log(m.content);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
# PLACE THIS IN A FILE CALLED pretty_print.sh
|
||||
pretty_print() {
|
||||
local type="$1"
|
||||
local content="$2"
|
||||
local padded=" $type "
|
||||
local total_width=80
|
||||
local sep_len=$(( (total_width - ${#padded}) / 2 ))
|
||||
local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
|
||||
local second_sep=$sep
|
||||
if (( (total_width - ${#padded}) % 2 )); then
|
||||
second_sep="${second_sep}="
|
||||
fi
|
||||
|
||||
echo "${sep}${padded}${second_sep}"
|
||||
echo
|
||||
echo "$content"
|
||||
}
|
||||
```
|
||||
|
||||
Then, let's import our required packages and instantiate our client, assistant, and thread.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
import httpx
|
||||
from langchain_core.messages import convert_to_messages
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
thread = await client.threads.create()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
const thread = await client.threads.create();
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
## Create runs
|
||||
|
||||
Now let's start two runs, with the second interrupting the first one with a multitask strategy of "enqueue":
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
first_run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
)
|
||||
second_run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
|
||||
multitask_strategy="enqueue",
|
||||
)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const firstRun = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
)
|
||||
|
||||
const secondRun = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
|
||||
multitask_strategy="enqueue",
|
||||
)
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
|
||||
}" && curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
|
||||
\"multitask_strategy\": \"enqueue\"
|
||||
}"
|
||||
```
|
||||
|
||||
## View run results
|
||||
|
||||
Verify that the thread has data from both runs:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# wait until the second run completes
|
||||
await client.runs.join(thread["thread_id"], second_run["run_id"])
|
||||
|
||||
state = await client.threads.get_state(thread["thread_id"])
|
||||
|
||||
for m in convert_to_messages(state["values"]["messages"]):
|
||||
m.pretty_print()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.runs.join(thread["thread_id"], secondRun["run_id"]);
|
||||
|
||||
const state = await client.threads.getState(thread["thread_id"]);
|
||||
|
||||
for (const m of state["values"]["messages"]) {
|
||||
prettyPrint(m);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
source pretty_print.sh && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join && \
|
||||
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
|
||||
jq -c '.values.messages[]' | while read -r element; do
|
||||
type=$(echo "$element" | jq -r '.type')
|
||||
content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
|
||||
pretty_print "$type" "$content"
|
||||
done
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in sf?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'id': 'toolu_01Dez1sJre4oA2Y7NsKJV6VT', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01Dez1sJre4oA2Y7NsKJV6VT)
|
||||
Call ID: toolu_01Dez1sJre4oA2Y7NsKJV6VT
|
||||
Args:
|
||||
query: weather in san francisco
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.accuweather.com/en/us/san-francisco/94103/weather-forecast/347629", "content": "Get the current and future weather conditions for San Francisco, CA, including temperature, precipitation, wind, air quality and more. See the hourly and 10-day outlook, radar maps, alerts and allergy information."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
According to AccuWeather, the current weather conditions in San Francisco are:
|
||||
|
||||
Temperature: 57°F (14°C)
|
||||
Conditions: Mostly Sunny
|
||||
Wind: WSW 10 mph
|
||||
Humidity: 72%
|
||||
|
||||
The forecast for the next few days shows partly sunny skies with highs in the upper 50s to mid 60s F (14-18°C) and lows in the upper 40s to low 50s F (9-11°C). Typical mild, dry weather for San Francisco this time of year.
|
||||
|
||||
Some key details from the AccuWeather forecast:
|
||||
|
||||
Today: Mostly sunny, high of 62°F (17°C)
|
||||
Tonight: Partly cloudy, low of 49°F (9°C)
|
||||
Tomorrow: Partly sunny, high of 59°F (15°C)
|
||||
Saturday: Mostly sunny, high of 64°F (18°C)
|
||||
Sunday: Partly sunny, high of 61°F (16°C)
|
||||
|
||||
So in summary, expect seasonable spring weather in San Francisco over the next several days, with a mix of sun and clouds and temperatures ranging from the upper 40s at night to the low 60s during the days. Typical dry conditions with no rain in the forecast.
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in nyc?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'text': 'Here are the current weather conditions and forecast for New York City:', 'type': 'text'}, {'id': 'toolu_01FFft5Sx9oS6AdVJuRWWcGp', 'input': {'query': 'weather in new york city'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01FFft5Sx9oS6AdVJuRWWcGp)
|
||||
Call ID: toolu_01FFft5Sx9oS6AdVJuRWWcGp
|
||||
Args:
|
||||
query: weather in new york city
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}"}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
According to the weather data from WeatherAPI:
|
||||
|
||||
Current Conditions in New York City (as of 2:00 PM local time):
|
||||
- Temperature: 85°F (29°C)
|
||||
- Conditions: Sunny
|
||||
- Wind: 2 mph (4 km/h) from the SSE
|
||||
- Humidity: 63%
|
||||
- Heat Index: 85°F (30°C)
|
||||
|
||||
The forecast shows sunny and warm conditions persisting over the next few days:
|
||||
|
||||
Today: Sunny, high of 85°F (29°C)
|
||||
Tonight: Clear, low of 68°F (20°C)
|
||||
Tomorrow: Sunny, high of 88°F (31°C)
|
||||
Thursday: Mostly sunny, high of 90°F (32°C)
|
||||
Friday: Partly cloudy, high of 87°F (31°C)
|
||||
|
||||
So New York City is experiencing beautiful sunny weather with seasonably warm temperatures in the mid-to-upper 80s Fahrenheit (around 30°C). Humidity is moderate in the 60% range. Overall, ideal late spring/early summer conditions for being outdoors in the city over the next several days.
|
||||
|
||||
@@ -0,0 +1,538 @@
|
||||
# How to implement Generative User Interfaces with LangGraph
|
||||
|
||||
!!! info "Prerequisites"
|
||||
|
||||
- [LangGraph Platform](../../concepts/langgraph_platform.md)
|
||||
- [LangGraph Server](../../concepts/langgraph_server.md)
|
||||
- [`useStream()` React Hook](./use_stream_react.md)
|
||||
|
||||
Generative user interfaces (Generative UI) allows agents to go beyond text and generate rich user interfaces. This enables creating more interactive and context-aware applications where the UI adapts based on the conversation flow and AI responses.
|
||||
|
||||
|
||||
|
||||
LangGraph Platform supports colocating your React components with your graph code. This allows you to focus on building specific UI components for your graph while easily plugging into existing chat interfaces such as [Agent Chat](https://agentchat.vercel.app) and loading the code only when actually needed.
|
||||
|
||||
## Tutorial
|
||||
|
||||
### 1. Define and configure UI components
|
||||
|
||||
First, create your first UI component. For each component you need to provide an unique identifier that will be used to reference the component in your graph code.
|
||||
|
||||
```tsx title="src/agent/ui.tsx"
|
||||
const WeatherComponent = (props: { city: string }) => {
|
||||
return <div>Weather for {props.city}</div>;
|
||||
};
|
||||
|
||||
export default {
|
||||
weather: WeatherComponent,
|
||||
};
|
||||
```
|
||||
|
||||
Next, define your UI components in your `langgraph.json` configuration:
|
||||
|
||||
=== "Python agent"
|
||||
|
||||
```json title="langgraph.json"
|
||||
{
|
||||
"node_version": "20",
|
||||
"graphs": {
|
||||
"agent": "./src/agent.py:graph"
|
||||
},
|
||||
"ui": {
|
||||
"agent": "./src/agent/ui.tsx"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
=== "JS agent"
|
||||
|
||||
```json title="langgraph.json"
|
||||
{
|
||||
"node_version": "20",
|
||||
"graphs": {
|
||||
"agent": "./src/agent/index.ts:graph"
|
||||
},
|
||||
"ui": {
|
||||
"agent": "./src/agent/ui.tsx"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
|
||||
|
||||
LangGraph Platform will automatically bundle your UI components code and styles and serve them as external assets that can be loaded by the `LoadExternalComponent` component. Some dependencies such as `react` and `react-dom` will be automatically excluded from the bundle.
|
||||
|
||||
CSS and Tailwind 4.x is also supported out of the box, so you can freely use Tailwind classes as well as `shadcn/ui` in your UI components.
|
||||
|
||||
=== "`src/agent/ui.tsx`"
|
||||
|
||||
```tsx
|
||||
import "./styles.css";
|
||||
|
||||
const WeatherComponent = (props: { city: string }) => {
|
||||
return <div className="bg-red-500">Weather for {props.city}</div>;
|
||||
};
|
||||
|
||||
export default {
|
||||
weather: WeatherComponent,
|
||||
};
|
||||
```
|
||||
|
||||
=== "`src/agent/styles.css`"
|
||||
|
||||
```css
|
||||
@import "tailwindcss";
|
||||
```
|
||||
|
||||
### 2. Send the UI components in your graph
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python title="src/agent.py"
|
||||
import uuid
|
||||
from typing import Annotated, Sequence, TypedDict
|
||||
|
||||
from langchain_core.messages import AIMessage, BaseMessage
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.graph.ui import AnyUIMessage, ui_message_reducer, push_ui_message
|
||||
|
||||
|
||||
class AgentState(TypedDict): # noqa: D101
|
||||
messages: Annotated[Sequence[BaseMessage], add_messages]
|
||||
ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
|
||||
|
||||
|
||||
async def weather(state: AgentState):
|
||||
class WeatherOutput(TypedDict):
|
||||
city: str
|
||||
|
||||
weather: WeatherOutput = (
|
||||
await ChatOpenAI(model="gpt-4o-mini")
|
||||
.with_structured_output(WeatherOutput)
|
||||
.with_config({"tags": ["nostream"]})
|
||||
.ainvoke(state["messages"])
|
||||
)
|
||||
|
||||
message = AIMessage(
|
||||
id=str(uuid.uuid4()),
|
||||
content=f"Here's the weather for {weather['city']}",
|
||||
)
|
||||
|
||||
# Emit UI elements associated with the message
|
||||
push_ui_message("weather", weather, message=message)
|
||||
return {"messages": [message]}
|
||||
|
||||
|
||||
workflow = StateGraph(AgentState)
|
||||
workflow.add_node(weather)
|
||||
workflow.add_edge("__start__", "weather")
|
||||
graph = workflow.compile()
|
||||
```
|
||||
|
||||
=== "JS"
|
||||
|
||||
Use the `typedUi` utility to emit UI elements from your agent nodes:
|
||||
|
||||
```typescript title="src/agent/index.ts"
|
||||
import {
|
||||
typedUi,
|
||||
uiMessageReducer,
|
||||
} from "@langchain/langgraph-sdk/react-ui/server";
|
||||
|
||||
import { ChatOpenAI } from "@langchain/openai";
|
||||
import { v4 as uuidv4 } from "uuid";
|
||||
import { z } from "zod";
|
||||
|
||||
import type ComponentMap from "./ui.js";
|
||||
|
||||
import {
|
||||
Annotation,
|
||||
MessagesAnnotation,
|
||||
StateGraph,
|
||||
type LangGraphRunnableConfig,
|
||||
} from "@langchain/langgraph";
|
||||
|
||||
const AgentState = Annotation.Root({
|
||||
...MessagesAnnotation.spec,
|
||||
ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
|
||||
});
|
||||
|
||||
export const graph = new StateGraph(AgentState)
|
||||
.addNode("weather", async (state, config) => {
|
||||
// Provide the type of the component map to ensure
|
||||
// type safety of `ui.push()` calls as well as
|
||||
// pushing the messages to the `ui` and sending a custom event as well.
|
||||
const ui = typedUi<typeof ComponentMap>(config);
|
||||
|
||||
const weather = await new ChatOpenAI({ model: "gpt-4o-mini" })
|
||||
.withStructuredOutput(z.object({ city: z.string() }))
|
||||
.withConfig({ tags: ["nostream"] })
|
||||
.invoke(state.messages);
|
||||
|
||||
const response = {
|
||||
id: uuidv4(),
|
||||
type: "ai",
|
||||
content: `Here's the weather for ${weather.city}`,
|
||||
};
|
||||
|
||||
// Emit UI elements associated with the AI message
|
||||
ui.push({ name: "weather", props: weather }, { message: response });
|
||||
|
||||
return { messages: [response] };
|
||||
})
|
||||
.addEdge("__start__", "weather")
|
||||
.compile();
|
||||
```
|
||||
|
||||
### 3. Handle UI elements in your React application
|
||||
|
||||
On the client side, you can use `useStream()` and `LoadExternalComponent` to display the UI elements.
|
||||
|
||||
```tsx title="src/app/page.tsx"
|
||||
"use client";
|
||||
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
import { LoadExternalComponent } from "@langchain/langgraph-sdk/react-ui";
|
||||
|
||||
export default function Page() {
|
||||
const { thread, values } = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
});
|
||||
|
||||
return (
|
||||
<div>
|
||||
{thread.messages.map((message) => (
|
||||
<div key={message.id}>
|
||||
{message.content}
|
||||
{values.ui
|
||||
?.filter((ui) => ui.metadata?.message_id === message.id)
|
||||
.map((ui) => (
|
||||
<LoadExternalComponent key={ui.id} stream={thread} message={ui} />
|
||||
))}
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI components from LangGraph Platform and render them in a shadow DOM, thus ensuring style isolation from the rest of your application.
|
||||
|
||||
## How-to guides
|
||||
|
||||
### Provide custom components on the client side
|
||||
|
||||
If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangGraph Platform.
|
||||
|
||||
```tsx
|
||||
const clientComponents = {
|
||||
weather: WeatherComponent,
|
||||
};
|
||||
|
||||
<LoadExternalComponent
|
||||
stream={thread}
|
||||
message={ui}
|
||||
components={clientComponents}
|
||||
/>;
|
||||
```
|
||||
|
||||
### Show loading UI when components are loading
|
||||
|
||||
You can provide a fallback UI to be rendered when the components are loading.
|
||||
|
||||
```tsx
|
||||
<LoadExternalComponent
|
||||
stream={thread}
|
||||
message={ui}
|
||||
fallback={<div>Loading...</div>}
|
||||
/>
|
||||
```
|
||||
|
||||
### Customise the namespace of UI components.
|
||||
|
||||
By default `LoadExternalComponent` will use the `assistantId` from `useStream()` hook to fetch the code for UI components. You can customise this by providing a `namespace` prop to the `LoadExternalComponent` component.
|
||||
|
||||
=== "`src/app/page.tsx`"
|
||||
|
||||
```tsx
|
||||
<LoadExternalComponent
|
||||
stream={thread}
|
||||
message={ui}
|
||||
namespace="custom-namespace"
|
||||
/>
|
||||
```
|
||||
|
||||
=== "`langgraph.json`"
|
||||
|
||||
```json
|
||||
{
|
||||
"ui": {
|
||||
"custom-namespace": "./src/agent/ui.tsx"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Access and interact with the thread state from the UI component
|
||||
|
||||
You can access the thread state inside the UI component by using the `useStreamContext` hook.
|
||||
|
||||
```tsx
|
||||
import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";
|
||||
|
||||
const WeatherComponent = (props: { city: string }) => {
|
||||
const { thread, submit } = useStreamContext();
|
||||
return (
|
||||
<>
|
||||
<div>Weather for {props.city}</div>
|
||||
|
||||
<button
|
||||
onClick={() => {
|
||||
const newMessage = {
|
||||
type: "human",
|
||||
content: `What's the weather in ${props.city}?`,
|
||||
};
|
||||
|
||||
submit({ messages: [newMessage] });
|
||||
}}
|
||||
>
|
||||
Retry
|
||||
</button>
|
||||
</>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Pass additional context to the client components
|
||||
|
||||
You can pass additional context to the client components by providing a `meta` prop to the `LoadExternalComponent` component.
|
||||
|
||||
```tsx
|
||||
<LoadExternalComponent stream={thread} message={ui} meta={{ userId: "123" }} />
|
||||
```
|
||||
|
||||
Then, you can access the `meta` prop in the UI component by using the `useStreamContext` hook.
|
||||
|
||||
```tsx
|
||||
import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";
|
||||
|
||||
const WeatherComponent = (props: { city: string }) => {
|
||||
const { meta } = useStreamContext<
|
||||
{ city: string },
|
||||
{ MetaType: { userId?: string } }
|
||||
>();
|
||||
|
||||
return (
|
||||
<div>
|
||||
Weather for {props.city} (user: {meta?.userId})
|
||||
</div>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Streaming UI messages from the server
|
||||
|
||||
You can stream UI messages before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook. This is especially useful when updating the UI component as the LLM is generating the response.
|
||||
|
||||
```tsx
|
||||
import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";
|
||||
|
||||
const { thread, submit } = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
onCustomEvent: (event, options) => {
|
||||
options.mutate((prev) => {
|
||||
const ui = uiMessageReducer(prev.ui ?? [], event);
|
||||
return { ...prev, ui };
|
||||
});
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
Then you can push updates to the UI component by calling `ui.push()` / `push_ui_message()` with the same ID as the UI message you wish to update.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from typing import Annotated, Sequence, TypedDict
|
||||
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
|
||||
from langgraph.graph import StateGraph
|
||||
from langgraph.graph.message import add_messages
|
||||
from langgraph.graph.ui import AnyUIMessage, push_ui_message, ui_message_reducer
|
||||
|
||||
|
||||
class AgentState(TypedDict): # noqa: D101
|
||||
messages: Annotated[Sequence[BaseMessage], add_messages]
|
||||
ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
|
||||
|
||||
|
||||
class CreateTextDocument(TypedDict):
|
||||
"""Prepare a document heading for the user."""
|
||||
|
||||
title: str
|
||||
|
||||
|
||||
async def writer_node(state: AgentState):
|
||||
model = ChatAnthropic(model="claude-3-5-sonnet-latest")
|
||||
message: AIMessage = await model.bind_tools(
|
||||
tools=[CreateTextDocument],
|
||||
tool_choice={"type": "tool", "name": "CreateTextDocument"},
|
||||
).ainvoke(state["messages"])
|
||||
|
||||
tool_call = next(
|
||||
(x["args"] for x in message.tool_calls if x["name"] == "CreateTextDocument"),
|
||||
None,
|
||||
)
|
||||
|
||||
if tool_call:
|
||||
ui_message = push_ui_message("writer", tool_call, message=message)
|
||||
ui_message_id = ui_message["id"]
|
||||
|
||||
# We're already streaming the LLM response to the client through UI messages
|
||||
# so we don't need to stream it again to the `messages` stream mode.
|
||||
content_stream = model.with_config({"tags": ["nostream"]}).astream(
|
||||
f"Create a document with the title: {tool_call['title']}"
|
||||
)
|
||||
|
||||
content: AIMessageChunk | None = None
|
||||
async for chunk in content_stream:
|
||||
content = content + chunk if content else chunk
|
||||
|
||||
push_ui_message(
|
||||
"writer",
|
||||
{"content": content.text()},
|
||||
id=ui_message_id,
|
||||
message=message,
|
||||
# Use `merge=rue` to merge props with the existing UI message
|
||||
merge=True,
|
||||
)
|
||||
|
||||
return {"messages": [message]}
|
||||
```
|
||||
|
||||
=== "JS"
|
||||
|
||||
```tsx
|
||||
import {
|
||||
Annotation,
|
||||
MessagesAnnotation,
|
||||
type LangGraphRunnableConfig,
|
||||
} from "@langchain/langgraph";
|
||||
import { z } from "zod";
|
||||
import { ChatAnthropic } from "@langchain/anthropic";
|
||||
import {
|
||||
typedUi,
|
||||
uiMessageReducer,
|
||||
} from "@langchain/langgraph-sdk/react-ui/server";
|
||||
import type { AIMessageChunk } from "@langchain/core/messages";
|
||||
|
||||
import type ComponentMap from "./ui";
|
||||
|
||||
const AgentState = Annotation.Root({
|
||||
...MessagesAnnotation.spec,
|
||||
ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
|
||||
});
|
||||
|
||||
async function writerNode(
|
||||
state: typeof AgentState.State,
|
||||
config: LangGraphRunnableConfig
|
||||
): Promise<typeof AgentState.Update> {
|
||||
const ui = typedUi<typeof ComponentMap>(config);
|
||||
|
||||
const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
|
||||
const message = await model
|
||||
.bindTools(
|
||||
[
|
||||
{
|
||||
name: "create_text_document",
|
||||
description: "Prepare a document heading for the user.",
|
||||
schema: z.object({ title: z.string() }),
|
||||
},
|
||||
],
|
||||
{ tool_choice: { type: "tool", name: "create_text_document" } }
|
||||
)
|
||||
.invoke(state.messages);
|
||||
|
||||
type ToolCall = { name: "create_text_document"; args: { title: string } };
|
||||
const toolCall = message.tool_calls?.find(
|
||||
(tool): tool is ToolCall => tool.name === "create_text_document"
|
||||
);
|
||||
|
||||
if (toolCall) {
|
||||
const { id, name } = ui.push(
|
||||
{ name: "writer", props: { title: toolCall.args.title } },
|
||||
{ message }
|
||||
);
|
||||
|
||||
const contentStream = await model
|
||||
// We're already streaming the LLM response to the client through UI messages
|
||||
// so we don't need to stream it again to the `messages` stream mode.
|
||||
.withConfig({ tags: ["nostream"] })
|
||||
.stream(`Create a short poem with the topic: ${message.text}`);
|
||||
|
||||
let content: AIMessageChunk | undefined;
|
||||
for await (const chunk of contentStream) {
|
||||
content = content?.concat(chunk) ?? chunk;
|
||||
|
||||
ui.push(
|
||||
{ id, name, props: { content: content?.text } },
|
||||
// Use `merge: true` to merge props with the existing UI message
|
||||
{ message, merge: true }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
return { messages: [message] };
|
||||
}
|
||||
```
|
||||
|
||||
=== "`ui.tsx`"
|
||||
|
||||
```tsx
|
||||
function WriterComponent(props: { title: string; content?: string }) {
|
||||
return (
|
||||
<article>
|
||||
<h2>{props.title}</h2>
|
||||
<p style={{ whiteSpace: "pre-wrap" }}>{props.content}</p>
|
||||
</article>
|
||||
);
|
||||
}
|
||||
|
||||
export default {
|
||||
weather: WriterComponent,
|
||||
};
|
||||
```
|
||||
|
||||
### Remove UI messages from state
|
||||
|
||||
Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `remove_ui_message` / `ui.delete` with the ID of the UI message.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph.graph.ui import push_ui_message, delete_ui_message
|
||||
|
||||
# push message
|
||||
message = push_ui_message("weather", {"city": "London"})
|
||||
|
||||
# remove said message
|
||||
delete_ui_message(message["id"])
|
||||
```
|
||||
|
||||
=== "JS"
|
||||
|
||||
```tsx
|
||||
// push message
|
||||
const message = ui.push({ name: "weather", props: { city: "London" } });
|
||||
|
||||
// remove said message
|
||||
ui.delete(message.id);
|
||||
```
|
||||
|
||||
## Learn more
|
||||
|
||||
- [JS/TS SDK Reference](../reference/sdk/js_ts_sdk_ref.md)
|
||||
@@ -0,0 +1,238 @@
|
||||
# Time travel using Server API
|
||||
|
||||
LangGraph provides the [**time travel**](../../concepts/time-travel.md) functionality to resume execution from a prior checkpoint, either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a new fork in the history.
|
||||
|
||||
To time travel using the LangGraph Server API (via the LangGraph SDK):
|
||||
|
||||
1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs.
|
||||
2. **Identify a checkpoint in an existing thread**: Use [`client.threads.get_history`][langgraph_sdk.client.ThreadsClient.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
|
||||
Alternatively, set a [breakpoint](./human_in_the_loop_breakpoint.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
|
||||
3. **(Optional) modify the graph state**: Use the [`client.threads.update_state`][langgraph_sdk.client.ThreadsClient.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
|
||||
4. **Resume execution from the checkpoint**: Use the [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.
|
||||
|
||||
## Use time travel in a workflow
|
||||
|
||||
??? example "Example graph"
|
||||
|
||||
```python
|
||||
from typing_extensions import TypedDict, NotRequired
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langchain.chat_models import init_chat_model
|
||||
from langgraph.checkpoint.memory import InMemorySaver
|
||||
|
||||
class State(TypedDict):
|
||||
topic: NotRequired[str]
|
||||
joke: NotRequired[str]
|
||||
|
||||
llm = init_chat_model(
|
||||
"anthropic:claude-3-7-sonnet-latest",
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
def generate_topic(state: State):
|
||||
"""LLM call to generate a topic for the joke"""
|
||||
msg = llm.invoke("Give me a funny topic for a joke")
|
||||
return {"topic": msg.content}
|
||||
|
||||
def write_joke(state: State):
|
||||
"""LLM call to write a joke based on the topic"""
|
||||
msg = llm.invoke(f"Write a short joke about {state['topic']}")
|
||||
return {"joke": msg.content}
|
||||
|
||||
# Build workflow
|
||||
builder = StateGraph(State)
|
||||
|
||||
# Add nodes
|
||||
builder.add_node("generate_topic", generate_topic)
|
||||
builder.add_node("write_joke", write_joke)
|
||||
|
||||
# Add edges to connect nodes
|
||||
builder.add_edge(START, "generate_topic")
|
||||
builder.add_edge("generate_topic", "write_joke")
|
||||
|
||||
# Compile
|
||||
graph = builder.compile()
|
||||
```
|
||||
|
||||
### 1. Run the graph
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# Run the graph
|
||||
result = await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={}
|
||||
)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// Run the graph
|
||||
const result = await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{ input: {}}
|
||||
);
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Run the graph:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {}
|
||||
}"
|
||||
```
|
||||
|
||||
### 2. Identify a checkpoint
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# The states are returned in reverse chronological order.
|
||||
states = await client.threads.get_history(thread_id)
|
||||
selected_state = states[1]
|
||||
print(selected_state)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
// The states are returned in reverse chronological order.
|
||||
const states = await client.threads.getHistory(threadID);
|
||||
const selectedState = states[1];
|
||||
console.log(selectedState);
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
### 3. Update the state (optional)
|
||||
|
||||
`update_state` will create a new checkpoint. The new checkpoint will be associated with the same thread, but a new checkpoint ID.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
new_config = await client.threads.update_state(
|
||||
thread_id,
|
||||
{"topic": "chickens"},
|
||||
# highlight-next-line
|
||||
checkpoint_id=selected_state["checkpoint_id"]
|
||||
)
|
||||
print(new_config)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const newConfig = await client.threads.updateState(
|
||||
threadID,
|
||||
{
|
||||
values: { "topic": "chickens" },
|
||||
checkpointId: selectedState["checkpoint_id"]
|
||||
}
|
||||
);
|
||||
console.log(newConfig);
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"checkpoint_id\": <CHECKPOINT_ID>,
|
||||
\"values\": {\"topic\": \"chickens\"}
|
||||
}"
|
||||
```
|
||||
|
||||
### 4. Resume execution from the checkpoint
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
await client.runs.wait(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
# highlight-next-line
|
||||
input=None,
|
||||
# highlight-next-line
|
||||
checkpoint_id=new_config["checkpoint_id"]
|
||||
)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
await client.runs.wait(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
// highlight-next-line
|
||||
input: null,
|
||||
// highlight-next-line
|
||||
checkpointId: newConfig["checkpoint_id"]
|
||||
}
|
||||
);
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"checkpoint_id\": <CHECKPOINT_ID>
|
||||
}"
|
||||
```
|
||||
|
||||
## Learn more
|
||||
|
||||
- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.md): learn more about using time travel in LangGraph.
|
||||
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@@ -0,0 +1,253 @@
|
||||
# How to use the interrupt option
|
||||
|
||||
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
|
||||
|
||||
The guide covers the `interrupt` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option does not delete the first run, but rather keeps it in the database but sets its status to `interrupted`. Below is a quick example of using the `interrupt` option.
|
||||
|
||||
## Setup
|
||||
|
||||
First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
function prettyPrint(m) {
|
||||
const padded = " " + m['type'] + " ";
|
||||
const sepLen = Math.floor((80 - padded.length) / 2);
|
||||
const sep = "=".repeat(sepLen);
|
||||
const secondSep = sep + (padded.length % 2 ? "=" : "");
|
||||
|
||||
console.log(`${sep}${padded}${secondSep}`);
|
||||
console.log("\n\n");
|
||||
console.log(m.content);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
# PLACE THIS IN A FILE CALLED pretty_print.sh
|
||||
pretty_print() {
|
||||
local type="$1"
|
||||
local content="$2"
|
||||
local padded=" $type "
|
||||
local total_width=80
|
||||
local sep_len=$(( (total_width - ${#padded}) / 2 ))
|
||||
local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
|
||||
local second_sep=$sep
|
||||
if (( (total_width - ${#padded}) % 2 )); then
|
||||
second_sep="${second_sep}="
|
||||
fi
|
||||
|
||||
echo "${sep}${padded}${second_sep}"
|
||||
echo
|
||||
echo "$content"
|
||||
}
|
||||
```
|
||||
|
||||
Now, let's import our required packages and instantiate our client, assistant, and thread.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
from langchain_core.messages import convert_to_messages
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
thread = await client.threads.create()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
const thread = await client.threads.create();
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
## Create runs
|
||||
|
||||
Now we can start our two runs and join the second one until it has completed:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# the first run will be interrupted
|
||||
interrupted_run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
)
|
||||
# sleep a bit to get partial outputs from the first run
|
||||
await asyncio.sleep(2)
|
||||
run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
|
||||
multitask_strategy="interrupt",
|
||||
)
|
||||
# wait until the second run completes
|
||||
await client.runs.join(thread["thread_id"], run["run_id"])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
// the first run will be interrupted
|
||||
let interruptedRun = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
{ input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
|
||||
);
|
||||
// sleep a bit to get partial outputs from the first run
|
||||
await new Promise(resolve => setTimeout(resolve, 2000));
|
||||
|
||||
let run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
{
|
||||
input: { messages: [{ role: "human", content: "what's the weather in nyc?" }] },
|
||||
multitaskStrategy: "interrupt"
|
||||
}
|
||||
);
|
||||
|
||||
// wait until the second run completes
|
||||
await client.runs.join(thread["thread_id"], run["run_id"]);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
|
||||
}" && sleep 2 && curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
|
||||
\"multitask_strategy\": \"interrupt\"
|
||||
}" && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join
|
||||
```
|
||||
|
||||
## View run results
|
||||
|
||||
We can see that the thread has partial data from the first run + data from the second run
|
||||
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
state = await client.threads.get_state(thread["thread_id"])
|
||||
|
||||
for m in convert_to_messages(state["values"]["messages"]):
|
||||
m.pretty_print()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const state = await client.threads.getState(thread["thread_id"]);
|
||||
|
||||
for (const m of state['values']['messages']) {
|
||||
prettyPrint(m);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
source pretty_print.sh && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
|
||||
jq -c '.values.messages[]' | while read -r element; do
|
||||
type=$(echo "$element" | jq -r '.type')
|
||||
content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
|
||||
pretty_print "$type" "$content"
|
||||
done
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in sf?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'id': 'toolu_01MjNtVJwEcpujRGrf3x6Pih', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01MjNtVJwEcpujRGrf3x6Pih)
|
||||
Call ID: toolu_01MjNtVJwEcpujRGrf3x6Pih
|
||||
Args:
|
||||
query: weather in san francisco
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.wunderground.com/hourly/us/ca/san-francisco/KCASANFR2002/date/2024-6-18", "content": "High 64F. Winds W at 10 to 20 mph. A few clouds from time to time. Low 49F. Winds W at 10 to 20 mph. Temp. San Francisco Weather Forecasts. Weather Underground provides local & long-range weather ..."}]
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in nyc?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'id': 'toolu_01KtE1m1ifPLQAx4fQLyZL9Q', 'input': {'query': 'weather in new york city'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01KtE1m1ifPLQAx4fQLyZL9Q)
|
||||
Call ID: toolu_01KtE1m1ifPLQAx4fQLyZL9Q
|
||||
Args:
|
||||
query: weather in new york city
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.accuweather.com/en/us/new-york/10021/june-weather/349727", "content": "Get the monthly weather forecast for New York, NY, including daily high/low, historical averages, to help you plan ahead."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
The search results provide weather forecasts and information for New York City. Based on the top result from AccuWeather, here are some key details about the weather in NYC:
|
||||
|
||||
- This is a monthly weather forecast for New York City for the month of June.
|
||||
- It includes daily high and low temperatures to help plan ahead.
|
||||
- Historical averages for June in NYC are also provided as a reference point.
|
||||
- More detailed daily or hourly forecasts with precipitation chances, humidity, wind, etc. can be found by visiting the AccuWeather page.
|
||||
|
||||
So in summary, the search provides a convenient overview of the expected weather conditions in New York City over the next month to give you an idea of what to prepare for if traveling or making plans there. Let me know if you need any other details!
|
||||
|
||||
|
||||
Verify that the original, interrupted run was interrupted
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print((await client.runs.get(thread["thread_id"], interrupted_run["run_id"]))["status"])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log((await client.runs.get(thread['thread_id'], interruptedRun["run_id"]))["status"])
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
'interrupted'
|
||||
```
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
# Run application
|
||||
|
||||
!!!info "Prerequisites"
|
||||
- [Running agents](../../agents/run_agents.md#running-agents)
|
||||
|
||||
This guide shows how to submit a [run](../../concepts/assistants.md#execution) to your application.
|
||||
|
||||
## Graph mode
|
||||
|
||||
### Specify input
|
||||
First define the input to your graph with in the "Input" section on the left side of the page, below the graph interface.
|
||||
|
||||
Studio will attempt to render a form for your input based on the graph's defined [state schema](../../concepts/low_level.md/#schema). To disable this, click the "View Raw" button, which will present you with a JSON editor.
|
||||
|
||||
Click the up/down arrows at the top of the "Input" section to toggle through and use previously submitted inputs.
|
||||
|
||||
### Run settings
|
||||
|
||||
#### Assistant
|
||||
|
||||
To specify the [assistant](../../concepts/assistants.md) that is used for the run click the settings button in the bottom left corner. If an assistant is currently selected the button will also list the assistant name. If no assistant is selected it will say "Manage Assistants".
|
||||
|
||||
Select the assistant to run and click the "Active" toggle at the top of the modal to activate it. [See here](./studio/manage_assistants.md) for more information on managing assistants.
|
||||
|
||||
#### Streaming
|
||||
Click the dropdown next to "Submit" and click the toggle to enable/disable streaming.
|
||||
|
||||
#### Breakpoints
|
||||
To run your graph with breakpoints, click the "Interrupt" button. Select a node and whether to pause before and/or after that node has executed. Click "Continue" in the thread log to resume execution.
|
||||
|
||||
|
||||
For more information on breakpoints see [here](../../concepts/human_in_the_loop.md).
|
||||
|
||||
### Submit run
|
||||
|
||||
To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../../concepts/assistants.md#execution) to the existing selected [thread](../../concepts/persistence.md#threads). If no thread is currently selected, a new one will be created.
|
||||
|
||||
To cancel the ongoing run, click the "Cancel" button.
|
||||
|
||||
|
||||
## Chat mode
|
||||
Specify the input to your chat application in the bottom of the conversation panel. Click the "Send message" button to submit the input as a Human message and have the response streamed back.
|
||||
|
||||
To cancel the ongoing run, click the "Cancel" button. Click the "Show tool calls" toggle to hide/show tool calls in the conversation.
|
||||
|
||||
## Learn more
|
||||
|
||||
To run your application from a specific checkpoint in an existing thread, see [this guide](./threads_studio.md#edit-thread-history).
|
||||
@@ -0,0 +1,134 @@
|
||||
# Iterate on prompts
|
||||
|
||||
## Overview
|
||||
|
||||
LangGraph Studio supports two methods for modifying prompts in your graph: direct node editing and the LangSmith Playground interface.
|
||||
|
||||
## Direct Node Editing
|
||||
|
||||
Studio allows you to edit prompts used inside individual nodes, directly from the graph interface.
|
||||
|
||||
!!! info "Prerequisites"
|
||||
|
||||
- [Assistants overview](../../concepts/assistants.md)
|
||||
|
||||
### Graph Configuration
|
||||
|
||||
Define your [configuration](https://langchain-ai.github.io/langgraph/how-tos/configuration/) to specify prompt fields and their associated nodes using `langgraph_nodes` and `langgraph_type` keys.
|
||||
|
||||
#### Configuration Reference
|
||||
|
||||
##### `langgraph_nodes`
|
||||
|
||||
- **Description**: Specifies which nodes of the graph a configuration field is associated with.
|
||||
- **Value Type**: Array of strings, where each string is the name of a node in your graph.
|
||||
- **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
|
||||
- **Example**:
|
||||
```python
|
||||
system_prompt: str = Field(
|
||||
default="You are a helpful AI assistant.",
|
||||
json_schema_extra={"langgraph_nodes": ["call_model", "other_node"]},
|
||||
)
|
||||
```
|
||||
|
||||
##### `langgraph_type`
|
||||
|
||||
- **Description**: Specifies the type of configuration field, which determines how it's handled in the UI.
|
||||
- **Value Type**: String
|
||||
- **Supported Values**:
|
||||
- `"prompt"`: Indicates the field contains prompt text that should be treated specially in the UI.
|
||||
- **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
|
||||
- **Example**:
|
||||
```python
|
||||
system_prompt: str = Field(
|
||||
default="You are a helpful AI assistant.",
|
||||
json_schema_extra={
|
||||
"langgraph_nodes": ["call_model"],
|
||||
"langgraph_type": "prompt",
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
#### Example Configuration
|
||||
|
||||
```python
|
||||
## Using Pydantic
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Annotated, Literal
|
||||
|
||||
class Configuration(BaseModel):
|
||||
"""The configuration for the agent."""
|
||||
|
||||
system_prompt: str = Field(
|
||||
default="You are a helpful AI assistant.",
|
||||
description="The system prompt to use for the agent's interactions. "
|
||||
"This prompt sets the context and behavior for the agent.",
|
||||
json_schema_extra={
|
||||
"langgraph_nodes": ["call_model"],
|
||||
"langgraph_type": "prompt",
|
||||
},
|
||||
)
|
||||
|
||||
model: Annotated[
|
||||
Literal[
|
||||
"anthropic/claude-3-7-sonnet-latest",
|
||||
"anthropic/claude-3-5-haiku-latest",
|
||||
"openai/o1",
|
||||
"openai/gpt-4o-mini",
|
||||
"openai/o1-mini",
|
||||
"openai/o3-mini",
|
||||
],
|
||||
{"__template_metadata__": {"kind": "llm"}},
|
||||
] = Field(
|
||||
default="openai/gpt-4o-mini",
|
||||
description="The name of the language model to use for the agent's main interactions. "
|
||||
"Should be in the form: provider/model-name.",
|
||||
json_schema_extra={"langgraph_nodes": ["call_model"]},
|
||||
)
|
||||
|
||||
## Using Dataclasses
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
@dataclass(kw_only=True)
|
||||
class Configuration:
|
||||
"""The configuration for the agent."""
|
||||
|
||||
system_prompt: str = field(
|
||||
default="You are a helpful AI assistant.",
|
||||
metadata={
|
||||
"description": "The system prompt to use for the agent's interactions. "
|
||||
"This prompt sets the context and behavior for the agent.",
|
||||
"json_schema_extra": {"langgraph_nodes": ["call_model"]},
|
||||
},
|
||||
)
|
||||
|
||||
model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
|
||||
default="anthropic/claude-3-5-sonnet-20240620",
|
||||
metadata={
|
||||
"description": "The name of the language model to use for the agent's main interactions. "
|
||||
"Should be in the form: provider/model-name.",
|
||||
"json_schema_extra": {"langgraph_nodes": ["call_model"]},
|
||||
},
|
||||
)
|
||||
|
||||
```
|
||||
|
||||
### Editing prompts in UI
|
||||
|
||||
1. Locate the gear icon on nodes with associated configuration fields
|
||||
2. Click to open the configuration modal
|
||||
3. Edit the values
|
||||
4. Save to update the current assistant version or create a new one
|
||||
|
||||
## LangSmith Playground
|
||||
|
||||
The [LangSmith Playground](https://
|
||||
docs.smith.langchain.com/prompt_engineering/how_to_guides#playground) interface allows testing individual LLM calls without running the full graph:
|
||||
|
||||
1. Select a thread
|
||||
2. Click "View LLM Runs" on a node. This lists all the LLM calls (if any) made inside the node.
|
||||
3. Select an LLM run to open in Playground
|
||||
4. Modify prompts and test different model and tool settings
|
||||
5. Copy updated prompts back to your graph
|
||||
|
||||
For advanced Playground features, click the expand button in the top right corner.
|
||||
@@ -0,0 +1,229 @@
|
||||
# Reject
|
||||
|
||||
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
|
||||
|
||||
The guide covers the `reject` option for double texting, which rejects the new run of the graph by throwing an error and continues with the original run until completion. Below is a quick example of using the `reject` option.
|
||||
|
||||
## Setup
|
||||
|
||||
First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
function prettyPrint(m) {
|
||||
const padded = " " + m['type'] + " ";
|
||||
const sepLen = Math.floor((80 - padded.length) / 2);
|
||||
const sep = "=".repeat(sepLen);
|
||||
const secondSep = sep + (padded.length % 2 ? "=" : "");
|
||||
|
||||
console.log(`${sep}${padded}${secondSep}`);
|
||||
console.log("\n\n");
|
||||
console.log(m.content);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
# PLACE THIS IN A FILE CALLED pretty_print.sh
|
||||
pretty_print() {
|
||||
local type="$1"
|
||||
local content="$2"
|
||||
local padded=" $type "
|
||||
local total_width=80
|
||||
local sep_len=$(( (total_width - ${#padded}) / 2 ))
|
||||
local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
|
||||
local second_sep=$sep
|
||||
if (( (total_width - ${#padded}) % 2 )); then
|
||||
second_sep="${second_sep}="
|
||||
fi
|
||||
|
||||
echo "${sep}${padded}${second_sep}"
|
||||
echo
|
||||
echo "$content"
|
||||
}
|
||||
```
|
||||
|
||||
Now, let's import our required packages and instantiate our client, assistant, and thread.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import httpx
|
||||
from langchain_core.messages import convert_to_messages
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
thread = await client.threads.create()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
const thread = await client.threads.create();
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
## Create runs
|
||||
|
||||
Now we can run a thread and try to run a second one with the "reject" option, which should fail since we have already started a run:
|
||||
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
)
|
||||
try:
|
||||
await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={
|
||||
"messages": [{"role": "user", "content": "what's the weather in nyc?"}]
|
||||
},
|
||||
multitask_strategy="reject",
|
||||
)
|
||||
except httpx.HTTPStatusError as e:
|
||||
print("Failed to start concurrent run", e)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
);
|
||||
|
||||
try {
|
||||
await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
{
|
||||
input: {"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
|
||||
multitask_strategy:"reject"
|
||||
},
|
||||
);
|
||||
} catch (e) {
|
||||
console.error("Failed to start concurrent run", e);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
|
||||
}" && curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
|
||||
\"multitask_strategy\": \"reject\"
|
||||
}" || { echo "Failed to start concurrent run"; echo "Error: $?" >&2; }
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
Failed to start concurrent run Client error '409 Conflict' for url 'http://localhost:8123/threads/f9e7088b-8028-4e5c-88d2-9cc9a2870e50/runs'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/409
|
||||
|
||||
## View run results
|
||||
|
||||
We can verify that the original thread finished executing:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# wait until the original run completes
|
||||
await client.runs.join(thread["thread_id"], run["run_id"])
|
||||
|
||||
state = await client.threads.get_state(thread["thread_id"])
|
||||
|
||||
for m in convert_to_messages(state["values"]["messages"]):
|
||||
m.pretty_print()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
await client.runs.join(thread["thread_id"], run["run_id"]);
|
||||
|
||||
const state = await client.threads.getState(thread["thread_id"]);
|
||||
|
||||
for (const m of state["values"]["messages"]) {
|
||||
prettyPrint(m);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
source pretty_print.sh && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join && \
|
||||
curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
|
||||
jq -c '.values.messages[]' | while read -r element; do
|
||||
type=$(echo "$element" | jq -r '.type')
|
||||
content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
|
||||
pretty_print "$type" "$content"
|
||||
done
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in sf?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'id': 'toolu_01CyewEifV2Kmi7EFKHbMDr1', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01CyewEifV2Kmi7EFKHbMDr1)
|
||||
Call ID: toolu_01CyewEifV2Kmi7EFKHbMDr1
|
||||
Args:
|
||||
query: weather in san francisco
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.accuweather.com/en/us/san-francisco/94103/june-weather/347629", "content": "Get the monthly weather forecast for San Francisco, CA, including daily high/low, historical averages, to help you plan ahead."}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
According to the search results from Tavily, the current weather in San Francisco is:
|
||||
|
||||
The average high temperature in San Francisco in June is around 65°F (18°C), with average lows around 54°F (12°C). June tends to be one of the cooler and foggier months in San Francisco due to the marine layer of fog that often blankets the city during the summer months.
|
||||
|
||||
Some key points about the typical June weather in San Francisco:
|
||||
|
||||
- Mild temperatures with highs in the 60s F and lows in the 50s F
|
||||
- Foggy mornings that often burn off to sunny afternoons
|
||||
- Little to no rainfall, as June falls in the dry season
|
||||
- Breezy conditions, with winds off the Pacific Ocean
|
||||
- Layers are recommended for changing weather conditions
|
||||
|
||||
So in summary, you can expect mild, foggy mornings giving way to sunny but cool afternoons in San Francisco this time of year. The marine layer keeps temperatures moderate compared to other parts of California in June.
|
||||
|
||||
@@ -0,0 +1,233 @@
|
||||
# How to use the Rollback option
|
||||
|
||||
|
||||
This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
|
||||
|
||||
The guide covers the `rollback` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the `interrupt` option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the `rollback` option.
|
||||
|
||||
## Setup
|
||||
|
||||
First, we will define a quick helper function for printing out JS and CURL model outputs (you can skip this if using Python):
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
function prettyPrint(m) {
|
||||
const padded = " " + m['type'] + " ";
|
||||
const sepLen = Math.floor((80 - padded.length) / 2);
|
||||
const sep = "=".repeat(sepLen);
|
||||
const secondSep = sep + (padded.length % 2 ? "=" : "");
|
||||
|
||||
console.log(`${sep}${padded}${secondSep}`);
|
||||
console.log("\n\n");
|
||||
console.log(m.content);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
# PLACE THIS IN A FILE CALLED pretty_print.sh
|
||||
pretty_print() {
|
||||
local type="$1"
|
||||
local content="$2"
|
||||
local padded=" $type "
|
||||
local total_width=80
|
||||
local sep_len=$(( (total_width - ${#padded}) / 2 ))
|
||||
local sep=$(printf '=%.0s' $(eval "echo {1.."${sep_len}"}"))
|
||||
local second_sep=$sep
|
||||
if (( (total_width - ${#padded}) % 2 )); then
|
||||
second_sep="${second_sep}="
|
||||
fi
|
||||
|
||||
echo "${sep}${padded}${second_sep}"
|
||||
echo
|
||||
echo "$content"
|
||||
}
|
||||
```
|
||||
|
||||
Now, let's import our required packages and instantiate our client, assistant, and thread.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
import httpx
|
||||
from langchain_core.messages import convert_to_messages
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
thread = await client.threads.create()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
const thread = await client.threads.create();
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
## Create runs
|
||||
|
||||
Now let's run a thread with the multitask parameter set to "rollback":
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
# the first run will be rolled back
|
||||
rolled_back_run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
|
||||
)
|
||||
run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
input={"messages": [{"role": "user", "content": "what's the weather in nyc?"}]},
|
||||
multitask_strategy="rollback",
|
||||
)
|
||||
# wait until the second run completes
|
||||
await client.runs.join(thread["thread_id"], run["run_id"])
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
// the first run will be interrupted
|
||||
let rolledBackRun = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistantId,
|
||||
{ input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
|
||||
);
|
||||
|
||||
let run = await client.runs.create(
|
||||
thread["thread_id"],
|
||||
assistant_id,
|
||||
{
|
||||
input: { messages: [{ role: "human", content: "what's the weather in nyc?" }] },
|
||||
multitaskStrategy: "rollback"
|
||||
}
|
||||
);
|
||||
|
||||
// wait until the second run completes
|
||||
await client.runs.join(thread["thread_id"], run["run_id"]);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
|
||||
}" && curl --request POST \
|
||||
--url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in nyc?\"}]},
|
||||
\"multitask_strategy\": \"rollback\"
|
||||
}" && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/join
|
||||
```
|
||||
|
||||
## View run results
|
||||
|
||||
We can see that the thread has data only from the second run
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
state = await client.threads.get_state(thread["thread_id"])
|
||||
|
||||
for m in convert_to_messages(state["values"]["messages"]):
|
||||
m.pretty_print()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const state = await client.threads.getState(thread["thread_id"]);
|
||||
|
||||
for (const m of state['values']['messages']) {
|
||||
prettyPrint(m);
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
source pretty_print.sh && curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
|
||||
jq -c '.values.messages[]' | while read -r element; do
|
||||
type=$(echo "$element" | jq -r '.type')
|
||||
content=$(echo "$element" | jq -r '.content | if type == "array" then tostring else . end')
|
||||
pretty_print "$type" "$content"
|
||||
done
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
================================ Human Message =================================
|
||||
|
||||
what's the weather in nyc?
|
||||
================================== Ai Message ==================================
|
||||
|
||||
[{'id': 'toolu_01JzPqefao1gxwajHQ3Yh3JD', 'input': {'query': 'weather in nyc'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
|
||||
Tool Calls:
|
||||
tavily_search_results_json (toolu_01JzPqefao1gxwajHQ3Yh3JD)
|
||||
Call ID: toolu_01JzPqefao1gxwajHQ3Yh3JD
|
||||
Args:
|
||||
query: weather in nyc
|
||||
================================= Tool Message =================================
|
||||
Name: tavily_search_results_json
|
||||
|
||||
[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}"}]
|
||||
================================== Ai Message ==================================
|
||||
|
||||
The weather API results show that the current weather in New York City is sunny with a temperature of around 85°F (29°C). The wind is light at around 2-3 mph from the south-southeast. Overall it looks like a nice sunny summer day in NYC.
|
||||
|
||||
|
||||
Verify that the original, rolled back run was deleted
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
try:
|
||||
await client.runs.get(thread["thread_id"], rolled_back_run["run_id"])
|
||||
except httpx.HTTPStatusError as _:
|
||||
print("Original run was correctly deleted")
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
try {
|
||||
await client.runs.get(thread["thread_id"], rolledBackRun["run_id"]);
|
||||
} catch (e) {
|
||||
console.log("Original run was correctly deleted");
|
||||
}
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
Original run was correctly deleted
|
||||
|
||||
@@ -0,0 +1,318 @@
|
||||
# How to run multiple agents on the same thread
|
||||
|
||||
In LangGraph Platform, a thread is not explicitly associated with a particular agent.
|
||||
This means that you can run multiple agents on the same thread, which allows a different agent to continue from an initial agent's progress.
|
||||
|
||||
In this example, we will create two agents and then call them both on the same thread.
|
||||
You'll see that the second agent will respond using information from the [checkpoint](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer-state) generated in the thread by the first agent as context.
|
||||
|
||||
## Setup
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
openai_assistant = await client.assistants.create(
|
||||
graph_id="agent", config={"configurable": {"model_name": "openai"}}
|
||||
)
|
||||
|
||||
# There should always be a default assistant with no configuration
|
||||
assistants = await client.assistants.search()
|
||||
default_assistant = [a for a in assistants if not a["config"]][0]
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
const openAIAssistant = await client.assistants.create(
|
||||
{ graphId: "agent", config: {"configurable": {"model_name": "openai"}}}
|
||||
);
|
||||
|
||||
const assistants = await client.assistants.search();
|
||||
const defaultAssistant = assistants.find(a => !a.config);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"graph_id": "agent",
|
||||
"config": { "configurable": { "model_name": "openai" } }
|
||||
}' && \
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"limit": 10,
|
||||
"offset": 0
|
||||
}' | jq -c 'map(select(.config == null or .config == {})) | .[0]'
|
||||
```
|
||||
|
||||
We can see that these agents are different:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(openai_assistant)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log(openAIAssistant);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/assistants/<OPENAI_ASSISTANT_ID>
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"assistant_id": "db87f39d-b2b1-4da8-ac65-cf81beb3c766",
|
||||
"graph_id": "agent",
|
||||
"created_at": "2024-08-30T21:18:51.850581+00:00",
|
||||
"updated_at": "2024-08-30T21:18:51.850581+00:00",
|
||||
"config": {
|
||||
"configurable": {
|
||||
"model_name": "openai"
|
||||
}
|
||||
},
|
||||
"metadata": {}
|
||||
}
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(default_assistant)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log(defaultAssistant);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/assistants/<DEFAULT_ASSISTANT_ID>
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"assistant_id": "fe096781-5601-53d2-b2f6-0d3403f7e9ca",
|
||||
"graph_id": "agent",
|
||||
"created_at": "2024-08-08T22:45:24.562906+00:00",
|
||||
"updated_at": "2024-08-08T22:45:24.562906+00:00",
|
||||
"config": {},
|
||||
"metadata": {
|
||||
"created_by": "system"
|
||||
}
|
||||
}
|
||||
|
||||
## Run assistants on thread
|
||||
|
||||
### Run OpenAI assistant
|
||||
|
||||
We can now run the OpenAI assistant on the thread first.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
thread = await client.threads.create()
|
||||
input = {"messages": [{"role": "user", "content": "who made you?"}]}
|
||||
async for event in client.runs.stream(
|
||||
thread["thread_id"],
|
||||
openai_assistant["assistant_id"],
|
||||
input=input,
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving event of type: {event.event}")
|
||||
print(event.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const thread = await client.threads.create();
|
||||
let input = {"messages": [{"role": "user", "content": "who made you?"}]}
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
thread["thread_id"],
|
||||
openAIAssistant["assistant_id"],
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const event of streamResponse) {
|
||||
console.log(`Receiving event of type: ${event.event}`);
|
||||
console.log(event.data);
|
||||
console.log("\n\n");
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
thread_id=$(curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}' | jq -r '.thread_id') && \
|
||||
curl --request POST \
|
||||
--url "<DEPLOYMENT_URL>/threads/${thread_id}/runs/stream" \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <OPENAI_ASSISTANT_ID>,
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "who made you?"
|
||||
}
|
||||
]
|
||||
},
|
||||
"stream_mode": [
|
||||
"updates"
|
||||
]
|
||||
}' | \
|
||||
sed 's/\r$//' | \
|
||||
awk '
|
||||
/^event:/ {
|
||||
if (data_content != "") {
|
||||
print data_content "\n"
|
||||
}
|
||||
sub(/^event: /, "Receiving event of type: ", $0)
|
||||
printf "%s...\n", $0
|
||||
data_content = ""
|
||||
}
|
||||
/^data:/ {
|
||||
sub(/^data: /, "", $0)
|
||||
data_content = $0
|
||||
}
|
||||
END {
|
||||
if (data_content != "") {
|
||||
print data_content "\n\n"
|
||||
}
|
||||
}
|
||||
'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
Receiving event of type: metadata
|
||||
{'run_id': '1ef671c5-fb83-6e70-b698-44dba2d9213e'}
|
||||
|
||||
|
||||
Receiving event of type: updates
|
||||
{'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-f5735b86-b80d-4c71-8dc3-4782b5a9c7c8', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
|
||||
|
||||
### Run default assistant
|
||||
|
||||
Now, we can run it on the default assistant and see that this second assistant is aware of the initial question, and can answer the question, "and you?":
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
input = {"messages": [{"role": "user", "content": "and you?"}]}
|
||||
async for event in client.runs.stream(
|
||||
thread["thread_id"],
|
||||
default_assistant["assistant_id"],
|
||||
input=input,
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving event of type: {event.event}")
|
||||
print(event.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let input = {"messages": [{"role": "user", "content": "and you?"}]}
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
thread["thread_id"],
|
||||
defaultAssistant["assistant_id"],
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const event of streamResponse) {
|
||||
console.log(`Receiving event of type: ${event.event}`);
|
||||
console.log(event.data);
|
||||
console.log("\n\n");
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <DEFAULT_ASSISTANT_ID>,
|
||||
"input": {
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "and you?"
|
||||
}
|
||||
]
|
||||
},
|
||||
"stream_mode": [
|
||||
"updates"
|
||||
]
|
||||
}' | \
|
||||
sed 's/\r$//' | \
|
||||
awk '
|
||||
/^event:/ {
|
||||
if (data_content != "") {
|
||||
print data_content "\n"
|
||||
}
|
||||
sub(/^event: /, "Receiving event of type: ", $0)
|
||||
printf "%s...\n", $0
|
||||
data_content = ""
|
||||
}
|
||||
/^data:/ {
|
||||
sub(/^data: /, "", $0)
|
||||
data_content = $0
|
||||
}
|
||||
END {
|
||||
if (data_content != "") {
|
||||
print data_content "\n\n"
|
||||
}
|
||||
}
|
||||
'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
Receiving event of type: metadata
|
||||
{'run_id': '1ef6722d-80b3-6fbb-9324-253796b1cd13'}
|
||||
|
||||
|
||||
Receiving event of type: updates
|
||||
{'agent': {'messages': [{'content': [{'text': 'I am an artificial intelligence created by Anthropic, not by OpenAI. I should not have stated that OpenAI created me, as that is incorrect. Anthropic is the company that developed and trained me using advanced language models and AI technology. I will be more careful about providing accurate information regarding my origins in the future.', 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ebaacf62-9dd9-4165-9535-db432e4793ec', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 302, 'output_tokens': 72, 'total_tokens': 374}}]}}
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,180 @@
|
||||
# Stateless Runs
|
||||
|
||||
Most of the time, you provide a `thread_id` to your client when you run your graph in order to keep track of prior runs through the persistent state implemented in LangGraph Platform. However, if you don't need to persist the runs you don't need to use the built in persistent state and can create stateless runs.
|
||||
|
||||
## Setup
|
||||
|
||||
First, let's setup our client:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
# create thread
|
||||
thread = await client.threads.create()
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantId = "agent";
|
||||
// create thread
|
||||
const thread = await client.threads.create();
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"limit": 10,
|
||||
"offset": 0
|
||||
}' | jq -c 'map(select(.config == null or .config == {})) | .[0].graph_id' && \
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
## Stateless streaming
|
||||
|
||||
We can stream the results of a stateless run in an almost identical fashion to how we stream from a run with the state attribute, but instead of passing a value to the `thread_id` parameter, we pass `None`:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
input = {
|
||||
"messages": [
|
||||
{"role": "user", "content": "Hello! My name is Bagatur and I am 26 years old."}
|
||||
]
|
||||
}
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
# Don't pass in a thread_id and the stream will be stateless
|
||||
None,
|
||||
assistant_id,
|
||||
input=input,
|
||||
stream_mode="updates",
|
||||
):
|
||||
if chunk.data and "run_id" not in chunk.data:
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let input = {
|
||||
messages: [
|
||||
{ role: "user", content: "Hello! My name is Bagatur and I am 26 years old." }
|
||||
]
|
||||
};
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
// Don't pass in a thread_id and the stream will be stateless
|
||||
null,
|
||||
assistantId,
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
if (chunk.data && !("run_id" in chunk.data)) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Hello! My name is Bagatur and I am 26 years old.\"}]},
|
||||
\"stream_mode\": [
|
||||
\"updates\"
|
||||
]
|
||||
}" | jq -c 'select(.data and (.data | has("run_id") | not)) | .data'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{'agent': {'messages': [{'content': "Hello Bagatur! It's nice to meet you. Thank you for introducing yourself and sharing your age. Is there anything specific you'd like to know or discuss? I'm here to help with any questions or topics you're interested in.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-489ec573-1645-4ce2-a3b8-91b391d50a71', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
|
||||
|
||||
## Waiting for stateless results
|
||||
|
||||
In addition to streaming, you can also wait for a stateless result by using the `.wait` function like follows:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
stateless_run_result = await client.runs.wait(
|
||||
None,
|
||||
assistant_id,
|
||||
input=input,
|
||||
)
|
||||
print(stateless_run_result)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
let statelessRunResult = await client.runs.wait(
|
||||
null,
|
||||
assistantId,
|
||||
{ input: input }
|
||||
);
|
||||
console.log(statelessRunResult);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/runs/wait \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <ASSISTANT_IDD>,
|
||||
}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
'messages': [
|
||||
{
|
||||
'content': 'Hello! My name is Bagatur and I am 26 years old.',
|
||||
'additional_kwargs': {},
|
||||
'response_metadata': {},
|
||||
'type': 'human',
|
||||
'name': None,
|
||||
'id': '5e088543-62c2-43de-9d95-6086ad7f8b48',
|
||||
'example': False}
|
||||
,
|
||||
{
|
||||
'content': "Hello Bagatur! It's nice to meet you. Thank you for introducing yourself and sharing your age. Is there anything specific you'd like to know or discuss? I'm here to help with any questions or topics you'd like to explore.",
|
||||
'additional_kwargs': {},
|
||||
'response_metadata': {},
|
||||
'type': 'ai',
|
||||
'name': None,
|
||||
'id': 'run-d6361e8d-4d4c-45bd-ba47-39520257f773',
|
||||
'example': False,
|
||||
'tool_calls': [],
|
||||
'invalid_tool_calls': [],
|
||||
'usage_metadata': None
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,957 @@
|
||||
# Streaming API
|
||||
|
||||
[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to [stream outputs](../../concepts/streaming.md) from the LangGraph API server.
|
||||
|
||||
!!! note
|
||||
|
||||
LangGraph SDK and LangGraph Server are a part of [LangGraph Platform](../../concepts/langgraph_platform.md).
|
||||
|
||||
## Basic usage
|
||||
|
||||
Basic usage example:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# create a streaming run
|
||||
# highlight-next-line
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input=inputs,
|
||||
stream_mode="updates"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// create a streaming run
|
||||
// highlight-next-line
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Create a streaming run:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'x-api-key: <API_KEY>'
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": <inputs>,
|
||||
\"stream_mode\": \"updates\"
|
||||
}"
|
||||
```
|
||||
|
||||
??? example "Extended example: streaming updates"
|
||||
|
||||
This is an example graph you can run in the LangGraph API server.
|
||||
See [LangGraph Platform quickstart](../quick_start.md) for more details.
|
||||
|
||||
```python
|
||||
# graph.py
|
||||
from typing import TypedDict
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
|
||||
class State(TypedDict):
|
||||
topic: str
|
||||
joke: str
|
||||
|
||||
def refine_topic(state: State):
|
||||
return {"topic": state["topic"] + " and cats"}
|
||||
|
||||
def generate_joke(state: State):
|
||||
return {"joke": f"This is a joke about {state['topic']}"}
|
||||
|
||||
graph = (
|
||||
StateGraph(State)
|
||||
.add_node(refine_topic)
|
||||
.add_node(generate_joke)
|
||||
.add_edge(START, "refine_topic")
|
||||
.add_edge("refine_topic", "generate_joke")
|
||||
.add_edge("generate_joke", END)
|
||||
.compile()
|
||||
)
|
||||
```
|
||||
|
||||
Once you have a running LangGraph API server, you can interact with it using
|
||||
[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
# create a streaming run
|
||||
# highlight-next-line
|
||||
async for chunk in client.runs.stream( # (1)!
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="updates" # (2)!
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
1. The `client.runs.stream()` method returns an iterator that yields streamed outputs.
|
||||
2. Set `stream_mode="updates"` to stream only the updates to the graph state after each node. Other stream modes are also available. See [supported stream modes](#supported-stream-modes) for details.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// create a streaming run
|
||||
// highlight-next-line
|
||||
const streamResponse = client.runs.stream( // (1)!
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "updates" // (2)!
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
1. The `client.runs.stream()` method returns an iterator that yields streamed outputs.
|
||||
2. Set `streamMode: "updates"` to stream only the updates to the graph state after each node. Other stream modes are also available. See [supported stream modes](#supported-stream-modes) for details.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Create a streaming run:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"updates\"
|
||||
}"
|
||||
```
|
||||
|
||||
```output
|
||||
{'run_id': '1f02c2b3-3cef-68de-b720-eec2a4a8e920', 'attempt': 1}
|
||||
{'refine_topic': {'topic': 'ice cream and cats'}}
|
||||
{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}
|
||||
```
|
||||
|
||||
|
||||
### Supported stream modes
|
||||
|
||||
| Mode | Description | LangGraph Library Method |
|
||||
|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
|
||||
| [`values`](#stream-graph-state) | Stream the full graph state after each [super-step](../../concepts/low_level.md#graphs). | `.stream()` / `.astream()` with [`stream_mode="values"`](../../how-tos/streaming.md#stream-graph-state) |
|
||||
| [`updates`](#stream-graph-state) | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. | `.stream()` / `.astream()` with [`stream_mode="updates"`](../../how-tos/streaming.md#stream-graph-state) |
|
||||
| [`messages-tuple`](#messages) | Streams LLM tokens and metadata for the graph node where the LLM is invoked (useful for chat apps). | `.stream()` / `.astream()` with [`stream_mode="messages"`](../../how-tos/streaming.md#messages) |
|
||||
| [`debug`](#debug) | Streams as much information as possible throughout the execution of the graph. | `.stream()` / `.astream()` with [`stream_mode="debug"`](../../how-tos/streaming.md#stream-graph-state) |
|
||||
| [`custom`](#stream-custom-data) | Streams custom data from inside your graph | `.stream()` / `.astream()` with [`stream_mode="custom"`](../../how-tos/streaming.md#stream-custom-data) |
|
||||
| [`events`](#stream-events) | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. | `.astream_events()` |
|
||||
|
||||
### Stream multiple modes
|
||||
|
||||
You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
|
||||
|
||||
The streamed outputs will be tuples of `(mode, chunk)` where `mode` is the name of the stream mode and `chunk` is the data streamed by that mode.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input=inputs,
|
||||
stream_mode=["updates", "custom"]
|
||||
):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input,
|
||||
streamMode: ["updates", "custom"]
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": <inputs>,
|
||||
\"stream_mode\": [
|
||||
\"updates\"
|
||||
\"custom\"
|
||||
]
|
||||
}"
|
||||
```
|
||||
|
||||
## Stream graph state
|
||||
|
||||
Use the stream modes `updates` and `values` to stream the state of the graph as it executes.
|
||||
|
||||
* `updates` streams the **updates** to the state after each step of the graph.
|
||||
* `values` streams the **full value** of the state after each step of the graph.
|
||||
|
||||
??? example "Example graph"
|
||||
|
||||
```python
|
||||
from typing import TypedDict
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
|
||||
class State(TypedDict):
|
||||
topic: str
|
||||
joke: str
|
||||
|
||||
def refine_topic(state: State):
|
||||
return {"topic": state["topic"] + " and cats"}
|
||||
|
||||
def generate_joke(state: State):
|
||||
return {"joke": f"This is a joke about {state['topic']}"}
|
||||
|
||||
graph = (
|
||||
StateGraph(State)
|
||||
.add_node(refine_topic)
|
||||
.add_node(generate_joke)
|
||||
.add_edge(START, "refine_topic")
|
||||
.add_edge("refine_topic", "generate_joke")
|
||||
.add_edge("generate_joke", END)
|
||||
.compile()
|
||||
)
|
||||
```
|
||||
|
||||
!!! note "Stateful runs"
|
||||
|
||||
Examples below assume that you want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB and have created a thread. To create a thread:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"]
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
If you don't need to persist the outputs of a run, you can pass `None` instead of `thread_id` when streaming.
|
||||
|
||||
=== "updates"
|
||||
|
||||
Use this to stream only the **state updates** returned by the nodes after each step. The streamed outputs include the name of the node as well as the update.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="updates"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"updates\"
|
||||
}"
|
||||
```
|
||||
|
||||
=== "values"
|
||||
|
||||
Use this to stream the **full state** of the graph after each step.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="values"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "values"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"values\"
|
||||
}"
|
||||
```
|
||||
|
||||
|
||||
## Subgraphs
|
||||
|
||||
To include outputs from [subgraphs](../../concepts/subgraphs.md) in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.
|
||||
|
||||
```python
|
||||
for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"foo": "foo"},
|
||||
# highlight-next-line
|
||||
stream_subgraphs=True, # (1)!
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
1. Set `stream_subgraphs=True` to stream outputs from subgraphs.
|
||||
|
||||
??? example "Extended example: streaming from subgraphs"
|
||||
|
||||
This is an example graph you can run in the LangGraph API server.
|
||||
See [LangGraph Platform quickstart](../quick_start.md) for more details.
|
||||
|
||||
```python
|
||||
# graph.py
|
||||
from langgraph.graph import START, StateGraph
|
||||
from typing import TypedDict
|
||||
|
||||
# Define subgraph
|
||||
class SubgraphState(TypedDict):
|
||||
foo: str # note that this key is shared with the parent graph state
|
||||
bar: str
|
||||
|
||||
def subgraph_node_1(state: SubgraphState):
|
||||
return {"bar": "bar"}
|
||||
|
||||
def subgraph_node_2(state: SubgraphState):
|
||||
return {"foo": state["foo"] + state["bar"]}
|
||||
|
||||
subgraph_builder = StateGraph(SubgraphState)
|
||||
subgraph_builder.add_node(subgraph_node_1)
|
||||
subgraph_builder.add_node(subgraph_node_2)
|
||||
subgraph_builder.add_edge(START, "subgraph_node_1")
|
||||
subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2")
|
||||
subgraph = subgraph_builder.compile()
|
||||
|
||||
# Define parent graph
|
||||
class ParentState(TypedDict):
|
||||
foo: str
|
||||
|
||||
def node_1(state: ParentState):
|
||||
return {"foo": "hi! " + state["foo"]}
|
||||
|
||||
builder = StateGraph(ParentState)
|
||||
builder.add_node("node_1", node_1)
|
||||
builder.add_node("node_2", subgraph)
|
||||
builder.add_edge(START, "node_1")
|
||||
builder.add_edge("node_1", "node_2")
|
||||
graph = builder.compile()
|
||||
```
|
||||
|
||||
Once you have a running LangGraph API server, you can interact with it using
|
||||
[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
|
||||
# Using the graph deployed with the name "agent"
|
||||
assistant_id = "agent"
|
||||
|
||||
# create a thread
|
||||
thread = await client.threads.create()
|
||||
thread_id = thread["thread_id"]
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"foo": "foo"},
|
||||
# highlight-next-line
|
||||
stream_subgraphs=True, # (1)!
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
1. Set `stream_subgraphs=True` to stream outputs from subgraphs.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
|
||||
// Using the graph deployed with the name "agent"
|
||||
const assistantID = "agent";
|
||||
|
||||
// create a thread
|
||||
const thread = await client.threads.create();
|
||||
const threadID = thread["thread_id"];
|
||||
|
||||
// create a streaming run
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { foo: "foo" },
|
||||
// highlight-next-line
|
||||
streamSubgraphs: true, // (1)!
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk);
|
||||
}
|
||||
```
|
||||
|
||||
1. Set `streamSubgraphs: true` to stream outputs from subgraphs.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
Create a thread:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Create a streaming run:
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"foo\": \"foo\"},
|
||||
\"stream_subgraphs\": true,
|
||||
\"stream_mode\": [
|
||||
\"updates\"
|
||||
]
|
||||
}"
|
||||
```
|
||||
|
||||
**Note** that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from.
|
||||
|
||||
## Debugging {#debug}
|
||||
|
||||
Use the `debug` streaming mode to stream as much information as possible throughout the execution of the graph. The streamed outputs include the name of the node as well as the full state.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="debug"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "debug"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"debug\"
|
||||
}"
|
||||
```
|
||||
|
||||
## LLM tokens {#messages}
|
||||
|
||||
Use the `messages-tuple` streaming mode to stream Large Language Model (LLM) outputs **token by token** from any part of your graph, including nodes, tools, subgraphs, or tasks.
|
||||
|
||||
The streamed output from [`messages-tuple` mode](#supported-stream-modes) is a tuple `(message_chunk, metadata)` where:
|
||||
|
||||
- `message_chunk`: the token or message segment from the LLM.
|
||||
- `metadata`: a dictionary containing details about the graph node and LLM invocation.
|
||||
|
||||
??? example "Example graph"
|
||||
|
||||
```python
|
||||
from dataclasses import dataclass
|
||||
|
||||
from langchain.chat_models import init_chat_model
|
||||
from langgraph.graph import StateGraph, START
|
||||
|
||||
@dataclass
|
||||
class MyState:
|
||||
topic: str
|
||||
joke: str = ""
|
||||
|
||||
llm = init_chat_model(model="openai:gpt-4o-mini")
|
||||
|
||||
def call_model(state: MyState):
|
||||
"""Call the LLM to generate a joke about a topic"""
|
||||
# highlight-next-line
|
||||
llm_response = llm.invoke( # (1)!
|
||||
[
|
||||
{"role": "user", "content": f"Generate a joke about {state.topic}"}
|
||||
]
|
||||
)
|
||||
return {"joke": llm_response.content}
|
||||
|
||||
graph = (
|
||||
StateGraph(MyState)
|
||||
.add_node(call_model)
|
||||
.add_edge(START, "call_model")
|
||||
.compile()
|
||||
)
|
||||
```
|
||||
|
||||
1. Note that the message events are emitted even when the LLM is run using `.invoke` rather than `.stream`.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="messages-tuple",
|
||||
):
|
||||
if chunk.event != "messages":
|
||||
continue
|
||||
|
||||
message_chunk, metadata = chunk.data # (1)!
|
||||
if message_chunk["content"]:
|
||||
print(message_chunk["content"], end="|", flush=True)
|
||||
```
|
||||
|
||||
1. The "messages-tuple" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "messages-tuple"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
if (chunk.event !== "messages") {
|
||||
continue;
|
||||
}
|
||||
console.log(chunk.data[0]["content"]); // (1)!
|
||||
}
|
||||
```
|
||||
|
||||
1. The "messages-tuple" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"messages-tuple\"
|
||||
}"
|
||||
```
|
||||
|
||||
### Filter LLM tokens
|
||||
|
||||
* To filter the streamed tokens by LLM invocation, you can [associate `tags` with LLM invocations](../../how-tos/streaming.md#filter-by-llm-invocation).
|
||||
* To stream tokens only from specific nodes, use `stream_mode="messages"` and [filter the outputs by the `langgraph_node` field](../../how-tos/streaming.md#filter-by-node) in the streamed metadata.
|
||||
|
||||
## Stream custom data
|
||||
|
||||
To send **custom user-defined data**:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"query": "example"},
|
||||
# highlight-next-line
|
||||
stream_mode="custom"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { query: "example" },
|
||||
// highlight-next-line
|
||||
streamMode: "custom"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"query\": \"example\"},
|
||||
\"stream_mode\": \"custom\"
|
||||
}"
|
||||
```
|
||||
|
||||
## Stream events
|
||||
|
||||
To stream all events, including the state of the graph:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id,
|
||||
assistant_id,
|
||||
input={"topic": "ice cream"},
|
||||
# highlight-next-line
|
||||
stream_mode="events"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const streamResponse = client.runs.stream(
|
||||
threadID,
|
||||
assistantID,
|
||||
{
|
||||
input: { topic: "ice cream" },
|
||||
// highlight-next-line
|
||||
streamMode: "events"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {\"topic\": \"ice cream\"},
|
||||
\"stream_mode\": \"events\"
|
||||
}"
|
||||
```
|
||||
|
||||
## Stateless runs
|
||||
|
||||
If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
# highlight-next-line
|
||||
None, # (1)!
|
||||
assistant_id,
|
||||
input=inputs,
|
||||
stream_mode="updates"
|
||||
):
|
||||
print(chunk.data)
|
||||
```
|
||||
|
||||
1. We are passing `None` instead of a `thread_id` UUID.
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
|
||||
|
||||
// create a streaming run
|
||||
// highlight-next-line
|
||||
const streamResponse = client.runs.stream(
|
||||
// highlight-next-line
|
||||
null, // (1)!
|
||||
assistantID,
|
||||
{
|
||||
input,
|
||||
streamMode: "updates"
|
||||
}
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk.data);
|
||||
}
|
||||
```
|
||||
|
||||
1. We are passing `None` instead of a `thread_id` UUID.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'x-api-key: <API_KEY>'
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": <inputs>,
|
||||
\"stream_mode\": \"updates\"
|
||||
}"
|
||||
```
|
||||
|
||||
## Join and stream
|
||||
|
||||
LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
|
||||
|
||||
# highlight-next-line
|
||||
async for chunk in client.runs.join_stream(
|
||||
thread_id,
|
||||
# highlight-next-line
|
||||
run_id, # (1)!
|
||||
):
|
||||
print(chunk)
|
||||
```
|
||||
|
||||
1. This is the `run_id` of an existing run you want to join.
|
||||
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
|
||||
|
||||
// highlight-next-line
|
||||
const streamResponse = client.runs.joinStream(
|
||||
threadID,
|
||||
// highlight-next-line
|
||||
runId // (1)!
|
||||
);
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(chunk);
|
||||
}
|
||||
```
|
||||
|
||||
1. This is the `run_id` of an existing run you want to join.
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'x-api-key: <API_KEY>'
|
||||
```
|
||||
|
||||
!!! warning "Outputs not buffered"
|
||||
|
||||
When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
|
||||
|
||||
## API Reference
|
||||
|
||||
For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream).
|
||||
@@ -0,0 +1,19 @@
|
||||
# Manage assistants
|
||||
|
||||
!!! info "Prerequisites"
|
||||
|
||||
- [Assistants Overview](../../../concepts/assistants.md)
|
||||
|
||||
LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
|
||||
|
||||
## Graph mode
|
||||
|
||||
To view your assistants, click the "Manage Assistants" button in the bottom left corner.
|
||||
|
||||
This opens a modal for you to view all the assistants for the selected graph. Specify the assistant and its version you would like to mark as "Active", and this assistant will be used when submitting runs.
|
||||
|
||||
By default, the "Default configuration" option will be active. This option reflects the default configuration defined in your graph. Edits made to this configuration will be used to update the run-time configuration, but will not update or create a new assistant unless you click "Create new assistant".
|
||||
|
||||
## Chat mode
|
||||
|
||||
Chat mode enables you to switch through the different assistants in your graph via the dropdown selector at the top of the page. To create, edit, or delete assistants, use Graph mode.
|
||||
@@ -0,0 +1,112 @@
|
||||
!!! info "Prerequisites"
|
||||
|
||||
- [LangGraph Studio Overview](../../../concepts/langgraph_studio.md)
|
||||
|
||||
LangGraph Studio supports connecting to two types of graphs:
|
||||
|
||||
- Graphs deployed on [LangGraph Platform](../../../cloud/quick_start.md)
|
||||
- Graphs running locally via the [LangGraph Server](../../../tutorials/langgraph-platform/local-server.md).
|
||||
|
||||
LangGraph Studio is accessed from the LangSmith UI, within the LangGraph Platform Deployments tab.
|
||||
|
||||
## Deployed application
|
||||
|
||||
For applications that are [deployed](../../quick_start.md) on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
|
||||
|
||||
This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../../concepts/persistence.md#threads), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
|
||||
|
||||
## Local development server
|
||||
|
||||
To test your locally running application using LangGraph Studio, ensure your application is set up following [this guide](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/).
|
||||
|
||||
!!! info "LangSmith Tracing"
|
||||
For local development, if you do not wish to have data traced to LangSmith, set `LANGSMITH_TRACING=false` in your application's `.env` file. With tracing disabled, no data will leave your local server.
|
||||
|
||||
Next, install the [LangGraph CLI](../../../concepts/langgraph_cli.md):
|
||||
|
||||
```
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
```
|
||||
|
||||
and run:
|
||||
|
||||
```
|
||||
langgraph dev
|
||||
```
|
||||
|
||||
!!! warning "Browser Compatibility"
|
||||
Safari blocks `localhost` connections to Studio. To work around this, run the above command with `--tunnel` to access Studio via a secure tunnel.
|
||||
|
||||
This will start the LangGraph Server locally, running in-memory. The server will run in watch mode, listening for and automatically restarting on code changes. Read this [reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#dev) to learn about all the options for starting the API server.
|
||||
|
||||
If successful, you will see the following logs:
|
||||
|
||||
> Ready!
|
||||
>
|
||||
> - API: [http://localhost:2024](http://localhost:2024/)
|
||||
>
|
||||
> - Docs: http://localhost:2024/docs
|
||||
>
|
||||
> - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
|
||||
|
||||
Once running, you will automatically be directed to LangGraph Studio.
|
||||
|
||||
For an already running server, access Studio by either:
|
||||
|
||||
1. Directly navigate to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024`.
|
||||
2. Within LangSmith, navigate to the LangGraph Platform Deployments tab, click the "LangGraph Studio" button, enter `http://127.0.0.1:2024` and click "Connect".
|
||||
|
||||
If running your server at a different host or port, simply update the `baseUrl` to match.
|
||||
|
||||
### (Optional) Attach a debugger
|
||||
|
||||
For step-by-step debugging with breakpoints and variable inspection:
|
||||
|
||||
```bash
|
||||
# Install debugpy package
|
||||
pip install debugpy
|
||||
|
||||
# Start server with debugging enabled
|
||||
langgraph dev --debug-port 5678
|
||||
```
|
||||
|
||||
Then attach your preferred debugger:
|
||||
|
||||
=== "VS Code"
|
||||
|
||||
Add this configuration to `launch.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"name": "Attach to LangGraph",
|
||||
"type": "debugpy",
|
||||
"request": "attach",
|
||||
"connect": {
|
||||
"host": "0.0.0.0",
|
||||
"port": 5678
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
=== "PyCharm"
|
||||
|
||||
1. Go to Run → Edit Configurations
|
||||
2. Click + and select "Python Debug Server"
|
||||
3. Set IDE host name: `localhost`
|
||||
4. Set port: `5678` (or the port number you chose in the previous step)
|
||||
5. Click "OK" and start debugging
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
For issues getting started, please see this [troubleshooting guide](../../../troubleshooting/studio.md).
|
||||
|
||||
## Next steps
|
||||
|
||||
See the following guides for more information on how to use Studio:
|
||||
|
||||
- [Run application](../invoke_studio.md)
|
||||
- [Manage assistants](./manage_assistants.md)
|
||||
- [Manage threads](../threads_studio.md)
|
||||
- [Iterate on prompts](../iterate_graph_studio.md)
|
||||
- [Debug LangSmith traces](../clone_traces_studio.md)
|
||||
- [Add node to dataset](../datasets_studio.md)
|
||||
@@ -0,0 +1,57 @@
|
||||
# Run experiments over a dataset
|
||||
|
||||
LangGraph Studio supports evaluations by allowing you to run your assistant over a pre-defined LangSmith dataset. This enables you to understand how your application performs over a variety of inputs, compare the results to reference outputs, and score the results using [evaluators](../../../agents/evals.md).
|
||||
|
||||
This guide shows you how to run an experiment end-to-end from Studio.
|
||||
|
||||
---
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before running an experiment, ensure you have the following:
|
||||
|
||||
1. **A LangSmith dataset**: Your dataset should contain the inputs you want to test and optionally, reference outputs for comparison.
|
||||
|
||||
- The schema for the inputs must match the required input schema for the assistant. For more information on schemas, see [here](../../../concepts/low_level.md#schema).
|
||||
- For more on creating datasets, see [How to Manage Datasets](https://docs.smith.langchain.com/evaluation/how_to_guides/manage_datasets_in_application#set-up-your-dataset).
|
||||
|
||||
2. **(Optional) Evaluators**: You can attach evaluators (e.g., LLM-as-a-Judge, heuristics, or custom functions) to your dataset in LangSmith. These will run automatically after the graph has processed all inputs.
|
||||
|
||||
- To learn more, read about [Evaluation Concepts](https://docs.smith.langchain.com/evaluation/concepts#evaluators).
|
||||
|
||||
3. **A running application**: The experiment can be run against:
|
||||
- An application deployed on [LangGraph Platform](../../quick_start.md).
|
||||
- A locally running application started via the [langgraph-cli](../../../tutorials/langgraph-platform/local-server.md).
|
||||
|
||||
---
|
||||
|
||||
## Step-by-step guide
|
||||
|
||||
### 1. Launch the experiment
|
||||
|
||||
Click the **Run experiment** button in the top right corner of the Studio page.
|
||||
|
||||
### 2. Select your dataset
|
||||
|
||||
In the modal that appears, select the dataset (or a specific dataset split) to use for the experiment and click **Start**.
|
||||
|
||||
### 3. Monitor the progress
|
||||
|
||||
All of the inputs in the dataset will now be run against the active assistant. Monitor the experiment's progress via the badge in the top right corner.
|
||||
|
||||
You can continue to work in Studio while the experiment runs in the background. Click the arrow icon button at any time to navigate to LangSmith and view the detailed experiment results.
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "Run experiment" button is disabled
|
||||
|
||||
If the "Run experiment" button is disabled, check the following:
|
||||
|
||||
- **Deployed application**: If your application is deployed on LangGraph Platform, you may need to create a new revision to enable this feature.
|
||||
- **Local development server**: If you are running your application locally, make sure you have upgraded to the latest version of the `langgraph-cli` (`pip install -U langgraph-cli`). Additionally, ensure you have tracing enabled by setting the `LANGSMITH_API_KEY` in your project's `.env` file.
|
||||
|
||||
### Evaluator results are missing
|
||||
|
||||
When you run an experiment, any attached evaluators are scheduled for execution in a queue. If you don't see results immediately, it likely means they are still pending.
|
||||
@@ -0,0 +1,37 @@
|
||||
# Manage threads
|
||||
|
||||
Studio allows you to view [threads](../../concepts/persistence.md#threads) from the server and edit their state.
|
||||
|
||||
## View threads
|
||||
|
||||
### Graph mode
|
||||
|
||||
1. In the top of the right-hand pane, select the dropdown menu to view existing threads.
|
||||
1. Select the desired thread, and the thread history will populate in the right-hand side of the page.
|
||||
1. To create a new thread, click `+ New Thread` and [submit a run](../how-tos/invoke_studio.md#graph-mode).
|
||||
|
||||
To view more granular information in the thread, drag the slider at the top of the page to the right. To view less information, drag the slider to the left. Additionally, collapse or expand individual turns, nodes, and keys of the state.
|
||||
|
||||
Switch between `Pretty` and `JSON` mode for different rendering formats.
|
||||
|
||||
### Chat mode
|
||||
|
||||
1. View all threads in the right-hand pane of the page.
|
||||
2. Select the desired thread and the thread history will populate in the center panel.
|
||||
3. To create a new thread, click the plus button and [submit a run](../how-tos/invoke_studio.md#chat-mode).
|
||||
|
||||
## Edit thread history
|
||||
|
||||
### Graph mode
|
||||
|
||||
To edit the state of the thread, select "edit node state" next to the desired node. Edit the node's output as desired and click "fork" to confirm. This will create a new forked run from the checkpoint of the selected node.
|
||||
|
||||
If you instead want to re-run the thread from a given checkpoint without editing the state, click the "Re-run from here". This will again create a new forked run from the selected checkpoint. This is useful for re-running with changes that are not specific to the state, such as the selected assistant.
|
||||
|
||||
### Chat mode
|
||||
|
||||
To edit a human message in the thread, click the edit button below the human message. Edit the message as desired and submit. This will create a new fork of the conversation history. To re-generate an AI message, click the retry icon below the AI message.
|
||||
|
||||
## Learn more
|
||||
|
||||
For more information about time travel, [see here](../../concepts/time-travel.md).
|
||||
@@ -0,0 +1,661 @@
|
||||
# How to integrate LangGraph into your React application
|
||||
|
||||
!!! info "Prerequisites"
|
||||
|
||||
- [LangGraph Platform](../../concepts/langgraph_platform.md)
|
||||
- [LangGraph Server](../../concepts/langgraph_server.md)
|
||||
|
||||
The `useStream()` React hook provides a seamless way to integrate LangGraph into your React applications. It handles all the complexities of streaming, state management, and branching logic, letting you focus on building great chat experiences.
|
||||
|
||||
Key features:
|
||||
|
||||
- Messages streaming: Handle a stream of message chunks to form a complete message
|
||||
- Automatic state management for messages, interrupts, loading states, and errors
|
||||
- Conversation branching: Create alternate conversation paths from any point in the chat history
|
||||
- UI-agnostic design: bring your own components and styling
|
||||
|
||||
Let's explore how to use `useStream()` in your React application.
|
||||
|
||||
The `useStream()` provides a solid foundation for creating bespoke chat experiences. For pre-built chat components and interfaces, we also recommend checking out [CopilotKit](https://docs.copilotkit.ai/coagents/quickstart/langgraph) and [assistant-ui](https://www.assistant-ui.com/docs/runtimes/langgraph).
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
npm install @langchain/langgraph-sdk @langchain/core
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
```tsx
|
||||
"use client";
|
||||
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
import type { Message } from "@langchain/langgraph-sdk";
|
||||
|
||||
export default function App() {
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
return (
|
||||
<div>
|
||||
<div>
|
||||
{thread.messages.map((message) => (
|
||||
<div key={message.id}>{message.content as string}</div>
|
||||
))}
|
||||
</div>
|
||||
|
||||
<form
|
||||
onSubmit={(e) => {
|
||||
e.preventDefault();
|
||||
|
||||
const form = e.target as HTMLFormElement;
|
||||
const message = new FormData(form).get("message") as string;
|
||||
|
||||
form.reset();
|
||||
thread.submit({ messages: [{ type: "human", content: message }] });
|
||||
}}
|
||||
>
|
||||
<input type="text" name="message" />
|
||||
|
||||
{thread.isLoading ? (
|
||||
<button key="stop" type="button" onClick={() => thread.stop()}>
|
||||
Stop
|
||||
</button>
|
||||
) : (
|
||||
<button keytype="submit">Send</button>
|
||||
)}
|
||||
</form>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
## Customizing Your UI
|
||||
|
||||
The `useStream()` hook takes care of all the complex state management behind the scenes, providing you with simple interfaces to build your UI. Here's what you get out of the box:
|
||||
|
||||
- Thread state management
|
||||
- Loading and error states
|
||||
- Interrupts
|
||||
- Message handling and updates
|
||||
- Branching support
|
||||
|
||||
Here are some examples on how to use these features effectively:
|
||||
|
||||
### Loading States
|
||||
|
||||
The `isLoading` property tells you when a stream is active, enabling you to:
|
||||
|
||||
- Show a loading indicator
|
||||
- Disable input fields during processing
|
||||
- Display a cancel button
|
||||
|
||||
```tsx
|
||||
export default function App() {
|
||||
const { isLoading, stop } = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
return (
|
||||
<form>
|
||||
{isLoading && (
|
||||
<button key="stop" type="button" onClick={() => stop()}>
|
||||
Stop
|
||||
</button>
|
||||
)}
|
||||
</form>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
### Resume a stream after page refresh
|
||||
|
||||
The `useStream()` hook can automatically resume an ongoing run upon mounting by setting `reconnectOnMount: true`. This is useful for continuing a stream after a page refresh, ensuring no messages and events generated during the downtime are lost.
|
||||
|
||||
```tsx
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
reconnectOnMount: true,
|
||||
});
|
||||
```
|
||||
|
||||
By default the ID of the created run is stored in `window.sessionStorage`, which can be swapped by passing a custom storage in `reconnectOnMount` instead. The storage is used to persist the in-flight run ID for a thread (under `lg:stream:${threadId}` key).
|
||||
|
||||
```tsx
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
reconnectOnMount: () => window.localStorage,
|
||||
});
|
||||
```
|
||||
|
||||
You can also manually manage the resuming process by using the run callbacks to persist the run metadata and the `joinStream` function to resume the stream. Make sure to pass `streamResumable: true` when creating the run; otherwise some events might be lost.
|
||||
|
||||
````tsx
|
||||
import type { Message } from "@langchain/langgraph-sdk";
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
import { useCallback, useState, useEffect, useRef } from "react";
|
||||
|
||||
export default function App() {
|
||||
const [threadId, onThreadId] = useSearchParam("threadId");
|
||||
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
|
||||
threadId,
|
||||
onThreadId,
|
||||
|
||||
onCreated: (run) => {
|
||||
window.sessionStorage.setItem(`resume:${run.thread_id}`, run.run_id);
|
||||
},
|
||||
onFinish: (_, run) => {
|
||||
window.sessionStorage.removeItem(`resume:${run?.thread_id}`);
|
||||
},
|
||||
});
|
||||
|
||||
// Ensure that we only join the stream once per thread.
|
||||
const joinedThreadId = useRef<string | null>(null);
|
||||
useEffect(() => {
|
||||
if (!threadId) return;
|
||||
|
||||
const resume = window.sessionStorage.getItem(`resume:${threadId}`);
|
||||
if (resume && joinedThreadId.current !== threadId) {
|
||||
thread.joinStream(resume);
|
||||
joinedThreadId.current = threadId;
|
||||
}
|
||||
}, [threadId]);
|
||||
|
||||
return (
|
||||
<form
|
||||
onSubmit={(e) => {
|
||||
e.preventDefault();
|
||||
const form = e.target as HTMLFormElement;
|
||||
const message = new FormData(form).get("message") as string;
|
||||
thread.submit(
|
||||
{ messages: [{ type: "human", content: message }] },
|
||||
{ streamResumable: true }
|
||||
);
|
||||
}}
|
||||
>
|
||||
<div>
|
||||
{thread.messages.map((message) => (
|
||||
<div key={message.id}>{message.content as string}</div>
|
||||
))}
|
||||
</div>
|
||||
<input type="text" name="message" />
|
||||
<button type="submit">Send</button>
|
||||
</form>
|
||||
);
|
||||
}
|
||||
|
||||
// Utility method to retrieve and persist data in URL as search param
|
||||
function useSearchParam(key: string) {
|
||||
const [value, setValue] = useState<string | null>(() => {
|
||||
const params = new URLSearchParams(window.location.search);
|
||||
return params.get(key) ?? null;
|
||||
});
|
||||
|
||||
const update = useCallback(
|
||||
(value: string | null) => {
|
||||
setValue(value);
|
||||
|
||||
const url = new URL(window.location.href);
|
||||
if (value == null) {
|
||||
url.searchParams.delete(key);
|
||||
} else {
|
||||
url.searchParams.set(key, value);
|
||||
}
|
||||
|
||||
window.history.pushState({}, "", url.toString());
|
||||
},
|
||||
[key]
|
||||
);
|
||||
|
||||
return [value, update] as const;
|
||||
}
|
||||
```
|
||||
|
||||
### Thread Management
|
||||
|
||||
Keep track of conversations with built-in thread management. You can access the current thread ID and get notified when new threads are created:
|
||||
|
||||
```tsx
|
||||
const [threadId, setThreadId] = useState<string | null>(null);
|
||||
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
|
||||
threadId: threadId,
|
||||
onThreadId: setThreadId,
|
||||
});
|
||||
````
|
||||
|
||||
We recommend storing the `threadId` in your URL's query parameters to let users resume conversations after page refreshes.
|
||||
|
||||
### Messages Handling
|
||||
|
||||
The `useStream()` hook will keep track of the message chunks received from the server and concatenate them together to form a complete message. The completed message chunks can be retrieved via the `messages` property.
|
||||
|
||||
By default, the `messagesKey` is set to `messages`, where it will append the new messages chunks to `values["messages"]`. If you store messages in a different key, you can change the value of `messagesKey`.
|
||||
|
||||
```tsx
|
||||
import type { Message } from "@langchain/langgraph-sdk";
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
|
||||
export default function HomePage() {
|
||||
const thread = useStream<{ messages: Message[] }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
return (
|
||||
<div>
|
||||
{thread.messages.map((message) => (
|
||||
<div key={message.id}>{message.content as string}</div>
|
||||
))}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
Under the hood, the `useStream()` hook will use the `streamMode: "messages-tuple"` to receive a stream of messages (i.e. individual LLM tokens) from any LangChain chat model invocations inside your graph nodes. Learn more about messages streaming in the [streaming](../how-tos/streaming.md#messages) guide.
|
||||
|
||||
### Interrupts
|
||||
|
||||
The `useStream()` hook exposes the `interrupt` property, which will be filled with the last interrupt from the thread. You can use interrupts to:
|
||||
|
||||
- Render a confirmation UI before executing a node
|
||||
- Wait for human input, allowing agent to ask the user with clarifying questions
|
||||
|
||||
Learn more about interrupts in the [How to handle interrupts](../../how-tos/human_in_the_loop/wait-user-input.ipynb) guide.
|
||||
|
||||
```tsx
|
||||
const thread = useStream<{ messages: Message[] }, { InterruptType: string }>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
if (thread.interrupt) {
|
||||
return (
|
||||
<div>
|
||||
Interrupted! {thread.interrupt.value}
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => {
|
||||
// `resume` can be any value that the agent accepts
|
||||
thread.submit(undefined, { command: { resume: true } });
|
||||
}}
|
||||
>
|
||||
Resume
|
||||
</button>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
### Branching
|
||||
|
||||
For each message, you can use `getMessagesMetadata()` to get the first checkpoint from which the message has been first seen. You can then create a new run from the checkpoint preceding the first seen checkpoint to create a new branch in a thread.
|
||||
|
||||
A branch can be created in following ways:
|
||||
|
||||
1. Edit a previous user message.
|
||||
2. Request a regeneration of a previous assistant message.
|
||||
|
||||
```tsx
|
||||
"use client";
|
||||
|
||||
import type { Message } from "@langchain/langgraph-sdk";
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
import { useState } from "react";
|
||||
|
||||
function BranchSwitcher({
|
||||
branch,
|
||||
branchOptions,
|
||||
onSelect,
|
||||
}: {
|
||||
branch: string | undefined;
|
||||
branchOptions: string[] | undefined;
|
||||
onSelect: (branch: string) => void;
|
||||
}) {
|
||||
if (!branchOptions || !branch) return null;
|
||||
const index = branchOptions.indexOf(branch);
|
||||
|
||||
return (
|
||||
<div className="flex items-center gap-2">
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => {
|
||||
const prevBranch = branchOptions[index - 1];
|
||||
if (!prevBranch) return;
|
||||
onSelect(prevBranch);
|
||||
}}
|
||||
>
|
||||
Prev
|
||||
</button>
|
||||
<span>
|
||||
{index + 1} / {branchOptions.length}
|
||||
</span>
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => {
|
||||
const nextBranch = branchOptions[index + 1];
|
||||
if (!nextBranch) return;
|
||||
onSelect(nextBranch);
|
||||
}}
|
||||
>
|
||||
Next
|
||||
</button>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
function EditMessage({
|
||||
message,
|
||||
onEdit,
|
||||
}: {
|
||||
message: Message;
|
||||
onEdit: (message: Message) => void;
|
||||
}) {
|
||||
const [editing, setEditing] = useState(false);
|
||||
|
||||
if (!editing) {
|
||||
return (
|
||||
<button type="button" onClick={() => setEditing(true)}>
|
||||
Edit
|
||||
</button>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<form
|
||||
onSubmit={(e) => {
|
||||
e.preventDefault();
|
||||
const form = e.target as HTMLFormElement;
|
||||
const content = new FormData(form).get("content") as string;
|
||||
|
||||
form.reset();
|
||||
onEdit({ type: "human", content });
|
||||
setEditing(false);
|
||||
}}
|
||||
>
|
||||
<input name="content" defaultValue={message.content as string} />
|
||||
<button type="submit">Save</button>
|
||||
</form>
|
||||
);
|
||||
}
|
||||
|
||||
export default function App() {
|
||||
const thread = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
return (
|
||||
<div>
|
||||
<div>
|
||||
{thread.messages.map((message) => {
|
||||
const meta = thread.getMessagesMetadata(message);
|
||||
const parentCheckpoint = meta?.firstSeenState?.parent_checkpoint;
|
||||
|
||||
return (
|
||||
<div key={message.id}>
|
||||
<div>{message.content as string}</div>
|
||||
|
||||
{message.type === "human" && (
|
||||
<EditMessage
|
||||
message={message}
|
||||
onEdit={(message) =>
|
||||
thread.submit(
|
||||
{ messages: [message] },
|
||||
{ checkpoint: parentCheckpoint }
|
||||
)
|
||||
}
|
||||
/>
|
||||
)}
|
||||
|
||||
{message.type === "ai" && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={() =>
|
||||
thread.submit(undefined, { checkpoint: parentCheckpoint })
|
||||
}
|
||||
>
|
||||
<span>Regenerate</span>
|
||||
</button>
|
||||
)}
|
||||
|
||||
<BranchSwitcher
|
||||
branch={meta?.branch}
|
||||
branchOptions={meta?.branchOptions}
|
||||
onSelect={(branch) => thread.setBranch(branch)}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
|
||||
<form
|
||||
onSubmit={(e) => {
|
||||
e.preventDefault();
|
||||
|
||||
const form = e.target as HTMLFormElement;
|
||||
const message = new FormData(form).get("message") as string;
|
||||
|
||||
form.reset();
|
||||
thread.submit({ messages: [message] });
|
||||
}}
|
||||
>
|
||||
<input type="text" name="message" />
|
||||
|
||||
{thread.isLoading ? (
|
||||
<button key="stop" type="button" onClick={() => thread.stop()}>
|
||||
Stop
|
||||
</button>
|
||||
) : (
|
||||
<button key="submit" type="submit">
|
||||
Send
|
||||
</button>
|
||||
)}
|
||||
</form>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
For advanced use cases you can use the `experimental_branchTree` property to get the tree representation of the thread, which can be used to render branching controls for non-message based graphs.
|
||||
|
||||
### Optimistic Updates
|
||||
|
||||
You can optimistically update the client state before performing a network request to the agent, allowing you to provide immediate feedback to the user, such as showing the user message immediately before the agent has seen the request.
|
||||
|
||||
```tsx
|
||||
const stream = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
const handleSubmit = (text: string) => {
|
||||
const newMessage = { type: "human" as const, content: text };
|
||||
|
||||
stream.submit(
|
||||
{ messages: [newMessage] },
|
||||
{
|
||||
optimisticValues(prev) {
|
||||
const prevMessages = prev.messages ?? [];
|
||||
const newMessages = [...prevMessages, newMessage];
|
||||
return { ...prev, messages: newMessages };
|
||||
},
|
||||
}
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Cached Thread Display
|
||||
|
||||
Use the `initialValues` option to display cached thread data immediately while the history is being loaded from the server. This improves user experience by showing cached data instantly when navigating to existing threads.
|
||||
|
||||
```tsx
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
|
||||
const CachedThreadExample = ({ threadId, cachedThreadData }) => {
|
||||
const stream = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
threadId,
|
||||
// Show cached data immediately while history loads
|
||||
initialValues: cachedThreadData?.values,
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
return (
|
||||
<div>
|
||||
{stream.messages.map((message) => (
|
||||
<div key={message.id}>{message.content as string}</div>
|
||||
))}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### Optimistic Thread Creation
|
||||
|
||||
Use the `threadId` option in `submit` function to enable optimistic UI patterns where you need to know the thread ID before the thread is actually created.
|
||||
|
||||
```tsx
|
||||
import { useState } from "react";
|
||||
import { useStream } from "@langchain/langgraph-sdk/react";
|
||||
|
||||
const OptimisticThreadExample = () => {
|
||||
const [threadId, setThreadId] = useState<string | null>(null);
|
||||
const [optimisticThreadId] = useState(() => crypto.randomUUID());
|
||||
|
||||
const stream = useStream({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
threadId,
|
||||
onThreadId: setThreadId, // (3) Updated after thread has been created.
|
||||
messagesKey: "messages",
|
||||
});
|
||||
|
||||
const handleSubmit = (text: string) => {
|
||||
// (1) Perform a soft navigation to /threads/${optimisticThreadId}
|
||||
// without waiting for thread creation.
|
||||
window.history.pushState({}, "", `/threads/${optimisticThreadId}`);
|
||||
|
||||
// (2) Submit message to create thread with the predetermined ID.
|
||||
stream.submit(
|
||||
{ messages: [{ type: "human", content: text }] },
|
||||
{ threadId: optimisticThreadId }
|
||||
);
|
||||
};
|
||||
|
||||
return (
|
||||
<div>
|
||||
<p>Thread ID: {threadId ?? optimisticThreadId}</p>
|
||||
{/* Rest of component */}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
```
|
||||
|
||||
### TypeScript
|
||||
|
||||
The `useStream()` hook is friendly for apps written in TypeScript and you can specify types for the state to get better type safety and IDE support.
|
||||
|
||||
```tsx
|
||||
// Define your types
|
||||
type State = {
|
||||
messages: Message[];
|
||||
context?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
// Use them with the hook
|
||||
const thread = useStream<State>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
```
|
||||
|
||||
You can also optionally specify types for different scenarios, such as:
|
||||
|
||||
- `ConfigurableType`: Type for the `config.configurable` property (default: `Record<string, unknown>`)
|
||||
- `InterruptType`: Type for the interrupt value - i.e. contents of `interrupt(...)` function (default: `unknown`)
|
||||
- `CustomEventType`: Type for the custom events (default: `unknown`)
|
||||
- `UpdateType`: Type for the submit function (default: `Partial<State>`)
|
||||
|
||||
```tsx
|
||||
const thread = useStream<
|
||||
State,
|
||||
{
|
||||
UpdateType: {
|
||||
messages: Message[] | Message;
|
||||
context?: Record<string, unknown>;
|
||||
};
|
||||
InterruptType: string;
|
||||
CustomEventType: {
|
||||
type: "progress" | "debug";
|
||||
payload: unknown;
|
||||
};
|
||||
ConfigurableType: {
|
||||
model: string;
|
||||
};
|
||||
}
|
||||
>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
```
|
||||
|
||||
If you're using LangGraph.js, you can also reuse your graph's annotation types. However, make sure to only import the types of the annotation schema in order to avoid importing the entire LangGraph.js runtime (i.e. via `import type { ... }` directive).
|
||||
|
||||
```tsx
|
||||
import {
|
||||
Annotation,
|
||||
MessagesAnnotation,
|
||||
type StateType,
|
||||
type UpdateType,
|
||||
} from "@langchain/langgraph/web";
|
||||
|
||||
const AgentState = Annotation.Root({
|
||||
...MessagesAnnotation.spec,
|
||||
context: Annotation<string>(),
|
||||
});
|
||||
|
||||
const thread = useStream<
|
||||
StateType<typeof AgentState.spec>,
|
||||
{ UpdateType: UpdateType<typeof AgentState.spec> }
|
||||
>({
|
||||
apiUrl: "http://localhost:2024",
|
||||
assistantId: "agent",
|
||||
messagesKey: "messages",
|
||||
});
|
||||
```
|
||||
|
||||
## Event Handling
|
||||
|
||||
The `useStream()` hook provides several callback options to help you respond to different events:
|
||||
|
||||
- `onError`: Called when an error occurs.
|
||||
- `onFinish`: Called when the stream is finished.
|
||||
- `onUpdateEvent`: Called when an update event is received.
|
||||
- `onCustomEvent`: Called when a custom event is received. See the [streaming](../../how-tos/streaming.md#stream-custom-data) guide to learn how to stream custom events.
|
||||
- `onMetadataEvent`: Called when a metadata event is received, which contains the Run ID and Thread ID.
|
||||
|
||||
## Learn More
|
||||
|
||||
- [JS/TS SDK Reference](../reference/sdk/js_ts_sdk_ref.md)
|
||||
@@ -0,0 +1,487 @@
|
||||
# Use threads
|
||||
|
||||
In this guide, we will show how to create, view, and inspect [threads](../../concepts/persistence.md#threads).
|
||||
|
||||
## Create a thread
|
||||
|
||||
To run your graph and the state persisted, you must first create a thread.
|
||||
|
||||
### Empty thread
|
||||
|
||||
To create a new thread, use the [LangGraph SDK](../../concepts/sdk.md) `create` method. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.create) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#create_3) SDK reference docs for more information.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
thread = await client.threads.create()
|
||||
|
||||
print(thread)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
const thread = await client.threads.create();
|
||||
|
||||
console.log(thread);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"thread_id": "123e4567-e89b-12d3-a456-426614174000",
|
||||
"created_at": "2025-05-12T14:04:08.268Z",
|
||||
"updated_at": "2025-05-12T14:04:08.268Z",
|
||||
"metadata": {},
|
||||
"status": "idle",
|
||||
"values": {}
|
||||
}
|
||||
|
||||
### Copy thread
|
||||
|
||||
Alternatively, if you already have a thread in your application whose state you wish to copy, you can use the `copy` method. This will create an independent thread whose history is identical to the original thread at the time of the operation. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.copy) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#copy) SDK reference docs for more information.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
copied_thread = await client.threads.copy(<THREAD_ID>)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const copiedThread = await client.threads.copy(<THREAD_ID>);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/copy \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
### Prepopulated State
|
||||
|
||||
Finally, you can create a thread with an arbitrary pre-defined state by providing a list of `supersteps` into the `create` method. The `supersteps` describe a list of a sequence of state updates. For example:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
thread = await client.threads.create(
|
||||
graph_id="agent",
|
||||
supersteps=[
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {},
|
||||
as_node: '__input__',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {
|
||||
messages: [
|
||||
{
|
||||
type: 'human',
|
||||
content: 'hello',
|
||||
},
|
||||
],
|
||||
},
|
||||
as_node: '__start__',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {
|
||||
messages: [
|
||||
{
|
||||
content: 'Hello! How can I assist you today?',
|
||||
type: 'ai',
|
||||
},
|
||||
],
|
||||
},
|
||||
as_node: 'call_model',
|
||||
},
|
||||
],
|
||||
},
|
||||
])
|
||||
|
||||
print(thread)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
const thread = await client.threads.create({
|
||||
graphId: 'agent',
|
||||
supersteps: [
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {},
|
||||
asNode: '__input__',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {
|
||||
messages: [
|
||||
{
|
||||
type: 'human',
|
||||
content: 'hello',
|
||||
},
|
||||
],
|
||||
},
|
||||
asNode: '__start__',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
updates: [
|
||||
{
|
||||
values: {
|
||||
messages: [
|
||||
{
|
||||
content: 'Hello! How can I assist you today?',
|
||||
type: 'ai',
|
||||
},
|
||||
],
|
||||
},
|
||||
asNode: 'call_model',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
console.log(thread);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{"metadata":{"graph_id":"agent"},"supersteps":[{"updates":[{"values":{},"as_node":"__input__"}]},{"updates":[{"values":{"messages":[{"type":"human","content":"hello"}]},"as_node":"__start__"}]},{"updates":[{"values":{"messages":[{"content":"Hello\u0021 How can I assist you today?","type":"ai"}]},"as_node":"call_model"}]}]}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
|
||||
"created_at": "2025-05-12T15:37:08.935038+00:00",
|
||||
"updated_at": "2025-05-12T15:37:08.935046+00:00",
|
||||
"metadata": {"graph_id": "agent"},
|
||||
"status": "idle",
|
||||
"config": {},
|
||||
"values": {
|
||||
"messages": [
|
||||
{
|
||||
"content": "hello",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "human",
|
||||
"name": null,
|
||||
"id": "8701f3be-959c-4b7c-852f-c2160699b4ab",
|
||||
"example": false
|
||||
},
|
||||
{
|
||||
"content": "Hello! How can I assist you today?",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "ai",
|
||||
"name": null,
|
||||
"id": "4d8ea561-7ca1-409a-99f7-6b67af3e1aa3",
|
||||
"example": false,
|
||||
"tool_calls": [],
|
||||
"invalid_tool_calls": [],
|
||||
"usage_metadata": null
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
## List threads
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
To list threads, use the [LangGraph SDK](../../concepts/sdk.md) `search` method. This will list the threads in the application that match the provided filters. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.search) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#search_2) SDK reference docs for more information.
|
||||
|
||||
#### Filter by thread status
|
||||
|
||||
Use the `status` field to filter threads based on their status. Supported values are `idle`, `busy`, `interrupted`, and `error`. See [here](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/?h=thread+status#langgraph_sdk.auth.types.ThreadStatus) for information on each status. For example, to view `idle` threads:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print(await client.threads.search(status="idle",limit=1))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log(await client.threads.search({ status: "idle", limit: 1 }));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{"status": "idle", "limit": 1}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
[
|
||||
{
|
||||
'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
|
||||
'created_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'updated_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'metadata': {'graph_id': 'agent'},
|
||||
'status': 'idle',
|
||||
'config': {'configurable': {}}
|
||||
}
|
||||
]
|
||||
|
||||
#### Filter by metadata
|
||||
|
||||
The `search` method allows you to filter on metadata:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print((await client.threads.search(metadata={"graph_id":"agent"},limit=1)))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log((await client.threads.search({ metadata: { "graph_id": "agent" }, limit: 1 })));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{"metadata": {"graph_id":"agent"}, "limit": 1}'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
[
|
||||
{
|
||||
'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
|
||||
'created_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'updated_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'metadata': {'graph_id': 'agent'},
|
||||
'status': 'idle',
|
||||
'config': {'configurable': {}}
|
||||
}
|
||||
]
|
||||
|
||||
#### Sorting
|
||||
|
||||
The SDK also supports sorting threads by `thread_id`, `status`, `created_at`, and `updated_at` using the `sort_by` and `sort_order` params.
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
You can also view threads in a deployment via the LangGraph Platform UI.
|
||||
|
||||
Inside your deployment, select the "Threads" tab. This will load a table of all of the threads in your deployment.
|
||||
|
||||
To filter by thread status, select a status in the top bar. To sort by a supported property, click on the arrow icon for the desired column.
|
||||
|
||||
## Inspect threads
|
||||
|
||||
### LangGraph SDK
|
||||
|
||||
#### Get Thread
|
||||
|
||||
To view a specific thread given its `thread_id`, use the `get` method:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print((await client.threads.get(<THREAD_ID>)))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log((await client.threads.get(<THREAD_ID>)));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID> \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
|
||||
'created_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'updated_at': '2024-08-14T17:36:38.921660+00:00',
|
||||
'metadata': {'graph_id': 'agent'},
|
||||
'status': 'idle',
|
||||
'config': {'configurable': {}}
|
||||
}
|
||||
|
||||
#### Inspect Thread State
|
||||
|
||||
To view the current state of a given thread, use the `get_state` method:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
print((await client.threads.get_state(<THREAD_ID>)))
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
console.log((await client.threads.getState(<THREAD_ID>)));
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
{
|
||||
"values": {
|
||||
"messages": [
|
||||
{
|
||||
"content": "hello",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "human",
|
||||
"name": null,
|
||||
"id": "8701f3be-959c-4b7c-852f-c2160699b4ab",
|
||||
"example": false
|
||||
},
|
||||
{
|
||||
"content": "Hello! How can I assist you today?",
|
||||
"additional_kwargs": {},
|
||||
"response_metadata": {},
|
||||
"type": "ai",
|
||||
"name": null,
|
||||
"id": "4d8ea561-7ca1-409a-99f7-6b67af3e1aa3",
|
||||
"example": false,
|
||||
"tool_calls": [],
|
||||
"invalid_tool_calls": [],
|
||||
"usage_metadata": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"next": [],
|
||||
"tasks": [],
|
||||
"metadata": {
|
||||
"thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
|
||||
"checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955",
|
||||
"graph_id": "agent_with_quite_a_long_name",
|
||||
"source": "update",
|
||||
"step": 1,
|
||||
"writes": {
|
||||
"call_model": {
|
||||
"messages": [
|
||||
{
|
||||
"content": "Hello! How can I assist you today?",
|
||||
"type": "ai"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"parents": {}
|
||||
},
|
||||
"created_at": "2025-05-12T15:37:09.008055+00:00",
|
||||
"checkpoint": {
|
||||
"checkpoint_id": "1f02f46f-733f-6b58-8001-ea90dcabb1bd",
|
||||
"thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
|
||||
"checkpoint_ns": ""
|
||||
},
|
||||
"parent_checkpoint": {
|
||||
"checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955",
|
||||
"thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
|
||||
"checkpoint_ns": ""
|
||||
},
|
||||
"checkpoint_id": "1f02f46f-733f-6b58-8001-ea90dcabb1bd",
|
||||
"parent_checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955"
|
||||
}
|
||||
|
||||
Optionally, to view the state of a thread at a given checkpoint, simply pass in the checkpoint id (or the entire checkpoint object):
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
thread_state = await client.threads.get_state(
|
||||
thread_id=<THREAD_ID>
|
||||
checkpoint_id=<CHECKPOINT_ID>
|
||||
)
|
||||
```
|
||||
|
||||
=== "Javascript"
|
||||
|
||||
```js
|
||||
const threadState = await client.threads.getState(<THREAD_ID>, <CHECKPOINT_ID>);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request GET \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state/<CHECKPOINT_ID> \
|
||||
--header 'Content-Type: application/json'
|
||||
```
|
||||
|
||||
#### Inspect Full Thread History
|
||||
|
||||
To view a thread's history, use the `get_history` method. This returns a list of every state the thread experienced. For more information see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/?h=thread+status#langgraph_sdk.client.ThreadsClient.get_history) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#gethistory) reference docs.
|
||||
|
||||
### LangGraph Platform UI
|
||||
|
||||
You can also view threads in a deployment via the LangGraph Platform UI.
|
||||
|
||||
Inside your deployment, select the "Threads" tab. This will load a table of all of the threads in your deployment.
|
||||
|
||||
Select a thread to inspect its current state. To view its full history and for further debugging, open the thread in [LangGraph Studio](../../concepts//langgraph_studio.md).
|
||||
@@ -0,0 +1,166 @@
|
||||
# Use webhooks
|
||||
|
||||
When working with LangGraph Platform, you may want to use webhooks to receive updates after an API call completes. Webhooks are useful for triggering actions in your service once a run has finished processing. To implement this, you need to expose an endpoint that can accept `POST` requests and pass this endpoint as a `webhook` parameter in your API request.
|
||||
|
||||
Currently, the SDK does not provide built-in support for defining webhook endpoints, but you can specify them manually using API requests.
|
||||
|
||||
## Supported endpoints
|
||||
|
||||
The following API endpoints accept a `webhook` parameter:
|
||||
|
||||
| Operation | HTTP Method | Endpoint |
|
||||
|----------------------|-------------|-----------------------------------|
|
||||
| Create Run | `POST` | `/thread/{thread_id}/runs` |
|
||||
| Create Thread Cron | `POST` | `/thread/{thread_id}/runs/crons` |
|
||||
| Stream Run | `POST` | `/thread/{thread_id}/runs/stream` |
|
||||
| Wait Run | `POST` | `/thread/{thread_id}/runs/wait` |
|
||||
| Create Cron | `POST` | `/runs/crons` |
|
||||
| Stream Run Stateless | `POST` | `/runs/stream` |
|
||||
| Wait Run Stateless | `POST` | `/runs/wait` |
|
||||
|
||||
In this guide, we’ll show how to trigger a webhook after streaming a run.
|
||||
|
||||
## Set up your assistant and thread
|
||||
|
||||
Before making API calls, set up your assistant and thread.
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url=<DEPLOYMENT_URL>)
|
||||
assistant_id = "agent"
|
||||
thread = await client.threads.create()
|
||||
print(thread)
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
import { Client } from "@langchain/langgraph-sdk";
|
||||
|
||||
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
|
||||
const assistantID = "agent";
|
||||
const thread = await client.threads.create();
|
||||
console.log(thread);
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/assistants/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{ "limit": 10, "offset": 0 }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{}'
|
||||
```
|
||||
|
||||
Example response:
|
||||
|
||||
```json
|
||||
{
|
||||
"thread_id": "9dde5490-2b67-47c8-aa14-4bfec88af217",
|
||||
"created_at": "2024-08-30T23:07:38.242730+00:00",
|
||||
"updated_at": "2024-08-30T23:07:38.242730+00:00",
|
||||
"metadata": {},
|
||||
"status": "idle",
|
||||
"config": {},
|
||||
"values": null
|
||||
}
|
||||
```
|
||||
|
||||
## Use a webhook with a graph run
|
||||
|
||||
To use a webhook, specify the `webhook` parameter in your API request. When the run completes, LangGraph Platform sends a `POST` request to the specified webhook URL.
|
||||
|
||||
For example, if your server listens for webhook events at `https://my-server.app/my-webhook-endpoint`, include this in your request:
|
||||
|
||||
=== "Python"
|
||||
|
||||
```python
|
||||
input = { "messages": [{ "role": "user", "content": "Hello!" }] }
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
thread_id=thread["thread_id"],
|
||||
assistant_id=assistant_id,
|
||||
input=input,
|
||||
stream_mode="events",
|
||||
webhook="https://my-server.app/my-webhook-endpoint"
|
||||
):
|
||||
pass
|
||||
```
|
||||
|
||||
=== "JavaScript"
|
||||
|
||||
```js
|
||||
const input = { messages: [{ role: "human", content: "Hello!" }] };
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
thread["thread_id"],
|
||||
assistantID,
|
||||
{
|
||||
input: input,
|
||||
webhook: "https://my-server.app/my-webhook-endpoint"
|
||||
}
|
||||
);
|
||||
|
||||
for await (const chunk of streamResponse) {
|
||||
// Handle stream output
|
||||
}
|
||||
```
|
||||
|
||||
=== "CURL"
|
||||
|
||||
```bash
|
||||
curl --request POST \
|
||||
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--data '{
|
||||
"assistant_id": <ASSISTANT_ID>,
|
||||
"input": {"messages": [{"role": "user", "content": "Hello!"}]},
|
||||
"webhook": "https://my-server.app/my-webhook-endpoint"
|
||||
}'
|
||||
```
|
||||
|
||||
## Webhook payload
|
||||
|
||||
LangGraph Platform sends webhook notifications in the format of a [Run](../../concepts/assistants.md#execution). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.
|
||||
|
||||
## Secure webhooks
|
||||
|
||||
To ensure only authorized requests hit your webhook endpoint, consider adding a security token as a query parameter:
|
||||
|
||||
```
|
||||
https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
|
||||
```
|
||||
|
||||
Your server should extract and validate this token before processing requests.
|
||||
|
||||
## Disable webhooks
|
||||
|
||||
As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
|
||||
|
||||
```json
|
||||
{
|
||||
"http": {
|
||||
"disable_webhooks": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
|
||||
|
||||
For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
|
||||
|
||||
## Test webhooks
|
||||
|
||||
You can test your webhook using online services like:
|
||||
|
||||
- **[Beeceptor](https://beeceptor.com/)** – Quickly create a test endpoint and inspect incoming webhook payloads.
|
||||
- **[Webhook.site](https://webhook.site/)** – View, debug, and log incoming webhook requests in real time.
|
||||
|
||||
These tools help you verify that LangGraph Platform is correctly triggering and sending webhooks to your service.
|
||||
@@ -0,0 +1,184 @@
|
||||
# Deployment quickstart
|
||||
|
||||
This guide shows you how to set up and use LangGraph Platform for a cloud deployment.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before you begin, ensure you have the following:
|
||||
|
||||
- A [GitHub account](https://github.com/)
|
||||
- A [LangSmith account](https://smith.langchain.com/) – free to sign up
|
||||
|
||||
## 1. Create a repository on GitHub
|
||||
|
||||
To deploy an application to **LangGraph Platform**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [`new-langgraph-project` template](https://github.com/langchain-ai/react-agent) for your application:
|
||||
|
||||
1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project).
|
||||
1. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
|
||||
1. Click **Create fork**.
|
||||
|
||||
## 2. Deploy to LangGraph Platform
|
||||
|
||||
1. Log in to [LangSmith](https://smith.langchain.com/).
|
||||
1. In the left sidebar, select **Deployments**.
|
||||
1. Click the **+ New Deployment** button. A pane will open where you can fill in the required fields.
|
||||
1. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
|
||||
1. Select your New LangGraph Project repository.
|
||||
1. Click **Submit** to deploy.
|
||||
|
||||
This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.
|
||||
|
||||
## 3. Test your application in LangGraph Studio
|
||||
|
||||
Once your application is deployed:
|
||||
|
||||
1. Select the deployment you just created to view more details.
|
||||
1. Click the **LangGraph Studio** button in the top right corner.
|
||||
|
||||
LangGraph Studio will open to display your graph.
|
||||
|
||||
<figure markdown="1">
|
||||
[{: style="max-height:400px"}](deployment/img/langgraph_studio.png)
|
||||
<figcaption>
|
||||
Sample graph run in LangGraph Studio.
|
||||
</figcaption>
|
||||
</figure>
|
||||
|
||||
## 4. Get the API URL for your deployment
|
||||
|
||||
1. In the **Deployment details** view in LangGraph, click the **API URL** to copy it to your clipboard.
|
||||
1. Click the `URL` to copy it to the clipboard.
|
||||
|
||||
## 5. Test the API
|
||||
|
||||
You can now test the API:
|
||||
|
||||
=== "Python SDK (Async)"
|
||||
|
||||
1. Install the LangGraph Python SDK:
|
||||
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
```
|
||||
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_client
|
||||
|
||||
client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")
|
||||
|
||||
async for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "Python SDK (Sync)"
|
||||
|
||||
1. Install the LangGraph Python SDK:
|
||||
|
||||
```shell
|
||||
pip install langgraph-sdk
|
||||
```
|
||||
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
```python
|
||||
from langgraph_sdk import get_sync_client
|
||||
|
||||
client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")
|
||||
|
||||
for chunk in client.runs.stream(
|
||||
None, # Threadless run
|
||||
"agent", # Name of assistant. Defined in langgraph.json.
|
||||
input={
|
||||
"messages": [{
|
||||
"role": "human",
|
||||
"content": "What is LangGraph?",
|
||||
}],
|
||||
},
|
||||
stream_mode="updates",
|
||||
):
|
||||
print(f"Receiving new event of type: {chunk.event}...")
|
||||
print(chunk.data)
|
||||
print("\n\n")
|
||||
```
|
||||
|
||||
=== "JavaScript SDK"
|
||||
|
||||
1. Install the LangGraph JS SDK
|
||||
|
||||
```shell
|
||||
npm install @langchain/langgraph-sdk
|
||||
```
|
||||
|
||||
1. Send a message to the assistant (threadless run):
|
||||
|
||||
```js
|
||||
const { Client } = await import("@langchain/langgraph-sdk");
|
||||
|
||||
const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });
|
||||
|
||||
const streamResponse = client.runs.stream(
|
||||
null, // Threadless run
|
||||
"agent", // Assistant ID
|
||||
{
|
||||
input: {
|
||||
"messages": [
|
||||
{ "role": "user", "content": "What is LangGraph?"}
|
||||
]
|
||||
},
|
||||
streamMode: "messages",
|
||||
}
|
||||
);
|
||||
|
||||
for await (const chunk of streamResponse) {
|
||||
console.log(`Receiving new event of type: ${chunk.event}...`);
|
||||
console.log(JSON.stringify(chunk.data));
|
||||
console.log("\n\n");
|
||||
}
|
||||
```
|
||||
|
||||
=== "Rest API"
|
||||
|
||||
```bash
|
||||
curl -s --request POST \
|
||||
--url <DEPLOYMENT_URL>/runs/stream \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header "X-Api-Key: <LANGSMITH API KEY> \
|
||||
--data "{
|
||||
\"assistant_id\": \"agent\",
|
||||
\"input\": {
|
||||
\"messages\": [
|
||||
{
|
||||
\"role\": \"human\",
|
||||
\"content\": \"What is LangGraph?\"
|
||||
}
|
||||
]
|
||||
},
|
||||
\"stream_mode\": \"updates\"
|
||||
}"
|
||||
```
|
||||
|
||||
|
||||
## Next steps
|
||||
|
||||
Congratulations! You have deployed an application using LangGraph Platform.
|
||||
|
||||
Here are some other resources to check out:
|
||||
|
||||
- [LangGraph Platform overview](../concepts/langgraph_platform.md)
|
||||
- [Deployment options](../concepts/deployment_options.md)
|
||||
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
# LangGraph Server API Reference
|
||||
|
||||
The LangGraph Server API reference is available within each deployment at the `/docs` endpoint (e.g. `http://localhost:8124/docs`).
|
||||
|
||||
Click <a href="/langgraph/cloud/reference/api/api_ref.html" target="_blank">here</a> to view the API reference.
|
||||
|
||||
## Authentication
|
||||
|
||||
For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Server. The value of the header should be set to a valid LangSmith API key for the organization where the LangGraph Server is deployed.
|
||||
|
||||
Example `curl` command:
|
||||
```shell
|
||||
curl --request POST \
|
||||
--url http://localhost:8124/assistants/search \
|
||||
--header 'Content-Type: application/json' \
|
||||
--header 'X-Api-Key: LANGSMITH_API_KEY' \
|
||||
--data '{
|
||||
"metadata": {},
|
||||
"limit": 10,
|
||||
"offset": 0
|
||||
}'
|
||||
```
|
||||
@@ -0,0 +1,247 @@
|
||||
# LangGraph Control Plane API Reference
|
||||
|
||||
The LangGraph Control Plane API is used to programmatically create and manage LangGraph Server deployments. For example, the APIs can be orchestrated to create custom CI/CD workflows.
|
||||
|
||||
Click <a href="https://api.host.langchain.com/docs" target="_blank">here</a> to view the API reference.
|
||||
|
||||
## Host
|
||||
|
||||
LangGraph Control Plane hosts for Cloud SaaS data regions:
|
||||
|
||||
| US | EU |
|
||||
|----|----|
|
||||
| `https://api.host.langchain.com` | `https://eu.api.host.langchain.com` |
|
||||
|
||||
**Note**: Self-hosted deployments of LangGraph Platform will have a custom host for the LangGraph Control Plane.
|
||||
|
||||
## Authentication
|
||||
|
||||
To authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key.
|
||||
|
||||
Example `curl` command:
|
||||
```shell
|
||||
curl --request GET \
|
||||
--url http://localhost:8124/v2/deployments \
|
||||
--header 'X-Api-Key: LANGSMITH_API_KEY'
|
||||
```
|
||||
|
||||
## Versioning
|
||||
|
||||
Each endpoint path is prefixed with a version (e.g. `v1`, `v2`).
|
||||
|
||||
## Quick Start
|
||||
|
||||
1. Call `POST /v2/deployments` to create a new Deployment. The response body contains the Deployment ID (`id`) and the ID of the latest (and first) revision (`latest_revision_id`).
|
||||
1. Call `GET /v2/deployments/{deployment_id}` to retrieve the Deployment. Set `deployment_id` in the URL to the value of Deployment ID (`id`).
|
||||
1. Poll for revision `status` until `status` is `DEPLOYED` by calling `GET /v2/deployments/{deployment_id}/revisions/{latest_revision_id}`.
|
||||
1. Call `PATCH /v2/deployments/{deployment_id}` to update the deployment.
|
||||
|
||||
## Example Code
|
||||
Below is example Python code that demonstrates how to orchestrate the LangGraph Control Plane APIs to create a deployment, update the deployment, and delete the deployment.
|
||||
```python
|
||||
import os
|
||||
import time
|
||||
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
|
||||
|
||||
load_dotenv()
|
||||
|
||||
# required environment variables
|
||||
CONTROL_PLANE_HOST = os.getenv("CONTROL_PLANE_HOST")
|
||||
LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
|
||||
INTEGRATION_ID = os.getenv("INTEGRATION_ID")
|
||||
MAX_WAIT_TIME = 1800 # 30 mins
|
||||
|
||||
|
||||
def get_headers() -> dict:
|
||||
"""Return common headers for requests to LangGraph Control Plane API."""
|
||||
return {
|
||||
"X-Api-Key": LANGSMITH_API_KEY,
|
||||
}
|
||||
|
||||
|
||||
def create_deployment() -> str:
|
||||
"""Create deployment. Return deployment ID."""
|
||||
headers = get_headers()
|
||||
headers["Content-Type"] = "application/json"
|
||||
|
||||
deployment_name = "my_deployment"
|
||||
|
||||
request_body = {
|
||||
"name": deployment_name,
|
||||
"source": "github",
|
||||
"source_config": {
|
||||
"integration_id": INTEGRATION_ID,
|
||||
"repo_url": "https://github.com/langchain-ai/langgraph-example",
|
||||
"deployment_type": "dev",
|
||||
"build_on_push": False,
|
||||
"custom_url": None,
|
||||
"resource_spec": None,
|
||||
},
|
||||
"source_revision_config": {
|
||||
"repo_ref": "main",
|
||||
"langgraph_config_path": "langgraph.json",
|
||||
"image_uri": None,
|
||||
},
|
||||
"secrets": [
|
||||
{
|
||||
"name": "OPENAI_API_KEY",
|
||||
"value": "test_openai_api_key",
|
||||
},
|
||||
{
|
||||
"name": "ANTHROPIC_API_KEY",
|
||||
"value": "test_anthropic_api_key",
|
||||
},
|
||||
{
|
||||
"name": "TAVILY_API_KEY",
|
||||
"value": "test_tavily_api_key",
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
response = requests.post(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments",
|
||||
headers=headers,
|
||||
json=request_body,
|
||||
)
|
||||
|
||||
if response.status_code != 201:
|
||||
raise Exception(f"Failed to create deployment: {response.text}")
|
||||
|
||||
deployment_id = response.json()["id"]
|
||||
print(f"Created deployment {deployment_name} ({deployment_id})")
|
||||
return deployment_id
|
||||
|
||||
|
||||
def get_deployment(deployment_id: str) -> dict:
|
||||
"""Get deployment."""
|
||||
response = requests.get(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
|
||||
headers=get_headers(),
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"Failed to get deployment ID {deployment_id}: {response.text}")
|
||||
|
||||
return response.json()
|
||||
|
||||
|
||||
def list_revisions(deployment_id: str) -> list[dict]:
|
||||
"""List revisions.
|
||||
|
||||
Return list is sorted by created_at in descending order (latest first).
|
||||
"""
|
||||
response = requests.get(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions",
|
||||
headers=get_headers(),
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(
|
||||
f"Failed to list revisions for deployment ID {deployment_id}: {response.text}"
|
||||
)
|
||||
|
||||
return response.json()
|
||||
|
||||
|
||||
def get_revision(
|
||||
deployment_id: str,
|
||||
revision_id: str,
|
||||
) -> dict:
|
||||
"""Get revision."""
|
||||
response = requests.get(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions/{revision_id}",
|
||||
headers=get_headers(),
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"Failed to get revision ID {revision_id}: {response.text}")
|
||||
|
||||
return response.json()
|
||||
|
||||
|
||||
def patch_deployment(deployment_id: str) -> None:
|
||||
"""Patch deployment."""
|
||||
headers = get_headers()
|
||||
headers["Content-Type"] = "application/json"
|
||||
|
||||
response = requests.patch(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
|
||||
headers=headers,
|
||||
json={
|
||||
"source_config": {
|
||||
"build_on_push": True,
|
||||
},
|
||||
"source_revision_config": {
|
||||
"repo_ref": "main",
|
||||
"langgraph_config_path": "langgraph.json",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"Failed to patch deployment: {response.text}")
|
||||
|
||||
print(f"Patched deployment ID {deployment_id}")
|
||||
|
||||
|
||||
def wait_for_deployment(deployment_id: str, revision_id: str) -> None:
|
||||
"""Wait for revision status to be DEPLOYED."""
|
||||
start_time = time.time()
|
||||
revision, status = None, None
|
||||
while time.time() - start_time < MAX_WAIT_TIME:
|
||||
revision = get_revision(deployment_id, revision_id)
|
||||
status = revision["status"]
|
||||
if status == "DEPLOYED":
|
||||
break
|
||||
elif "FAILED" in status:
|
||||
raise Exception(f"Revision ID {revision_id} failed: {revision}")
|
||||
|
||||
print(f"Waiting for revision ID {revision_id} to be DEPLOYED...")
|
||||
time.sleep(60)
|
||||
|
||||
if status != "DEPLOYED":
|
||||
raise Exception(
|
||||
f"Timeout waiting for revision ID {revision_id} to be DEPLOYED: {revision}"
|
||||
)
|
||||
|
||||
|
||||
def delete_deployment(deployment_id: str) -> None:
|
||||
"""Delete deployment."""
|
||||
response = requests.delete(
|
||||
url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
|
||||
headers=get_headers(),
|
||||
)
|
||||
|
||||
if response.status_code != 204:
|
||||
raise Exception(
|
||||
f"Failed to delete deployment ID {deployment_id}: {response.text}"
|
||||
)
|
||||
|
||||
print(f"Deployment ID {deployment_id} deleted")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# create deployment and get the latest revision
|
||||
deployment_id = create_deployment()
|
||||
revisions = list_revisions(deployment_id)
|
||||
latest_revision = revisions["resources"][0]
|
||||
latest_revision_id = latest_revision["id"]
|
||||
|
||||
# wait for latest revision to be DEPLOYED
|
||||
wait_for_deployment(deployment_id, latest_revision_id)
|
||||
|
||||
# patch the deployment and get the latest revision
|
||||
patch_deployment(deployment_id)
|
||||
revisions = list_revisions(deployment_id)
|
||||
latest_revision = revisions["resources"][0]
|
||||
latest_revision_id = latest_revision["id"]
|
||||
|
||||
# wait for latest revision to be DEPLOYED
|
||||
wait_for_deployment(deployment_id, latest_revision_id)
|
||||
|
||||
# delete the deployment
|
||||
delete_deployment(deployment_id)
|
||||
```
|
||||
@@ -0,0 +1,544 @@
|
||||
# LangGraph CLI
|
||||
|
||||
The LangGraph command line interface includes commands to build and run a LangGraph Platform API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server.
|
||||
|
||||
## Installation
|
||||
|
||||
1. Ensure that Docker is installed (e.g. `docker --version`).
|
||||
2. Install the CLI package:
|
||||
|
||||
=== "Python"
|
||||
```bash
|
||||
pip install langgraph-cli
|
||||
```
|
||||
|
||||
=== "JS"
|
||||
```bash
|
||||
npx @langchain/langgraph-cli
|
||||
|
||||
# Install globally, will be available as `langgraphjs`
|
||||
npm install -g @langchain/langgraph-cli
|
||||
```
|
||||
|
||||
3. Run the command `langgraph --help` or `npx @langchain/langgraph-cli --help` to confirm that the CLI is working correctly.
|
||||
|
||||
[](){#langgraph.json}
|
||||
|
||||
## Configuration File {#configuration-file}
|
||||
|
||||
The LangGraph CLI requires a JSON configuration file that follows this [schema](https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.json). It contains the following properties:
|
||||
|
||||
<div class="admonition tip">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>
|
||||
The LangGraph CLI defaults to using the configuration file <strong>langgraph.json</strong> in the current directory.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
=== "Python"
|
||||
|
||||
| Key | Description |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| <span style="white-space: nowrap;">`dependencies`</span> | **Required**. Array of dependencies for LangGraph Platform API server. Dependencies can be one of the following: <ul><li>A single period (`"."`), which will look for local Python packages.</li><li>The directory path where `pyproject.toml`, `setup.py` or `requirements.txt` is located.</br></br>For example, if `requirements.txt` is located in the root of the project directory, specify `"./"`. If it's located in a subdirectory called `local_package`, specify `"./local_package"`. Do not specify the string `"requirements.txt"` itself.</li><li>A Python package name.</li></ul> |
|
||||
| <span style="white-space: nowrap;">`graphs`</span> | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and returns an instance of `langgraph.graph.state.StateGraph` or `langgraph.graph.state.CompiledStateGraph`. See [how to rebuild a graph at runtime](../../cloud/deployment/graph_rebuild.md) for more details.</li></ul> |
|
||||
| <span style="white-space: nowrap;">`auth`</span> | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details. |
|
||||
| <span style="white-space: nowrap;">`base_image`</span> | Optional. Base image to use for the LangGraph API server. Defaults to `langchain/langgraph-api` or `langchain/langgraphjs-api`. Use this to pin your builds to a particular version of the langgraph API, such as `"langchain/langgraph-server:0.2"`. See https://hub.docker.com/r/langchain/langgraph-server/tags for more details. (added in `langgraph-cli==0.2.8`) |
|
||||
| <span style="white-space: nowrap;">`image_distro`</span> | Optional. Linux distribution for the base image. Must be either `"debian"` or `"wolfi"`. If omitted, defaults to `"debian"`. Available in `langgraph-cli>=0.2.11`.|
|
||||
| <span style="white-space: nowrap;">`env`</span> | Path to `.env` file or a mapping from environment variable to its value. |
|
||||
| <span style="white-space: nowrap;">`store`</span> | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
|
||||
| <span style="white-space: nowrap;">`ui`</span> | Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file. (added in `langgraph-cli==0.1.84`) |
|
||||
| <span style="white-space: nowrap;">`python_version`</span> | `3.11`, `3.12`, or `3.13`. Defaults to `3.11`. |
|
||||
| <span style="white-space: nowrap;">`node_version`</span> | Specify `node_version: 20` to use LangGraph.js. |
|
||||
| <span style="white-space: nowrap;">`pip_config_file`</span> | Path to `pip` config file. |
|
||||
| <span style="white-space: nowrap;">`pip_installer`</span> | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version 0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. |
|
||||
| <span style="white-space: nowrap;">`keep_pkg_tools`</span> | _(Added in v0.3.4)_ Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values: <ul><li><code>true</code> : Keep all three tools (skip uninstall).</li><li><code>false</code> / omitted : Uninstall all three tools (default behaviour).</li><li><code>list[str]</code> : Names of tools <strong>to retain</strong>. Each value must be one of "pip", "setuptools", "wheel".</li></ul>. By default, all three tools are uninstalled. |
|
||||
| <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image. |
|
||||
| <span style="white-space: nowrap;">`checkpointer`</span> | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
|
||||
| <span style="white-space: nowrap;">`http`</span> | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_ui`: Disable `/ui` routes</li><li>`disable_webhooks`: Disable webhooks calls on run completion in all routes</li><li>`mount_prefix`: Prefix for mounted routes (e.g., "/my-deployment/api")</li></ul> |
|
||||
|
||||
=== "JS"
|
||||
|
||||
| Key | Description |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| <span style="white-space: nowrap;">`graphs`</span> | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and returns an instance of `StateGraph` or `CompiledStateGraph`. See [how to rebuild a graph at runtime](../../cloud/deployment/graph_rebuild.md) for more details.</li></ul> |
|
||||
| <span style="white-space: nowrap;">`env`</span> | Path to `.env` file or a mapping from environment variable to its value. |
|
||||
| <span style="white-space: nowrap;">`store`</span> | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
|
||||
| <span style="white-space: nowrap;">`node_version`</span> | Specify `node_version: 20` to use LangGraph.js. |
|
||||
| <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image. |
|
||||
| <span style="white-space: nowrap;">`checkpointer`</span> | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
|
||||
|
||||
### Examples
|
||||
|
||||
=== "Python"
|
||||
|
||||
#### Basic Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"chat": "./chat/graph.py:graph"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Using Wolfi Base Images
|
||||
|
||||
You can specify the Linux distribution for your base image using the `image_distro` field. Valid options are `debian` or `wolfi`. Wolfi is the recommended option as it provides smaller and more secure images. This is available in `langgraph-cli>=0.2.11`.
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"chat": "./chat/graph.py:graph"
|
||||
},
|
||||
"image_distro": "wolfi"
|
||||
}
|
||||
```
|
||||
|
||||
#### Adding semantic search to the store
|
||||
|
||||
All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
|
||||
|
||||
The `index.fields` configuration determines which parts of your documents to embed:
|
||||
|
||||
- If omitted or set to `["$"]`, the entire document will be embedded
|
||||
- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
|
||||
- Documents missing specified fields will still be stored but won't have embeddings for those fields
|
||||
- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"memory_agent": "./agent/graph.py:graph"
|
||||
},
|
||||
"store": {
|
||||
"index": {
|
||||
"embed": "openai:text-embedding-3-small",
|
||||
"dims": 1536,
|
||||
"fields": ["$"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
!!! note "Common model dimensions"
|
||||
- `openai:text-embedding-3-large`: 3072
|
||||
- `openai:text-embedding-3-small`: 1536
|
||||
- `openai:text-embedding-ada-002`: 1536
|
||||
- `cohere:embed-english-v3.0`: 1024
|
||||
- `cohere:embed-english-light-v3.0`: 384
|
||||
- `cohere:embed-multilingual-v3.0`: 1024
|
||||
- `cohere:embed-multilingual-light-v3.0`: 384
|
||||
|
||||
#### Semantic search with a custom embedding function
|
||||
|
||||
If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"memory_agent": "./agent/graph.py:graph"
|
||||
},
|
||||
"store": {
|
||||
"index": {
|
||||
"embed": "./embeddings.py:embed_texts",
|
||||
"dims": 768,
|
||||
"fields": ["text", "summary"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
|
||||
|
||||
```python
|
||||
# embeddings.py
|
||||
def embed_texts(texts: list[str]) -> list[list[float]]:
|
||||
"""Custom embedding function for semantic search."""
|
||||
# Implementation using your preferred embedding model
|
||||
return [[0.1, 0.2, ...] for _ in texts] # dims-dimensional vectors
|
||||
```
|
||||
|
||||
#### Adding custom authentication
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"chat": "./chat/graph.py:graph"
|
||||
},
|
||||
"auth": {
|
||||
"path": "./auth.py:auth",
|
||||
"openapi": {
|
||||
"securitySchemes": {
|
||||
"apiKeyAuth": {
|
||||
"type": "apiKey",
|
||||
"in": "header",
|
||||
"name": "X-API-Key"
|
||||
}
|
||||
},
|
||||
"security": [{ "apiKeyAuth": [] }]
|
||||
},
|
||||
"disable_studio_auth": false
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
See the [authentication conceptual guide](../../concepts/auth.md) for details, and the [setting up custom authentication](../../tutorials/auth/getting_started.md) guide for a practical walk through of the process.
|
||||
|
||||
#### Configuring Store Item Time-to-Live (TTL)
|
||||
|
||||
You can configure default data expiration for items/memories in the BaseStore using the `store.ttl` key. This determines how long items are retained after they are last accessed (with reads potentially refreshing the timer based on `refresh_on_read`). Note that these defaults can be overwritten on a per-call basis by modifying the corresponding arguments in `get`, `search`, etc.
|
||||
|
||||
The `ttl` configuration is an object containing optional fields:
|
||||
|
||||
- `refresh_on_read`: If `true` (the default), accessing an item via `get` or `search` resets its expiration timer. Set to `false` to only refresh TTL on writes (`put`).
|
||||
- `default_ttl`: The default lifespan of an item in **minutes**. If not set, items do not expire by default.
|
||||
- `sweep_interval_minutes`: How frequently (in minutes) the system should run a background process to delete expired items. If not set, sweeping does not occur automatically.
|
||||
|
||||
Here is an example enabling a 7-day TTL (10080 minutes), refreshing on reads, and sweeping every hour:
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"memory_agent": "./agent/graph.py:graph"
|
||||
},
|
||||
"store": {
|
||||
"ttl": {
|
||||
"refresh_on_read": true,
|
||||
"sweep_interval_minutes": 60,
|
||||
"default_ttl": 10080
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Configuring Checkpoint Time-to-Live (TTL)
|
||||
|
||||
You can configure the time-to-live (TTL) for checkpoints using the `checkpointer` key. This determines how long checkpoint data is retained before being automatically handled according to the specified strategy (e.g., deletion). The `ttl` configuration is an object containing:
|
||||
|
||||
- `strategy`: The action to take on expired checkpoints (currently `"delete"` is the only accepted option).
|
||||
- `sweep_interval_minutes`: How frequently (in minutes) the system checks for expired checkpoints.
|
||||
- `default_ttl`: The default lifespan of a checkpoint in **minutes**.
|
||||
|
||||
Here's an example setting a default TTL of 30 days (43200 minutes):
|
||||
|
||||
```json
|
||||
{
|
||||
"dependencies": ["."],
|
||||
"graphs": {
|
||||
"chat": "./chat/graph.py:graph"
|
||||
},
|
||||
"checkpointer": {
|
||||
"ttl": {
|
||||
"strategy": "delete",
|
||||
"sweep_interval_minutes": 10,
|
||||
"default_ttl": 43200
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
In this example, checkpoints older than 30 days will be deleted, and the check runs every 10 minutes.
|
||||
|
||||
|
||||
=== "JS"
|
||||
|
||||
#### Basic Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"graphs": {
|
||||
"chat": "./src/graph.ts:graph"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
## Commands
|
||||
|
||||
**Usage**
|
||||
|
||||
=== "Python"
|
||||
|
||||
The base command for the LangGraph CLI is `langgraph`.
|
||||
|
||||
```
|
||||
langgraph [OPTIONS] COMMAND [ARGS]
|
||||
```
|
||||
=== "JS"
|
||||
|
||||
The base command for the LangGraph.js CLI is `langgraphjs`.
|
||||
|
||||
```
|
||||
npx @langchain/langgraph-cli [OPTIONS] COMMAND [ARGS]
|
||||
```
|
||||
|
||||
We recommend using `npx` to always use the latest version of the CLI.
|
||||
|
||||
### `dev`
|
||||
|
||||
=== "Python"
|
||||
|
||||
Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
|
||||
|
||||
!!! note
|
||||
|
||||
Currently, the CLI only supports Python >= 3.11.
|
||||
|
||||
**Installation**
|
||||
|
||||
This command requires the "inmem" extra to be installed:
|
||||
|
||||
```bash
|
||||
pip install -U "langgraph-cli[inmem]"
|
||||
```
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
langgraph dev [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
|
||||
| `--host TEXT` | `127.0.0.1` | Host to bind the server to |
|
||||
| `--port INTEGER` | `2024` | Port to bind the server to |
|
||||
| `--no-reload` | | Disable auto-reload |
|
||||
| `--n-jobs-per-worker INTEGER` | | Number of jobs per worker. Default is 10 |
|
||||
| `--debug-port INTEGER` | | Port for debugger to listen on |
|
||||
| `--wait-for-client` | `False` | Wait for a debugger client to connect to the debug port before starting the server |
|
||||
| `--no-browser` | | Skip automatically opening the browser when the server starts |
|
||||
| `--studio-url TEXT` | | URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com |
|
||||
| `--allow-blocking` | `False` | Do not raise errors for synchronous I/O blocking operations in your code (added in `0.2.6`) |
|
||||
| `--tunnel` | `False` | Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers like Safari or networks blocking localhost connections |
|
||||
| `--help` | | Display command documentation |
|
||||
|
||||
|
||||
=== "JS"
|
||||
|
||||
Run LangGraph API server in development mode with hot reloading capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
npx @langchain/langgraph-cli dev [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
|
||||
| `--host TEXT` | `127.0.0.1` | Host to bind the server to |
|
||||
| `--port INTEGER` | `2024` | Port to bind the server to |
|
||||
| `--no-reload` | | Disable auto-reload |
|
||||
| `--n-jobs-per-worker INTEGER` | | Number of jobs per worker. Default is 10 |
|
||||
| `--debug-port INTEGER` | | Port for debugger to listen on |
|
||||
| `--wait-for-client` | `False` | Wait for a debugger client to connect to the debug port before starting the server |
|
||||
| `--no-browser` | | Skip automatically opening the browser when the server starts |
|
||||
| `--studio-url TEXT` | | URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com |
|
||||
| `--allow-blocking` | `False` | Do not raise errors for synchronous I/O blocking operations in your code |
|
||||
| `--tunnel` | `False` | Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers or networks blocking localhost connections |
|
||||
| `--help` | | Display command documentation |
|
||||
|
||||
### `build`
|
||||
|
||||
=== "Python"
|
||||
|
||||
Build LangGraph Platform API server Docker image.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
langgraph build [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
|
||||
| `--platform TEXT` | | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64` |
|
||||
| `-t, --tag TEXT` | | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image` |
|
||||
| `--pull / --no-pull` | `--pull` | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Platform API server with locally built images. |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
|
||||
| `--help` | | Display command documentation. |
|
||||
|
||||
=== "JS"
|
||||
|
||||
Build LangGraph Platform API server Docker image.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
npx @langchain/langgraph-cli build [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| -------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
|
||||
| `--platform TEXT` | | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64` |
|
||||
| `-t, --tag TEXT` | | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image` |
|
||||
| `--no-pull` | | Use locally built images. Defaults to `false` to build with latest remote Docker image. |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
|
||||
| `--help` | | Display command documentation. |
|
||||
|
||||
|
||||
### `up`
|
||||
|
||||
=== "Python"
|
||||
|
||||
Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
langgraph up [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
|
||||
| `--wait` | | Wait for services to start before returning. Implies --detach |
|
||||
| `--base-image TEXT` | `langchain/langgraph-api` | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
|
||||
| `--image TEXT` | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
|
||||
| `--postgres-uri TEXT` | Local database | Postgres URI to use for the database. |
|
||||
| `--watch` | | Restart on file changes |
|
||||
| `--debugger-base-url TEXT` | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API. |
|
||||
| `--debugger-port INTEGER` | | Pull the debugger image locally and serve the UI on specified port |
|
||||
| `--verbose` | | Show more output from the server logs. |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
|
||||
| `-d, --docker-compose FILE` | | Path to docker-compose.yml file with additional services to launch. |
|
||||
| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph up --port 8000` |
|
||||
| `--pull / --no-pull` | `pull` | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
|
||||
| `--recreate / --no-recreate` | `no-recreate` | Recreate containers even if their configuration and image haven't changed |
|
||||
| `--help` | | Display command documentation. |
|
||||
|
||||
=== "JS"
|
||||
|
||||
Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
npx @langchain/langgraph-cli up [OPTIONS]
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
|
||||
| <span style="white-space: nowrap;">`--wait`</span> | | Wait for services to start before returning. Implies --detach |
|
||||
| <span style="white-space: nowrap;">`--base-image TEXT`</span> | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
|
||||
| <span style="white-space: nowrap;">`--image TEXT`</span> | | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
|
||||
| <span style="white-space: nowrap;">`--postgres-uri TEXT`</span> | Local database | Postgres URI to use for the database. |
|
||||
| <span style="white-space: nowrap;">`--watch`</span> | | Restart on file changes |
|
||||
| <span style="white-space: nowrap;">`-c, --config FILE`</span> | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
|
||||
| <span style="white-space: nowrap;">`-d, --docker-compose FILE`</span> | | Path to docker-compose.yml file with additional services to launch. |
|
||||
| <span style="white-space: nowrap;">`-p, --port INTEGER`</span> | `8123` | Port to expose. Example: `langgraph up --port 8000` |
|
||||
| <span style="white-space: nowrap;">`--no-pull`</span> | | Use locally built images. Defaults to `false` to build with latest remote Docker image. |
|
||||
| <span style="white-space: nowrap;">`--recreate`</span> | | Recreate containers even if their configuration and image haven't changed |
|
||||
| <span style="white-space: nowrap;">`--help`</span> | | Display command documentation. |
|
||||
|
||||
### `dockerfile`
|
||||
|
||||
=== "Python"
|
||||
|
||||
Generate a Dockerfile for building a LangGraph Platform API server Docker image.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
langgraph dockerfile [OPTIONS] SAVE_PATH
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
|
||||
| `--help` | | Show this message and exit. |
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
langgraph dockerfile -c langgraph.json Dockerfile
|
||||
```
|
||||
|
||||
This generates a Dockerfile that looks similar to:
|
||||
|
||||
```dockerfile
|
||||
FROM langchain/langgraph-api:3.11
|
||||
|
||||
ADD ./pipconf.txt /pipconfig.txt
|
||||
|
||||
RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
|
||||
|
||||
ADD ./graphs /deps/__outer_graphs/src
|
||||
RUN set -ex && \
|
||||
for line in '[project]' \
|
||||
'name = "graphs"' \
|
||||
'version = "0.1"' \
|
||||
'[tool.setuptools.package-data]' \
|
||||
'"*" = ["**/*"]'; do \
|
||||
echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
|
||||
done
|
||||
|
||||
RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
|
||||
|
||||
ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
|
||||
```
|
||||
|
||||
???+ note "Updating your langgraph.json file"
|
||||
The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
|
||||
|
||||
=== "JS"
|
||||
|
||||
Generate a Dockerfile for building a LangGraph Platform API server Docker image.
|
||||
|
||||
**Usage**
|
||||
|
||||
```
|
||||
npx @langchain/langgraph-cli dockerfile [OPTIONS] SAVE_PATH
|
||||
```
|
||||
|
||||
**Options**
|
||||
|
||||
| Option | Default | Description |
|
||||
| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
|
||||
| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
|
||||
| `--help` | | Show this message and exit. |
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
npx @langchain/langgraph-cli dockerfile -c langgraph.json Dockerfile
|
||||
```
|
||||
|
||||
This generates a Dockerfile that looks similar to:
|
||||
|
||||
```dockerfile
|
||||
FROM langchain/langgraphjs-api:20
|
||||
|
||||
ADD . /deps/agent
|
||||
|
||||
RUN cd /deps/agent && yarn install
|
||||
|
||||
ENV LANGSERVE_GRAPHS='{"agent":"./src/react_agent/graph.ts:graph"}'
|
||||
|
||||
WORKDIR /deps/agent
|
||||
|
||||
RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts
|
||||
```
|
||||
|
||||
???+ note "Updating your langgraph.json file"
|
||||
The `npx @langchain/langgraph-cli dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
|
||||
@@ -0,0 +1,150 @@
|
||||
# Environment Variables
|
||||
|
||||
The LangGraph Server supports specific environment variables for configuring a deployment.
|
||||
|
||||
## `BG_JOB_ISOLATED_LOOPS`
|
||||
|
||||
Set `BG_JOB_ISOLATED_LOOPS` to `True` to execute background runs in an isolated event loop separate from the serving API event loop.
|
||||
|
||||
This environment variable should be set to `True` if the implementation of a graph/node contains synchronous code. In this situation, the synchronous code will block the serving API event loop, which may cause the API to be unavailable. A symptom of an unavailable API is continuous application restarts due to failing health checks.
|
||||
|
||||
Defaults to `False`.
|
||||
|
||||
## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
|
||||
|
||||
Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `180` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
|
||||
|
||||
## `BG_JOB_TIMEOUT_SECS`
|
||||
|
||||
The timeout of a background run can be increased. However, the infrastructure for a Cloud SaaS deployment enforces a 1 hour timeout limit for API requests. This means the connection between client and server will timeout after 1 hour. This is not configurable.
|
||||
|
||||
A background run can execute for longer than 1 hour, but a client must reconnect to the server (e.g. join stream via `POST /threads/{thread_id}/runs/{run_id}/stream`) to retrieve output from the run if the run is taking longer than 1 hour.
|
||||
|
||||
Defaults to `3600`.
|
||||
|
||||
## `DD_API_KEY`
|
||||
|
||||
Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.
|
||||
|
||||
If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
|
||||
|
||||
!!! note
|
||||
Enabling `DD_API_KEY` (and thus `ddtrace-run`) can override or interfere with other auto-instrumentation solutions (such as OpenTelemetry) that you may have instrumented into your application code.
|
||||
|
||||
## `LANGCHAIN_TRACING_SAMPLING_RATE`
|
||||
|
||||
Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.
|
||||
|
||||
See <a href="https://docs.smith.langchain.com/how_to_guides/tracing/sample_traces" target="_blank">LangSmith documentation</a> for more details.
|
||||
|
||||
## `LANGGRAPH_AUTH_TYPE`
|
||||
|
||||
Type of authentication for the LangGraph Server deployment. Valid values: `langsmith`, `noop`.
|
||||
|
||||
For deployments to LangGraph Platform, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to `noop`.
|
||||
|
||||
## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`
|
||||
|
||||
Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool (per replica) can be controlled using the `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` environment variable. By setting this variable, you can determine the upper bound on the number of simultaneous connections the server will establish with the Postgres database.
|
||||
|
||||
For example, if a deployment is scaled up to 10 replicas and `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` is configured to `150`, then up to `1500` connections to Postgres can be established. This is particularly useful for deployments where database resources are limited (or more available) or where you need to tune connection behavior for performance or scaling reasons.
|
||||
|
||||
Defaults to `150` connections.
|
||||
|
||||
## `LANGSMITH_RUNS_ENDPOINTS`
|
||||
|
||||
For deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
|
||||
|
||||
Set this environment variable to have a deployment send traces to a self-hosted LangSmith instance. The value of `LANGSMITH_RUNS_ENDPOINTS` is a JSON string: `{"<SELF_HOSTED_LANGSMITH_HOSTNAME>":"<LANGSMITH_API_KEY>"}`.
|
||||
|
||||
`SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.
|
||||
|
||||
## `LANGSMITH_TRACING`
|
||||
|
||||
Set `LANGSMITH_TRACING` to `false` to disable tracing to LangSmith.
|
||||
|
||||
Defaults to `true`.
|
||||
|
||||
## `LOG_COLOR`
|
||||
|
||||
This is mainly relevant in the context of using the dev server via the `langgraph dev` command. Set `LOG_COLOR` to `true` to enable ANSI-colored console output when using the default console renderer. Disabling color output by setting this variable to `false` produces monochrome logs. Defaults to `true`.
|
||||
|
||||
## `LOG_LEVEL`
|
||||
|
||||
Configure [log level](https://docs.python.org/3/library/logging.html#logging-levels). Defaults to `INFO`.
|
||||
|
||||
## `LOG_JSON`
|
||||
|
||||
Set `LOG_JSON` to `true` to render all log messages as JSON objects using the configured `JSONRenderer`. This produces structured logs that can be easily parsed or ingested by log management systems. Defaults to `false`.
|
||||
|
||||
## `MOUNT_PREFIX`
|
||||
|
||||
!!! info "Only Allowed in Self-Hosted Deployments"
|
||||
The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
|
||||
|
||||
Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
|
||||
|
||||
For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.
|
||||
|
||||
## `N_JOBS_PER_WORKER`
|
||||
|
||||
Number of jobs per worker for the LangGraph Server task queue. Defaults to `10`.
|
||||
|
||||
## `POSTGRES_URI_CUSTOM`
|
||||
|
||||
!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
|
||||
Custom Postgres instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
|
||||
|
||||
Specify `POSTGRES_URI_CUSTOM` to use a custom Postgres instance. The value of `POSTGRES_URI_CUSTOM` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
|
||||
|
||||
Postgres:
|
||||
|
||||
- Version 15.8 or higher.
|
||||
- An initial database must be present and the connection URI must reference the database.
|
||||
|
||||
Control Plane Functionality:
|
||||
|
||||
- If `POSTGRES_URI_CUSTOM` is specified, the LangGraph Control Plane will not provision a database for the server.
|
||||
- If `POSTGRES_URI_CUSTOM` is removed, the LangGraph Control Plane will not provision a database for the server and will not delete the externally managed Postgres instance.
|
||||
- If `POSTGRES_URI_CUSTOM` is removed, deployment of the revision will not succeed. Once `POSTGRES_URI_CUSTOM` is specified, it must always be set for the lifecycle of the deployment.
|
||||
- If the deployment is deleted, the LangGraph Control Plane will not delete the externally managed Postgres instance.
|
||||
- The value of `POSTGRES_URI_CUSTOM` can be updated. For example, a password in the URI can be updated.
|
||||
|
||||
Database Connectivity:
|
||||
|
||||
- The custom Postgres instance must be accessible by the LangGraph Server. The user is responsible for ensuring connectivity.
|
||||
|
||||
## `REDIS_CLUSTER`
|
||||
|
||||
!!! info "Only Allowed in Self-Hosted Deployments"
|
||||
Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Platform SaaS will provision a redis instance for you by default.
|
||||
|
||||
Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
|
||||
|
||||
Defaults to `False`.
|
||||
|
||||
## `REDIS_KEY_PREFIX`
|
||||
|
||||
!!! info "Available in API Server version 0.1.9+"
|
||||
This environment variable is supported in API Server version 0.1.9 and above.
|
||||
|
||||
Specify a prefix for Redis keys. This allows multiple LangGraph Server instances to share the same Redis instance by using different key prefixes.
|
||||
|
||||
Defaults to `''`.
|
||||
|
||||
## `REDIS_URI_CUSTOM`
|
||||
|
||||
!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
|
||||
Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
|
||||
|
||||
Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
|
||||
|
||||
## `RESUMABLE_STREAM_TTL_SECONDS`
|
||||
|
||||
Time-to-live in seconds for resumable stream data in Redis.
|
||||
|
||||
When a run is created and the output is streamed, the stream can be configured to be resumable (e.g. `stream_resumable=True`). If a stream is resumable, output from the stream is temporarily stored in Redis. The TTL for this data can be configured by setting `RESUMABLE_STREAM_TTL_SECONDS`.
|
||||
|
||||
See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream) and [JS/TS](https://langchain-ai.github.io/langgraphjs/reference/classes/sdk_client.RunsClient.html#stream) SDKs for more details on how to implement resumable streams.
|
||||
|
||||
Defaults to `120` seconds.
|
||||
@@ -0,0 +1,225 @@
|
||||
# LangGraph Server Changelog
|
||||
|
||||
[LangGraph Server](../../concepts/langgraph_server.md) is an API platform for creating and managing agent-based applications. It provides built-in persistence, a task queue, and supports deploying, configuring, and running assistants (agentic workflows) at scale. This changelog documents all notable updates, features, and fixes to LangGraph Server releases.
|
||||
|
||||
---
|
||||
|
||||
## v0.2.109 (2025-07-28)
|
||||
- Fixed an issue where missing config schema occurred when `config_type` was not set.
|
||||
|
||||
## v0.2.108 (2025-07-28)
|
||||
- Added compatibility for langgraph v0.6, including new context API support and a migration to enhance context handling in assistant operations.
|
||||
|
||||
## v0.2.107 (2025-07-27)
|
||||
- Implemented caching for authentication processes to improve performance.
|
||||
- Merged count and select queries to improve database query efficiency.
|
||||
|
||||
## v0.2.106 (2025-07-27)
|
||||
- Log whether run uses resumable streams.
|
||||
|
||||
## v0.2.105 (2025-07-27)
|
||||
- Added a `/heapdump` endpoint to capture and save JS process heap data.
|
||||
|
||||
## v0.2.103 (2025-07-25)
|
||||
- Corrected the metadata endpoint to ensure accurate data retrieval.
|
||||
|
||||
## v0.2.102 (2025-07-24)
|
||||
- Captured interrupt events in the wait method to preserve legacy behavior and stream updates by default.
|
||||
- Added support for SDK structlog in the JavaScript environment, enhancing logging capabilities.
|
||||
|
||||
## v0.2.101 (2025-07-24)
|
||||
- Used the correct metadata endpoint for self-hosted environments, resolving an access issue.
|
||||
|
||||
## v0.2.99 (2025-07-22)
|
||||
- Improved license validation by adding an in-memory cache and handling Redis connection errors more effectively.
|
||||
- Automatically remove agents from memory that are removed from `langgraph.json` to prevent persistence issues.
|
||||
- Ensured the UI namespace for generated UI is a valid JavaScript property name to prevent errors.
|
||||
- Raised a 422 error for improved request validation feedback.
|
||||
|
||||
## v0.2.98 (2025-07-19)
|
||||
- Added langgraph node context for improved log filtering and trace visibility.
|
||||
|
||||
## v0.2.97 (2025-07-19)
|
||||
- Fixed scheduling issue with ckpt ingestion worker that occurred on isolated background loops.
|
||||
- Ensured queue worker starts only after all migrations have completed.
|
||||
- Added more detailed error messages for thread state issues and improved response handling when state updates fail.
|
||||
- Exposed interrupt ID while retrieving thread state for enhanced API response details.
|
||||
|
||||
## v0.2.96 (2025-07-17)
|
||||
- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
|
||||
|
||||
## v0.2.95 (2025-07-17)
|
||||
- Avoided setting the future if it is already done to prevent redundant operations.
|
||||
- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
|
||||
|
||||
## v0.2.94 (2025-07-16)
|
||||
- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
|
||||
- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
|
||||
|
||||
## v0.2.93 (2025-07-16)
|
||||
- Removed the GIN index for run metadata to improve performance.
|
||||
|
||||
## v0.2.92 (2025-07-16)
|
||||
- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
|
||||
|
||||
## v0.2.91 (2025-07-16)
|
||||
- Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.
|
||||
|
||||
## v0.2.90 (2025-07-16)
|
||||
- Improve checkpoint writes via node-local background queueing.
|
||||
|
||||
|
||||
## v0.2.89 (2025-07-15)
|
||||
- Decoupled checkpoint writing from thread/run state by removing foreign keys and updated logger to prevent timeout-related failures.
|
||||
|
||||
## v0.2.88 (2025-07-14)
|
||||
- Removed the foreign key constraint for `thread` in the `run` table to simplify database schema.
|
||||
|
||||
## v0.2.87 (2025-07-14)
|
||||
- Added more detailed logs for Redis worker signaling to improve debugging.
|
||||
|
||||
## v0.2.86 (2025-07-11)
|
||||
- Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.
|
||||
|
||||
## v0.2.85 (2025-07-10)
|
||||
- Added support for the `on_disconnect` field to `runs/wait` and included disconnect logs for better debugging.
|
||||
|
||||
## v0.2.84 (2025-07-09)
|
||||
- Removed unnecessary status updates to streamline thread handling and updated version to 0.2.84.
|
||||
|
||||
## v0.2.83 (2025-07-09)
|
||||
- Reduced the default time-to-live for resumable streams to 2 minutes.
|
||||
- Enhanced data submission logic to send data to both Beacon and LangSmith instance based on license configuration.
|
||||
- Enabled submission of self-hosted data to a Langsmith instance when the endpoint is configured.
|
||||
|
||||
## v0.2.82 (2025-07-03)
|
||||
- Addressed a race condition in background runs by implementing a lock using join, ensuring reliable execution across CTEs.
|
||||
|
||||
## v0.2.81 (2025-07-03)
|
||||
- Optimized run streams by reducing initial wait time to improve responsiveness for older or non-existent runs.
|
||||
|
||||
## v0.2.80 (2025-07-03)
|
||||
- Corrected parameter passing in the `logger.ainfo()` API call to resolve a TypeError.
|
||||
|
||||
## v0.2.79 (2025-07-02)
|
||||
- Fixed a JsonDecodeError in checkpointing with remote graph by correcting JSON serialization to handle trailing slashes properly.
|
||||
- Introduced a configuration flag to disable webhooks globally across all routes.
|
||||
|
||||
## v0.2.78 (2025-07-02)
|
||||
- Added timeout retries to webhook calls to improve reliability.
|
||||
- Added HTTP request metrics, including a request count and latency histogram, for enhanced monitoring capabilities.
|
||||
|
||||
## v0.2.77 (2025-07-02)
|
||||
- Added HTTP metrics to improve performance monitoring.
|
||||
- Changed the Redis cache delimiter to reduce conflicts with subgraph message names and updated caching behavior.
|
||||
|
||||
## v0.2.76 (2025-07-01)
|
||||
- Updated Redis cache delimiter to prevent conflicts with subgraph messages.
|
||||
|
||||
## v0.2.74 (2025-06-30)
|
||||
- Scheduled webhooks in an isolated loop to ensure thread-safe operations and prevent errors with PYTHONASYNCIODEBUG=1.
|
||||
|
||||
## v0.2.73 (2025-06-27)
|
||||
- Fixed an infinite frame loop issue and removed the dict_parser due to structlog's unexpected behavior.
|
||||
- Throw a 409 error on deadlock occurrence during run cancellations to handle lock conflicts gracefully.
|
||||
|
||||
## v0.2.72 (2025-06-27)
|
||||
- Ensured compatibility with future langgraph versions.
|
||||
- Implemented a 409 response status to handle deadlock issues during cancellation.
|
||||
|
||||
## v0.2.71 (2025-06-26)
|
||||
- Improved logging for better clarity and detail regarding log types.
|
||||
|
||||
## v0.2.70 (2025-06-26)
|
||||
- Improved error handling to better distinguish and log TimeoutErrors caused by users from internal run timeouts.
|
||||
|
||||
## v0.2.69 (2025-06-26)
|
||||
- Added sorting and pagination to the crons API and updated schema definitions for improved accuracy.
|
||||
|
||||
## v0.2.66 (2025-06-26)
|
||||
- Fixed a 404 error when creating multiple runs with the same thread_id using `on_not_exist="create"`.
|
||||
|
||||
## v0.2.65 (2025-06-25)
|
||||
- Ensured that only fields from `assistant_versions` are returned when necessary.
|
||||
- Ensured consistent data types for in-memory and PostgreSQL users, improving internal authentication handling.
|
||||
|
||||
## v0.2.64 (2025-06-24)
|
||||
- Added descriptions to version entries for better clarity.
|
||||
|
||||
## v0.2.62 (2025-06-23)
|
||||
- Improved user handling for custom authentication in the JS Studio.
|
||||
- Added Prometheus-format run statistics to the metrics endpoint for better monitoring.
|
||||
- Added run statistics in Prometheus format to the metrics endpoint.
|
||||
|
||||
## v0.2.61 (2025-06-20)
|
||||
- Set a maximum idle time for Redis connections to prevent unnecessary open connections.
|
||||
|
||||
## v0.2.60 (2025-06-20)
|
||||
- Enhanced error logging to include traceback details for dictionary operations.
|
||||
- Added a `/metrics` endpoint to expose queue worker metrics for monitoring.
|
||||
|
||||
## v0.2.57 (2025-06-18)
|
||||
- Removed CancelledError from retriable exceptions to allow local interrupts while maintaining retriability for workers.
|
||||
- Introduced middleware to gracefully shut down the server after completing in-flight requests upon receiving a SIGINT.
|
||||
- Reduced metadata stored in checkpoint to only include necessary information.
|
||||
- Improved error handling in join runs to return error details when present.
|
||||
|
||||
## v0.2.56 (2025-06-17)
|
||||
- Improved application stability by adding a handler for SIGTERM signals.
|
||||
|
||||
## v0.2.55 (2025-06-17)
|
||||
- Improved the handling of cancellations in the queue entrypoint.
|
||||
- Improved cancellation handling in the queue entry point.
|
||||
|
||||
## v0.2.54 (2025-06-16)
|
||||
- Enhanced error message for LuaLock timeout during license validation.
|
||||
- Fixed the $contains filter in custom auth by requiring an explicit ::text cast and updated tests accordingly.
|
||||
- Ensured project and tenant IDs are formatted as UUIDs for consistency.
|
||||
|
||||
## v0.2.53 (2025-06-13)
|
||||
- Resolved a timing issue to ensure the queue starts only after the graph is registered.
|
||||
- Improved performance by setting thread and run status in a single query and enhanced error handling during checkpoint writes.
|
||||
- Reduced the default background grace period to 3 minutes.
|
||||
|
||||
## v0.2.52 (2025-06-12)
|
||||
- Now logging expected graphs when one is omitted to improve traceability.
|
||||
- Implemented a time-to-live (TTL) feature for resumable streams.
|
||||
- Improved query efficiency and consistency by adding a unique index and optimizing row locking.
|
||||
|
||||
## v0.2.51 (2025-06-12)
|
||||
- Handled `CancelledError` by marking tasks as ready to retry, improving error management in worker processes.
|
||||
- Added LG API version and request ID to metadata and logs for better tracking.
|
||||
- Added LG API version and request ID to metadata and logs to improve traceability.
|
||||
- Improved database performance by creating indexes concurrently.
|
||||
- Ensured postgres write is committed only after the Redis running marker is set to prevent race conditions.
|
||||
- Enhanced query efficiency and reliability by adding a unique index on thread_id/running, optimizing row locks, and ensuring deterministic run selection.
|
||||
- Resolved a race condition by ensuring Postgres updates only occur after the Redis running marker is set.
|
||||
|
||||
## v0.2.46 (2025-06-07)
|
||||
- Introduced a new connection for each operation while preserving transaction characteristics in Threads state `update()` and `bulk()` commands.
|
||||
|
||||
## v0.2.45 (2025-06-05)
|
||||
- Enhanced streaming feature by incorporating tracing contexts.
|
||||
- Removed an unnecessary query from the Crons.search function.
|
||||
- Resolved connection reuse issue when scheduling next run for multiple cron jobs.
|
||||
- Removed an unnecessary query in the Crons.search function to improve efficiency.
|
||||
- Resolved an issue with scheduling the next cron run by improving connection reuse.
|
||||
|
||||
## v0.2.44 (2025-06-04)
|
||||
- Enhanced the worker logic to exit the pipeline before continuing when the Redis message limit is reached.
|
||||
- Introduced a ceiling for Redis message size with an option to skip messages larger than 128 MB for improved performance.
|
||||
- Ensured the pipeline always closes properly to prevent resource leaks.
|
||||
|
||||
## v0.2.43 (2025-06-04)
|
||||
- Improved performance by omitting logs in metadata calls and ensuring output schema compliance in value streaming.
|
||||
- Ensured the connection is properly closed after use.
|
||||
- Aligned output format to strictly adhere to the specified schema.
|
||||
- Stopped sending internal logs in metadata requests to improve privacy.
|
||||
|
||||
## v0.2.42 (2025-06-04)
|
||||
- Added timestamps to track the start and end of a request's run.
|
||||
- Added tracer information to the configuration settings.
|
||||
- Added support for streaming with tracing contexts.
|
||||
|
||||
## v0.2.41 (2025-06-03)
|
||||
- Added locking mechanism to prevent errors in pipelined executions.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,17 @@
|
||||
# Python SDK Reference
|
||||
|
||||
::: langgraph_sdk.client
|
||||
handler: python
|
||||
|
||||
|
||||
::: langgraph_sdk.schema
|
||||
handler: python
|
||||
|
||||
::: langgraph_sdk.auth
|
||||
handler: python
|
||||
|
||||
::: langgraph_sdk.auth.types
|
||||
handler: python
|
||||
|
||||
::: langgraph_sdk.auth.exceptions
|
||||
handler: python
|
||||
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Reference in New Issue
Block a user