Port checkpointer/store fixtures in langgraph-prebuilt to idiomatic pattern (#4629)

- Use a single fixture that returns the store, instead of list of names

.gitignore

@@ -180,3 +180,4 @@ Chinook.db
 
 .vercel
 .turbo
+.editorconfig

docs/Makefile

@@ -19,7 +19,7 @@ build-prebuilt:
 		poetry run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
 		set +x; \
 	fi
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/prebuilt.md --language python
+	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
 
 build-docs: build-typedoc build-prebuilt
 	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict

docs/README.md

@@ -58,4 +58,4 @@ To delete cassettes for a notebook, you can run:
 
 ```bash
 rm cassettes/<notebook_name>*
-```
+```

docs/_scripts/generate_api_reference_links.py

@@ -22,6 +22,12 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
         "create_react_agent",
         "prebuilt",
     ),
+    (
+        [],
+        "langgraph.prebuilt.chat_agent_executor",
+        "AgentState",
+        "prebuilt",
+    ),
     (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
     (
         ["langgraph.prebuilt"],
@@ -45,6 +51,8 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
     (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "Command", "types"),
+    (["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
+    (["langgraph.config"], "langgraph.config", "get_store", "config"),
     (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
     (["langgraph.func"], "langgraph.func", "task", "func"),
     (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
@@ -56,10 +64,23 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
     ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
     ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
     ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
+    ([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
     ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
     ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
     ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
     ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
+    # other prebuilts
+    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
+    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
+    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
+    ([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
+    ([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
+    ([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
+    ([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
 ]
 
 WELL_KNOWN_LANGGRAPH_OBJECTS = {
@@ -141,7 +162,9 @@ def get_imports(code: str, path: str) -> List[ImportInformation]:
     for found_import in found_imports:
         module = found_import["source"]
 
-        if module.startswith("langchain"):
+        if module.startswith("langchain_mcp_adapters"):
+            package_ecosystem = "langgraph"
+        elif module.startswith("langchain"):
             # Handles things like `langchain` or `langchain_anthropic`
             package_ecosystem = "langchain"
         elif module.startswith("langgraph"):
@@ -214,7 +237,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
         path: The path of the file where the markdown content originated.
 
     Returns:
-        Updated markdown with API reference links appended to Python code blocks.
+        Updated markdown with API reference links prepended to Python code blocks.
 
     Example:
         Given a markdown with a Python code block:
@@ -237,7 +260,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
             match (re.Match): The regex match object containing the code block.
 
         Returns:
-            str: The modified code block with API reference links appended if applicable.
+            str: The modified code block with API reference links prepended if applicable.
         """
         indent = match.group("indent")
         code_block = match.group("code")
@@ -253,8 +276,8 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
         api_links = " | ".join(
             f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
         )
-        # Return the code block with appended API reference links
-        return f"{original_code_block}\n\n{indent}API Reference: {api_links}"
+        # Return the code block with prepended API reference links
+        return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
 
     # Apply the replace_code_block function to all matches in the markdown
     updated_markdown = code_block_pattern.sub(replace_code_block, markdown)

docs/_scripts/notebook_convert.py

@@ -1,7 +1,6 @@
 import ast
 import os
 import re
-from pathlib import Path
 from typing import Literal
 
 import nbformat
@@ -26,7 +25,7 @@ def _uses_input(source: str) -> bool:
 
 
 def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.:w
+    """Process a code block that uses cell magic.
 
     - Lines starting with "%%capture" are ignored.
     - Lines starting with "%pip" are rewritten by removing the leading "%" character.
@@ -52,10 +51,14 @@ def _rewrite_cell_magic(code: str) -> str:
         if stripped.startswith("%%capture"):
             continue
         # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%pip"):
-            # Drop the leading '%' character
-            rewritten_lines.append(stripped[1:])
-        # Anything else is not supported
+        elif stripped.startswith("%") or stripped.startswith("!"):
+            # Drop the leading '%' character and then drop all leading whitespace
+            stripped = stripped.lstrip("%! \t")
+            # Check if the line starts with "pip"
+            if stripped.startswith("pip"):
+                rewritten_lines.append(stripped)
+            else:
+                raise NotImplementedError(f"Unhandled line: {line}")
         else:
             raise NotImplementedError(f"Unhandled line: {line}")
 
@@ -217,6 +220,24 @@ def _convert_links_in_markdown(markdown: str) -> str:
     )
 
 
+class HideCellTagPreprocessor(Preprocessor):
+    """
+    Removes cells that have '# hide-cell' at the beginning of the cell content.
+    This allows authors to include cells in the notebook that should not
+    appear in the generated markdown output.
+    """
+
+    def preprocess(self, nb, resources):
+        # Filter out cells with the '# hide-cell' comment at the beginning
+        nb.cells = [
+            cell
+            for cell in nb.cells
+            if not (cell.source.strip().startswith("# hide-cell"))
+        ]
+
+        return nb, resources
+
+
 class EscapePreprocessor(Preprocessor):
     def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
         super().__init__(**kwargs)
@@ -247,13 +268,10 @@ class EscapePreprocessor(Preprocessor):
             )
             cell.metadata["exec"] = is_exec
 
-            if self.markdown_exec_migration:
-                # For markdown exec migration we'll re-write cell magic as bash commands
-                if source.startswith("%%"):
-                    cell.source = _rewrite_cell_magic(source)
-                    cell.metadata["language"] = "shell"
-
-                cell.metadata["has_output"] = _has_output(source)
+            # For markdown exec migration we'll re-write cell magic as bash commands
+            if source.startswith("%%"):
+                cell.source = _rewrite_cell_magic(source)
+                cell.metadata["language"] = "shell"
 
             # Remove noqa comments
             cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
@@ -341,6 +359,7 @@ class ExtractAttachmentsPreprocessor(Preprocessor):
 
 exporter = MarkdownExporter(
     preprocessors=[
+        HideCellTagPreprocessor,
         EscapePreprocessor,
         ExtractAttachmentsPreprocessor,
     ],
@@ -352,7 +371,7 @@ exporter = MarkdownExporter(
 
 
 def convert_notebook(
-    notebook_path: Path,
+    notebook_path: str,
     mode: Literal["markdown", "exec"] = "markdown",
 ) -> str:
     with open(notebook_path) as f:

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -1,5 +1,18 @@
 {% extends 'markdown/index.md.j2' %}
 
+{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
+```{%- if 'language' in cell.metadata -%}
+{{ cell.metadata.language }}
+{%- elif 'magics_language' in cell.metadata -%}
+{{ cell.metadata.magics_language }}
+{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
+{{ nb.metadata.language_info.name }}
+{%- endif %}
+{{ cell.source }}
+```
+{% endblock input %}
+
+
 {%- block traceback_line -%}
 ```output
 {{ line.rstrip() | strip_ansi }}
@@ -8,13 +21,13 @@
 
 {%- block stream -%}
 ```output
-{{ output.text.rstrip() }}
+{{ output.text.rstrip() | strip_ansi }}
 ```
 {%- endblock stream -%}
 
 {%- block data_text scoped -%}
 ```output
-{{ output.data['text/plain'].rstrip() }}
+{{ output.data['text/plain'].rstrip() | strip_ansi }}
 ```
 {%- endblock data_text -%}
 

docs/_scripts/notebook_hooks.py

@@ -31,6 +31,15 @@ REDIRECT_MAP = {
     "cloud/concepts/api.md": "concepts/langgraph_server.md",
     "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
     "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+    # prebuit redirects
+    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
+    "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
+    "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
+    "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
+    "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
+    # misc
+    "prebuilt.md": "agents/prebuilt.md",
+    "reference/prebuilt.md": "reference/agents.md"
 }
 
 

docs/_scripts/prepare_notebooks_for_ci.py

@@ -20,7 +20,6 @@ BLOCKLIST_COMMANDS = (
 
 NOTEBOOKS_NO_CASSETTES = (
     "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/many-tools.ipynb"
 )
 
 NOTEBOOKS_NO_EXECUTION = [
@@ -49,7 +48,10 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
     "docs/tutorials/tot/tot.ipynb",
     "docs/how-tos/visualization.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "docs/how-tos/streaming-specific-nodes.ipynb",
+    "docs/tutorials/llm-compiler/LLMCompiler.ipynb",
+    "docs/tutorials/customer-support/customer-support.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
+    "docs/how-tos/many-tools.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
 ]
 
 
@@ -86,6 +88,13 @@ def has_blocklisted_command(code: str, metadata: dict) -> bool:
             return True
     return False
 
+def remove_mermaid(code: str) -> str:
+    return code.replace(
+        "display(Image(graph.get_graph().draw_mermaid_png()))",
+        # replace with a dummy statement
+        "print()"
+    )
+
 
 def add_vcr_to_notebook(
     notebook: nbformat.NotebookNode, cassette_prefix: str
@@ -180,6 +189,15 @@ def add_vcr_to_notebook(
     return notebook
 
 
+def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
+    for cell in notebook.cells:
+        if cell.cell_type != "code":
+            continue
+
+        cell.source = remove_mermaid(cell.source)
+    return notebook
+
+
 def process_notebooks(should_comment_install_cells: bool) -> None:
     for directory in NOTEBOOK_DIRS:
         for root, _, files in os.walk(directory):
@@ -201,6 +219,8 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                             notebook, cassette_prefix=cassette_prefix
                         )
 
+                    notebook = remove_mermaid_from_notebook(notebook)
+
                     if notebook_path in NOTEBOOKS_NO_EXECUTION:
                         # Add a cell at the beginning to indicate that this notebook should not be executed
                         warning_cell = nbformat.v4.new_markdown_cell(

docs/_scripts/third_party_page/create_third_party_page.py

@@ -9,10 +9,7 @@ import yaml
 
 MARKDOWN = """\
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# 🚀 Prebuilt Agents
-
-LangGraph includes a prebuilt React agent. For more information on how to use it, 
-check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
+# Community Agents
 
 If you’re looking for other prebuilt libraries, explore the community-built options 
 below. These libraries can extend LangGraph's functionality in various ways.

docs/cassettes/agent_supervisor_0fdc7345-2967-484b-881a-114dfac3f534.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/agent_supervisor_f803943c-6dd2-4b42-93ba-0210ec276132.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/create-react-agent-hitl_740bbaeb.msgpack.zlib

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docs/cassettes/create-react-agent-hitl_83148e08-63e8-49e5-a08b-02dc907bed1d.msgpack.zlib

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docs/cassettes/create-react-agent-hitl_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-memory_187479f9-32fa-4611-9487-cf816ba2e147.msgpack.zlib

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docs/cassettes/create-react-agent-memory_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

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docs/cassettes/create-react-agent-system-prompt_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib

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docs/cassettes/introduction_6a385b06-8d34-4a2d-aded-1cf4bb0ca590.msgpack.zlib

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docs/cassettes/introduction_7df33d9e-cc76-4a0f-8307-01e619483b3e.msgpack.zlib

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docs/cassettes/introduction_9f318020-ab7e-415b-a5e2-eddec6d9f3a6.msgpack.zlib

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docs/cassettes/introduction_df6d81bb-e674-44ef-a46e-17b1a8d2381a.msgpack.zlib

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docs/cassettes/introduction_f5447778-53d7-47f3-801b-f47bcf2185a0.msgpack.zlib

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docs/cassettes/sql-agent_1040233f-3751-4bd3-902f-709fc2e1ecf5.msgpack.zlib

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docs/cassettes/sql-agent_293017e8f05ac2b3.msgpack.zlib

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docs/cassettes/sql-agent_cf02e843-438d-4168-a27a-8f1e0266f8d7.msgpack.zlib

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docs/cassettes/sql-agent_eb814b85-70ba-4699-9038-20266b53efbd.msgpack.zlib

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docs/docs/agents/agents.md

@@ -0,0 +1,218 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Agents
+
+## 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.
+
+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.
+
+<figure markdown="1">
+![image](./assets/agent.png){: style="max-height:400px"}
+<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
+</figure>
+
+## Basic configuration
+
+Use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] to instantiate an agent:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def get_weather(city: str) -> str:  # (1)!
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",  # (2)!
+    tools=[get_weather],  # (3)!
+    prompt="You are a helpful assistant"  # (4)!
+)
+
+# Run the agent
+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 as vanilla Python functions. 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.
+
+## LLM configuration
+
+Use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) to configure an LLM with specific parameters,
+such as temperature:
+
+```python
+from langchain.chat_models import init_chat_model
+from langgraph.prebuilt import create_react_agent
+
+# highlight-next-line
+model = init_chat_model(
+    "anthropic:claude-3-7-sonnet-latest",
+    # highlight-next-line
+    temperature=0
+)
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model,
+    tools=[get_weather],
+)
+```
+
+See the [models](./models.md) page for more information on how to configure LLMs.
+
+## Custom Prompts
+
+Prompts instruct the LLM how to behave. They can be:
+
+* **Static**: A string is interpreted as a **system message**
+* **Dynamic**: a list of messages generated at **runtime** based on input or configuration
+
+### Static prompts
+
+Define a fixed prompt string or list of messages.
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # A static prompt that never changes
+    # highlight-next-line
+    prompt="Never answer questions about the weather."
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)
+```
+
+### Dynamic prompts
+
+Define a function that returns a message list based on the agent's state and configuration:
+
+```python
+from langchain_core.messages import AnyMessage
+from langchain_core.runnables import RunnableConfig
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.prebuilt import create_react_agent
+
+# highlight-next-line
+def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
+    user_name = config["configurable"].get("user_name")
+    system_msg = f"You are a helpful assistant. Address the user as {user_name}."
+    return [{"role": "system", "content": system_msg}] + state["messages"]
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # highlight-next-line
+    prompt=prompt
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    # highlight-next-line
+    config={"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.
+
+See the [context](./context.md) page for more information.
+
+## 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
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import InMemorySaver
+
+# highlight-next-line
+checkpointer = InMemorySaver()
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # highlight-next-line
+    checkpointer=checkpointer  # (1)!
+)
+
+# Run the agent
+# highlight-next-line
+config = {"configurable": {"thread_id": "1"}}
+sf_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    # highlight-next-line
+    config  # (2)!
+)
+ny_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what about new york?"}]},
+    # highlight-next-line
+    config
+)
+```
+
+1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities.
+2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
+
+When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
+
+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.
+
+Please see the [memory guide](./memory.md) for more details on how to work with memory.
+
+
+## Structured output
+
+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
+from pydantic import BaseModel
+from langgraph.prebuilt import create_react_agent
+
+class WeatherResponse(BaseModel):
+    conditions: str
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # highlight-next-line
+    response_format=WeatherResponse  # (1)!
+)
+
+response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)
+
+# highlight-next-line
+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.
+
+    To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
+
+!!! Note "LLM post-processing"
+
+    Structured output requires an additional call to the LLM to format the response according to the schema.
+

docs/docs/agents/context.md

@@ -0,0 +1,236 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Context
+
+Agents often require more than a list of messages to function effectively. They need **context**.
+
+Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
+
+- 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.
+
+LangGraph provides **three** primary ways to supply context:
+
+| Type                                                                         | Description                                   | Mutable? | Lifetime                |
+|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
+| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
+| [**State**](#state-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    |
+
+You can use context to:
+
+- Adjust the system prompt the model sees
+- Feed tools with necessary inputs
+- Track facts during an ongoing conversation
+
+## Providing Runtime Context
+
+Use this when you need to inject data into an agent at runtime.
+
+### Config (static context)
+
+Config is for immutable data like user metadata or API keys. Use
+when you have values that don't change mid-run.
+
+Specify configuration using a key called **"configurable"** which is reserved
+for this purpose:
+
+```python
+agent.invoke(
+    {"messages": [{"role": "user", "content": "hi!"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+### State (mutable context)
+
+State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
+
+```python
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+agent = create_react_agent(
+    # Other agent parameters...
+    # highlight-next-line
+    state_schema=CustomState,
+)
+
+agent.invoke({
+    "messages": "hi!",
+    "user_name": "Jane"
+})
+```
+
+!!! tip "Turning on memory"
+
+    Please see the [memory guide](./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 agent run.
+
+
+
+### Long-Term Memory (cross-conversation context)
+
+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, see the [Memory guide](./memory.md).
+
+## Customizing Prompts with Context { #prompts }
+
+Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
+
+Common use cases:
+
+- Personalization
+- Role or goal customization
+- Conditional behavior (e.g., user is admin)
+
+=== "Using config"
+
+    ```python
+    from langchain_core.messages import AnyMessage
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.prebuilt import create_react_agent
+    from langgraph.prebuilt.chat_agent_executor import AgentState
+
+    def prompt(
+        state: AgentState,
+        # highlight-next-line
+        config: RunnableConfig,
+    ) -> list[AnyMessage]:
+        # highlight-next-line
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. User's name is {user_name}"
+        return [{"role": "system", "content": system_msg}] + state["messages"]
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # highlight-next-line
+        prompt=prompt
+    )
+
+    agent.invoke(
+        ...,
+        # highlight-next-line
+        config={"configurable": {"user_name": "John Smith"}}
+    )
+    ```
+
+=== "Using state"
+
+    ```python
+    from langchain_core.messages import AnyMessage
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.prebuilt import create_react_agent
+    from langgraph.prebuilt.chat_agent_executor import AgentState
+
+    class CustomState(AgentState):
+        # highlight-next-line
+        user_name: str
+
+    def prompt(
+        # highlight-next-line
+        state: CustomState
+    ) -> list[AnyMessage]:
+        # highlight-next-line
+        user_name = state["user_name"]
+        system_msg = f"You are a helpful assistant. User's name is {user_name}"
+        return [{"role": "system", "content": system_msg}] + state["messages"]
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[...],
+        # highlight-next-line
+        state_schema=CustomState,
+        # highlight-next-line
+        prompt=prompt
+    )
+
+    agent.invoke({
+        "messages": "hi!",
+        # highlight-next-line
+        "user_name": "John Smith"
+    })
+    ```
+
+## Accessing Context in Tools { #tools }
+
+Tools can access context through special parameter **annotations**.
+
+* Use `RunnableConfig` for config access
+* Use `Annotated[StateSchema, InjectedState]` for agent state
+
+
+!!! tip 
+
+    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
+
+=== "Using config"
+
+    ```python
+    def get_user_info(
+        # highlight-next-line
+        config: RunnableConfig,
+    ) -> str:
+        """Look up user info."""
+        # highlight-next-line
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_user_info],
+    )
+
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "look up user information"}]},
+        # highlight-next-line
+        config={"configurable": {"user_id": "user_123"}}
+    )
+    ```
+
+=== "Using State"
+
+    ```python
+    from typing import Annotated
+    from langgraph.prebuilt import InjectedState
+
+    class CustomState(AgentState):
+        # highlight-next-line
+        user_id: str
+
+    def get_user_info(
+        # highlight-next-line
+        state: Annotated[CustomState, InjectedState]
+    ) -> str:
+        """Look up user info."""
+        # highlight-next-line
+        user_id = state["user_id"]
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_user_info],
+        # highlight-next-line
+        state_schema=CustomState,
+    )
+
+    agent.invoke({
+        "messages": "look up user information",
+        # highlight-next-line
+        "user_id": "user_123"
+    })
+    ```
+
+### Update Context from Tools
+
+Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.

docs/docs/agents/deployment.md

@@ -0,0 +1,92 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Deployment
+
+To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.
+
+Features: 
+
+* 🖥️ Local server for development
+* 🧩 Studio Web UI for visual debugging
+* ☁️ Cloud and 🔧 self-hosted deployment options
+* 📊 LangSmith integration for tracing and observability
+
+!!! info "Requirements" 
+
+    - ✅ You **must** have a [LangSmith account](https://www.langchain.com/langsmith). You can sign up for **free** and get started with the free tier.
+
+## Create a LangGraph app
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+langgraph new path/to/your/app --template new-langgraph-project-python
+```
+
+This will create an empty LangGraph project. You can modify it by replacing the code in `src/agent/graph.py` with your agent code. For example:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+graph = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    prompt="You are a helpful assistant"
+)
+```
+
+### Install dependencies
+
+In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
+
+```shell
+pip install -e .
+```
+
+### Create an `.env` file
+
+You will find a `.env.example` in the root of your new LangGraph app. Create
+a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
+
+```bash
+LANGSMITH_API_KEY=lsv2...
+ANTHROPIC_API_KEY=sk-
+```
+
+## Launch LangGraph server locally
+
+```shell
+langgraph dev
+```
+
+This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
+
+>    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
+
+See this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) to learn more about running LangGraph app locally.
+
+## LangGraph Studio Web UI
+
+LangGraph Studio Web is a specialized UI that you can connect to LangGraph API server to enable visualization, interaction, and debugging of your application locally. Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph dev` command.
+
+>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
+
+## Deployment
+
+Once your LangGraph app is running locally, you can deploy it using LangGraph Cloud or self-hosted options. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

docs/docs/agents/evals.md

@@ -0,0 +1,128 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Evals
+
+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
+def evaluator(*, outputs: dict, reference_outputs: dict):
+    # compare agent outputs against reference outputs
+    output_messages = outputs["messages"]
+    reference_messages = reference["messages"]
+    score = compare_messages(output_messages, reference_messages)
+    return {"key": "evaluator_score", "score": score}
+```
+
+To get started, you can use prebuilt evaluators from `AgentEvals` package:
+
+```bash
+pip install -U 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
+import json
+# highlight-next-line
+from agentevals.trajectory.match import create_trajectory_match_evaluator
+
+outputs = [
+    {
+        "role": "assistant",
+        "tool_calls": [
+            {
+                "function": {
+                    "name": "get_weather",
+                    "arguments": json.dumps({"city": "san francisco"}),
+                }
+            },
+            {
+                "function": {
+                    "name": "get_directions",
+                    "arguments": json.dumps({"destination": "presidio"}),
+                }
+            }
+        ],
+    }
+]
+reference_outputs = [
+    {
+        "role": "assistant",
+        "tool_calls": [
+            {
+                "function": {
+                    "name": "get_weather",
+                    "arguments": json.dumps({"city": "san francisco"}),
+                }
+            },
+        ],
+    }
+]
+
+# Create the evaluator
+evaluator = create_trajectory_match_evaluator(
+    # highlight-next-line
+    trajectory_match_mode="superset",  # (1)!
+)
+
+# Run the evaluator
+result = evaluator(
+    outputs=outputs, reference_outputs=reference_outputs
+)
+```
+
+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
+import json
+from agentevals.trajectory.llm import (
+    # highlight-next-line
+    create_trajectory_llm_as_judge,
+    TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE
+)
+
+evaluator = create_trajectory_llm_as_judge(
+    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:
+
+- **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
+from langsmith import Client
+from langgraph.prebuilt import create_react_agent
+from agentevals.trajectory.match import create_trajectory_match_evaluator
+
+client = Client()
+agent = create_react_agent(...)
+evaluator = create_trajectory_match_evaluator(...)
+
+experiment_results = client.evaluate(
+    lambda inputs: agent.invoke(inputs),
+    # replace with your dataset name
+    data="<Name of your dataset>",
+    evaluators=[evaluator]
+)
+```

docs/docs/agents/human-in-the-loop.md

@@ -0,0 +1,238 @@
+---
+search:
+  boost: 2
+tags:
+  - human-in-the-loop
+  - hil
+  - agent
+hide:
+  - tags
+---
+
+# Human-in-the-loop
+
+To review, edit and approve tool calls in an agent you can use LangGraph's built-in [Human-In-the-Loop (HIL)](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
+
+LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.
+
+This is possible because the agent state is **checkpointed into a database**, which allows the system to persist execution context and later resume the workflow, continuing from where it left off.
+
+For a deeper dive into the **human-in-the-loop** concept, see the [concept guide](../concepts/human_in_the_loop.md).
+
+<figure markdown="1">
+![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
+<figcaption>
+A human can review and edit the output from the agent before proceeding. This is particularly critical in applications where the tool calls requested may be sensitive or require human oversight.
+</figcaption>
+</figure>
+
+
+## Review tool calls
+
+To add a human approval step to a tool:
+
+1. Use `interrupt()` in the tool to pause execution.
+2. Resume with a `Command(resume=...)` to continue based on human input.
+
+```python
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import interrupt
+from langgraph.prebuilt import create_react_agent
+
+# An example of a sensitive tool that requires human review / approval
+def book_hotel(hotel_name: str):
+    """Book a hotel"""
+    # highlight-next-line
+    response = interrupt(  # (1)!
+        f"Trying to call `book_hotel` with args {{'hotel_name': {hotel_name}}}. "
+        "Please approve or suggest edits."
+    )
+    if response["type"] == "accept":
+        pass
+    elif response["type"] == "edit":
+        hotel_name = response["args"]["hotel_name"]
+    else:
+        raise ValueError(f"Unknown response type: {response['type']}")
+    return f"Successfully booked a stay at {hotel_name}."
+
+# highlight-next-line
+checkpointer = InMemorySaver() # (2)!
+
+agent = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    tools=[book_hotel],
+    # highlight-next-line
+    checkpointer=checkpointer, # (3)!
+)
+```
+
+1. The [`interrupt` function][langgraph.types.interrupt] pauses the agent graph at a specific node. In this case, we call `interrupt()` at the beginning of the tool function, which pauses the graph at the node that executes the tool. The information inside `interrupt()` (e.g., tool calls) can be presented to a human, and the graph can be resumed with the user input (tool call approval, edit or feedback).
+2. The `InMemorySaver` is used to store the agent state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities. In this example, we use `InMemorySaver` to store the agent state in memory. In a production application, the agent state will be stored in a database.
+3. Initialize the agent with the `checkpointer`.
+
+Run the agent with the `stream()` method, passing the `config` object to specify the thread ID. This allows the agent to resume the same conversation on future invocations.
+
+```python
+config = {
+   "configurable": {
+      # highlight-next-line
+      "thread_id": "1"
+   }
+}
+
+for chunk in agent.stream(
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
+    # highlight-next-line
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+> You should see that the agent runs until it reaches the `interrupt()` call, at which point it pauses and waits for human input.
+
+Resume the agent with a `Command(resume=...)` to continue based on human input.
+
+```python
+from langgraph.types import Command
+
+for chunk in agent.stream(
+    # highlight-next-line
+    Command(resume={"type": "accept"}),  # (1)!
+    # Command(resume={"type": "edit", "args": {"hotel_name": "McKittrick Hotel"}}),
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`](../reference/types.md#langgraph.types.Command) object to resume the graph with a value provided by the human.
+
+## Using with Agent Inbox
+
+You can create a wrapper to add interrupts to *any* tool. 
+
+The example below provides a reference implementation compatible with [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox) and [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui).
+
+```python title="Wrapper that adds human-in-the-loop to any tool"
+from typing import Callable
+from langchain_core.tools import BaseTool, tool as create_tool
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import interrupt 
+from langgraph.prebuilt.interrupt import HumanInterruptConfig, HumanInterrupt
+
+def add_human_in_the_loop(
+    tool: Callable | BaseTool,
+    *,
+    interrupt_config: HumanInterruptConfig = None,
+) -> BaseTool:
+    """Wrap a tool to support human-in-the-loop review.""" 
+    if not isinstance(tool, BaseTool):
+        tool = create_tool(tool)
+
+    if interrupt_config is None:
+        interrupt_config = {
+            "allow_accept": True,
+            "allow_edit": True,
+            "allow_respond": True,
+        }
+
+    @create_tool(  # (1)!
+        tool.name,
+        description=tool.description,
+        args_schema=tool.args_schema
+    )
+    def call_tool_with_interrupt(config: RunnableConfig, **tool_input):
+        request: HumanInterrupt = {
+            "action_request": {
+                "action": tool.name,
+                "args": tool_input
+            },
+            "config": interrupt_config,
+            "description": "Please review the tool call"
+        }
+        # highlight-next-line
+        response = interrupt([request])[0]  # (2)!
+        # approve the tool call
+        if response["type"] == "accept":
+            tool_response = tool.invoke(tool_input, config)
+        # update tool call args
+        elif response["type"] == "edit":
+            tool_input = response["args"]["args"]
+            tool_response = tool.invoke(tool_input, config)
+        # respond to the LLM with user feedback
+        elif response["type"] == "response":
+            user_feedback = response["args"]
+            tool_response = user_feedback
+        else:
+            raise ValueError(f"Unsupported interrupt response type: {response['type']}")
+
+        return tool_response
+
+    return call_tool_with_interrupt
+```
+
+1. This wrapper creates a new tool that calls `interrupt()` **before** executing the wrapped tool.
+2. `interrupt()` is using special input and output format that's expected by [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox):
+    - a list of [`HumanInterrupt`][langgraph.prebuilt.interrupt.HumanInterrupt] objects is sent to `AgentInbox` render interrupt information to the end user
+    - resume value is provided by `AgentInbox` as a list (i.e., `Command(resume=[...])`)
+
+You can use the `add_human_in_the_loop` wrapper to add `interrupt()` to any tool without having to add it *inside* the tool:
+
+```python
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.prebuilt import create_react_agent
+
+# highlight-next-line
+checkpointer = InMemorySaver()
+
+def book_hotel(hotel_name: str):
+   """Book a hotel"""
+   return f"Successfully booked a stay at {hotel_name}."
+
+
+agent = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    tools=[
+        # highlight-next-line
+        add_human_in_the_loop(book_hotel), # (1)!
+    ],
+    # highlight-next-line
+    checkpointer=checkpointer,
+)
+
+config = {"configurable": {"thread_id": "1"}}
+
+# Run the agent
+for chunk in agent.stream(
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
+    # highlight-next-line
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+1. The `add_human_in_the_loop` wrapper is used to add `interrupt()` to the tool. This allows the agent to pause execution and wait for human input before proceeding with the tool call.
+
+> You should see that the agent runs until it reaches the `interrupt()` call, 
+>  at which point it pauses and waits for human input.
+
+Resume the agent with a `Command(resume=...)`  to continue based on human input.
+
+```python
+from langgraph.types import Command 
+
+for chunk in agent.stream(
+    # highlight-next-line
+    Command(resume=[{"type": "accept"}]),
+    # Command(resume=[{"type": "edit", "args": {"args": {"hotel_name": "McKittrick Hotel"}}}]),
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+## Additional resources
+
+* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)

docs/docs/agents/mcp.md

@@ -0,0 +1,107 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# MCP Integration
+
+[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.
+
+![MCP](./assets/mcp.png)
+
+Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
+
+```bash
+pip install langchain-mcp-adapters
+```
+
+## Use MCP tools
+
+The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
+
+```python title="Agent using tools defined on MCP servers"
+# highlight-next-line
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langgraph.prebuilt import create_react_agent
+
+# highlight-next-line
+async with MultiServerMCPClient(
+    {
+        "math": {
+            "command": "python",
+            # Replace with absolute path to your math_server.py file
+            "args": ["/path/to/math_server.py"],
+            "transport": "stdio",
+        },
+        "weather": {
+            # Ensure your start your weather server on port 8000
+            "url": "http://localhost:8000/sse",
+            "transport": "sse",
+        }
+    }
+) as client:
+    agent = create_react_agent(
+        "anthropic:claude-3-7-sonnet-latest",
+        # highlight-next-line
+        client.get_tools()
+    )
+    math_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+    )
+    weather_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+    )
+```
+
+## Custom MCP servers
+
+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:
+
+```bash
+pip install mcp
+```
+Use the following reference implementations to test your agent with MCP tool servers.
+
+```python title="Example Math Server (stdio transport)"
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("Math")
+
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+@mcp.tool()
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers"""
+    return a * b
+
+if __name__ == "__main__":
+    mcp.run(transport="stdio")
+```
+
+```python title="Example Weather Server (SSE transport)"
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("Weather")
+
+@mcp.tool()
+async def get_weather(location: str) -> str:
+    """Get weather for location."""
+    return "It's always sunny in New York"
+
+if __name__ == "__main__":
+    mcp.run(transport="sse")
+```
+
+## Additional resources
+
+- [MCP documentation](https://modelcontextprotocol.io/introduction)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)

docs/docs/agents/memory.md

@@ -0,0 +1,423 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Memory
+
+LangGraph supports two types of memory essential for building conversational agents:
+
+- **[Short-term memory](#short-term-memory)**: Tracks the ongoing conversation by maintaining message history within a session.
+- **[Long-term memory](#long-term-memory)**: Stores user-specific or application-level data across sessions.
+
+This guide demonstrates how to use both memory types with agents in LangGraph. For a deeper
+understanding of memory concepts, refer to the [LangGraph memory documentation](../concepts/memory.md).
+
+<figure markdown="1">
+![image](./assets/memory.png){: style="max-height:400px"}
+<figcaption>Both <strong>short-term</strong> and <strong>long-term</strong> memory require persistent storage to maintain continuity across LLM interactions. In production environments, this data is typically stored in a database.</figcaption>
+</figure>
+
+!!! note "Terminology"
+
+    In LangGraph:
+
+    - *Short-term memory* is also referred to as **thread-level memory**.
+    - *Long-term memory* is also called **cross-thread memory**.
+
+    A [thread](../concepts/persistence.md#threads) represents a sequence of related runs
+    grouped by the same `thread_id`.
+
+## Short-term memory
+
+Short-term memory enables agents to track multi-turn conversations. To use it, you must:
+
+1. Provide a `checkpointer` when creating the agent. The `checkpointer` enables [persistence](../concepts/persistence.md) of the agent's state.
+2. Supply a `thread_id` in the config when running the agent. The `thread_id` is a unique identifier for the conversation session.
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import InMemorySaver
+
+# highlight-next-line
+checkpointer = InMemorySaver() # (1)!
+
+
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # highlight-next-line
+    checkpointer=checkpointer # (2)!
+)
+
+# Run the agent
+config = {
+    "configurable": {
+        # highlight-next-line
+        "thread_id": "1"  # (3)!
+    }
+}
+
+sf_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    # highlight-next-line
+    config
+)
+
+# Continue the conversation using the same thread_id
+ny_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what about new york?"}]},
+    # highlight-next-line
+    config # (4)!
+)
+```
+
+1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
+2. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations. Please note that 
+3. A unique `thread_id` is provided in the config. This ID is used to identify the conversation session. The value is controlled by the user and can be any string.
+4. The agent will continue the conversation using the same `thread_id`. This will allow the agent to infer that the user is asking specifically about the **weather** in New York. 
+
+When the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, allowing the agent to infer that the user is asking specifically about the **weather** in New York.
+
+!!! Note "LangGraph Platform providers a production-ready checkpointer"
+
+    If you're using [LangGraph Platform](./deployment.md), during deployment your checkpointer will be automatically configured to use a production-ready database.
+
+### Manage message history
+
+Long conversations can exceed the LLM's context window. Common solutions are:
+
+* [Summarization](#summarize-message-history): Maintain a running summary of the conversation
+* [Trimming](#trim-message-history): Remove first or last N messages in the history
+
+This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+
+To manage message history, specify `pre_model_hook` — a function ([node](../concepts/low_level.md#nodes)) that will always run before calling the language model.
+
+#### Summarize message history
+
+<figure markdown="1">
+![image](./assets/summary.png){: style="max-height:400px"}
+<figcaption>Long conversations can exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+</figcaption>
+</figure>
+
+To summarize message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with a prebuilt [`SummarizationNode`](https://langchain-ai.github.io/langmem/reference/short_term/#langmem.short_term.SummarizationNode):
+
+```python
+from langchain_anthropic import ChatAnthropic
+from langmem.short_term import SummarizationNode
+from langchain_core.messages.utils import count_tokens_approximately
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.checkpoint.memory import InMemorySaver
+from typing import Any
+
+model = ChatAnthropic(model="claude-3-7-sonnet-latest")
+
+summarization_node = SummarizationNode( # (1)!
+    token_counter=count_tokens_approximately,
+    model=model,
+    max_tokens=384,
+    max_summary_tokens=128,
+    output_messages_key="llm_input_messages",
+)
+
+class State(AgentState):
+    # NOTE: we're adding this key to keep track of previous summary information
+    # to make sure we're not summarizing on every LLM call
+    # highlight-next-line
+    context: dict[str, Any]  # (2)!
+
+
+checkpointer = InMemorySaver() # (3)!
+
+agent = create_react_agent(
+    model=model,
+    tools=tools,
+    # highlight-next-line
+    pre_model_hook=summarization_node, # (4)!
+    # highlight-next-line
+    state_schema=State, # (5)!
+    checkpointer=checkpointer,
+)
+```
+
+1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
+2. The `context` key is added to the agent's state. The key contains book-keeping information for the summarization node. It is used to keep track of the last summary information and ensure that the agent doesn't summarize on every LLM call, which can be inefficient.
+3. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations.
+4. The `pre_model_hook` is set to the `SummarizationNode`. This node will summarize the message history before sending it to the LLM. The summarization node will automatically handle the summarization process and update the agent's state with the new summary. You can replace this with a custom implementation if you prefer. Please see the [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] API reference for more details.
+5. The `state_schema` is set to the `State` class, which is the custom state that contains an extra `context` key.
+
+#### Trim message history
+
+To trim message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with [`trim_messages`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html) function:
+
+```python
+# highlight-next-line
+from langchain_core.messages.utils import (
+    # highlight-next-line
+    trim_messages,
+    # highlight-next-line
+    count_tokens_approximately
+# highlight-next-line
+)
+from langgraph.prebuilt import create_react_agent
+
+# This function will be called every time before the node that calls LLM
+def pre_model_hook(state):
+    trimmed_messages = trim_messages(
+        state["messages"],
+        strategy="last",
+        token_counter=count_tokens_approximately,
+        max_tokens=384,
+        start_on="human",
+        end_on=("human", "tool"),
+    )
+    # highlight-next-line
+    return {"llm_input_messages": trimmed_messages}
+
+checkpointer = InMemorySaver()
+agent = create_react_agent(
+    model,
+    tools,
+    # highlight-next-line
+    pre_model_hook=pre_model_hook,
+    checkpointer=checkpointer,
+)
+```
+
+To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
+
+### Read in tools { #read-short-term }
+
+LangGraph allows agent to access its short-term memory (state) inside the tools.
+
+```python
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, create_react_agent
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_id: str
+
+def get_user_info(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Look up user info."""
+    # highlight-next-line
+    user_id = state["user_id"]
+    return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    state_schema=CustomState,
+)
+
+agent.invoke({
+    "messages": "look up user information",
+    # highlight-next-line
+    "user_id": "user_123"
+})
+```
+
+See the [Context](./context.md#__tabbed_2_2) guide for more information.
+
+### Write from tools { #write-short-term }
+
+To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```python
+from typing import Annotated
+from langchain_core.tools import InjectedToolCallId
+from langchain_core.runnables import RunnableConfig
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.types import Command
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+def update_user_info(
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    config: RunnableConfig
+) -> Command:
+    """Look up and update user info."""
+    user_id = config["configurable"].get("user_id")
+    name = "John Smith" if user_id == "user_123" else "Unknown user"
+    # highlight-next-line
+    return Command(update={
+        # highlight-next-line
+        "user_name": name,
+        # update the message history
+        "messages": [
+            ToolMessage(
+                "Successfully looked up user information",
+                tool_call_id=tool_call_id
+            )
+        ]
+    })
+
+def greet(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Use this to greet the user once you found their info."""
+    user_name = state["user_name"]
+    return f"Hello {user_name}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[update_user_info, greet],
+    # highlight-next-line
+    state_schema=CustomState
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "greet the user"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
+
+## Long-term memory
+
+Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
+
+To use long-term memory, you need to:
+
+1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
+2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
+
+### Read { #read-long-term }
+
+```python title="A tool the agent can use to look up user information"
+from langchain_core.runnables import RunnableConfig
+from langgraph.config import get_store
+from langgraph.prebuilt import create_react_agent
+from langgraph.store.memory import InMemoryStore
+
+# highlight-next-line
+store = InMemoryStore() # (1)!
+
+# highlight-next-line
+store.put(  # (2)!
+    ("users",),  # (3)!
+    "user_123",  # (4)!
+    {
+        "name": "John Smith",
+        "language": "English",
+    } # (5)!
+)
+
+def get_user_info(config: RunnableConfig) -> str:
+    """Look up user info."""
+    # Same as that provided to `create_react_agent`
+    # highlight-next-line
+    store = get_store() # (6)!
+    user_id = config["configurable"].get("user_id")
+    # highlight-next-line
+    user_info = store.get(("users",), user_id) # (7)!
+    return str(user_info.value) if user_info else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    store=store # (8)!
+)
+
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "look up user information"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+2. For this example, we write some sample data to the store using the `put` method. Please see the [BaseStore.put][langgraph.store.base.BaseStore.put] API reference for more details.
+3. The first argument is the namespace. This is used to group related data together. In this case, we are using the `users` namespace to group user data.
+4. A key within the namespace. This example uses a user ID for the key.
+5. The data that we want to store for the given user.
+6. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
+7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
+8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
+
+### Write { #write-long-term }
+
+```python title="Example of a tool that updates user information"
+from typing_extensions import TypedDict
+
+from langgraph.config import get_store
+from langgraph.prebuilt import create_react_agent
+from langgraph.store.memory import InMemoryStore
+
+store = InMemoryStore() # (1)!
+
+class UserInfo(TypedDict): # (2)!
+    name: str
+
+def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
+    """Save user info."""
+    # Same as that provided to `create_react_agent`
+    # highlight-next-line
+    store = get_store() # (4)!
+    user_id = config["configurable"].get("user_id")
+    # highlight-next-line
+    store.put(("users",), user_id, user_info) # (5)!
+    return "Successfully saved user info."
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[save_user_info],
+    # highlight-next-line
+    store=store
+)
+
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}} # (6)!
+)
+
+# You can access the store directly to get the value
+store.get(("users",), "user_123").value
+```
+
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+2. The `UserInfo` class is a `TypedDict` that defines the structure of the user information. The LLM will use this to format the response according to the schema.
+3. The `save_user_info` function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
+4. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
+5. The `put` method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
+6. The `user_id` is passed in the config. This is used to identify the user whose information is being updated.
+
+### Semantic search
+
+LangGraph also allows you to [search](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/#using-in-create-react-agent) for items in long-term memory by semantic similarity.
+
+### Prebuilt memory tools
+
+**LangMem** is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the [LangMem documentation](https://langchain-ai.github.io/langmem/) for usage examples.
+
+
+## Additional resources
+
+* [Memory in LangGraph](../concepts/memory.md)

docs/docs/agents/models.md

@@ -0,0 +1,145 @@
+---
+search:
+  boost: 2
+tags:
+  - anthropic
+  - openai
+  - agent
+hide:
+  - tags
+---
+
+# Models
+
+This page describes how to configure the chat model used by an agent.
+
+## Tool calling support
+
+To enable tool-calling agents, the underlying LLM must support [tool calling](https://python.langchain.com/docs/concepts/tool_calling/).
+
+Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
+
+## Specifying a model by name
+
+You can configure an agent with a model name string:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    # highlight-next-line
+    model="anthropic:claude-3-7-sonnet-latest",
+    # other parameters
+)
+```
+
+## Using `init_chat_model`
+
+The [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) utility simplifies model initialization with configurable parameters:
+
+```python
+from langchain.chat_models import init_chat_model
+
+model = init_chat_model(
+    "anthropic:claude-3-7-sonnet-latest",
+    temperature=0,
+    max_tokens=2048
+)
+```
+
+Refer to the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for advanced options.
+
+## Using provider-specific LLMs 
+
+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
+from langchain_anthropic import ChatAnthropic
+from langgraph.prebuilt import create_react_agent
+
+model = ChatAnthropic(
+    model="claude-3-7-sonnet-latest",
+    temperature=0,
+    max_tokens=2048
+)
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model,
+    # other parameters
+)
+```
+
+!!! note "Illustrative example" 
+
+    The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.
+
+## Disable streaming
+
+To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
+
+=== "`init_chat_model`"
+
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    model = init_chat_model(
+        "anthropic:claude-3-7-sonnet-latest",
+        # highlight-next-line
+        disable_streaming=True
+    )
+    ```
+
+=== "`ChatModel`"
+
+    ```python
+    from langchain_anthropic import ChatAnthropic
+
+    model = ChatAnthropic(
+        model="claude-3-7-sonnet-latest",
+        # highlight-next-line
+        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`
+
+## Adding model fallbacks
+
+You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
+
+=== "`init_chat_model`"
+
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    model_with_fallbacks = (
+        init_chat_model("anthropic:claude-3-5-haiku-latest")
+        # highlight-next-line
+        .with_fallbacks([
+            init_chat_model("openai:gpt-4.1-mini"),
+        ])
+    )
+    ```
+
+=== "`ChatModel`"
+
+    ```python
+    from langchain_anthropic import ChatAnthropic
+    from langchain_openai import ChatOpenAI
+
+    model_with_fallbacks = (
+        ChatAnthropic(model="claude-3-5-haiku-latest")
+        # highlight-next-line
+        .with_fallbacks([
+            ChatOpenAI(model="gpt-4.1-mini"),
+        ])
+    )
+    ```
+
+See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
+
+## Additional resources
+
+- [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
+- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)

docs/docs/agents/multi-agent.md

@@ -0,0 +1,308 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Multi-agent
+
+A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).
+
+In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.
+
+Two of the most popular multi-agent architectures are:
+
+- [supervisor](#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.
+- [swarm](#swarm) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent.
+
+## Supervisor
+
+![Supervisor](./assets/supervisor.png)
+
+Use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent system:
+
+```bash
+pip install langgraph-supervisor
+```
+
+```python
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+# highlight-next-line
+from langgraph_supervisor import create_supervisor
+
+def book_hotel(hotel_name: str):
+    """Book a hotel"""
+    return f"Successfully booked a stay at {hotel_name}."
+
+def book_flight(from_airport: str, to_airport: str):
+    """Book a flight"""
+    return f"Successfully booked a flight from {from_airport} to {to_airport}."
+
+flight_assistant = create_react_agent(
+    model="openai:gpt-4o",
+    tools=[book_flight],
+    prompt="You are a flight booking assistant",
+    # highlight-next-line
+    name="flight_assistant"
+)
+
+hotel_assistant = create_react_agent(
+    model="openai:gpt-4o",
+    tools=[book_hotel],
+    prompt="You are a hotel booking assistant",
+    # highlight-next-line
+    name="hotel_assistant"
+)
+
+# highlight-next-line
+supervisor = create_supervisor(
+    agents=[flight_assistant, hotel_assistant],
+    model=ChatOpenAI(model="gpt-4o"),
+    prompt=(
+        "You manage a hotel booking assistant and a"
+        "flight booking assistant. Assign work to them."
+    )
+).compile()
+
+for chunk in supervisor.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
+    print(chunk)
+    print("\n")
+```
+
+## Swarm
+
+![Swarm](./assets/swarm.png)
+
+Use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent system:
+
+```bash
+pip install langgraph-swarm
+```
+
+```python
+from langgraph.prebuilt import create_react_agent
+# highlight-next-line
+from langgraph_swarm import create_swarm, create_handoff_tool
+
+transfer_to_hotel_assistant = create_handoff_tool(
+    agent_name="hotel_assistant",
+    description="Transfer user to the hotel-booking assistant.",
+)
+transfer_to_flight_assistant = create_handoff_tool(
+    agent_name="flight_assistant",
+    description="Transfer user to the flight-booking assistant.",
+)
+
+flight_assistant = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    # highlight-next-line
+    tools=[book_flight, transfer_to_hotel_assistant],
+    prompt="You are a flight booking assistant",
+    # highlight-next-line
+    name="flight_assistant"
+)
+hotel_assistant = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    # highlight-next-line
+    tools=[book_hotel, transfer_to_flight_assistant],
+    prompt="You are a hotel booking assistant",
+    # highlight-next-line
+    name="hotel_assistant"
+)
+
+# highlight-next-line
+swarm = create_swarm(
+    agents=[flight_assistant, hotel_assistant],
+    default_active_agent="flight_assistant"
+).compile()
+
+for chunk in swarm.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
+    print(chunk)
+    print("\n")
+```
+
+## Handoffs
+
+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
+
+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
+
+    ```python
+    def transfer_to_bob():
+        """Transfer to bob."""
+        return 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,
+        )
+    ```
+
+1. Create individual agents that have access to handoff tools:
+
+    ```python
+    flight_assistant = create_react_agent(
+        ..., tools=[book_flight, transfer_to_hotel_assistant]
+    )
+    hotel_assistant = create_react_agent(
+        ..., tools=[book_hotel, transfer_to_flight_assistant]
+    )
+    ```
+
+1. Define a parent graph that contains individual agents as nodes:
+
+    ```python
+    from langgraph.graph import StateGraph, MessagesState
+    multi_agent_graph = (
+        StateGraph(MessagesState)
+        .add_node(flight_assistant)
+        .add_node(hotel_assistant)
+        ...
+    )
+    ```
+
+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
+from typing import Annotated
+from langchain_core.tools import tool, InjectedToolCallId
+from langgraph.prebuilt import create_react_agent, InjectedState
+from langgraph.graph import StateGraph, START, MessagesState
+from langgraph.types import Command
+
+def create_handoff_tool(*, agent_name: str, description: str | None = None):
+    name = f"transfer_to_{agent_name}"
+    description = description or f"Transfer to {agent_name}"
+
+    @tool(name, description=description)
+    def handoff_tool(
+        # highlight-next-line
+        state: Annotated[MessagesState, InjectedState], # (1)!
+        # highlight-next-line
+        tool_call_id: Annotated[str, InjectedToolCallId],
+    ) -> Command:
+        tool_message = {
+            "role": "tool",
+            "content": f"Successfully transferred to {agent_name}",
+            "name": name,
+            "tool_call_id": tool_call_id,
+        }
+        return Command(  # (2)!
+            # highlight-next-line
+            goto=agent_name,  # (3)!
+            # highlight-next-line
+            update={"messages": state["messages"] + [tool_message]},  # (4)!
+            # highlight-next-line
+            graph=Command.PARENT,  # (5)!
+        )
+    return handoff_tool
+
+# Handoffs
+transfer_to_hotel_assistant = create_handoff_tool(
+    agent_name="hotel_assistant",
+    description="Transfer user to the hotel-booking assistant.",
+)
+transfer_to_flight_assistant = create_handoff_tool(
+    agent_name="flight_assistant",
+    description="Transfer user to the flight-booking assistant.",
+)
+
+# Simple agent tools
+def book_hotel(hotel_name: str):
+    """Book a hotel"""
+    return f"Successfully booked a stay at {hotel_name}."
+
+def book_flight(from_airport: str, to_airport: str):
+    """Book a flight"""
+    return f"Successfully booked a flight from {from_airport} to {to_airport}."
+
+# Define agents
+flight_assistant = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    # highlight-next-line
+    tools=[book_flight, transfer_to_hotel_assistant],
+    prompt="You are a flight booking assistant",
+    # highlight-next-line
+    name="flight_assistant"
+)
+hotel_assistant = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    # highlight-next-line
+    tools=[book_hotel, transfer_to_flight_assistant],
+    prompt="You are a hotel booking assistant",
+    # highlight-next-line
+    name="hotel_assistant"
+)
+
+# Define multi-agent graph
+multi_agent_graph = (
+    StateGraph(MessagesState)
+    .add_node(flight_assistant)
+    .add_node(hotel_assistant)
+    .add_edge(START, "flight_assistant")
+    .compile()
+)
+
+# Run the multi-agent graph
+for chunk in multi_agent_graph.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
+    print(chunk)
+    print("\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
+
+    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.

docs/docs/agents/overview.md

@@ -0,0 +1,44 @@
+---
+title: Overview
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Agent development with LangGraph
+
+**LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
+
+## Key features
+
+LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
+
+- [**Memory integration**](./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**](./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**](./streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
+- [**Deployment tooling**](./deployment.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/tutorials/deployment/) for production.
+
+## High-level building blocks
+
+LangGraph comes with a set of prebuilt components that implement common agent behaviors and workflows. These abstractions are built on top of the LangGraph framework, offering a faster path to production while remaining flexible for advanced customization.
+
+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.
+
+## 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**](./memory.md)        | `pip install -U langmem`                |
+| `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
+

docs/docs/agents/prebuilt.md

@@ -1,4 +1,11 @@
-# 🚀 Prebuilt Agents
+---
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Community Agents
 
 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.
 

docs/docs/agents/run_agents.md

@@ -0,0 +1,168 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Running agents
+
+
+Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](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:
+
+- **Synchronous** using `.invoke()` or `.stream()`
+- **Asynchronous** using `await .invoke()` or `async for` with `.astream()`
+
+=== "Sync invocation"
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    agent = create_react_agent(...)
+
+    # highlight-next-line
+    response = agent.invoke({"messages": [{"role": "user", "content": "what is the weather in sf"}]})
+    ```
+
+=== "Async invocation"
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    agent = create_react_agent(...)
+    # highlight-next-line
+    response = await agent.ainvoke({"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).
+
+## Input format
+
+Agent input must be a dictionary with a `messages` key. Supported formats are:
+
+| 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`               |
+
+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.
+
+!!! tip "Using custom agent state"
+
+    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.
+
+!!! note
+
+    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.
+
+
+## Output format
+
+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#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.
+
+See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.
+
+## Streaming output
+
+Agents support streaming responses for more responsive applications. This includes:
+
+- **Progress updates** after each step
+- **LLM tokens** as they're generated
+- **Custom tool messages** during execution
+
+Streaming is available in both sync and async modes:
+
+=== "Sync streaming"
+
+    ```python
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        stream_mode="updates"
+    ):
+        print(chunk)
+    ```
+
+=== "Async streaming"
+
+    ```python
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        stream_mode="updates"
+    ):
+        print(chunk)
+    ```
+
+!!! tip
+
+    For full details, see the [streaming guide](./streaming.md).
+
+## Max iterations
+
+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()`:
+
+=== "Runtime"
+
+    ```python
+    from langgraph.errors import GraphRecursionError
+    from langgraph.prebuilt import create_react_agent
+
+    max_iterations = 3
+    # highlight-next-line
+    recursion_limit = 2 * max_iterations + 1
+    agent = create_react_agent(
+        model="anthropic:claude-3-5-haiku-latest",
+        tools=[get_weather]
+    )
+
+    try:
+        response = agent.invoke(
+            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
+            # highlight-next-line
+            {"recursion_limit": recursion_limit},
+        )
+    except GraphRecursionError:
+        print("Agent stopped due to max iterations.")
+    ```
+
+=== "`.with_config()`"
+
+    ```python
+    from langgraph.errors import GraphRecursionError
+    from langgraph.prebuilt import create_react_agent
+
+    max_iterations = 3
+    # highlight-next-line
+    recursion_limit = 2 * max_iterations + 1
+    agent = create_react_agent(
+        model="anthropic:claude-3-5-haiku-latest",
+        tools=[get_weather]
+    )
+    # highlight-next-line
+    agent_with_recursion_limit = agent.with_config(recursion_limit=recursion_limit)
+
+    try:
+        response = agent_with_recursion_limit.invoke(
+            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
+        )
+    except GraphRecursionError:
+        print("Agent stopped due to max iterations.")
+    ```
+
+## Additional Resources
+
+* [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)

docs/docs/agents/streaming.md

@@ -0,0 +1,223 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Streaming
+
+Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
+
+1. [**Agent progress**](#agent-progress) — get updates after each node in the agent graph is executed.
+2. [**LLM tokens**](#llm-tokens) — stream tokens as they are generated by the language model.
+3. [**Custom updates**](#tool-updates) — emit custom data from tools during execution (e.g., "Fetched 10/100 records")
+
+You can stream [more than one type of data](#stream-multiple-modes) at a time. 
+
+
+<figure markdown="1">
+![image](./assets/fast_parrot.png){: style="max-height:300px"}
+<figcaption>
+Waiting is for pigeons.
+</figcaption>
+</figure>
+
+## Agent progress
+
+To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with [`stream_mode="updates"`](https://langchain-ai.github.io/langgraph/how-tos/streaming/#updates). This emits an event after every agent step.
+
+For example, if you have an agent that calls a tool once, you should see the following updates:
+
+* **LLM node**: AI message with tool call requests
+* **Tool node**: Tool message with execution result
+* **LLM node**: Final AI response
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+## LLM tokens
+
+To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for token, metadata in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for token, metadata in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+## Tool updates
+
+To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
+
+=== "Sync"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+!!! Note
+    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
+
+## Stream multiple modes
+
+You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for stream_mode, chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for stream_mode, chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+## Disable streaming
+
+In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
+
+See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
+
+## Additional resources
+
+* [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)

docs/docs/agents/tools.md

@@ -0,0 +1,296 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Tools
+
+[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
+
+You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
+
+## Define simple tools
+
+You can pass a vanilla function to `create_react_agent` to use as a tool:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+create_react_agent(
+    model="anthropic:claude-3-7-sonnet",
+    tools=[multiply]
+)
+```
+
+`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
+
+## Customize tools
+
+For more control over tool behavior, use the `@tool` decorator:
+
+```python
+# highlight-next-line
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool("multiply_tool", parse_docstring=True)
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+
+    Args:
+        a: First operand
+        b: Second operand
+    """
+    return a * b
+```
+
+You can also define a custom input schema using Pydantic:
+
+```python
+from pydantic import BaseModel, Field
+
+class MultiplyInputSchema(BaseModel):
+    """Multiply two numbers"""
+    a: int = Field(description="First operand")
+    b: int = Field(description="Second operand")
+
+# highlight-next-line
+@tool("multiply_tool", args_schema=MultiplyInputSchema)
+def multiply(a: int, b: int) -> int:
+   return a * b
+```
+
+For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
+
+## Hide arguments from the model
+
+Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
+
+You can put these arguments in the `state` or `config` of the agent, and access
+this information inside the tool:
+
+```python
+from langgraph.prebuilt import InjectedState
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langchain_core.runnables import RunnableConfig
+
+def my_tool(
+    # This will be populated by an LLM
+    tool_arg: str,
+    # access information that's dynamically updated inside the agent
+    # highlight-next-line
+    state: Annotated[AgentState, InjectedState],
+    # access static data that is passed at agent invocation
+    # highlight-next-line
+    config: RunnableConfig,
+) -> str:
+    """My tool."""
+    do_something_with_state(state["messages"])
+    do_something_with_config(config)
+    ...
+```
+
+## Disable parallel tool calling
+
+Some model providers support executing multiple tools in parallel, but
+allow users to disable this feature.
+
+For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
+
+```python
+from langchain.chat_models import init_chat_model
+
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
+tools = [add, multiply]
+agent = create_react_agent(
+    # disable parallel tool calls
+    # highlight-next-line
+    model=model.bind_tools(tools, parallel_tool_calls=False),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
+)
+```
+
+## Return tool results directly
+
+Use `return_direct=True` to return tool results immediately and stop the agent loop:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[add]
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
+)
+```
+
+## Force tool use
+
+To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def greet(user_name: str) -> int:
+    """Greet user."""
+    return f"Hello {user_name}!"
+
+tools = [greet]
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
+)
+```
+
+!!! Warning "Avoid infinite loops"
+
+    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
+
+    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
+    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
+
+## Handle tool errors
+
+By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
+
+=== "Enable error handling (default)"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # Run with error handling (default)
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[multiply]
+    )
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+=== "Disable error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=False  # (1)!
+    )
+    agent_no_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_no_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+=== "Custom error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=(
+            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
+        )
+    )
+    agent_custom_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_custom_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
+
+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
+
+* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
+* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
+
+## Prebuilt tools
+
+LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+
+You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
+
+Some commonly used tool categories include:
+
+- **Search**: Bing, SerpAPI, Tavily
+- **Code interpreters**: Python REPL, Node.js REPL
+- **Databases**: SQL, MongoDB, Redis
+- **Web data**: Web scraping and browsing
+- **APIs**: OpenWeatherMap, NewsAPI, and others
+
+These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
+

docs/docs/agents/ui.md

@@ -0,0 +1,40 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# UI
+
+You can use a prebuilt chat UI for interacting with any LangGraph agent through the [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui). Using the [deployed version](https://agentchat.vercel.app) is the quickest way to get started, and allows you to interact with both local and deployed graphs.
+
+## Run agent in UI
+
+First, set up LangGraph API server [locally](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
+
+Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):
+
+<video controls src="../assets/base-chat-ui.mp4" type="video/mp4"></video>
+
+!!! Tip
+
+    UI has out-of-box support for rendering tool calls, and tool result messages. To customize what messages are shown, see the [Hiding Messages in the Chat](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#hiding-messages-in-the-chat) section in the Agent Chat UI documentation.
+
+## Add human-in-the-loop
+
+Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](./deployment.md) guide) with this [agent implementation](./human-in-the-loop.md#using-with-agent-inbox):
+
+<video controls src="../assets/interrupt-chat-ui.mp4" type="video/mp4"></video>
+
+!!! Important
+
+    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
+
+You can also use generative UI in the Agent Chat UI.
+
+Generative UI allows you to define [React](https://react.dev/) components, and push them to the UI from the LangGraph server. For more documentation on building generative UI LangGraph agents, read [these docs](https://langchain-ai.github.io/langgraph/cloud/how-tos/generative_ui_react/).

docs/docs/cloud/deployment/cloud.md

@@ -107,13 +107,13 @@ After installing and authorizing LangChain's `hosted-langserve` GitHub app, repo
 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  | 34.13.192.67   |
-| 34.145.102.123 | 34.147.105.64  |
-| 34.169.45.153  | 34.90.22.166   |
-| 34.82.222.17   | 34.147.36.213  |
-| 35.227.171.135 | 34.32.137.113  | 
-| 34.169.88.30   | 34.91.238.184  |
-| 34.19.93.202   | 35.204.101.241 |
-| 34.19.34.50    | 35.204.48.32   |
+| 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   |

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -12,19 +12,13 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 
          helm repo add kedacore https://kedacore.github.io/charts 
          helm install keda kedacore/keda --namespace keda --create-namespace
-
-1. Ingress Configuration (recommended)
-    1. Install `Ingress Nginx` to serve as a reverse proxy for your deployment.
-
-            helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
-            helm repo update
-            helm install ingress-nginx ingress-nginx/ingress-nginx
-
-    1. Provision a root domain that will suffix all domains for your workloads (e.g. `us.langgraph.app`).
-    1. Provision wildcard certificates to terminate TLS for your deployments.
-    1. Note: If this step is skipped, you will need to provision domains/certs for each of your deployments.
-
+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
 
 ## Setup
 
@@ -44,13 +38,12 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
           pullPolicy: IfNotPresent
           tag: "aa9dff4"
 
-1. In your `values.yaml` file, enable the `langgraphPlatform` option.
-
+1. In your `values.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
         config:
           langgraphPlatform:
             enabled: true
             langgraphPlatformLicenseKey: "YOUR_LANGGRAPH_PLATFORM_LICENSE_KEY"
-            rootDomain: "YOUR_ROOT_DOMAIN"
+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).

docs/docs/cloud/deployment/setup_javascript.md

@@ -40,6 +40,14 @@ Example `package.json` file:
 }
 ```
 
+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
@@ -82,14 +90,10 @@ import { ChatOpenAI } from "@langchain/openai";
 import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";
 import { ToolNode } from "@langchain/langgraph/prebuilt";
 
-const tools = [
-  new TavilySearchResults({ maxResults: 3, }),
-];
+const tools = [new TavilySearchResults({ maxResults: 3 })];
 
 // Define the function that calls the model
-async function callModel(
-  state: typeof MessagesAnnotation.State,
-) {
+async function callModel(state: typeof MessagesAnnotation.State) {
   /**
    * Call the LLM powering our agent.
    * Feel free to customize the prompt, model, and other logic!
@@ -101,9 +105,9 @@ async function callModel(
   const response = await model.invoke([
     {
       role: "system",
-      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`
+      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`,
     },
-    ...state.messages
+    ...state.messages,
   ]);
 
   // MessagesAnnotation supports returning a single message or array of messages
@@ -141,10 +145,7 @@ const workflow = new StateGraph(MessagesAnnotation)
     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__"
-    ],
+    ["tools", "__end__"]
   )
   // This means that after `tools` is called, `callModel` node is called next.
   .addEdge("tools", "callModel");
@@ -155,6 +156,7 @@ export const graph = workflow.compile();
 ```
 
 !!! info "Assign `CompiledGraph` to Variable"
+
     The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
 
 Example file directory:
@@ -193,6 +195,7 @@ Example `langgraph.json` file:
 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 API 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

docs/docs/cloud/how-tos/clone_traces_studio.md

@@ -22,7 +22,7 @@ To support this, LangGraph Studio, in combination with LangSmith, allows you to
 
 First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".
 
-![Run in Studio](../img/run_in_studio.png){width=1200}
+![Run in Studio](img/run_in_studio.png){width=1200}
 
 This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.
 

docs/docs/cloud/how-tos/configurable_headers.md

@@ -0,0 +1,86 @@
+# 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 (see the [configuration how-to](../../how-tos/configuration.ipynb) for more details on how to access within your graph).
+
+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"}
+}
+```
+
+For more examples on how to use runtime configuration, check out the [configuration how-to](../../how-tos/configuration.ipynb).
+
+### 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.

docs/docs/cloud/how-tos/generative_ui_react.md

@@ -207,18 +207,6 @@ Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI
 
 ## How-to guides
 
-### 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>}
-/>
-```
-
 ### 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.
@@ -235,6 +223,18 @@ const 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.
@@ -316,9 +316,9 @@ const WeatherComponent = (props: { city: string }) => {
 };
 ```
 
-### Streaming UI updates before the node execution is finished
+### Streaming UI messages from the server
 
-You can stream UI updates before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook.
+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";
@@ -335,6 +335,162 @@ const { thread, submit } = useStream({
 });
 ```
 
+Then you can pushing 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.

docs/docs/cloud/how-tos/human_in_the_loop_breakpoint.md

@@ -1,12 +1,16 @@
-# How to Add Breakpoints
+# How to add static breakpoints
 
-When creating LangGraph agents, it is often nice to add a human-in-the-loop component.
-This can be helpful when giving them access to tools.
-Often in these situations you may want to manually approve an action before taking.
+!!! tip "Prerequisites"
 
-This can be in several ways, but the primary supported way is to add an "interrupt" before a node is executed.
-This interrupts execution at that node.
-You can then resume from that spot to continue.  
+    This guide assumes familiarity with the following concepts:
+
+    * [Breakpoints](../../concepts/breakpoints.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+    
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). [Breakpoints](../../concepts/low_level.md#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).
+
+Breakpoints are built on top of LangGraph [checkpoints](../../concepts/low_level.md#persistence), which save the graph's state after each node execution. Checkpoints are saved in [threads](../../concepts/low_level.md#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.
 
 ## Setup
 

docs/docs/cloud/how-tos/human_in_the_loop_review_tool_calls.md

@@ -1,6 +1,14 @@
-# Review Tool Calls
+# How to review tool calls
 
-Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:
+!!! tip "Prerequisites"
+
+    This guide assumes familiarity with the following concepts:
+
+    * [Tool calling](https://python.langchain.com/docs/concepts/tool_calling/)
+    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:
 
 - A tool call to execute SQL, which will then be run by the tool
 - A tool call to generate a summary, which will then be saved to the State of the graph
@@ -11,13 +19,46 @@ There are typically a few different interactions you may want to do here:
 
 1. Approve the tool call and continue
 2. Modify the tool call manually and then continue
-3. Give natural language feedback, and then pass that back to the agent instead of continuing
+3. Give natural language feedback, and then pass that back to the agent
 
-We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above
+
+We can implement these in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input:
+
+
+```python
+def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
+    # this is the value we'll be providing via Command(resume=<human_review>)
+    human_review = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface tool calls for review
+            "tool_call": tool_call
+        }
+    )
+
+    review_action, review_data = human_review
+
+    # Approve the tool call and continue
+    if review_action == "continue":
+        return Command(goto="run_tool")
+
+    # Modify the tool call manually and then continue
+    elif review_action == "update":
+        ...
+        updated_msg = get_updated_msg(review_data)
+        return Command(goto="run_tool", update={"messages": [updated_message]})
+
+    # Give natural language feedback, and then pass that back to the agent
+    elif review_action == "feedback":
+        ...
+        feedback_msg = get_feedback_msg(review_data)
+        return Command(goto="call_llm", update={"messages": [feedback_msg]})
+
+```
 
 ## Setup
 
-We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 
+We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb). Once this graph is hosted, we are ready to invoke it and wait for user input. 
 
 ### SDK initialization
 
@@ -54,122 +95,9 @@ First, we need to setup our client so that we can communicate with our hosted gr
       --data '{}'
     ```
 
-## Example with no review
-
-Let's look at an example when no review is required (because no tools are called)
-
-=== "Python"
-
-    ```python
-    input = { 'messages':[{ "role":"user", "content":"hi!" }] }
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-        interrupt_before=["action"],
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const input = { "messages": [{ "role": "user", "content": "hi!" }] };
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-        interruptBefore: ["action"],
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        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\": {\"messages\": [{\"role\": \"human\", \"content\": \"hi!\"}]},
-       \"stream_mode\": [
-         \"updates\"
-       ],
-       \"interrupt_before\": [\"action\"]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
-    ```
-
-Output:
-
-    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}]}
-    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}, {'content': [{'text': "Hello! Welcome. How can I assist you today? Is there anything specific you'd like to know or any information you're looking for?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d65e07fb-43ff-4d98-ab6b-6316191b9c8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 355, 'output_tokens': 31, 'total_tokens': 386}}]}
-
-
-If we check the state, we can see that it is finished
-
-=== "Python"
-
-    ```python
-    state = await client.threads.get_state(thread["thread_id"])
-
-    print(state['next'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-
-    console.log(state.next);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
-    ```
-
-Output:
-
-    []
-
 ## Example of approving tool
 
-Let's now look at what it looks like to approve a tool call. Note that we don't need to pass an interrupt to our streaming calls because the graph (defined [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage)) was already compiled with an interrupt before the `human_review_node`.
+First, let's run the agent with an input that requires tool calls with approval:
 
 === "Python"
 
@@ -180,6 +108,7 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
         thread["thread_id"],
         assistant_id,
         input=input,
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -195,6 +124,7 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
       assistantId,
       {
         input: input,
+        streamMode: "updates"
       }
     );
 
@@ -213,75 +143,32 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
 Output:
 
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}]}
+    {'call_llm': {'messages': [{'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01Tdfufy4nZYXMbVZvgyNbhc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 379, 'output_tokens': 66}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-a33434b2-f5ca-40c6-98e2-6288d349d4ce-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 379, 'output_tokens': 66, 'total_tokens': 445, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:9caf42cf-1371-7213-a331-e6fe5d026be8'], 'when': 'during'}]}
 
-
-If we now check, we can see that it is waiting on human review:
+To approve the tool call, we need to let `human_review_node` know what value to use for the `human_review` variable we defined inside the node. We can provide this value by invoking the graph with a `Command(resume=<human_review>)` input.  Since we're approving the tool call, we'll provide `resume` value of `{"action": "continue"}` to navigate to `run_tool` node:
 
 === "Python"
 
     ```python
-    state = await client.threads.get_state(thread["thread_id"])
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
 
-    print(state['next'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-
-    console.log(state.next);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-        --url <DELPOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
-    ```
-
-Output:
-
-    ['human_review_node']
-
-To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs.
-
-=== "Python"
-
-    ```python
     async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id,
-        input=None,
-        stream_mode="values",
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -294,8 +181,9 @@ To approve the tool call, we can just continue the thread with no edits. To do t
       thread["thread_id"],
       assistantId,
       {
-        input: null,
-        streamMode: "values",
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
       }
     );
 
@@ -313,34 +201,21 @@ To approve the tool call, we can just continue the thread with no edits. To do t
      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
      --header 'Content-Type: application/json' \
      --data "{
-       \"assistant_id\": \"agent\"
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
 Output:
 
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5d5fd0f1-a939-447e-801a-9aaa812322d3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514}}]}
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01142G3woscA8JjFTLdqymtn'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01JJE9AtT4a9Lob91RRiW9rU', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 458, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-5e8d80b5-c46a-4aad-af37-b01f8bb15963-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 458, 'output_tokens': 18, 'total_tokens': 476, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
 
 ## Edit Tool Call
 
@@ -355,7 +230,7 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
         thread["thread_id"],
         assistant_id,
         input=input,
-        stream_mode="values",
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -371,7 +246,7 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
       assistantId,
       {
         input: input,
-        streamMode: "values",
+        streamMode: "updates",
       }
     );
 
@@ -390,84 +265,35 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
-Output:
+To do this, we will use `Command` with a different resume value of `{"action": "update", "data": <tool call args>}`. This will do the following:
 
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6326da9f-6061-4e12-8586-482e32ab4cab', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-
-To do this, we first need to update the state. We can do this by passing a message in with the **same** id of the message we want to overwrite. This will have the effect of **replacing** that old message. Note that this is only possible because of the **reducer** we are using that replaces messages with the same ID - read more about that [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state).
+* combine existing tool call with user-provided tool call arguments and update the existing AI message with the new tool call
+* navigate to `run_tool` node with the updated AI message and continue execution
 
 
 === "Python"
 
     ```python
-    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
-    state = await client.threads.get_state(thread['thread_id'])
-    print("Current State:")
-    print(state['values'])
-    print("\nCurrent Tool Call ID:")
-    current_content = state['values']['messages'][-1]['content']
-    current_id = state['values']['messages'][-1]['id']
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
-    print(tool_call_id)
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
 
-    # We now need to construct a replacement tool call.
-    # We will change the argument to be `San Francisco, USA`
-    # Note that we could change any number of arguments or tool names - it just has to be a valid one
-    new_message = {
-        "role": "assistant", 
-        "content": current_content,
-        "tool_calls": [
-            {
-                "id": tool_call_id,
-                "name": "weather_search",
-                "args": {"city": "San Francisco, USA"}
-            }
-        ],
-        # This is important - this needs to be the same as the message you replacing!
-        # Otherwise, it will show up as a separate message
-        "id": current_id
-    }
-    await client.threads.update_state(
-        # This is the config which represents this thread
-        thread['thread_id'], 
-        # This is the updated value we want to push
-        {"messages": [new_message]}, 
-        # We push this update acting as our human_review_node
-        as_node="human_review_node"
-    )
-
-    print("\nResuming Execution")
-    # Let's now continue executing from here
     async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id,
-        input=None,
+        # highlight-next-line
+        command=Command(
+            # highlight-next-line
+            resume={"action": "update", "data": {"city": "San Francisco, USA"}}
+            # highlight-next-line
+        ),
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -476,51 +302,21 @@ To do this, we first need to update the state. We can do this by passing a messa
 === "Javascript"
 
     ```js
-    const state = await client.threads.getState(thread.thread_id);
-    console.log("Current State:");
-    console.log(state.values);
-
-    console.log("\nCurrent Tool Call ID:");
-    const lastMessage = state.values.messages[state.values.messages.length - 1];
-    const currentContent = lastMessage.content;
-    const currentId = lastMessage.id;
-    const toolCallId = lastMessage.tool_calls[0].id;
-    console.log(toolCallId);
-
-    // Construct a replacement tool call
-    const newMessage = {
-      role: "assistant",
-      content: currentContent,
-      tool_calls: [
-        {
-          id: toolCallId,
-          name: "weather_search",
-          args: { city: "San Francisco, USA" }
-        }
-      ],
-      // Ensure the ID is the same as the message you're replacing
-      id: currentId
-    };
-
-    await client.threads.updateState(
-      thread.thread_id,  // Thread ID
-      {
-        values: { "messages": [newMessage] },  // Updated message
-        asNode: "human_review_node"
-      }  // Acting as human_review_node
-    );
-
-    console.log("\nResuming Execution");
-    // Continue executing from here
-    const streamResponseResumed = client.runs.stream(
+    const streamResponse = client.runs.stream(
       thread["thread_id"],
       assistantId,
       {
-        input: null,
+        // highlight-next-line
+        command: {
+          // highlight-next-line
+          resume: { "action": "update", "data": { "city": "San Francisco, USA" } }
+          // highlight-next-line
+        },
+        streamMode: "updates"
       }
     );
 
-    for await (const chunk of streamResponseResumed) {
+    for await (const chunk of streamResponse) {
       if (chunk.data && chunk.event !== "metadata") {
         console.log(chunk.data);
       }
@@ -531,76 +327,37 @@ To do this, we first need to update the state. We can do this by passing a messa
 
     ```bash
     curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-    --header 'Content-Type: application/json' \
-    --data "{
-        \"values\": { \"messages\": [$(curl --request GET \
-            --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
-            jq -c '{
-            role: "assistant",
-            content: .values.messages[-1].content,
-            tool_calls: [
-                {
-                id: .values.messages[-1].tool_calls[0].id,
-                name: "weather_search",
-                args: { city: "San Francisco, USA" }
-                }
-            ],
-            id: .values.messages[-1].id
-            }')
-        ]},
-        \"as_node\": \"human_review_node\"
-    }" && echo "Resuming Execution" && curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "assistant_id": "agent"
-    }' | \
-    sed 's/\r$//' | \
-    awk '
-    /^event:/ {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-        sub(/^event: /, "", $0)
-        event_type = $0
-        data_content = ""
-    }
-    /^data:/ {
-        sub(/^data: /, "", $0)
-        data_content = $0
-    }
-    END {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-    }
-    '
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"update\", \"data\": { \"city\": \"San Francisco, USA\" } }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
 Output:
 
-    Current State:
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-    Current Tool Call ID:
-    toolu_01VzagzsUGZsNMwW1wHkcw7h
-
-    Resuming Execution
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nBased on the search result, the weather in San Francisco is sunny! It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d90ce97a-39f9-4330-985e-67c5f351a0c5', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 455, 'output_tokens': 52, 'total_tokens': 507}}]}
+    {'human_review_node': {'messages': [{'role': 'ai', 'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'tool_calls': [{'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}}], 'id': 'run-b07f0c35-4e93-43a5-9b48-363767ada3ca-0'}]}}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01De5HurjNUMwMUpfRtMLbX1', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 460, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-85e2aaaa-6f61-4fa0-b594-b6e57129d7e7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 460, 'output_tokens': 18, 'total_tokens': 478, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
 
 ## Give feedback to a tool call
 
-Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock **RESULT** of the tool call.
+Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert this feedback as a mock **RESULT** of the tool call.
 
 There are multiple ways to do this:
 
-You could add a new message to the state (representing the "result" of a tool call)
-You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback
-Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_node` and how it handles different types of messages.
+1. You could add a new message to the state (representing the "result" of a tool call)
+2. You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback
 
-For this example we will just add a single tool call representing the feedback. Let's see this in action!
+Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_review_node` and how it handles different types of messages.
+
+For this example we will just add a single tool call representing the feedback (see `human_review_node` implementation). Let's see this in action!
 
 === "Python"
 
@@ -611,6 +368,7 @@ For this example we will just add a single tool call representing the feedback.
         thread["thread_id"],
         assistant_id,
         input=input,
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -626,6 +384,7 @@ For this example we will just add a single tool call representing the feedback.
       assistantId,
       {
         input: input,
+        streamMode: "updates"
       }
     );
 
@@ -644,74 +403,35 @@ For this example we will just add a single tool call representing the feedback.
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
-Output:
+To do this, we will use `Command` with a different resume value of `{"action": "feedback", "data": <feedback string>}`. This will do the following:
 
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-4911ac27-3d7c-4edf-a3ca-c2908e3922eb', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-To do this, we first need to update the state. We can do this by passing a message in with the same **tool call id** of the tool call we want to respond to. Note that this is a **different*** ID from above
+* create a new tool message that combines existing tool call from LLM with the with user-provided feedback as content
+* navigate to `call_llm` node with the updated tool message and continue execution
 
 === "Python"
 
     ```python
-    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
-    state = await client.threads.get_state(thread['thread_id'])
-    print("Current State:")
-    print(state['values'])
-    print("\nCurrent Tool Call ID:")
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
-    print(tool_call_id)
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
 
-    # We now need to construct a replacement tool call.
-    # We will change the argument to be `San Francisco, USA`
-    # Note that we could change any number of arguments or tool names - it just has to be a valid one
-    new_message = {
-        "role": "tool", 
-        # This is our natural language feedback
-        "content": "User requested changes: pass in the country as well",
-        "name": "weather_search",
-        "tool_call_id": tool_call_id
-    }
-    await client.threads.update_state(
-        # This is the config which represents this thread
-        thread['thread_id'], 
-        # This is the updated value we want to push
-        {"messages": [new_message]}, 
-        # We push this update acting as our human_review_node
-        as_node="human_review_node"
-    )
-
-    print("\nResuming execution")
-    # Let's now continue executing from here
     async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id,
-        input=None,
-        stream_mode="values",
+        # highlight-next-line
+        command=Command(
+            resume={
+                "action": "feedback", 
+                "data": "User requested changes: use <city, country> format for location"
+            }
+        ),
+        stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -720,133 +440,22 @@ To do this, we first need to update the state. We can do this by passing a messa
 === "Javascript"
 
     ```js
-    const state = await client.threads.getState(thread.thread_id);
-    console.log("Current State:");
-    console.log(state.values);
-
-    console.log("\nCurrent Tool Call ID:");
-    const lastMessage = state.values.messages[state.values.messages.length - 1];
-    const toolCallId = lastMessage.tool_calls[0].id;
-    console.log(toolCallId);
-
-    // Construct a replacement tool call
-    const newMessage = {
-      role: "tool",
-      content: "User requested changes: pass in the country as well",
-      name: "weather_search",
-      tool_call_id: toolCallId,
-    };
-
-    await client.threads.updateState(
-      thread.thread_id,  // Thread ID
-      {
-        values: { "messages": [newMessage] },  // Updated message
-        asNode: "human_review_node"
-      }  // Acting as human_review_node
-    );
-
-    console.log("\nResuming Execution");
-    // Continue executing from here
-    const streamResponseEdited = client.runs.stream(
+    const streamResponse = client.runs.stream(
       thread["thread_id"],
       assistantId,
       {
-        input: null,
-        streamMode: "values",
-        interruptBefore: ["action"],
+        // highlight-next-line
+        command: {
+          resume: {
+            "action": "feedback", 
+            "data": "User requested changes: use <city, country> format for location"
+          }
+        },
+        streamMode: "updates"
       }
     );
 
-    for await (const chunk of streamResponseEdited) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-    --header 'Content-Type: application/json' \
-    --data "{
-        \"values\": { \"messages\": [$(curl --request GET \
-            --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
-            jq -c '{
-            role: "tool",
-            content: "User requested changes: pass in the country as well",
-            name: "get_weather",
-            tool_call_id: .values.messages[-1].id.tool_calls[0].id
-            }')
-        ]},
-        \"as_node\": \"human_review_node\"
-    }" && echo "Resuming Execution" && curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "assistant_id": "agent"
-    }' | \
-    sed 's/\r$//' | \
-    awk '
-    /^event:/ {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-        sub(/^event: /, "", $0)
-        event_type = $0
-        data_content = ""
-    }
-    /^data:/ {
-        sub(/^data: /, "", $0)
-        data_content = $0
-    }
-    END {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-    }
-    '
-    ```
-
-
-Output:
-
-    Current State:
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-    Current Tool Call ID:
-    toolu_01NNw18j57GEGPZvsa9f1wvX
-
-    Resuming execution
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}]}
-
-We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue
-
-=== "Python"
-
-    ```python
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=None,
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const streamResponseResumed = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: null,
-      }
-    );
-
-    for await (const chunk of streamResponseResumed) {
+    for await (const chunk of streamResponse) {
       if (chunk.data && chunk.event !== "metadata") {
         console.log(chunk.data);
       }
@@ -860,31 +469,81 @@ We can see that we now get to another breakpoint - because it went back to the m
      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
      --header 'Content-Type: application/json' \
      --data "{
-       \"assistant_id\": \"agent\"
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"feedback\", \"data\": \"User requested changes: use <city, country> format for location\" }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+
+Output:
+
+    {'human_review_node': {'messages': [{'role': 'tool', 'content': 'User requested changes: use <city, country> format for location', 'name': 'weather_search', 'tool_call_id': 'toolu_01RkPHCjpfoUvPAktaq4Cqhm'}]}}
+    {'call_llm': {'messages': [{'content': [{'text': 'Let me try that again with the correct format:', 'type': 'text'}, {'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01EBan969yY5f6iGk6sPgKcj', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 469, 'output_tokens': 68}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-64bbc255-d126-4db0-8ae5-3197cf29bed1-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 469, 'output_tokens': 68, 'total_tokens': 537, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:e9856878-e28c-5dd1-d353-4d83aa1a3a2b'], 'when': 'during'}]}
+
+We can see that we now get to another interrupt - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue.
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        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\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
     ```
 
 Output:
 
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6a857bb1-f65b-4b86-93d6-c025e003c777', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 557, 'output_tokens': 38, 'total_tokens': 595}}]}
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01Rdrag6cVufHZG26BwVaiE7'}]}}
+    {'call_llm': {'messages': [{'content': 'The weather in San Francisco is sunny!', 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_013WTDHhbg8WiYLiQ9n2CaTk', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 550, 'output_tokens': 12}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-b6c815f0-989a-47cf-b150-33e3bbc4eab7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 550, 'output_tokens': 12, 'total_tokens': 562, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}

docs/docs/cloud/how-tos/human_in_the_loop_user_input.md

@@ -1,16 +1,16 @@
-# How to Wait for User Input
+# How to wait for user input using `interrupt`
 
-One of the main human-in-the-loop interaction patterns is waiting for human input. A key use case involves asking the user clarifying questions. One way to accomplish this is simply go to the `END` node and exit the graph. Then, any user response comes back in as fresh invocation of the graph. This is basically just creating a chatbot architecture.
+!!! tip "Prerequisites"
 
-The issue with this is it is tough to resume back in a particular point in the graph. Often times the agent is halfway through some process, and just needs a bit of a user input. Although it is possible to design your graph in such a way where you have a `conditional_entry_point` to route user messages back to the right place, that is not super scalable (as it essentially involves having a routing function that can end up almost anywhere).
+    This guide assumes familiarity with the following concepts:
 
-A separate way to do this is to have a node explicitly for getting user input. This is easy to implement in a notebook setting - you just put an `input()` call in the node. But that isn't exactly production ready.
+    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+    
 
-Luckily, LangGraph makes it possible to do similar things in a production way. The basic idea is:
+**Human-in-the-loop (HIL)** interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding.
 
-- Set up a node that represents human input. This can have specific incoming/outgoing edges (as you desire). There shouldn't actually be any logic inside this node.
-- Add a breakpoint before the node. This will stop the graph before this node executes (which is good, because there's no real logic in it anyways)
-- Use `.update_state` to update the state of the graph. Pass in whatever human response you get. The key here is to use the `as_node` parameter to apply this update **as if you were that node**. This will have the effect of making it so that when you resume execution next it resumes as if that node just acted, and not from the beginning.
+We can implement this in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input.
 
 ## Setup
 
@@ -54,7 +54,7 @@ First, we need to setup our client so that we can communicate with our hosted gr
 
 ### Initial invocation
 
-Now, let's invoke our graph by interrupting before `ask_human` node:
+Now, let's invoke our graph.
 
 === "Python"
 
@@ -73,7 +73,6 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
         assistant_id,
         input=input,
         stream_mode="updates",
-        interrupt_before=["ask_human"],
     ):
         if chunk.data and chunk.event != "metadata": 
             print(chunk.data)
@@ -95,7 +94,6 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
       {
         input: input,
         streamMode: "updates",
-        interruptBefore: ["ask_human"]
       }
     );
 
@@ -115,117 +113,34 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
      --data "{
        \"assistant_id\": \"agent\",
        \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Ask the user where they are, then look up the weather there\"}]},
-       \"interrupt_before\": [\"ask_human\"],
        \"stream_mode\": [
          \"updates\"
        ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+     }"
     ```
     
 Output:
 
-    {'agent': {'messages': [{'content': [{'text': "Certainly! I'll use the AskHuman function to ask the user about their location, and then I'll use the search function to look up the weather for that location. Let's start by asking the user where they are.", 'type': 'text'}, {'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR', 'input': {'question': 'Where are you currently located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-a8422215-71d3-4093-afb4-9db141c94ddb', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you currently located?'}, 'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
+    {'agent': {'messages': [{'content': [{'text': "I'll help you ask the user about their location and then search for weather information.", 'type': 'text'}, {'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01UBEdS6UvuFMetdokNsykVG', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 438, 'output_tokens': 76}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-1b1210d8-39e0-4607-9f0e-0ea932d28d5c-0', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you located?'}, 'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 438, 'output_tokens': 76, 'total_tokens': 514, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': 'Where are you located?', 'resumable': True, 'ns': ['ask_human:2d41f894-f297-211e-9bfe-1d162ecba54a'], 'when': 'during'}]}
 
+You can see that our graph got interrupted inside the `ask_human` node, which is now waiting for a `location` to be provided.
 
-### Adding user input to state
-
-We now want to update this thread with a response from the user. We then can kick off another run.
-
-Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call.
+### Providing human input
 
+We can provide human input (`location`) by invoking the graph with a `Command(resume="<location>")`:
 
 === "Python"
 
     ```python
-    state = await client.threads.get_state(thread['thread_id'])
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
 
-    # We now create the tool call with the id and the response we want
-    tool_message = [{"tool_call_id": tool_call_id, "type": "tool", "content": "san francisco"}]
-
-    await client.threads.update_state(thread['thread_id'], {"messages": tool_message}, as_node="ask_human")
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-    const toolCallId = state.values.messages[state.values.messages.length - 1].tool_calls[0].id;
-
-    // We now create the tool call with the id and the response we want
-    const toolMessage = [
-      {
-        tool_call_id: toolCallId,
-        type: "tool",
-        content: "san francisco"
-      }
-    ];
-
-    await client.threads.updateState(
-      thread["thread_id"],
-      { values: { messages: toolMessage } },
-      { asNode: "ask_human" }
-    );
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-     | jq -r '.values.messages[-1].tool_calls[0].id' \
-     | sh -c '
-         TOOL_CALL_ID="$1"
-         
-         # Construct the JSON payload
-         JSON_PAYLOAD=$(printf "{\"messages\": [{\"tool_call_id\": \"%s\", \"type\": \"tool\", \"content\": \"san francisco\"}], \"as_node\": \"ask_human\"}" "$TOOL_CALL_ID")
-         
-         # Send the updated state
-         curl --request POST \
-              --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-              --header "Content-Type: application/json" \
-              --data "${JSON_PAYLOAD}"
-     ' _ 
-    ```
-
-Output:
-
-    {'configurable': {'thread_id': 'a9f322ae-4ed1-41ec-942b-38cb3d342c3a',
-    'checkpoint_ns': '',
-    'checkpoint_id': '1ef58e97-a623-63dd-8002-39a9a9b20be3'}}
-
-
-### Invoking after receiving human input
-
-We can now tell the agent to continue. We can just pass in None as the input to the graph, since no additional input is needed:
-
-=== "Python"
-
-    ```python
     async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id,
-        input=None,
+        # highlight-next-line
+        command=Command(resume="san francisco"),
         stream_mode="updates",
     ):
         if chunk.data and chunk.event != "metadata": 
@@ -238,7 +153,8 @@ We can now tell the agent to continue. We can just pass in None as the input to
       thread["thread_id"],
       assistantId,
       {
-        input: null,
+        // highlight-next-line
+        command: { resume: "san francisco" },
         streamMode: "updates"
       }
     );
@@ -253,40 +169,23 @@ We can now tell the agent to continue. We can just pass in None as the input to
 === "CURL"
 
     ```bash
-    curl --request POST \                                                                             
+    curl --request POST \
      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": \"san francisco\"
+       },
        \"stream_mode\": [
          \"updates\"
        ]
-     }"| \ 
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+     }"
     ```
 
 Output:
 
-    {'agent': {'messages': [{'content': [{'text': "Thank you for letting me know that you're in San Francisco. Now, I'll use the search function to look up the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-241baed7-db5e-44ce-ac3c-56431705c22b', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-    {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '8b699b95-8546-4557-8e66-14ea71a15ed8', 'tool_call_id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. It's a beautiful day in the city! \n\nHowever, I should note that the search result included an unusual comment about Gemini zodiac signs. This appears to be either a joke or potentially irrelevant information added by the search engine. For accurate and detailed weather information, you might want to check a reliable weather service or app for San Francisco.\n\nIs there anything else you'd like to know about the weather or San Francisco?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-b4d7309f-f849-46aa-b6ef-475bcabd2be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
+    {'ask_human': {'messages': [{'tool_call_id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool', 'content': 'san francisco'}]}}
+    {'agent': {'messages': [{'content': [{'text': 'Let me search for the weather in San Francisco.', 'type': 'text'}, {'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'input': {'query': 'current weather in san francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_0152YFm7DtnzfZQuiMUzaSsw', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 527, 'output_tokens': 67}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f509b5b2-eb30-4200-a8da-fa79ed68812a-0', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in san francisco'}, 'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 527, 'output_tokens': 67, 'total_tokens': 594, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'action': {'messages': [{'content': "I looked up: current weather in san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'cbd0f623-cc12-48a2-8c18-3cbb943e46e0', 'tool_call_id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'artifact': None, 'status': 'success'}]}}
+    {'agent': {'messages': [{'content': "Based on the search results, it's currently sunny in San Francisco. Would you like any specific details about the weather forecast?", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01FhzXj72CehBYkJGX69vsBc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 639, 'output_tokens': 29}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f48e818e-dd88-415e-9a0b-4a958498b553-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 639, 'output_tokens': 29, 'total_tokens': 668, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}

docs/docs/cloud/how-tos/interrupt_concurrent.md

@@ -1,4 +1,4 @@
-# Interrupt
+# 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).
 

docs/docs/cloud/how-tos/iterate_graph_studio.md

@@ -119,11 +119,11 @@ With this set up, running your graph and viewing in LangGraph Studio will result
 
 **Note the configuration icon in the top right corner of the `call_model` node**:
 
-![Graph in Studio](../img/studio_graph_with_configuration.png){width=1200}
+![Graph in Studio](img/studio_graph_with_configuration.png){width=1200}
 
 Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the `call_model` node. From here, you can save your changes and apply them to the graph. Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
 
-![Configuration modal](../img/studio_node_configuration.png){width=1200}
+![Configuration modal](img/studio_node_configuration.png){width=1200}
 
 ### Playground
 
@@ -133,7 +133,7 @@ LangGraph Studio also supports prompt engineering through an integration with th
 2. Within the thread log, any nodes that have made an LLM call will have a "View LLM Runs" button. Clicking this will open a popover with the LLM runs for that node.
 3. Select the LLM run you want to edit. This will open the LangSmith Playground with the selected LLM run.
 
-![Playground in Studio](../img/studio_playground.png){width=1200}
+![Playground in Studio](img/studio_playground.png){width=1200}
 
 From here you can edit the prompt, test different model configurations and re-run just this LLM call without having to re-run the entire graph. When you are happy with your changes, you can copy the updated prompt back into your graph.
 

docs/docs/cloud/how-tos/rollback_concurrent.md

@@ -1,4 +1,5 @@
-# Rollback
+# 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).
 

docs/docs/cloud/how-tos/stream_events.md

@@ -1,7 +1,7 @@
 # How to stream events
 
 !!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md#streaming-llm-tokens-and-events-astream_events)
+    * [Streaming](../../concepts/streaming.md#streaming-graph-outputs-stream-and-astream)
 
 This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently.
 

docs/docs/cloud/how-tos/test_local_deployment.md

@@ -1,7 +1,7 @@
 # LangGraph Studio With Local Deployment
 
 !!! warning "Browser Compatibility"
-    Viewing the studio page of a local LangGraph deployment does not work in Safari. Use Chrome instead.
+    Safari blocks `localhost` connections to Studio. To work around this, start the server with `--tunnel` and you’ll be able to access Studio from Safari via a secure tunnel.
 
 ## Setup
 

docs/docs/cloud/how-tos/use_stream_react.md

@@ -476,7 +476,7 @@ The `useStream()` hook provides several callback options to help you respond to
 - `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 [Custom events](../../concepts/streaming.md#custom) to learn how to stream custom events.
+- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../how-tos/streaming.ipynb#custom) 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

docs/docs/cloud/reference/api/openapi_control_plane.json

@@ -3,7 +3,7 @@
   "info": {
     "title": "LangGraph Control Plane API (Beta)",
     "version": "0.0.1",
-    "description": "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.\n\n### Beta\nThis API is currently in beta and may change or break without notice. This API documentation may not be up-to-date with actual API functionality.\n### Host\nhttps://api.host.langchain.com/\n\n### Authentication\nTo authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key for each request.\n\n### Versioning\nEach endpoint path is prefixed with a version (e.g. `v1`).\n\n### Quick Start\n\n1. Call `GET /{version}/projects` to retrieve the `Project` `id`. The `Project` `id` is needed in subsequent API calls.\n2. Call `POST /{version}/projects/{project_id}/revisions` to create a new `Revision` for the `Project`.\n3. Call `GET /{version}/projects/{project_id}/revisions` to get the latest `Revision` (first element in returned list). Get the `Revision` `id`.\n4. Poll for `Revision` `status` until `status` is `DEPLOYED` by calling `GET /{version}/projects/{project_id}/revisions/{revision_id}`."
+    "description": "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.\n\n### Beta\nThis API is currently in beta and may change or break without notice. This API documentation may not be up-to-date with actual API functionality.\n### Host\nhttps://api.host.langchain.com/\n\n### Authentication\nTo authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key for each request.\n\n### Versioning\nEach endpoint path is prefixed with a version (e.g. `v1`).\n\n### Quick Start\n\n1. Call `POST /{version}/projects` to create a new `Project`.\n2. Call `GET /{version}/projects` to retrieve the `Project` `id`. The `Project` `id` is needed in subsequent API calls.\n3. Call `POST /{version}/projects/{project_id}/revisions` to create a new `Revision` for the `Project`.\n4. Call `GET /{version}/projects/{project_id}/revisions` to get the latest `Revision` (first element in returned list). Get the `Revision` `id`.\n5. Poll for `Revision` `status` until `status` is `DEPLOYED` by calling `GET /{version}/projects/{project_id}/revisions/{revision_id}`."
   },
   "servers": [
     {
@@ -22,6 +22,34 @@
   ],
   "paths": {
     "/v1/projects": {
+      "post": {
+        "tags": ["Projects (v1)"],
+        "summary": "Create Project",
+        "description": "Create a new project.",
+        "operationId": "create_project_projects_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/CreateProjectRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/Project"
+                }
+              }
+            }
+          }
+        }
+      },
       "get": {
         "tags": ["Projects (v1)"],
         "summary": "List Projects",
@@ -397,6 +425,83 @@
           }
         }
       },
+      "CreateProjectRequest":{
+        "type": "object",
+        "description": "Object for creating a new project.",
+        "properties": {
+          "name": {
+            "type": "string",
+            "description": "Name of the project.",
+            "required": true
+          },
+          "lc_hosted": {
+            "type": "boolean",
+            "description": "Whether the project is hosted on LangChain's cloud (i.e. Cloud SaaS deployment option). Set to `false` for Self-Hosted Data Plane and Self-Hosted Control Plane deployment options.",
+            "default": true
+          },
+          "repo_url": {
+            "type": ["string", "null"],
+            "description": "URL of the GitHub repository to use for the project. Omit this field if creating a new project from a Docker image.",
+            "default": "null"
+          },
+          "repo_path": {
+            "type": ["string", "null"],
+            "description": "Path to `langgraph.json` configuration file. For example, `langgraph.json` or `src/langgraph.json`.\n\nIf this field is omitted or set to `null`, the previous revision's `repo_path` value is used. Set this field for deployments from a GitHub repository. Omit this field if creating a new revision from a Docker image.",
+            "default": "null"
+          },
+          "repo_commit": {
+            "type": ["string", "null"],
+            "description": "Git branch name of deployment.\n\nThis field only applies to deployments from a GitHub repository.",
+            "default": "null"
+          },
+          "env_vars": {
+            "type": "array",
+            "description": "List of environment variables or secrets.\n\nIf this field is omitted or set to `null`, the previous revision's `env_vars` value is used.",
+            "items": {
+              "$ref": "#/components/schemas/EnvVar"
+            },
+            "default": "null"
+          },
+          "host_integration_id": {
+            "type": ["string", "null"],
+            "format": "uuid",
+            "description": "Do not use."
+          },
+          "deployment_type": {
+            "type": "string",
+            "description": "Development (`dev`) or Production (`prod`) type deployment.",
+            "enum": [
+              "dev",
+              "prod"
+            ]
+          },
+          "shareable": {
+            "type": ["boolean", "null"],
+            "description": "Boolean flag to configure if a deployment is shareable through LangGraph Studio.\n\nIf this field is omitted or set to `null`, the previous revision's `shareable` value is used. This field does not apply to BYOC deployments.",
+            "default": "null"
+          },
+          "platform": {
+            "type": "object",
+            "description": "Do not use.",
+            "default": "null"
+          },
+          "image_path": {
+            "type": ["string", "null"],
+            "description": "URI of the Docker image to deploy.\n\nIf this field is omitted or set to `null`, the previous revision's `image_path` value is used. Set this field for BYOC deployments. Omit this field if creating a new revision from a GitHub repository.",
+            "default": "null"
+          },
+          "build_on_push": {
+            "type": "boolean",
+            "description": "Boolean flag to indicate if a new revision is automatically created on push to GitHub branch (`repo_branch`).\n\nThis field does not apply for BYOC deployments.",
+            "default": false
+          },
+          "container_spec": {
+            "description": "If this field is omitted or set to `null`, the previous revision's `container_spec` value is used.",
+            "$ref": "#/components/schemas/ContainerSpec",
+            "default": "null"
+          }
+        }
+      },
       "CreateRevisionRequest": {
         "type": "object",
         "description": "Object for creating a new revision.",

docs/docs/cloud/reference/cli.md

@@ -10,9 +10,6 @@ The LangGraph command line interface includes commands to build and run a LangGr
     === "Python"
         ```bash
         pip install langgraph-cli
-
-        # Install via Homebrew
-        brew install langgraph-cli
         ```
 
     === "JS"
@@ -45,14 +42,16 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Cloud 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 creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</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;">`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;">`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>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</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>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</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></ul> |
 
 === "JS"
 
@@ -298,6 +297,11 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | `--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                                                       |
 
 
@@ -321,6 +325,11 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | `--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`

docs/docs/cloud/reference/env_var.md

@@ -55,6 +55,14 @@ Set `LANGSMITH_TRACING` to `false` to disable tracing to LangSmith.
 
 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`.
+
+## `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`.
+
 ## `N_JOBS_PER_WORKER`
 
 Number of jobs per worker for the LangGraph Server task queue. Defaults to `10`.
@@ -83,9 +91,31 @@ Database Connectivity:
 
 - The custom Postgres instance must be accessible by the LangGraph Server. The user is responsible for ensuring connectivity.
 
+## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`
+
+Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool 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. 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. If not specified, the pool size defaults to 150 connections.
+
 ## `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).
+
+## `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_CLUSTER`
+
+!!! info "Only Allowed in Self-Hosted Deployments"
+    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Cloud 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`.

docs/docs/concepts/agentic_concepts.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Agent architectures
 
 Many LLM applications implement a particular control flow of steps before and / or after LLM calls. As an example, [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of documents relevant to a user question, and passes those documents to an LLM in order to ground the model's response in the provided document context. 

docs/docs/concepts/application_structure.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Application Structure
 
 !!! info "Prerequisites"
@@ -15,7 +20,7 @@ This guide shows a typical structure for a LangGraph application and shows how t
 
 To deploy using the LangGraph Platform, the following information should be provided:
 
-1. A [LangGraph API Configuration file](#configuration-file) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
+1. A [LangGraph API Configuration file](#configuration-file-concepts) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
 2. The [graphs](#graphs) that implement the logic of the application.
 3. A file that specifies [dependencies](#dependencies) required to run the application.
 4. [Environment variable](#environment-variables) that are required for the application to run.
@@ -77,7 +82,7 @@ Below are examples of directory structures for Python and JavaScript application
     The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.
 
 
-## Configuration File
+## Configuration File {#configuration-file-concepts}
 
 The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.
 
@@ -145,18 +150,18 @@ A LangGraph application may depend on other Python packages or JavaScript librar
 You will generally need to specify the following information for dependencies to be set up correctly:
 
 1. A file in the directory that specifies the dependencies (e.g., `requirements.txt`, `pyproject.toml`, or `package.json`).
-2. A `dependencies` key in the [LangGraph configuration file](#configuration-file) that specifies the dependencies required to run the LangGraph application.
-3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file).
+2. A `dependencies` key in the [LangGraph configuration file](#configuration-file-concepts) that specifies the dependencies required to run the LangGraph application.
+3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file-concepts).
 
 ## Graphs
 
-Use the `graphs` key in the [LangGraph configuration file](#configuration-file) to specify which graphs will be available in the deployed LangGraph application.
+Use the `graphs` key in the [LangGraph configuration file](#configuration-file-concepts) to specify which graphs will be available in the deployed LangGraph application.
 
 You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.
 
 ## Environment Variables
 
-If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file).
+If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file-concepts).
 
 For a production deployment, you will typically want to configure the environment variables in the deployment environment.
 

docs/docs/concepts/assistants.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Assistants
 
 !!! info "Prerequisites"

docs/docs/concepts/auth.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Authentication & Access Control
 
 LangGraph Platform provides a flexible authentication and authorization system that can integrate with most authentication schemes.

docs/docs/concepts/breakpoints.md

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Breakpoints
 
 Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.