x

docs/_scripts/add_translation.py

@@ -1,157 +1,154 @@
-"""Add typescript translation to a given markdown file."""
+"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
 
 import argparse
-import re
 
 import requests
 from langchain_anthropic import ChatAnthropic
 
+# Load reference TypeScript snippets
 URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
 response = requests.get(URL)
 response.raise_for_status()
 reference_snippets = response.text
 
-model = ChatAnthropic(model="claude-3-5-sonnet-latest")
+# Initialize model
+model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
+
+TRANSLATION_PROMPT = (
+    "You are a helpful assistant that translates Python-based technical "
+    "documentation written in Markdown to equivalent TypeScript-based documentation. "
+    "The input is a Markdown file written in mkdocs format. It contains "
+    "Python code snippets embedded in prose. "
+    "Your task is to rewrite the content by translating the Python code to "
+    "idiomatic TypeScript, using the provided TypeScript reference snippets "
+    "to ensure accurate and consistent usage (e.g., correct imports, function "
+    "names, and patterns). "
+    "Remove the original Python code and replace it with the corresponding "
+    "TypeScript version. "
+    "Do not alter the surrounding prose unless a change is necessary to "
+    "reflect differences between Python and TypeScript. "
+    "Preserve the structure and formatting of the original Markdown document. "
+    "Do not make stylistic or structural changes unless they directly support "
+    "the translation. "
+    "Use the reference TypeScript snippets as guidance whenever possible to "
+    "maintain alignment with existing conventions.\n\n"
+    f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
+)
+
+CONSOLIDATION_PROMPT = (
+    "You are a helpful assistant that consolidates parallel Python and JavaScript (TypeScript) technical documentation "
+    "written in Markdown into a single unified Markdown document. "
+    "The input consists of two documents: the first is for Python users, and the second is for JavaScript/TypeScript users. "
+    "Your task is to merge these into one Markdown file using language-specific fenced blocks to separate the content where needed. "
+    "Use the following syntax to distinguish content for each language:\n\n"
+    ":::python\n"
+    "# Python-specific content\n"
+    ":::\n\n"
+    ":::js\n"
+    "# JavaScript/TypeScript-specific content\n"
+    ":::\n\n"
+    "Follow these consolidation rules:\n"
+    "- When content (prose or code) is the same or nearly identical in both versions, include it only once—outside of any fenced block.\n"
+    "- When content differs between the Python and JS versions, wrap each version in its corresponding fenced block.\n"
+    "- Prefer **paragraph-level separation** of language-specific content. Do not combine Python and JS snippets or terminology in the same sentence or paragraph using conditional phrases.\n"
+    "  For example, avoid inline constructs like:\n"
+    "  `The :::python add_messages ::: :::js reducer ::: function...`\n"
+    "  Instead, write two distinct paragraphs:\n\n"
+    "  :::python\n"
+    "  The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.\n"
+    "  ::: \n\n"
+    "  :::js\n"
+    "  The `reducer` function in our `StateAnnotation` will append the LLM's response messages to whatever messages are already in the state.\n"
+    "  :::\n\n"
+    "- Preserve the overall structure, ordering, and formatting of the original Markdown documents.\n"
+    "- Do not rephrase or unify content unless it is logically and semantically identical.\n"
+    "- Use the fenced blocks for both prose and code as needed, and ensure output is clean, readable Markdown suitable for tools that parse these directives.\n"
+    "Your goal is to produce a cleanly merged documentation file that serves both Python and JavaScript users without redundancy, while maximizing clarity and separation of language-specific details."
+)
 
 
-def _get_tqdm():
-    try:
-        from tqdm import tqdm
-    except ImportError:
-        # If not available return a simple identity function
-        def tqdm(iterable, *args, **kwargs):
-            return iterable
-
-    return tqdm
-
-
-_tqdm = _get_tqdm()
-
-opening_pattern = re.compile(r"^\s*```python(?:\s+.*)?\s*$")
-closing_pattern = re.compile(r"^\s*```\s*$")
-
-
-def extract_python_snippets(markdown: str) -> list[str]:
-    """
-    Extract all python code blocks (including their fence lines) from the markdown content.
-    A python block is defined as any block that starts with a line containing an opening fence
-    with '```python' (optionally with extra parameters) and ends with a closing fence '```'.
-    """
-    snippets = []
-    inside_block = False
-    current_snippet = []
-
-    for line in markdown.splitlines(keepends=True):
-        if not inside_block:
-            if opening_pattern.match(line):
-                inside_block = True
-                current_snippet = [line]
-        else:
-            current_snippet.append(line)
-            if closing_pattern.match(line):
-                inside_block = False
-                snippets.append("".join(current_snippet))
-                current_snippet = []
-    return snippets
-
-
-def translate_snippet(python_snippet: str) -> str:
-    """Translate a python code block into a TypeScript code block using Langchain.
-    The response is expected to be a properly fenced TypeScript code block (i.e.
-    starting with ```typescript and ending with ```).
-    """
-    ai_message = model.invoke(
+def translate_python_to_ts(markdown_content: str) -> str:
+    response = model.invoke(
         [
             {
                 "role": "system",
-                "content": (
-                    f"You have access to the following up-to-date example TypeScript code "
-                    f"snippets that show examples of building with langgraph "
-                    f"and langchain:\n\n{reference_snippets}\n\n"
-                    "Use this context to translate the following Python code to equivalent "
-                    "TypeScript. Ensure that your output is a valid fenced TypeScript "
-                    "code block (i.e. starts with ```typescript and ends with ```)."
-                ),
-            },
-            {
-                "role": "user",
-                "content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
+                "content": TRANSLATION_PROMPT,
+                "cache_control": {"type": "ephemeral"},
             },
+            {"role": "user", "content": markdown_content},
         ]
     )
-
-    # Use a regular expression to search for a TypeScript code block in the response.
-    pattern = r"```typescript\s*(.*?)\s*```"
-    match = re.search(pattern, ai_message.content, re.DOTALL)
-    if match:
-        # Reconstruct the code block with proper fences.
-        typescript_code = match.group(1).strip()
-        return f"```typescript\n{typescript_code}\n```"
-    else:
-        raise ValueError("No TypeScript code block found in the model's response.")
+    return response.content
 
 
-def insert_translations_into_markdown(
-    markdown: str, typescript_snippets: list[str]
-) -> str:
-    """Walks through the original markdown content and, after each
-    Python snippet block, inserts the corresponding translated TypeScript snippet.
-    It assumes that the ordering of the Python snippets
-    (from extract_python_snippets) matches the order they appear in the markdown.
-    """
-    output_lines = []
-    lines = markdown.splitlines(keepends=True)
-    inside_block = False
-    snippet_index = 0
-
-    for line in lines:
-        output_lines.append(line)
-        if not inside_block and opening_pattern.match(line):
-            # We've encountered the start of a python code block.
-            inside_block = True
-        elif inside_block:
-            if closing_pattern.match(line):
-                # End of a python snippet block.
-                inside_block = False
-                if snippet_index < len(typescript_snippets):
-                    # Insert an extra newline for clarity, then the translated TypeScript snippet.
-                    output_lines.append("\n")
-                    output_lines.append(typescript_snippets[snippet_index])
-                    output_lines.append("\n")
-                    snippet_index += 1
-    return "".join(output_lines)
+def consolidate_python_and_ts(combined_content: str) -> str:
+    response = model.invoke(
+        [
+            {
+                "role": "system",
+                "content": CONSOLIDATION_PROMPT,
+                "cache_control": {"type": "ephemeral"},
+            },
+            {"role": "user", "content": combined_content},
+        ]
+    )
+    return response.content
 
 
-def main(file_path: str) -> None:
-    # Read the markdown file.
-    with open(file_path, "r") as f:
+def main(file_path: str, translate_only: bool, consolidate_only: bool) -> None:
+    with open(file_path, "r", encoding="utf-8") as f:
         markdown_content = f.read()
 
-    # 1. Extract all Python snippets.
-    python_snippets = extract_python_snippets(markdown_content)[:1]
+    if translate_only:
+        translated = translate_python_to_ts(markdown_content)
+        output_path = file_path.replace(".md", ".translated.md")
+        with open(output_path, "w", encoding="utf-8") as f:
+            f.write(translated)
+        print(f"Translated JS/TS version written to: {output_path}")
 
-    # 2. Translate each Python snippet to TypeScript.
-    typescript_snippets = []
-    # Replace with .batch() for faster translation
-    for python_snippet in _tqdm(python_snippets):
-        ts_snippet = translate_snippet(python_snippet)
-        typescript_snippets.append(ts_snippet)
+    elif consolidate_only:
+        consolidated = consolidate_python_and_ts(markdown_content)
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(consolidated)
+        print(f"Consolidated content written to: {file_path}")
 
-    # 3. Insert the TypeScript translations after their respective Python snippets.
-    updated_markdown = insert_translations_into_markdown(
-        markdown_content, typescript_snippets
-    )
-
-    # Overwrite the original markdown file with the updated content.
-    with open(file_path, "w") as f:
-        f.write(updated_markdown)
+    else:
+        # Default behavior: translate first, then consolidate both
+        translated = translate_python_to_ts(markdown_content)
+        combined = f"{markdown_content.strip()}\n\n\n{translated.strip()}"
+        consolidated = consolidate_python_and_ts(combined)
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(consolidated)
+        print(f"Translated and consolidated content written to: {file_path}")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
+        description=(
+            "Translate Python markdown to TypeScript and/or consolidate "
+            "Python-JS markdown into one file."
+        )
     )
     parser.add_argument("file_path", type=str, help="Path to the markdown file.")
+    parser.add_argument(
+        "--translate-only",
+        action="store_true",
+        help="Only generate the JS translation.",
+    )
+    parser.add_argument(
+        "--consolidate-only",
+        action="store_true",
+        help="Only consolidate pre-paired Python and JS content.",
+    )
     args = parser.parse_args()
 
-    main(args.file_path)
+    if args.translate_only and args.consolidate_only:
+        raise ValueError(
+            "Cannot use both --translate-only and --consolidate-only at the same time."
+        )
+
+    main(
+        args.file_path,
+        translate_only=args.translate_only,
+        consolidate_only=args.consolidate_only,
+    )

docs/_scripts/generate_llms_text.py

@@ -3,19 +3,21 @@
 import asyncio
 import glob
 import os
-from typing import TypedDict, List, Optional
-import pydantic
 import re
-from pydantic import BaseModel, Field
-from langchain_core.rate_limiters import InMemoryRateLimiter
+from typing import TypedDict, List, Optional
 
 import yaml
 from langchain.chat_models import init_chat_model
+from langchain_core.rate_limiters import InMemoryRateLimiter
 from mkdocs.structure.files import File
 from mkdocs.structure.pages import Page
+from pydantic import BaseModel, Field
 from yaml import SafeLoader
 
-from _scripts.notebook_hooks import _on_page_markdown_with_config
+from _scripts.notebook_hooks import (
+    _on_page_markdown_with_config,
+    _apply_conditional_rendering,
+)
 
 HERE = os.path.dirname(os.path.abspath(__file__))
 # Get source directory (parent of HERE / docs)
@@ -211,7 +213,9 @@ async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
     # Remove any items that start with http:// or https:// looking only for
     # local file at this stages.
     nav_items = [
-        item for item in nav_items if not item["url"].startswith(("http://", "https://"))
+        item
+        for item in nav_items
+        if not item["url"].startswith(("http://", "https://"))
     ]
     # Process items in parallel
     tasks = [process_single_item(item) for item in nav_items]

docs/_scripts/link_map.py

@@ -0,0 +1,6 @@
+JS_LINK_MAP = {
+    "langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
+    "create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
+    "langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
+
+}

docs/_scripts/notebook_hooks.py

@@ -16,6 +16,7 @@ from mkdocs.structure.pages import Page
 
 from _scripts.generate_api_reference_links import update_markdown_with_imports
 from _scripts.notebook_convert import convert_notebook
+from _scripts.link_map import JS_LINK_MAP
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -61,7 +62,6 @@ REDIRECT_MAP = {
     "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
     "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
     "cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
-    "cloud/concepts/threads.md": "concepts/persistence.md#threads",
     # tool calling how-tos
     "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
     "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
@@ -87,8 +87,6 @@ REDIRECT_MAP = {
     "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
     "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
     "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
-    "cloud/concepts/streaming.md": "concepts/streaming.md",
-    "agents/streaming.md": "how-tos/streaming.md",
     # prebuit redirects
     "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
@@ -110,10 +108,8 @@ REDIRECT_MAP = {
     # deployment redirects
     "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
     "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
-    "tutorials/deployment.md": "concepts/deployment_options.md",
     # assistant redirects
     "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
-    "cloud/concepts/runs.md": "concepts/assistants.md#execution",
 }
 
 
@@ -163,6 +159,62 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
     return code_block_pattern.sub(replace_code_block_header, markdown)
 
 
+def _resolve_cross_references(md_text: str, link_map: dict[str, str]) -> str:
+    """Replace [title][identifier] with [title](url) using language-specific link_map.
+
+    Args:
+        md_text: The markdown text to process.
+        link_map: mapping of identifier to URL.
+
+    Returns:
+        The processed markdown text with cross-references resolved.
+    """
+    # Pattern to match [title][identifier]
+    pattern = re.compile(r"\[([^\]]+)\]\[([^\]]+)\]")
+
+    def replace_reference(match: re.Match) -> str:
+        """Replace the matched reference with the corresponding URL."""
+        title, identifier = match.group(1), match.group(2)
+        url = link_map.get(identifier)
+
+        if url:
+            return f"[{title}]({url})"
+        else:
+            # Leave it unchanged if not found
+            return match.group(0)
+
+    return pattern.sub(replace_reference, md_text)
+
+
+def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
+    if target_language not in {"python", "js"}:
+        raise ValueError("target_language must be 'python' or 'js'")
+
+    pattern = re.compile(
+        r"(?P<indent>[ \t]*):::(?P<language>\w+)\s*\n"
+        r"(?P<content>((?:.*\n)*?))"  # Capture the content inside the block
+        r"(?P=indent):::"  # Match closing with the same indentation
+    )
+
+    def replace_conditional_blocks(match: re.Match) -> str:
+        """Keep active conditionals."""
+        language = match.group("language")
+        content = match.group("content")
+
+        if language not in {"python", "js"}:
+            # If the language is not supported, return the original block
+            return match.group(0)
+
+        if language == target_language:
+            return content
+
+        # If the language does not match, return an empty string
+        return ""
+
+    processed = pattern.sub(replace_conditional_blocks, md_text)
+    return processed
+
+
 def _highlight_code_blocks(markdown: str) -> str:
     """Find code blocks with highlight comments and add hl_lines attribute.
 
@@ -262,6 +314,20 @@ def _on_page_markdown_with_config(
     # Apply highlight comments to code blocks
     markdown = _highlight_code_blocks(markdown)
 
+    # Apply conditional rendering for code blocks
+    target_language = kwargs.get("target_language", "js")
+    markdown = _apply_conditional_rendering(markdown, target_language)
+    if target_language == "js":
+        markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
+    elif target_language == "python":
+        # Via a dedicated plugin
+        pass
+    else:
+        raise ValueError(
+            f"Unsupported target language: {target_language}. "
+            "Supported languages are 'python' and 'js'."
+        )
+
     # Add file path as an attribute to code blocks that are executable.
     # This file path is used to associate fixtures with the executable code
     # which can be used in CI to test the docs without making network requests.

docs/docs/agents/agents.md

@@ -11,6 +11,8 @@ hide:
 
 This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable** components, which are designed to help you construct agentic systems quickly and reliably.
 
+:::python
+
 ## Prerequisites
 
 Before you start this tutorial, ensure you have the following:
@@ -228,3 +230,244 @@ response["structured_response"]
 - [Deploy your agent locally](../tutorials/langgraph-platform/local-server.md)
 - [Learn more about prebuilt agents](../agents/overview.md)
 - [LangGraph Platform quickstart](../cloud/quick_start.md)
+
+:::
+
+:::js
+
+## Prerequisites
+
+Before you start this tutorial, ensure you have the following:
+
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key
+
+## 1. Install dependencies
+
+If you haven't already, install LangGraph and LangChain:
+
+```
+npm install langchain @langchain/langgraph @langchain/anthropic
+```
+
+## 2. Create an agent
+
+Use [`createReactAgent`][create_react_agent] to instantiate an agent:
+
+```ts
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { initChatModel } from "langchain/chat_models/universal";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const getWeather = tool( // (1)!
+  async (input: { city: string }) => {
+    return `It's always sunny in ${input.city}!`;
+  },
+  {
+    name: "getWeather",
+    schema: z.object({
+      city: z.string().describe("The city to get the weather for"),
+    }),
+    description: "Get weather for a given city.",
+  }
+);
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest"); // (2)!
+const agent = createReactAgent({
+  llm,
+  tools: [getWeather], // (3)!
+  prompt: "You are a helpful assistant", // (4)!
+});
+
+// Run the agent
+await agent.invoke({
+  messages: [{ role: "user", content: "what is the weather in sf" }],
+});
+```
+
+1. Define a tool for the agent to use. For more advanced tool usage and customization, check the [tools](./tools.md) page.
+2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
+3. Provide a list of tools for the model to use.
+4. Provide a system prompt (instructions) to the language model used by the agent.
+
+## 3. Configure an LLM
+
+Use [`initChatModel`](https://api.js.langchain.com/functions/langchain.chat_models_universal.initChatModel.html) to configure an LLM with specific parameters, such as temperature:
+
+```ts
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { initChatModel } from "langchain/chat_models/universal";
+
+// highlight-next-line
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest", {
+  // highlight-next-line
+  temperature: 0,
+});
+
+const agent = createReactAgent({
+  // highlight-next-line
+  llm,
+  tools: [getWeather],
+});
+```
+
+See the [models](./models.md) page for more information on how to configure LLMs.
+
+## 4. Add a custom prompt
+
+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 prompt"
+
+    Define a fixed prompt string or list of messages.
+
+    ```ts
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { initChatModel } from "langchain/chat_models/universal";
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getWeather],
+      // A static prompt that never changes
+      // highlight-next-line
+      prompt: "Never answer questions about the weather.",
+    });
+
+    await agent.invoke({
+      messages: "what is the weather in sf",
+    });
+    ```
+
+=== "Dynamic prompt"
+
+    Define a function that returns a message list based on the agent's state and configuration:
+
+    ```ts
+    import { BaseMessageLike } from "@langchain/core/messages";
+    import { RunnableConfig } from "@langchain/core/runnables";
+    import { initChatModel } from "langchain/chat_models/universal";
+    import { MessagesAnnotation } from "@langchain/langgraph";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+    const prompt = (
+      state: typeof MessagesAnnotation.State,
+      config: RunnableConfig
+    ): BaseMessageLike[] => { // (1)!
+      const userName = config.configurable?.userName;
+      const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
+      return [{ role: "system", content: systemMsg }, ...state.messages];
+    };
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getWeather],
+      // highlight-next-line
+      prompt,
+    });
+
+    await agent.invoke(
+      { messages: [{ role: "user", content: "what is the weather in sf" }] },
+      // highlight-next-line
+      { configurable: { userName: "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 `userId` or API credentials (using `config`).
+      - Internal agent state updated during a multi-step reasoning process (using `state`).
+
+      Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
+
+For more information, see [Context](./context.md).
+
+## 5. Add memory
+
+To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
+
+```ts
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { MemorySaver } from "@langchain/langgraph-checkpoint";
+import { initChatModel } from "langchain/chat_models/universal";
+
+// highlight-next-line
+const checkpointer = new MemorySaver();
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+const agent = createReactAgent({
+  llm,
+  tools: [getWeather],
+  // highlight-next-line
+  checkpointer, // (1)!
+});
+
+// Run the agent
+// highlight-next-line
+const config = { configurable: { thread_id: "1" } };
+const sfResponse = await agent.invoke(
+  { messages: [{ role: "user", content: "what is the weather in sf" }] },
+  config // (2)!
+);
+const nyResponse = await agent.invoke(
+  { messages: [{ role: "user", content: "what about new york?" }] },
+  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.
+
+For more information, see [Memory](./memory.md).
+
+## 6. Configure structured output
+
+To produce structured responses conforming to a schema, use the `responseFormat` parameter. The schema can be defined with a `zod` schema. The result will be accessible via the `structuredResponse` field.
+
+```ts
+import { z } from "zod";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { initChatModel } from "langchain/chat_models/universal";
+
+const WeatherResponse = z.object({
+  conditions: z.string(),
+});
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+const agent = createReactAgent({
+  llm,
+  tools: [getWeather],
+  // highlight-next-line
+  responseFormat: WeatherResponse, // (1)!
+});
+
+const response = await agent.invoke({
+  messages: [{ role: "user", content: "what is the weather in sf" }],
+});
+// highlight-next-line
+response.structuredResponse;
+```
+
+1. When `responseFormat` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
+
+   To provide a system prompt to this LLM, use an object `{ prompt, schema }`, e.g., `responseFormat: { prompt, schema: WeatherResponse }`.
+
+!!! Note "LLM post-processing"
+
+    Structured output requires an additional call to the LLM to format the response according to the schema.
+
+## Next steps
+
+- [Deploy your agent locally](../tutorials/langgraph-platform/local-server.md)
+- [Learn more about prebuilt agents](../agents/overview.md)
+- [LangGraph Platform quickstart](../cloud/quick_start.md)
+
+:::

docs/docs/agents/context.md

@@ -43,6 +43,8 @@ when you have values that don't change mid-run.
 Specify configuration using a key called **"configurable"** which is reserved
 for this purpose:
 
+
+:::python
 ```python
 agent.invoke(
     {"messages": [{"role": "user", "content": "hi!"}]},
@@ -50,11 +52,23 @@ agent.invoke(
     config={"configurable": {"user_id": "user_123"}}
 )
 ```
+:::
+
+:::js
+```ts
+await agent.invoke(
+  { messages: "hi!" },
+  // highlight-next-line
+  { configurable: { userId: "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
 ```python
 class CustomState(AgentState):
     # highlight-next-line
@@ -71,6 +85,29 @@ agent.invoke({
     "user_name": "Jane"
 })
 ```
+:::
+
+:::js
+```ts
+const CustomState = Annotation.Root({
+    ...MessagesAnnotation.spec,
+    userName: Annotation<string>,
+});
+
+const agent = createReactAgent({
+    // Other agent parameters...
+    // highlight-next-line
+    stateSchema: CustomState,
+})
+
+await agent.invoke(
+    // highlight-next-line
+    { messages: "hi!", userName: "Jane" }
+)
+```
+:::
+
+
 
 !!! tip "Turning on memory"
 
@@ -93,6 +130,8 @@ Common use cases:
 - Role or goal customization
 - Conditional behavior (e.g., user is admin)
 
+:::python
+
 === "Using config"
 
     ```python
@@ -162,8 +201,90 @@ Common use cases:
     })
     ```
 
+:::
+
+:::js
+=== "Using config"
+
+    ```ts
+    import { BaseMessageLike } from "@langchain/core/messages";
+    import { RunnableConfig } from "@langchain/core/runnables";
+    import { initChatModel } from "langchain/chat_models/universal";
+    import { MessagesAnnotation } from "@langchain/langgraph";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+    const prompt = (
+      state: typeof MessagesAnnotation.State,
+      // highlight-next-line
+      config: RunnableConfig
+    ): BaseMessageLike[] => {
+      // highlight-next-line
+      const userName = config.configurable?.userName;
+      const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
+      return [{ role: "system", content: systemMsg }, ...state.messages];
+    };
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getWeather],
+      // highlight-next-line
+      prompt
+    });
+
+    await agent.invoke(
+      { messages: "hi!" },
+      // highlight-next-line
+      { configurable: { userName: "John Smith" } }
+    );
+    ```
+
+=== "Using state"
+
+    ```ts
+    import { BaseMessageLike } from "@langchain/core/messages";
+    import { RunnableConfig } from "@langchain/core/runnables";
+    import { initChatModel } from "langchain/chat_models/universal";
+    import { Annotation, MessagesAnnotation } from "@langchain/langgraph";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+    const CustomState = Annotation.Root({
+      ...MessagesAnnotation.spec,
+      // highlight-next-line
+      userName: Annotation<string>,
+    });
+
+    const prompt = (
+      // highlight-next-line
+      state: typeof CustomState.State,
+    ): BaseMessageLike[] => {
+      // highlight-next-line
+      const userName = state.userName;
+      const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
+      return [{ role: "system", content: systemMsg }, ...state.messages];
+    };
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getWeather],
+      // highlight-next-line
+      prompt,
+      // highlight-next-line
+      stateSchema: CustomState,
+    });
+
+    await agent.invoke(
+      // highlight-next-line
+      { messages: "hi!", userName: "John Smith" },
+    );
+    ```
+:::
+
+
 ## Accessing Context in Tools { #tools }
 
+:::python
 Tools can access context through special parameter **annotations**.
 
 * Use `RunnableConfig` for config access
@@ -230,7 +351,167 @@ Tools can access context through special parameter **annotations**.
         "user_id": "user_123"
     })
     ```
+:::
+
+:::js
+Tools can access context through:
+
+* Use `RunnableConfig` for config access
+* Use `getCurrentTaskInput()` for agent state
+
+=== "Using config"
+
+    ```ts
+    import { RunnableConfig } from "@langchain/core/runnables";
+    import { initChatModel } from "langchain/chat_models/universal";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { tool } from "@langchain/core/tools";
+    import { z } from "zod";
+
+    const getUserInfo = tool(
+      async (input: Record<string, any>, config: RunnableConfig) => {
+        // highlight-next-line
+        const userId = config.configurable?.userId;
+        return userId === "user_123" ? "User is John Smith" : "Unknown user";
+      },
+      {
+        name: "get_user_info",
+        description: "Look up user info.",
+        schema: z.object({}),
+      }
+    );
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getUserInfo],
+    });
+
+    await agent.invoke(
+      { messages: "look up user information" },
+      // highlight-next-line
+      { configurable: { userId: "user_123" } }
+    );
+    ```
+
+=== "Using state"
+
+    ```ts
+    import { initChatModel } from "langchain/chat_models/universal";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { Annotation, MessagesAnnotation, getCurrentTaskInput } from "@langchain/langgraph";
+    import { tool } from "@langchain/core/tools";
+    import { z } from "zod";
+
+    const CustomState = Annotation.Root({
+      ...MessagesAnnotation.spec,
+      // highlight-next-line
+      userId: Annotation<string>(),
+    });
+
+    const getUserInfo = tool(
+      async (
+        input: Record<string, any>,
+      ) => {
+        // highlight-next-line
+        const state = getCurrentTaskInput() as typeof CustomState.State;
+        // highlight-next-line
+        const userId = state.userId;
+        return userId === "user_123" ? "User is John Smith" : "Unknown user";
+      },
+      {
+        name: "get_user_info",
+        description: "Look up user info.",
+        schema: z.object({})
+      }
+    );
+
+    const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+    const agent = createReactAgent({
+      llm,
+      tools: [getUserInfo],
+      // highlight-next-line
+      stateSchema: CustomState,
+    });
+
+    await agent.invoke(
+      // highlight-next-line
+      { messages: "look up user information", userId: "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.
+:::python
+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.
+:::
+
+:::js
+Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```ts
+import { Annotation, MessagesAnnotation, LangGraphRunnableConfig, Command } from "@langchain/langgraph";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+import { ToolMessage } from "@langchain/core/messages";
+import { initChatModel } from "langchain/chat_models/universal";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+const CustomState = Annotation.Root({
+  ...MessagesAnnotation.spec,
+  // highlight-next-line
+  userName: Annotation<string>(), // Will be updated by the tool
+});
+
+const getUserInfo = tool(
+  async (
+    _input: Record<string, never>,
+    config: LangGraphRunnableConfig
+  ): Promise<Command> => {
+    const userId = config.configurable?.userId;
+    if (!userId) {
+      throw new Error("Please provide a user id in config.configurable");
+    }
+
+    const toolCallId = config.toolCall?.id;
+
+    const name = userId === "user_123" ? "John Smith" : "Unknown user";
+    // Return command to update state
+    return new Command({
+      update: {
+        // highlight-next-line
+        userName: name,
+        // Update the message history
+        // highlight-next-line
+        messages: [
+          new ToolMessage({
+            content: "Successfully looked up user information",
+            tool_call_id: toolCallId,
+          }),
+        ],
+      },
+    });
+  },
+  {
+    name: "get_user_info",
+    description: "Look up user information.",
+    schema: z.object({}),
+  }
+);
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+const agent = createReactAgent({
+  llm,
+  tools: [getUserInfo],
+  // highlight-next-line
+  stateSchema: CustomState,
+});
+
+await agent.invoke(
+  { messages: "look up user information" },
+  // highlight-next-line
+  { configurable: { userId: "user_123" } }
+);
+```
+:::

docs/docs/agents/deployment.md

@@ -89,4 +89,4 @@ LangGraph Studio Web is a specialized UI that you can connect to LangGraph API s
 
 ## Deployment
 
-Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../concepts/deployment_options.md) for detailed instructions on all supported deployment models.
+Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

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

@@ -27,6 +27,8 @@ A human can review and edit the output from the agent before proceeding. This is
 </figure>
 
 
+:::python
+
 ## Review tool calls
 
 To add a human approval step to a tool:
@@ -34,6 +36,7 @@ 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
@@ -233,6 +236,110 @@ for chunk in agent.stream(
     print("\n")
 ```
 
+:::
+
+:::js
+
+## 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.
+
+```ts
+import { MemorySaver } from "@langchain/langgraph-checkpoint";
+import { interrupt } from "@langchain/langgraph";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { initChatModel } from "langchain/chat_models/universal";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+// An example of a sensitive tool that requires human review / approval
+const bookHotel = tool(
+  async (input: { hotelName: string; }) => {
+    let hotelName = input.hotelName;
+    // highlight-next-line
+    const response = interrupt(  // (1)!
+      `Trying to call \`book_hotel\` with args {'hotel_name': ${hotelName}}. ` +
+      `Please approve or suggest edits.`
+    )
+    if (response.type === "accept") {
+      // proceed to execute the tool logic
+    } else if (response.type === "edit") {
+        hotelName = response.args["hotel_name"]
+    } else {
+        throw new Error(`Unknown response type: ${response.type}`)
+    }
+    return `Successfully booked a stay at ${hotelName}.`;
+  },
+  {
+    name: "bookHotel",
+    schema: z.object({
+      hotelName: z.string().describe("Hotel to book"),
+    }),
+    description: "Book a hotel.",
+  }
+);
+
+// highlight-next-line
+const checkpointer = new MemorySaver();  // (2)!
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+const agent = createReactAgent({
+  llm,
+  tools: [bookHotel],
+  // highlight-next-line
+  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.
+
+```ts
+const config = {
+   configurable: {
+      // highlight-next-line
+      "thread_id": "1"
+   }
+}
+
+for await (const chunk of await agent.stream(
+  { messages: "book a stay at McKittrick hotel" },
+  // highlight-next-line
+  config
+)) {
+  console.log(chunk);
+  console.log("\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.
+
+```ts
+import { Command } from "@langchain/langgraph";
+
+for await (const chunk of await agent.stream(
+  new Command({ resume: { type: "accept" } }),  // (1)!
+  // new Command({ resume: { type: "edit", args: { "hotel_name": "McKittrick Hotel" } } }),
+  // highlight-next-line
+  config
+)) {
+  console.log(chunk);
+  console.log("\n");
+};
+```
+
+1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`][langgraph.types.Command] object to resume the graph with a value provided by the human.
+
+:::
+
 ## Additional resources
 
 * [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)

docs/docs/agents/mcp.md

@@ -13,6 +13,8 @@ hide:
 
 ![MCP](./assets/mcp.png)
 
+:::python
+
 Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
 
 ```bash
@@ -58,6 +60,57 @@ weather_response = await agent.ainvoke(
     {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
 )
 ```
+:::
+
+:::js
+Install the `@langchain/mcp-adapters` library to use MCP tools in LangGraph:
+```bash
+npm install @langchain/mcp-adapters
+```
+
+## Use MCP tools
+
+The `@langchain/mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
+
+```ts
+// highlight-next-line
+import { MultiServerMCPClient } from "@langchain/mcp-adapters";
+import { initChatModel } from "langchain/chat_models/universal";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+// highlight-next-line
+const client = new MultiServerMCPClient({
+  mcpServers: {
+    "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",
+    }
+  }
+})
+
+const llm = await initChatModel("anthropic:claude-3-7-sonnet-latest");
+const agent = createReactAgent({
+  llm,
+  // highlight-next-line
+  tools: await client.getTools()
+});
+ 
+const mathResponse = await agent.invoke(
+  { messages: [ { role: "user", content: "what's (3 + 5) x 12?" } ] }
+);
+const weatherResponse = await agent.invoke(
+  { messages: [ { role: "user", content: "what is the weather in nyc?" } ] }
+);
+await client.close();
+```
+:::
 
 ## Custom MCP servers
 

docs/docs/agents/overview.md

@@ -29,10 +29,10 @@ LangGraph includes several capabilities essential for building robust, productio
 
 - [**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**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
+- [**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/concepts/deployment_options.md) for production.
+    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for production.
 
 ## High-level building blocks
 
@@ -40,6 +40,8 @@ LangGraph comes with a set of prebuilt components that implement common agent be
 
 Using LangGraph for agent development allows you to focus on your application's logic and behavior, instead of building and maintaining the supporting infrastructure for state, memory, and human feedback.
 
+
+:::python
 ## Package ecosystem
 
 The high-level components are organized into several packages, each with a specific focus.
@@ -189,3 +191,161 @@ function initializeWidget() {
 window.addEventListener("DOMContentLoaded", initializeWidget);
 document$.subscribe(initializeWidget);
 </script>
+
+:::
+
+:::js
+
+## Package ecosystem
+
+The high-level components are organized into several packages, each with a specific focus.
+
+| Package                  | Description                                                                 | Installation                                       |
+|--------------------------|-----------------------------------------------------------------------------|----------------------------------------------------|
+| `langgraph`              | Prebuilt components to [**create agents**](./agents.md)                     | `npm install @langchain/langgraph @langchain/core` |
+| `langgraph-supervisor`   | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `npm install @langchain/langgraph-supervisor`      |
+| `langgraph-swarm`        | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `npm install @langchain/langgraph-swarm`           |
+| `langchain-mcp-adapters` | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `npm install @langchain/mcp-adapters`              |
+| `agentevals`             | Utilities to [**evaluate agent performance**](./evals.md)                   | `npm install agentevals`                           |
+
+## Visualize an agent graph
+
+Use the following tool to visualize the graph generated by [`createReactAgent`][create_react_agent] and to view an outline of the corresponding code. It allows you to explore the infrastructure of the agent as defined by the presence of:
+
+- [`tools`](./tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+- `preModelHook`: A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
+- `postModelHook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
+- [`responseFormat`](./agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output (via Zod schemas).
+
+<div class="agent-layout">
+  <div class="agent-graph-features-container">
+    <div class="agent-graph-features">
+      <h3 class="agent-section-title">Features</h3>
+      <label><input type="checkbox" id="tools" checked> <code>tools</code></label>
+      <label><input type="checkbox" id="preModelHook"> <code>preModelHook</code></label>
+      <label><input type="checkbox" id="postModelHook"> <code>postModelHook</code></label>
+      <label><input type="checkbox" id="responseFormat"> <code>responseFormat</code></label>
+    </div>
+  </div>
+
+  <div class="agent-graph-container">
+    <h3 class="agent-section-title">Graph</h3>
+    <img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
+  </div>
+</div>
+
+The following code snippet shows how to create the above agent (and underlying graph) with [`createReactAgent`][create_react_agent]:
+
+```typescript
+
+<div class="language-typescript">
+  <pre><code id="agent-code" class="language-typescript"></code></pre>
+</div>
+
+<script>
+function getCheckedValue(id) {
+  return document.getElementById(id).checked ? "1" : "0";
+}
+
+function getKey() {
+  return [
+    getCheckedValue("responseFormat"),
+    getCheckedValue("postModelHook"),
+    getCheckedValue("preModelHook"),
+    getCheckedValue("tools")
+  ].join("");
+}
+
+function dedent(strings, ...values) {
+  const str = String.raw({ raw: strings }, ...values)
+  const [space] = str.split("\n").filter(Boolean).at(0).match(/^(\s*)/)
+  const spaceLen = space.length
+  return str.split("\n").map(line => line.slice(spaceLen)).join("\n").trim()
+}
+
+Object.assign(dedent, {
+  offset: (size) => (strings, ...values) => {
+    return dedent(strings, ...values).split("\n").map(line => " ".repeat(size) + line).join("\n")
+  }
+})
+
+
+
+
+function generateCodeSnippet({ tools, pre, post, response }) {
+  const lines = []
+
+  lines.push(dedent`
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { ChatOpenAI } from "@langchain/openai";
+  `)
+
+  if (tools) lines.push(`import { tool } from "@langchain/core/tools";`);  
+  if (response || tools) lines.push(`import { z } from "zod";`);
+
+  lines.push("", dedent`
+    const agent = createReactAgent({
+      llm: new ChatOpenAI({ model: "o4-mini" }),
+  `)
+
+  if (tools) {
+    lines.push(dedent.offset(2)`
+      tools: [
+        tool(() => "Sample tool output", {
+          name: "sampleTool",
+          schema: z.object({}),
+        }),
+      ],
+    `)
+  }
+
+  if (pre) {
+    lines.push(dedent.offset(2)`
+      preModelHook: (state) => ({ llmInputMessages: state.messages }),
+    `)
+  }
+
+  if (post) {
+    lines.push(dedent.offset(2)`
+      postModelHook: (state) => state,
+    `)
+  }
+
+  if (response) {
+    lines.push(dedent.offset(2)`
+      responseFormat: z.object({ result: z.string() }),
+    `)
+  }
+
+  lines.push(`});`);
+
+  return lines.join("\n");
+}
+
+function render() {
+  const key = getKey();
+  document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
+
+  const state = {
+    tools: document.getElementById("tools").checked,
+    pre: document.getElementById("preModelHook").checked,
+    post: document.getElementById("postModelHook").checked,
+    response: document.getElementById("responseFormat").checked
+  };
+
+  document.getElementById("agent-code").textContent = generateCodeSnippet(state);
+}
+
+function initializeWidget() {
+  render(); // no need for `await` here
+  document.querySelectorAll(".agent-graph-features input").forEach((input) => {
+    input.addEventListener("change", render);
+  });
+}
+
+// Init for both full reload and SPA nav (used by MkDocs Material)
+window.addEventListener("DOMContentLoaded", initializeWidget);
+document$.subscribe(initializeWidget);
+</script>
+
+:::

docs/docs/agents/run_agents.md

@@ -109,7 +109,7 @@ Streaming is available in both sync and async modes:
 
 !!! tip
 
-    For full details, see the [streaming guide](../how-tos/streaming.md).
+    For full details, see the [streaming guide](./streaming.md).
 
 ## Max iterations
 

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

@@ -1 +1,310 @@
-delete me
+---
+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
+
+You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, 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

@@ -23,6 +23,7 @@ Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the re
 
     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.
 
+:::python
 ## 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):
@@ -32,6 +33,7 @@ Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_
 !!! Important
 
     Agent Chat UI works best if your LangGraph agent interrupts using the [`HumanInterrupt` schema][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
 

docs/docs/cloud/concepts/runs.md

@@ -0,0 +1,5 @@
+# Runs
+
+A run is an invocation of an [assistant](../../concepts/assistants.md). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./threads.md).
+
+The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/cloud/concepts/streaming.md

@@ -0,0 +1,138 @@
+# Streaming
+
+Streaming is critical for making LLM applications feel responsive to end users.  
+When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
+
+## Supported streaming modes
+
+LangGraph Platform supports the following streaming modes:
+
+| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
+| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
+| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
+| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
+| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
+| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
+
+✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
+
+## Stateless runs
+
+If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    async for chunk in client.runs.stream(
+        # highlight-next-line
+        None,  # (1)!
+        assistant_id,
+        input=inputs,
+        stream_mode="updates"
+    ):
+        print(chunk.data)
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // create a streaming run
+    // highlight-next-line
+    const streamResponse = client.runs.stream(
+      // highlight-next-line
+      null,  // (1)!
+      assistantID,
+      {
+        input,
+        streamMode: "updates"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk.data);
+    }
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <inputs>,
+      \"stream_mode\": \"updates\"
+    }"
+    ```
+
+## Join and stream
+
+LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    # highlight-next-line
+    async for chunk in client.runs.join_stream(
+        thread_id,
+        # highlight-next-line
+        run_id,  # (1)!
+    ):
+        print(chunk)
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // highlight-next-line
+    const streamResponse = client.runs.joinStream(
+      threadID,
+      // highlight-next-line
+      runId  // (1)!
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk);
+    }
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+=== "cURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    ```
+
+!!! warning "Outputs not buffered"
+
+    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
+
+## API Reference
+
+For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
+

docs/docs/cloud/deployment/cloud.md

@@ -62,15 +62,6 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
 
-## View Deployment Metrics
-
-Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
-
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
-1. Select an existing deployment to monitor.
-1. Select the `Monitoring` tab to view the deployment metrics. See a list of [all available metrics](../../concepts/langgraph_control_plane.md#monitoring).
-1. Within the `Monitoring` tab, use the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
-
 ## Interrupt Revision
 
 Interrupting a revision will stop deployment of the revision.

docs/docs/cloud/deployment/graph_rebuild.md

@@ -20,7 +20,7 @@ my-app/
 |-- openai_agent.py     # code for your graph
 ```
 
-where the graph is defined in `openai_agent.py`. 
+where the graph is defined in `openai_agent.py`.
 
 ### No rebuild
 
@@ -28,11 +28,11 @@ In the standard LangGraph API configuration, the server uses the compiled graph
 
 ```python
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, MessageGraph
+from langgraph.graph import END, START, StateGraph, MessagesState
 
 model = ChatOpenAI(temperature=0)
 
-graph_workflow = MessageGraph()
+graph_workflow = StateGraph(MessagesState)
 
 graph_workflow.add_node("agent", model)
 graph_workflow.add_edge("agent", END)
@@ -61,7 +61,7 @@ To make your graph rebuild on each new run with custom configuration, you need t
 from typing import Annotated
 from typing_extensions import TypedDict
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, MessageGraph
+from langgraph.graph import END, START
 from langgraph.graph.state import StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode
@@ -144,4 +144,4 @@ Finally, you need to specify the path to your graph-making function (`make_graph
 }
 ```
 
-See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)

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

@@ -33,7 +33,7 @@ For more information on breakpoints see [here](../../concepts/breakpoints.md).
 
 ### Submit run
 
-To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../concepts/runs.md) to the existing selected [thread](../../concepts/persistence.md#threads). If no thread is currently selected, a new one will be created.
+To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../concepts/runs.md) to the existing selected [thread](../concepts/threads.md). If no thread is currently selected, a new one will be created.
 
 To cancel the ongoing run, click the "Cancel" button.
 

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

@@ -1,12 +1,8 @@
-# Streaming API
+# Stream outputs
 
-[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to [stream outputs](../../concepts/streaming.md) from the LangGraph API server.
+## Streaming API
 
-!!! note
-
-    LangGraph SDK and LangGraph Server are a part of [LangGraph Platform](../../concepts/langgraph_platform.md).
-
-## Basic usage
+[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to stream outputs from the LangGraph API server.
 
 Basic usage example:
 
@@ -837,121 +833,3 @@ To stream all events, including the state of the graph:
       \"stream_mode\": \"events\"
     }"
     ```
-
-## Stateless runs
-
-If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    async for chunk in client.runs.stream(
-        # highlight-next-line
-        None,  # (1)!
-        assistant_id,
-        input=inputs,
-        stream_mode="updates"
-    ):
-        print(chunk.data)
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // create a streaming run
-    // highlight-next-line
-    const streamResponse = client.runs.stream(
-      // highlight-next-line
-      null,  // (1)!
-      assistantID,
-      {
-        input,
-        streamMode: "updates"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk.data);
-    }
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "cURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <inputs>,
-      \"stream_mode\": \"updates\"
-    }"
-    ```
-
-## Join and stream
-
-LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    # highlight-next-line
-    async for chunk in client.runs.join_stream(
-        thread_id,
-        # highlight-next-line
-        run_id,  # (1)!
-    ):
-        print(chunk)
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // highlight-next-line
-    const streamResponse = client.runs.joinStream(
-      threadID,
-      // highlight-next-line
-      runId  // (1)!
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk);
-    }
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-=== "cURL"
-
-    ```bash
-    curl --request GET \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    ```
-
-!!! warning "Outputs not buffered"
-
-    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
-
-## API Reference
-
-For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 

docs/docs/cloud/how-tos/studio/quick_start.md

@@ -13,7 +13,7 @@ LangGraph Studio is accessed from the LangSmith UI, within the LangGraph Platfor
 
 For applications that are [deployed](../../quick_start.md) on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
 
-This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../../concepts/persistence.md#threads), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
+This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../concepts/threads.md), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
 
 ## Local development server
 

docs/docs/cloud/how-tos/studio/run_evals.md

@@ -1,57 +0,0 @@
-# Run experiments over a dataset
-
-LangGraph Studio supports evaluations by allowing you to run your assistant over a pre-defined LangSmith dataset. This enables you to understand how your application performs over a variety of inputs, compare the results to reference outputs, and score the results using [evaluators](../../../agents/evals.md).
-
-This guide shows you how to run an experiment end-to-end from Studio.
-
----
-
-## Prerequisites
-
-Before running an experiment, ensure you have the following:
-
-1.  **A LangSmith dataset**: Your dataset should contain the inputs you want to test and optionally, reference outputs for comparison.
-
-    - The schema for the inputs must match the required input schema for the assistant. For more information on schemas, see [here](../../../concepts/low_level.md#schema).
-    - For more on creating datasets, see [How to Manage Datasets](https://docs.smith.langchain.com/evaluation/how_to_guides/manage_datasets_in_application#set-up-your-dataset).
-
-2.  **(Optional) Evaluators**: You can attach evaluators (e.g., LLM-as-a-Judge, heuristics, or custom functions) to your dataset in LangSmith. These will run automatically after the graph has processed all inputs.
-
-    - To learn more, read about [Evaluation Concepts](https://docs.smith.langchain.com/evaluation/concepts#evaluators).
-
-3.  **A running application**: The experiment can be run against:
-    - An application deployed on [LangGraph Platform](../../quick_start.md).
-    - A locally running application started via the [langgraph-cli](../../../tutorials/langgraph-platform/local-server.md).
-
----
-
-## Step-by-step guide
-
-### 1. Launch the experiment
-
-Click the **Run experiment** button in the top right corner of the Studio page.
-
-### 2. Select your dataset
-
-In the modal that appears, select the dataset (or a specific dataset split) to use for the experiment and click **Start**.
-
-### 3. Monitor the progress
-
-All of the inputs in the dataset will now be run against the active assistant. Monitor the experiment's progress via the badge in the top right corner.
-
-You can continue to work in Studio while the experiment runs in the background. Click the arrow icon button at any time to navigate to LangSmith and view the detailed experiment results.
-
----
-
-## Troubleshooting
-
-### "Run experiment" button is disabled
-
-If the "Run experiment" button is disabled, check the following:
-
-- **Deployed application**: If your application is deployed on LangGraph Platform, you may need to create a new revision to enable this feature.
-- **Local development server**: If you are running your application locally, make sure you have upgraded to the latest version of the `langgraph-cli` (`pip install -U langgraph-cli`). Additionally, ensure you have tracing enabled by setting the `LANGSMITH_API_KEY` in your project's `.env` file.
-
-### Evaluator results are missing
-
-When you run an experiment, any attached evaluators are scheduled for execution in a queue. If you don't see results immediately, it likely means they are still pending.

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

@@ -1,6 +1,10 @@
 # Manage threads
 
-Studio allows you to view [threads](../../concepts/persistence.md#threads) from the server and edit their state.
+!!! info "Prerequisites"
+
+    - [Threads Overview](../concepts/threads.md)
+
+Studio allows you to view threads from the server and edit their state.
 
 ## View threads
 

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

@@ -1,6 +1,10 @@
 # Use threads
 
-In this guide, we will show how to create, view, and inspect [threads](../../concepts/persistence.md#threads).
+!!! info "Prerequisites"
+
+    - [Threads Overview](../concepts/threads.md)
+
+In this guide, we will show how to create, view, and inspect threads.
 
 ## Create a thread
 

docs/docs/concepts/assistants.md

@@ -1,31 +1,29 @@
 # Assistants
 
-**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through configuration variations rather than structural changes.
+!!! info "Prerequisites"
 
-For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
+    - [LangGraph Server](./langgraph_server.md)
+    - [Configuration](./low_level.md#configuration)
+
+When building agents, it is common to make rapid changes that _do not_ alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agent but does not require updating your graph's architecture. Assistants offer a straightforward way to manage these configurations separately from your graph's core logic.
+
+Imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
 
 ![assistant versions](img/assistants.png)
 
-The LangGraph Cloud API provides several endpoints for creating and managing assistants and their versions. See the [API reference](../cloud/reference/api/api_ref.html#tag/assistants) for more details.
-
-!!! info
-
-    Assistants are a [LangGraph Platform](langgraph_platform.md) concept. They are not available in the open source LangGraph library.
-
-## Configuration
+## Configuring assistants
 
 Assistants build on the LangGraph open source concept of [configuration](low_level.md#configuration).
-While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
+While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md).
+This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
 
 In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.
 
-## Versioning
+## Versioning assistants
 
 Assistants support versioning to track changes over time.
 Once you've created an assistant, subsequent edits to that assistant will create new versions. See [this how-to](../cloud/how-tos/configuration_cloud.md#create-a-new-version-for-your-assistant) for more details on how to manage assistant versions.
 
-## Execution
+## Learn more
 
-A **run** is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](../../concepts/persistence.md#threads).
-
-The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
+* The LangGraph Cloud API provides several endpoints for creating and managing assistants and their versions. See the [API reference](../cloud/reference/api/api_ref.html#tag/assistants) for more details.

docs/docs/concepts/deployment_options.md

@@ -5,16 +5,7 @@ search:
 
 # Deployment Options
 
-## Free deployment
-
-There are two free options for deploying LangGraph applications via the LangGraph Server:
-
-1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
-1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
-
-## Production deployment
-
-There are 4 main options for deploying with the [LangGraph Platform](langgraph_platform.md):
+There are 4 main options for deploying with the LangGraph Platform:
 
 1. [Cloud SaaS](#cloud-saas)
 
@@ -31,7 +22,7 @@ A quick comparison:
 |----------------------|----------------|----------------------------|-------------------------------|--------------------------|
 | **[Control plane UI/API](../concepts/langgraph_control_plane.md)** | Yes | Yes | Yes | No |
 | **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
-| **Data/compute residency** | LangChain's cloud | Your cloud | Your cloud | Your cloud |
+| **Data/compute residency** | LangChain’s cloud | Your cloud | Your cloud | Your cloud |
 | **LangSmith compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
 | **[Server version compatibility](../concepts/langgraph_server.md#server-versions)** | Enterprise | Enterprise | Enterprise | Lite, Enterprise |
 | **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |

docs/docs/concepts/langgraph_control_plane.md

@@ -19,7 +19,6 @@ From the control plane UI, you can:
 - Update a deployment.
 - Update environment variables for a deployment.
 - View build and server logs of a deployment.
-- View deployment metrics like CPU and memory usage.
 - Delete a deployment.
 
 The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com/langgraph_cloud).
@@ -89,15 +88,6 @@ Infrastructure for deployments and revisions are provisioned and deployed asynch
 
 The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to achieve asynchronous deployments.
 
-### Monitoring
-
-After a deployment is ready, the control plane monitors the deployment and records various metrics, such as:
-
-- CPU and memory usage of the deployment.
-- Number of container restarts.
-
-These metrics are displayed as charts in the Control Plane UI.
-
 ### LangSmith Integration
 
 A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.

docs/docs/concepts/langgraph_platform.md

@@ -17,7 +17,7 @@ Develop, deploy, scale, and manage agents with **LangGraph Platform** — the pu
 
 LangGraph Platform makes it easy to get your agent running in production —  whether it’s built with LangGraph or another framework — so you can focus on your app logic, not infrastructure. Deploy with one click to get a live endpoint, and use our robust APIs and built-in task queues to handle production scale. 
 
-- **[Streaming Support](../cloud/how-tos/streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides multiple streaming modes optimized for various application needs.
+- **[Streaming Support](../cloud/concepts/streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides multiple streaming modes optimized for various application needs.
 
 - **[Background Runs](../cloud/how-tos/background_run.md)**: For agents that take longer to process (e.g., hours), maintaining an open connection can be impractical. The LangGraph Server supports launching agent runs in the background and provides both polling endpoints and webhooks to monitor run status effectively.
  

docs/docs/concepts/langgraph_self_hosted_control_plane.md

@@ -3,7 +3,7 @@
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
 !!! info "Important"
-    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](plans.md) plan.
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/docs/concepts/langgraph_self_hosted_data_plane.md

@@ -8,7 +8,7 @@ search:
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
 !!! info "Important"
-    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](plans.md) plan.
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/docs/concepts/langgraph_server.md

@@ -7,7 +7,7 @@ search:
 
 **LangGraph Server** offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.
 
-Use LangGraph Server to create and manage [assistants](assistants.md), [threads](./persistence.md#threads), [runs](../cloud/concepts/runs.md), [cron jobs](../cloud/concepts/cron_jobs.md), [webhooks](../cloud/concepts/webhooks.md), and more.
+Use LangGraph Server to create and manage [assistants](assistants.md), [threads](../cloud/concepts/threads.md), [runs](../cloud/concepts/runs.md), [cron jobs](../cloud/concepts/cron_jobs.md), [webhooks](../cloud/concepts/webhooks.md), and more.
 
 !!! tip "API reference"
   

docs/docs/concepts/langgraph_studio.md

@@ -24,7 +24,6 @@ Key features of LangGraph Studio:
 - [Manage assistants](../cloud/how-tos/studio/manage_assistants.md)
 - [Manage threads](../cloud/how-tos/threads_studio.md)
 - [Iterate on prompts](../cloud/how-tos/iterate_graph_studio.md)
-- [Run experiments over a dataset](../cloud/how-tos/studio/run_evals.md)
 - Manage [long term memory](memory.md)
 - Debug agent state via [time travel](time-travel.md)
 
@@ -42,4 +41,4 @@ Chat mode is a simpler UI for iterating on and testing chat-specific agents. It
 
 ## Learn more
 
-- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.
+- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.

docs/docs/concepts/persistence.md

@@ -15,19 +15,15 @@ LangGraph has a built-in persistence layer, implemented through checkpointers. W
 
 ## Threads
 
-A thread is a unique ID or thread identifier assigned to each checkpoint saved by a checkpointer. It contains the accumulated state of a sequence of [runs](../cloud/concepts/runs.md). When a run is executed, the [state](../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
-
-When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
+A thread is a unique ID or [thread identifier](#threads) assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
 
 ```python
 {"configurable": {"thread_id": "1"}}
 ```
 
-A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run. The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../cloud/reference/api/api_ref.html#tag/threads) for more details.
-
 ## Checkpoints
 
-The state of a thread at a particular point in time is called a checkpoint. Checkpoint is a snapshot of the graph state saved at each super-step and is represented by `StateSnapshot` object with the following key properties:
+Checkpoint is a snapshot of the graph state saved at each super-step and is represented by `StateSnapshot` object with the following key properties:
 
 - `config`: Config associated with this checkpoint. 
 - `metadata`: Metadata associated with this checkpoint.
@@ -35,8 +31,6 @@ The state of a thread at a particular point in time is called a checkpoint. Chec
 - `next` A tuple of the node names to execute next in the graph.
 - `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.ipynb#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.
 
-Checkpoints are persisted and can be used to restore the state of a thread at a later time.
-
 Let's see what checkpoints are saved when a simple graph is invoked as follows:
 
 ```python

docs/docs/concepts/streaming.md

@@ -18,6 +18,6 @@ There are three main categories of data you can stream:
 
 - [**Stream LLM tokens**](../how-tos/streaming.md#messages) — capture token streams from anywhere: inside nodes, subgraphs, or tools.
 - [**Emit progress notifications from tools**](../how-tos/streaming.md#stream-custom-data) — send custom updates or progress signals directly from tool functions.
-- [**Stream from subgraphs**](../how-tos/streaming.md#stream-subgraph-outputs) — include outputs from both the parent graph and any nested subgraphs.
+- [**Stream from subgraphs**](../how-tos/streaming.md#subgraphs) — include outputs from both the parent graph and any nested subgraphs.
 - [**Use any LLM**](../how-tos/streaming.md#use-with-any-llm) — stream tokens from any LLM, even if it's not a LangChain model using the `custom` streaming mode.
 - [**Use multiple streaming modes**](../how-tos/streaming.md#stream-multiple-modes) — choose from `values` (full state), `updates` (state deltas), `messages` (LLM tokens + metadata), `custom` (arbitrary user data), or `debug` (detailed traces).

docs/docs/concepts/tools.md

@@ -1,64 +1,62 @@
 # Tools
 
-Many AI applications interact with users via natural language. However, some use cases require models to interface directly with external systems—such as APIs, databases, or file systems—using structured input. In these scenarios, **tool calling** enables models to generate requests that conform to a specified input schema.
+Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language.
+But what about cases where we want a model to also interact *directly* with systems, such as databases or an API?
+These systems often have a particular input schema; for example, APIs frequently have a required payload structure. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema.
 
-[Tools](https://python.langchain.com/docs/concepts/tools/) encapsulate a callable function and its input schema. These can be passed to compatible [chat models](https://python.langchain.com/docs/concepts/chat_models), allowing the model to decide whether to invoke a tool and with what arguments.
+[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.
+
+**Tools** can be passed to [chat models](https://python.langchain.com/docs/concepts/chat_models) that support [tool calling](https://python.langchain.com/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.
+
+You can [create custom tools](https://python.langchain.com/docs/how_to/custom_tools/) or use [prebuilt](#prebuilt-tools) tools.
 
 ## Tool calling
 
 ![Diagram of a tool call by a model](./img/tool_call.png)
 
-Tool calling is typically **conditional**. Based on the user input and available tools, the model may choose to issue a tool call request. This request is returned in an `AIMessage` object, which includes a `tool_calls` field that specifies the tool name and input arguments:
+A key principle of tool calling is that the model decides when to use a tool based on the input's relevance. The model doesn't always need to call a tool.
+For example, given an input that is *irrelevant to the tool*, the model would not call the tool:
 
 ```python
-llm_with_tools.invoke("What is 2 multiplied by 3?")
-# -> AIMessage(tool_calls=[{'name': 'multiply', 'args': {'a': 2, 'b': 3}, ...}])
+result = llm_with_tools.invoke("Hello world!")
 ```
 
-If the input is unrelated to any tool, the model returns only a natural language message:
+The result would be an `AIMessage` containing the model's response in natural language (e.g., "Hello!").
+However, if we pass an input *relevant to the tool*, the model should choose to call it:
 
 ```python
-llm_with_tools.invoke("Hello world!")  # -> AIMessage(content="Hello!")
+result = llm_with_tools.invoke("What is 2 multiplied by 3?")
 ```
 
-Importantly, the model does not execute the tool—it only generates a request. A separate executor (such as a runtime or agent) is responsible for handling the tool call and returning the result.
+As before, the output `result` will be an `AIMessage`. 
+But, if the tool was called, `result` will have a `tool_calls` attribute.
+This attribute includes everything needed to execute the tool, including the tool name and input arguments:
 
-See the [tool calling guide](../how-tos/tool-calling.md) for more details.
+```
+result.tool_calls
+{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
+```
+
+For more details on usage, see the [how-to guide](../how-tos/tool-calling.ipynb).
+
+## Execute tools
+
+LangGraph offers pre-built components — [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] and [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] — that invoke the tools on behalf of the user.
+
+See this [how-to guide](../how-tos/tool-calling.ipynb#use-prebuilt-toolnode) on tool calling.
 
 ## Prebuilt tools
 
-LangChain provides prebuilt tool integrations for common external systems including APIs, databases, file systems, and web data.
+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.
 
-Browse the [integrations directory](https://python.langchain.com/docs/integrations/tools/) for available tools.
+You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
 
-Common categories:
+Some commonly used tool categories include:
 
-* **Search**: Bing, SerpAPI, Tavily
-* **Code execution**: Python REPL, Node.js REPL
-* **Databases**: SQL, MongoDB, Redis
-* **Web data**: Scraping and browsing
-* **APIs**: OpenWeatherMap, NewsAPI, etc.
+- **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
 
-## Custom tools
-
-You can define custom tools using the `@tool` decorator or plain Python functions. For example:
-
-```python
-from langchain_core.tools import tool
-
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-```
-
-See the [tool calling guide](../how-tos/tool-calling.md) for more details.
-
-## Tool execution
-
-While the model determines *when* to call a tool, **execution** of the tool call must be handled by a runtime component.
-
-LangGraph provides prebuilt components for this:
-
-* [`ToolNode`][oolNode]: Executes tools based on AI tool calls.
-* [`create_react_agent`][create_react_agent]: Constructs a full agent that manages tool calling automatically.
+These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.

docs/docs/how-tos/streaming.md

@@ -1,220 +1,11 @@
 # Stream outputs
 
-You can [stream outputs](../concepts/streaming.md) from a LangGraph agent or workflow.
-
-## Supported stream modes
-
-Pass one or more of the following stream modes as a list to the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods:
-
-| Mode | Description |
-|------|-------------|
-| `values` | Streams the full value of the state after each step of the graph. |
-| `updates` | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. |
-| `custom` | Streams custom data from inside your graph nodes. |
-| `messages` | Streams 2-tuples (LLM token, metadata) from any graph nodes where an LLM is invoked. |
-| `debug` | Streams as much information as possible throughout the execution of the graph.   
-
-## Stream from an agent
-
-### 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"`. 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](../agents/multi-agent.md) systems to control which agents stream their output.
-
-See the [Models](../agents/models.md#disable-streaming) guide to learn how to disable streaming.
-
-## Stream from a workflow 
-
-### Basic usage example
+## Streaming API
 
 LangGraph graphs expose the [`.stream()`][langgraph.pregel.Pregel.stream] (sync) and [`.astream()`][langgraph.pregel.Pregel.astream] (async) methods to yield streamed outputs as iterators.
 
+Basic usage example:
+
 === "Sync"
 
     ```python
@@ -270,7 +61,18 @@ LangGraph graphs expose the [`.stream()`][langgraph.pregel.Pregel.stream] (sync)
       ```output
       {'refine_topic': {'topic': 'ice cream and cats'}}
       {'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}
-      ```                                                                                                   |
+      ```
+
+
+### Supported stream modes
+
+| Mode                             | Description                                                                                                                                                                         |
+|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [`values`](#stream-graph-state)  | Streams the full value of the state after each step of the graph.                                                                                                                   |
+| [`updates`](#stream-graph-state) | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. |
+| [`custom`](#stream-custom-data)  | Streams custom data from inside your graph nodes.                                                                                                                                   |
+| [`messages`](#messages)          | Streams 2-tuples (LLM token, metadata) from any graph nodes where an LLM is invoked.                                                                                                        |
+| [`debug`](#debug)                | Streams as much information as possible throughout the execution of the graph.                                                                                                      |
 
 ### Stream multiple modes
 
@@ -292,7 +94,7 @@ The streamed outputs will be tuples of `(mode, chunk)` where `mode` is the name
         print(chunk)
     ```
 
-### Stream graph state
+## Stream graph state
 
 Use the stream modes `updates` and `values` to stream the state of the graph as it executes.
 
@@ -355,7 +157,7 @@ graph = (
     ```
 
 
-### Stream subgraph outputs
+## Subgraphs
 
 To include outputs from [subgraphs](../concepts/subgraphs.md) in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.
 
@@ -431,7 +233,7 @@ for chunk in graph.stream(
 
       **Note** that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from.
 
-### Debugging {#debug}
+## Debugging {#debug}
 
 Use the `debug` streaming mode to stream as much information as possible throughout the execution of the graph. The streamed outputs include the name of the node as well as the full state.
 
@@ -445,7 +247,7 @@ for chunk in graph.stream(
 ```
 
 
-### LLM tokens {#messages}
+## LLM tokens {#messages}
 
 Use the `messages` streaming mode to stream Large Language Model (LLM) outputs **token by token** from any part of your graph, including nodes, tools, subgraphs, or tasks.
 
@@ -505,7 +307,7 @@ for message_chunk, metadata in graph.stream( # (2)!
 2. The "messages" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
 
 
-#### Filter by LLM invocation
+### Filter by LLM invocation
 
 You can associate `tags` with LLM invocations to filter the streamed tokens by LLM invocation.
 
@@ -589,7 +391,7 @@ async for msg, metadata in graph.astream(  # (3)!
       4. The `stream_mode` is set to "messages" to stream LLM tokens. The `metadata` contains information about the LLM invocation, including the tags.
 
 
-#### Filter by node
+### Filter by node
 
 To stream tokens only from specific nodes, use `stream_mode="messages"` and filter the outputs by the `langgraph_node` field in the streamed metadata:
 
@@ -662,7 +464,7 @@ for msg, metadata in graph.stream( # (1)!
       1. The "messages" stream mode returns a tuple of `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
       2. Filter the streamed tokens by the `langgraph_node` field in the metadata to only include the tokens from the `write_poem` node.
 
-### Stream custom data
+## Stream custom data
 
 To send **custom user-defined data** from inside a LangGraph node or tool, follow these steps:
 
@@ -739,7 +541,7 @@ To send **custom user-defined data** from inside a LangGraph node or tool, follo
       3. Emit another custom key-value pair.
       4. Set `stream_mode="custom"` to receive the custom data in the stream.
 
-### Use with any LLM
+## Use with any LLM
 
 You can use `stream_mode="custom"` to stream data from **any LLM API**  — even if that API does **not** implement the LangChain chat model interface.
 
@@ -899,7 +701,7 @@ for chunk in graph.stream(
       ```
 
 
-### Disable streaming for specific chat models
+## Disable streaming for specific chat models
 
 If your application mixes models that support streaming with those that do not, you may need to explicitly disable streaming for 
 models that do not support it.
@@ -931,7 +733,7 @@ Set `disable_streaming=True` when initializing the model.
       1. Set `disable_streaming=True` to disable streaming for the chat model.
 
 
-### Async with Python < 3.11 { #async }
+## Async with Python < 3.11 { #async }
 
 In Python versions < 3.11, [asyncio tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) do not support the `context` parameter.  
 This limits LangGraph ability to automatically propagate context, and affects LangGraph’s streaming mechanisms in two key ways:

docs/docs/tutorials/deployment.md

@@ -0,0 +1,22 @@
+---
+search:
+  boost: 2
+---
+
+# Deployment 🚀
+
+There are two free options for deploying LangGraph applications via the LangGraph Server:
+
+- [Local](./langgraph-platform/local-server.md): Deploy for local testing and development. 
+- [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+
+## Other deployment options
+
+Additionally, you can deploy to production with [LangGraph Platform](../concepts/langgraph_platform.md):
+
+- [Cloud SaaS](../concepts/langgraph_cloud.md): Connect your GitHub repositories and deploy LangGraph Servers within LangChain's cloud. *We manage everything.*
+- [Self-Hosted Data Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_data_plane.md): Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *We manage the [control plane](../concepts/langgraph_control_plane.md). You manage the deployments.*
+- [Self-Hosted Control Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_control_plane.md): Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *You manage everything.*
+- [Standalone Container](../concepts/langgraph_standalone_container.md): Deploy LangGraph Server Docker images however you like.
+
+For more information, see [Deployment options](../concepts/deployment_options.md).

docs/docs/tutorials/extraction/retries.ipynb

@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "baf669a0-04ee-492d-80d8-8fcb658ed128",
    "metadata": {},
    "outputs": [],
@@ -313,8 +313,8 @@
     "\n",
     "    builder.add_edge(\"finalizer\", END)\n",
     "\n",
-    "    # These functions let the step be used in a MessageGraph\n",
-    "    # or a StateGraph with 'messages' as the key.\n",
+    "    # These functions let the step be used in a\n",
+    "    # StateGraph with 'messages' as the key.\n",
     "    def encode(x: Union[Sequence[AnyMessage], PromptValue]) -> dict:\n",
     "        \"\"\"Ensure the input is the correct format.\"\"\"\n",
     "        if isinstance(x, PromptValue):\n",

docs/docs/tutorials/get-started/1-build-basic-chatbot.md

@@ -1,6 +1,6 @@
 # Build a basic chatbot
 
-In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let’s dive in! 🌟
+In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let's dive in! 🌟
 
 ## Prerequisites
 
@@ -13,9 +13,17 @@ tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
 
 Install the required packages:
 
+:::python
 ```bash
 pip install -U langgraph langsmith
 ```
+:::
+
+:::js
+```bash
+npm install @langchain/langgraph @langchain/core langsmith
+```
+:::
 
 !!! tip
 
@@ -27,6 +35,7 @@ Now you can create a basic chatbot using LangGraph. This chatbot will respond di
 
 Start by creating a `StateGraph`. A `StateGraph` object defines the structure of our chatbot as a "state machine". We'll add `nodes` to represent the llm and functions our chatbot can call and `edges` to specify how the bot should transition between these functions.
 
+:::python
 ```python
 from typing import Annotated
 
@@ -45,24 +54,53 @@ class State(TypedDict):
 
 graph_builder = StateGraph(State)
 ```
+:::
+
+:::js
+```typescript
+import { Annotation } from "@langchain/langgraph";
+import { BaseMessage } from "@langchain/core/messages";
+import { StateGraph, START, END } from "@langchain/langgraph";
+
+const StateAnnotation = Annotation.Root({
+  // Messages have the type "BaseMessage[]". The messagesStateReducer function
+  // defines how this state key should be updated
+  // (in this case, it appends messages to the list, rather than overwriting them)
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+});
+
+const graphBuilder = new StateGraph(StateAnnotation);
+```
+:::
 
 Our graph can now handle two key tasks:
 
 1. Each `node` can receive the current `State` as input and output an update to the state.
-2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.
+2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt function used with the annotation.
 
 ------
 
 !!! tip "Concept"
 
-    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
+    When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
+
+    :::python
+    In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it.
+    :::
+
+    :::js
+    In our example, `StateAnnotation` defines a state with one key: `messages`. The reducer function is used to append new messages to the list instead of overwriting it.
+    :::
 
 ## 3. Add a node
 
-Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular Python functions.
+Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular functions.
 
 Let's first select a chat model:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -72,10 +110,21 @@ from langchain.chat_models import init_chat_model
 llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
+:::
 
+:::js
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+});
+```
+:::
 
 We can now incorporate the chat model into a simple node:
 
+:::python
 ```python
 
 def chatbot(state: State):
@@ -87,26 +136,63 @@ def chatbot(state: State):
 # the node is used.
 graph_builder.add_node("chatbot", chatbot)
 ```
+:::
+
+:::js
+```typescript
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  return { messages: [await llm.invoke(state.messages)] };
+};
+
+// The first argument is the unique node name
+// The second argument is the function or object that will be called whenever
+// the node is used.
+graphBuilder.addNode("chatbot", chatbot);
+```
+:::
 
 **Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key "messages". This is the basic pattern for all LangGraph node functions.
 
+:::python
 The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.
+:::
+
+:::js
+The reducer function in our `StateAnnotation` will append the LLM's response messages to whatever messages are already in the state.
+:::
 
 ## 4. Add an `entry` point
 
 Add an `entry` point to tell the graph **where to start its work** each time it is run:
 
+:::python
 ```python
 graph_builder.add_edge(START, "chatbot")
 ```
+:::
+
+:::js
+```typescript
+graphBuilder.addEdge(START, "chatbot");
+```
+:::
 
 ## 5. Add an `exit` point
 
 Add an `exit` point to indicate **where the graph should finish execution**. This is helpful for more complex flows, but even in a simple graph like this, adding an end node improves clarity.
 
+:::python
 ```python
 graph_builder.add_edge("chatbot", END)
 ```
+:::
+
+:::js
+```typescript
+graphBuilder.addEdge("chatbot", END);
+```
+:::
+
 This tells the graph to terminate after running the chatbot node.
 
 ## 6. Compile the graph
@@ -114,14 +200,23 @@ This tells the graph to terminate after running the chatbot node.
 Before running the graph, we'll need to compile it. We can do so by calling `compile()`
 on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
 
+:::python
 ```python
 graph = graph_builder.compile()
 ```
+:::
+
+:::js
+```typescript
+const graph = graphBuilder.compile();
+```
+:::
 
 ## 7. Visualize the graph (optional)
 
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
+:::python
 ```python
 from IPython.display import Image, display
 
@@ -131,14 +226,31 @@ except Exception:
     # This requires some extra dependencies and is optional
     pass
 ```
+:::
+
+:::js
+```typescript
+import * as tslab from "tslab";
+
+try {
+  const drawableGraph = graph.getGraph();
+  const image = await drawableGraph.drawMermaidPng();
+  const arrayBuffer = await image.arrayBuffer();
+  await tslab.display.png(new Uint8Array(arrayBuffer));
+} catch (error) {
+  // This requires some extra dependencies and is optional
+  console.log("Graph visualization not available");
+}
+```
+:::
 
 ![basic chatbot diagram](basic-chatbot.png)
 
-
 ## 8. Run the chatbot
 
 Now run the chatbot! 
 
+:::python
 !!! tip
 
     You can exit the chat loop at any time by typing `quit`, `exit`, or `q`.
@@ -169,11 +281,41 @@ while True:
 Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
 Goodbye!
 ```
+:::
+
+:::js
+```typescript
+import { HumanMessage } from "@langchain/core/messages";
+
+async function streamGraphUpdates(userInput: string) {
+  const stream = await graph.stream({
+    messages: [new HumanMessage(userInput)]
+  });
+  
+  for await (const event of stream) {
+    for (const value of Object.values(event)) {
+      console.log("Assistant:", value.messages[value.messages.length - 1].content);
+    }
+  }
+}
+
+// Example usage
+const userInput = "What do you know about LangGraph?";
+console.log("User:", userInput);
+await streamGraphUpdates(userInput);
+```
+
+```
+User: What do you know about LangGraph?
+Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
+```
+:::
 
 **Congratulations!** You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a [LangSmith Trace](https://smith.langchain.com/public/7527e308-9502-4894-b347-f34385740d5a/r) for the call above.
 
 Below is the full code for this tutorial:
 
+:::python
 ```python
 from typing import Annotated
 
@@ -206,9 +348,41 @@ graph_builder.add_edge(START, "chatbot")
 graph_builder.add_edge("chatbot", END)
 graph = graph_builder.compile()
 ```
+:::
+
+:::js
+```typescript
+import { Annotation } from "@langchain/langgraph";
+import { BaseMessage, HumanMessage } from "@langchain/core/messages";
+import { StateGraph, START, END } from "@langchain/langgraph";
+import { ChatAnthropic } from "@langchain/anthropic";
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+});
+
+const graphBuilder = new StateGraph(StateAnnotation);
+
+const llm = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+});
+
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  return { messages: [await llm.invoke(state.messages)] };
+};
+
+// The first argument is the unique node name
+// The second argument is the function or object that will be called whenever
+// the node is used.
+graphBuilder.addNode("chatbot", chatbot);
+graphBuilder.addEdge(START, "chatbot");
+graphBuilder.addEdge("chatbot", END);
+const graph = graphBuilder.compile();
+```
+:::
 
 ## Next steps
 
-You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.
-
-
+You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.

docs/docs/tutorials/get-started/2-add-tools.md

@@ -8,19 +8,39 @@ To handle queries that your chatbot can't answer "from memory", integrate a web
 
 ## Prerequisites
 
+:::python
 Before you start this tutorial, ensure you have the following:
 
 - An API key for the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/).
+:::
+
+:::js
+Before you start this tutorial, ensure you have the following:
+
+- An API key for the [Tavily Search Engine](https://js.langchain.com/docs/integrations/tools/tavily_search/).
+:::
 
 ## 1. Install the search engine
 
+:::python
 Install the requirements to use the [Tavily Search Engine](https://python.langchain.com/docs/integrations/tools/tavily_search/):
 
 ```bash
 pip install -U langchain-tavily
 ```
+:::
+
+:::js
+Install the requirements to use the [Tavily Search Engine](https://js.langchain.com/docs/integrations/tools/tavily_search/):
+
+```bash
+npm install @langchain/community
+```
+:::
+
 ## 2. Configure your environment
 
+:::python
 Configure your environment with your search engine API key:
 
 ```bash
@@ -30,11 +50,21 @@ _set_env("TAVILY_API_KEY")
 ```
 TAVILY_API_KEY:  ········
 ```
+:::
+
+:::js
+Configure your environment with your search engine API key:
+
+```typescript
+process.env.TAVILY_API_KEY = "tvly-...";
+```
+:::
 
 ## 3. Define the tool
 
 Define the web search tool:
 
+:::python
 ```python
 from langchain_tavily import TavilySearch
 
@@ -42,9 +72,21 @@ tool = TavilySearch(max_results=2)
 tools = [tool]
 tool.invoke("What's a 'node' in LangGraph?")
 ```
+:::
+
+:::js
+```typescript
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+
+const tool = new TavilySearchResults({ maxResults: 2 });
+const tools = [tool];
+await tool.invoke("What's a 'node' in LangGraph?");
+```
+:::
 
 The results are page summaries our chat bot can use to answer questions:
 
+:::python
 ```
 {'query': "What's a 'node' in LangGraph?",
 'follow_up_questions': None,
@@ -62,9 +104,17 @@ The results are page summaries our chat bot can use to answer questions:
 'raw_content': None}],
 'response_time': 1.38}
 ```
+:::
+
+:::js
+```
+'[{"title":"Introduction to LangGraph: A Beginner\'s Guide - Medium","url":"https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141","content":"Stateful Graph: LangGraph revolves around the concept of a stateful graph, where each node in the graph represents a step in your computation, and the graph maintains a state that is passed around and updated as the computation progresses. LangGraph supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph. We define nodes for classifying the input, handling greetings, and handling search queries. def classify_input_node(state): LangGraph is a versatile tool for building complex, stateful applications with LLMs. By understanding its core concepts and working through simple examples, beginners can start to leverage its power for their projects. Remember to pay attention to state management, conditional edges, and ensuring there are no dead-end nodes in your graph.","score":0.7065353,"raw_content":null},{"title":"LangGraph Tutorial: What Is LangGraph and How to Use It?","url":"https://www.datacamp.com/tutorial/langgraph-tutorial","content":"LangGraph is a library within the LangChain ecosystem that provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured and efficient manner. By managing the flow of data and the sequence of operations, LangGraph allows developers to focus on the high-level logic of their applications rather than the intricacies of agent coordination. Whether you need a chatbot that can handle various types of user requests or a multi-agent system that performs complex tasks, LangGraph provides the tools to build exactly what you need. LangGraph significantly simplifies the development of complex LLM applications by providing a structured framework for managing state and coordinating agent interactions.","score":0.5008063,"raw_content":null}]'
+```
+:::
 
 ## 4. Define the graph
 
+:::python
 For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bind_tools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
 
 Let's first select our LLM:
@@ -103,9 +153,52 @@ def chatbot(state: State):
 
 graph_builder.add_node("chatbot", chatbot)
 ```
+:::
+
+:::js
+For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot.md), add `bindTools` on the LLM. This lets the LLM know the correct JSON format to use if it wants to use the search engine.
+
+Let's first select our LLM:
+
+```typescript
+import { ChatOpenAI } from "@langchain/openai";
+
+const llm = new ChatOpenAI({
+  model: "gpt-4o",
+  temperature: 0,
+});
+```
+
+We can now incorporate it into a `StateGraph`:
+
+```typescript hl_lines="15"
+import { Annotation } from "@langchain/langgraph";
+import { BaseMessage } from "@langchain/core/messages";
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+});
+
+import { StateGraph, START, END } from "@langchain/langgraph";
+
+const graphBuilder = new StateGraph(StateAnnotation);
+
+// Modification: tell the LLM which tools it can call
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  return { messages: [await llmWithTools.invoke(state.messages)] };
+};
+
+graphBuilder.addNode("chatbot", chatbot);
+```
+:::
 
 ## 5. Create a function to run the tools
 
+:::python
 Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called`BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
 
 ```python
@@ -143,6 +236,50 @@ class BasicToolNode:
 tool_node = BasicToolNode(tools=[tool])
 graph_builder.add_node("tools", tool_node)
 ```
+:::
+
+:::js
+Now, create a function to run the tools if they are called. Do this by adding the tools to a new node called `BasicToolNode` that checks the most recent message in the state and calls tools if the message contains `tool_calls`. It relies on the LLM's `tool_calling` support, which is available in Anthropic, OpenAI, Google Gemini, and a number of other LLM providers.
+
+```typescript
+import { ToolMessage } from "@langchain/core/messages";
+
+class BasicToolNode {
+  private toolsByName: Record<string, any>;
+
+  constructor(tools: any[]) {
+    this.toolsByName = {};
+    for (const tool of tools) {
+      this.toolsByName[tool.name] = tool;
+    }
+  }
+
+  async __call__(inputs: Record<string, any>): Promise<{ messages: ToolMessage[] }> {
+    const messages = inputs.messages || [];
+    if (messages.length === 0) {
+      throw new Error("No message found in input");
+    }
+    const message = messages[messages.length - 1];
+    const outputs: ToolMessage[] = [];
+    
+    for (const toolCall of message.tool_calls || []) {
+      const toolResult = await this.toolsByName[toolCall.name].invoke(toolCall.args);
+      outputs.push(
+        new ToolMessage({
+          content: JSON.stringify(toolResult),
+          name: toolCall.name,
+          tool_call_id: toolCall.id,
+        })
+      );
+    }
+    return { messages: outputs };
+  }
+}
+
+const toolNode = new BasicToolNode([tool]);
+graphBuilder.addNode("tools", async (state) => toolNode.__call__(state));
+```
+:::
 
 !!! note
 
@@ -154,6 +291,7 @@ With the tool node added, now you can define the `conditional_edges`.
 
 **Edges** route the control flow from one node to the next. **Conditional edges** start from a single node and usually contain "if" statements to route to different nodes depending on the current graph state. These functions receive the current graph `state` and return a string or list of strings indicating which node(s) to call next.
 
+:::python
 Next, define a router function called `route_tools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `add_conditional_edges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next. 
 
 The condition will route to `tools` if tool calls are present and `END` if not. Because the condition can return `END`, you do not need to explicitly set a `finish_point` this time.
@@ -194,6 +332,51 @@ graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 graph = graph_builder.compile()
 ```
+:::
+
+:::js
+Next, define a router function called `routeTools` that checks for `tool_calls` in the chatbot's output. Provide this function to the graph by calling `addConditionalEdges`, which tells the graph that whenever the `chatbot` node completes to check this function to see where to go next.
+
+The condition will route to `tools` if tool calls are present and `END` if not. Because the condition can return `END`, you do not need to explicitly set a `finish_point` this time.
+
+```typescript
+import { AIMessage } from "@langchain/core/messages";
+
+const routeTools = (state: typeof StateAnnotation.State) => {
+  /**
+   * Use in the conditional_edge to route to the ToolNode if the last message
+   * has tool calls. Otherwise, route to the end.
+   */
+  const messages = state.messages;
+  const lastMessage = messages[messages.length - 1] as AIMessage;
+  
+  if (lastMessage.tool_calls && lastMessage.tool_calls.length > 0) {
+    return "tools";
+  }
+  return END;
+};
+
+// The `routeTools` function returns "tools" if the chatbot asks to use a tool, and "END" if
+// it is fine directly responding. This conditional routing defines the main agent loop.
+graphBuilder.addConditionalEdges(
+  "chatbot",
+  routeTools,
+  // The following dictionary lets you tell the graph to interpret the condition's outputs as a specific node
+  // It defaults to the identity function, but if you
+  // want to use a node named something else apart from "tools",
+  // You can update the value of the dictionary to something else
+  // e.g., "tools": "my_tools"
+  {
+    tools: "tools",
+    [END]: END,
+  }
+);
+// Any time a tool is called, we return to the chatbot to decide the next step
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+const graph = graphBuilder.compile();
+```
+:::
 
 !!! note
 
@@ -201,6 +384,7 @@ graph = graph_builder.compile()
 
 ## 7. Visualize the graph (optional)
 
+:::python
 You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
 
 ```python
@@ -212,6 +396,26 @@ except Exception:
     # This requires some extra dependencies and is optional
     pass
 ```
+:::
+
+:::js
+You can visualize the graph using the `getGraph` method and one of the "draw" methods, like `drawAscii` or `drawMermaidPng`. The `draw` methods each require additional dependencies.
+
+```typescript
+import * as tslab from "tslab";
+
+try {
+  const representation = graph.getGraph();
+  const image = await representation.drawMermaidPng();
+  const arrayBuffer = await image.arrayBuffer();
+  
+  await tslab.display.png(new Uint8Array(arrayBuffer));
+} catch (error) {
+  // This requires some extra dependencies and is optional
+  console.log("Graph visualization not available");
+}
+```
+:::
 
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
@@ -219,6 +423,7 @@ except Exception:
 
 Now you can ask the chatbot questions outside its training data:
 
+:::python
 ```python
 def stream_graph_updates(user_input: str):
     for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -274,11 +479,71 @@ LangGraph appears to be a significant tool in the evolving landscape of LLM-base
 Goodbye!
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
+:::
+
+:::js
+```typescript
+import { HumanMessage } from "@langchain/core/messages";
+
+const streamGraphUpdates = async (userInput: string) => {
+  const stream = await graph.stream(
+    { messages: [new HumanMessage(userInput)] },
+    { streamMode: "values" }
+  );
+  
+  for await (const event of stream) {
+    const messages = event.messages;
+    const lastMessage = messages[messages.length - 1];
+    console.log("Assistant:", lastMessage.content);
+  }
+};
+
+// Example usage
+const userInput = "What do you know about LangGraph?";
+console.log("User:", userInput);
+await streamGraphUpdates(userInput);
+```
+
+```
+Assistant: I'll search for information about LangGraph to provide you with accurate details.
+Assistant: [{"title": "Introduction to LangGraph: A Beginner's Guide - Medium", "url": "https://medium.com/@cplog/introduction-to-langgraph-a-beginners-guide-14f9be027141", "content": "Stateful Graph: LangGraph revolves around the concept of a stateful graph, where each node in the graph represents a step in your computation, and the graph maintains a state that is passed around and updated as the computation progresses. LangGraph supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph. We define nodes for classifying the input, handling greetings, and handling search queries. def classify_input_node(state): LangGraph is a versatile tool for building complex, stateful applications with LLMs. By understanding its core concepts and working through simple examples, beginners can start to leverage its power for their projects. Remember to pay attention to state management, conditional edges, and ensuring there are no dead-end nodes in your graph.", "score": 0.7065353, "raw_content": null}, {"title": "LangGraph Tutorial: What Is LangGraph and How to Use It?", "url": "https://www.datacamp.com/tutorial/langgraph-tutorial", "content": "LangGraph is a library within the LangChain ecosystem that provides a framework for defining, coordinating, and executing multiple LLM agents or chains in a structured and efficient manner. By managing the flow of data and the sequence of operations, LangGraph allows developers to focus on the high-level logic of their applications rather than the intricacies of agent coordination. Whether you need a chatbot that can handle various types of user requests or a multi-agent system that performs complex tasks, LangGraph provides the tools to build exactly what you need. LangGraph significantly simplifies the development of complex LLM applications by providing a structured framework for managing state and coordinating agent interactions.", "score": 0.5008063, "raw_content": null}]
+Assistant: Based on the search results, I can provide you with comprehensive information about LangGraph:
+
+## What is LangGraph?
+
+LangGraph is a library within the LangChain ecosystem designed for building stateful, multi-actor applications with Large Language Models (LLMs). It provides a framework for defining, coordinating, and executing multiple LLM agents or chains in a structured and efficient manner.
+
+## Key Features:
+
+1. **Stateful Graph Architecture**: LangGraph revolves around the concept of a stateful graph where each node represents a step in your computation, and the graph maintains state that is passed around and updated as the computation progresses.
+
+2. **Conditional Edges**: It supports conditional edges, allowing you to dynamically determine the next node to execute based on the current state of the graph.
+
+3. **Multi-Agent Coordination**: LangGraph manages the flow of data and sequence of operations, allowing developers to focus on high-level logic rather than the intricacies of agent coordination.
+
+## Use Cases:
+
+- Building conversational agents
+- Creating chatbots that can handle various types of user requests
+- Developing multi-agent systems that perform complex tasks
+- Complex task automation
+- Custom LLM-backed experiences
+
+## Benefits:
+
+- **Simplified Development**: LangGraph significantly simplifies the development of complex LLM applications by providing a structured framework for managing state and coordinating agent interactions.
+- **Flexibility**: It's a versatile tool for building complex, stateful applications with LLMs.
+- **Focus on Logic**: Developers can focus on the high-level logic of their applications rather than coordination details.
+
+LangGraph is particularly valuable for projects that require sophisticated AI workflows with multiple steps, decision points, and state management across different components.
+```
+:::
 
 ## 9. Use prebuilts
 
 For ease of use, adjust your code to replace the following with LangGraph prebuilt components. These have built in functionality like parallel API execution.
 
+:::python
 - `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
 - `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
 
@@ -322,9 +587,56 @@ graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 graph = graph_builder.compile()
 ```
+:::
+
+:::js
+- `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
+- `routeTools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
+
+```typescript hl_lines="25 30"
+import { Annotation } from "@langchain/langgraph";
+import { BaseMessage } from "@langchain/core/messages";
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { ChatOpenAI } from "@langchain/openai";
+
+import { StateGraph, START, END } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+});
+
+const graphBuilder = new StateGraph(StateAnnotation);
+
+const tool = new TavilySearchResults({ maxResults: 2 });
+const tools = [tool];
+const llm = new ChatOpenAI({ model: "gpt-4o", temperature: 0 });
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  return { messages: [await llmWithTools.invoke(state.messages)] };
+};
+
+graphBuilder.addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+graphBuilder.addConditionalEdges(
+  "chatbot",
+  toolsCondition,
+);
+// Any time a tool is called, we return to the chatbot to decide the next step
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+const graph = graphBuilder.compile();
+```
+:::
 
 **Congratulations!** You've created a conversational agent in LangGraph that can use a search engine to retrieve updated information when needed. Now it can handle a wider range of user queries. To inspect all the steps your agent just took, check out this [LangSmith trace](https://smith.langchain.com/public/4fbd7636-25af-4638-9587-5a02fdbb0172/r).
 
 ## Next steps
 
-The chatbot cannot remember past interactions on its own, which limits its ability to have coherent, multi-turn conversations. In the next part, you will [add **memory**](./3-add-memory.md) to address this.
+The chatbot cannot remember past interactions on its own, which limits its ability to have coherent, multi-turn conversations. In the next part, you will [add **memory**](./3-add-memory.md) to address this.

docs/docs/tutorials/get-started/3-add-memory.md

@@ -14,11 +14,21 @@ We will see later that **checkpointing** is _much_ more powerful than simple cha
 
 Create a `MemorySaver` checkpointer:
 
+:::python
 ``` python
 from langgraph.checkpoint.memory import MemorySaver
 
 memory = MemorySaver()
 ```
+:::
+
+:::js
+```typescript
+import { MemorySaver } from "@langchain/langgraph";
+
+const memory = new MemorySaver();
+```
+:::
 
 This is in-memory checkpointer, which is convenient for the tutorial. However, in a production application, you would likely change this to use `SqliteSaver` or `PostgresSaver` and connect a database.
 
@@ -26,6 +36,7 @@ This is in-memory checkpointer, which is convenient for the tutorial. However, i
 
 Compile the graph with the provided checkpointer, which will checkpoint the `State` as the graph works through each node:
 
+:::python
 ``` python
 graph = graph_builder.compile(checkpointer=memory)
 ```
@@ -39,6 +50,27 @@ except Exception:
     # This requires some extra dependencies and is optional
     pass
 ```
+:::
+
+:::js
+```typescript
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+
+```typescript
+import * as tslab from "tslab";
+
+try {
+    const representation = graph.getGraph();
+    const image = await representation.drawMermaidPng();
+    const arrayBuffer = await image.arrayBuffer();
+    
+    await tslab.display.png(new Uint8Array(arrayBuffer));
+} catch (e) {
+    // This requires some extra dependencies and is optional
+}
+```
+:::
 
 ## 3. Interact with your chatbot
 
@@ -46,12 +78,21 @@ Now you can interact with your bot!
 
 1. Pick a thread to use as the key for this conversation.
 
+    :::python
     ```python
     config = {"configurable": {"thread_id": "1"}}
     ```
+    :::
+
+    :::js
+    ```typescript
+    const config = { configurable: { thread_id: "1" } };
+    ```
+    :::
 
 2. Call your chatbot:
 
+    :::python
     ```python
     user_input = "Hi there! My name is Will."
 
@@ -64,6 +105,24 @@ Now you can interact with your bot!
     for event in events:
         event["messages"][-1].pretty_print()
     ```
+    :::
+
+    :::js
+    ```typescript
+    const userInput = "Hi there! My name is Will.";
+
+    // The config is the **second positional argument** to stream() or invoke()!
+    const events = await graph.stream(
+        { messages: [{ role: "user", content: userInput }] },
+        { ...config, streamMode: "values" }
+    );
+    
+    for await (const event of events) {
+        const messages = event.messages;
+        console.log(messages[messages.length - 1]);
+    }
+    ```
+    :::
 
     ```
     ================================ Human Message =================================
@@ -74,14 +133,23 @@ Now you can interact with your bot!
     Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?
     ```
 
+    :::python
     !!! note 
 
         The config was provided as the **second positional argument** when calling our graph. It importantly is _not_ nested within the graph inputs (`{'messages': []}`).
+    :::
+
+    :::js
+    !!! note 
+
+        The config was provided as the **second positional argument** when calling our graph. It importantly is _not_ nested within the graph inputs (`{ messages: [] }`).
+    :::
 
 ## 4. Ask a follow up question
 
 Ask a follow up question:
 
+:::python
 ```python
 user_input = "Remember my name?"
 
@@ -94,6 +162,24 @@ events = graph.stream(
 for event in events:
     event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+```typescript
+const userInput2 = "Remember my name?";
+
+// The config is the **second positional argument** to stream() or invoke()!
+const events2 = await graph.stream(
+    { messages: [{ role: "user", content: userInput2 }] },
+    { ...config, streamMode: "values" }
+);
+
+for await (const event of events2) {
+    const messages = event.messages;
+    console.log(messages[messages.length - 1]);
+}
+```
+:::
 
 ```
 ================================ Human Message =================================
@@ -108,6 +194,7 @@ Of course, I remember your name, Will. I always try to pay attention to importan
 
 Don't believe me? Try this using a different config.
 
+:::python
 ```python
 # The only difference is we change the `thread_id` here to "2" instead of "1"
 events = graph.stream(
@@ -119,6 +206,23 @@ events = graph.stream(
 for event in events:
     event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+```typescript
+// The only difference is we change the `thread_id` here to "2" instead of "1"
+const events3 = await graph.stream(
+    { messages: [{ role: "user", content: userInput2 }] },
+    // highlight-next-line
+    { configurable: { thread_id: "2" }, streamMode: "values" }
+);
+
+for await (const event of events3) {
+    const messages = event.messages;
+    console.log(messages[messages.length - 1]);
+}
+```
+:::
 
 ```
 ================================ Human Message =================================
@@ -133,8 +237,15 @@ I apologize, but I don't have any previous context or memory of your name. As an
 
 ## 5. Inspect the state
 
+:::python
 By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `get_state(config)`.
+:::
 
+:::js
+By now, we have made a few checkpoints across two different threads. But what goes into a checkpoint? To inspect a graph's `state` for a given config at any time, call `getState(config)`.
+:::
+
+:::python
 ```python
 snapshot = graph.get_state(config)
 snapshot
@@ -147,6 +258,75 @@ StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Wi
 ```
 snapshot.next  # (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)
 ```
+:::
+
+:::js
+```typescript
+const snapshot = await graph.getState(config);
+console.log(snapshot);
+```
+
+```
+StateSnapshot {
+  values: {
+    messages: [
+      HumanMessage {
+        content: 'Hi there! My name is Will.',
+        id: '8c1ca919-c553-4ebf-95d4-b59a2d61e078'
+      },
+      AIMessage {
+        content: "Hello Will! It's nice to meet you. How can I assist you today? Is there anything specific you'd like to know or discuss?",
+        id: 'run-58587b77-8c82-41e6-8a90-d62c444a261d-0'
+      },
+      HumanMessage {
+        content: 'Remember my name?',
+        id: 'daba7df6-ad75-4d6b-8057-745881cea1ca'
+      },
+      AIMessage {
+        content: "Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.",
+        id: 'run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0'
+      }
+    ]
+  },
+  next: [],
+  config: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_ns: '',
+      checkpoint_id: '1ef7d06e-93e0-6acc-8004-f2ac846575d2'
+    }
+  },
+  metadata: {
+    source: 'loop',
+    writes: {
+      chatbot: {
+        messages: [
+          AIMessage {
+            content: "Of course, I remember your name, Will. I always try to pay attention to important details that users share with me. Is there anything else you'd like to talk about or any questions you have? I'm here to help with a wide range of topics or tasks.",
+            id: 'run-ffeaae5c-4d2d-4ddb-bd59-5d5cbf2a5af8-0'
+          }
+        ]
+      }
+    },
+    step: 4,
+    parents: {}
+  },
+  createdAt: '2024-09-27T19:30:10.820758+00:00',
+  parentConfig: {
+    configurable: {
+      thread_id: '1',
+      checkpoint_ns: '',
+      checkpoint_id: '1ef7d06e-859f-6206-8003-e1bd3c264b8f'
+    }
+  },
+  tasks: []
+}
+```
+
+```typescript
+console.log(snapshot.next);  // (since the graph ended this turn, `next` is empty. If you fetch a state from within a graph invocation, next tells which node will execute next)
+```
+:::
 
 The snapshot above contains the current state values, corresponding config, and the `next` node to process. In our case, the graph has reached an `END` state, so `next` is empty.
 
@@ -157,13 +337,24 @@ Check out the code snippet below to review the graph from this tutorial:
 {!snippets/chat_model_tabs.md!}
 
 <!---
+:::python
 ```python
 from langchain.chat_models import init_chat_model
 
 llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
+:::
+
+:::js
+```typescript
+import { ChatOpenAI } from "@langchain/openai";
+
+const llm = new ChatOpenAI({ model: "gpt-4" });
+```
+:::
 -->
 
+:::python
 ```python hl_lines="36 37"
 from typing import Annotated
 
@@ -203,7 +394,51 @@ graph_builder.set_entry_point("chatbot")
 memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
+:::
+
+:::js
+```typescript hl_lines="36 37"
+import { Annotation } from "@langchain/langgraph";
+import { ChatOpenAI } from "@langchain/openai";
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { BaseMessage } from "@langchain/core/messages";
+import { MemorySaver, StateGraph } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+
+const StateAnnotation = Annotation.Root({
+    messages: Annotation<BaseMessage[]>({
+        reducer: (x, y) => x.concat(y),
+    }),
+});
+
+const graphBuilder = new StateGraph(StateAnnotation);
+
+const tool = new TavilySearchResults({ maxResults: 2 });
+const tools = [tool];
+const llm = new ChatOpenAI({ model: "gpt-4" });
+const llmWithTools = llm.bindTools(tools);
+
+function chatbot(state: typeof StateAnnotation.State) {
+    return { messages: [llmWithTools.invoke(state.messages)] };
+}
+
+graphBuilder.addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+graphBuilder.addConditionalEdges(
+    "chatbot",
+    toolsCondition,
+);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge("__start__", "chatbot");
+
+const memory = new MemorySaver();
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+:::
 
 ## Next steps
 
-In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
+In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.

docs/docs/tutorials/get-started/4-human-in-the-loop.md

@@ -14,6 +14,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
 
 Let's first select a chat model:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 <!---
@@ -23,9 +24,21 @@ from langchain.chat_models import init_chat_model
 llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
+:::
+
+:::js
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+
+const llm = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+});
+```
+:::
 
 We can now incorporate it into our `StateGraph` with an additional tool:
 
+:::python
 ``` python hl_lines="12 19 20 21 22 23"
 from typing import Annotated
 
@@ -75,6 +88,60 @@ graph_builder.add_conditional_edges(
 graph_builder.add_edge("tools", "chatbot")
 graph_builder.add_edge(START, "chatbot")
 ```
+:::
+
+:::js
+```typescript hl_lines="12 19 20 21 22 23"
+import { tool } from "@langchain/core/tools";
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { z } from "zod";
+
+import { MemorySaver } from "@langchain/langgraph";
+import { StateGraph, START, END, MessagesAnnotation } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+
+import { interrupt, Command } from "@langchain/langgraph";
+
+const humanAssistance = tool(async ({ query }) => {
+  const humanResponse = interrupt({ query });
+  return humanResponse.data;
+}, {
+  name: "human_assistance",
+  description: "Request assistance from a human.",
+  schema: z.object({
+    query: z.string().describe("Human readable question for the human")
+  })
+});
+
+const searchTool = new TavilySearchResults({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof MessagesAnnotation.State) => {
+  const message = await llmWithTools.invoke(state.messages);
+  // Because we will be interrupting during tool execution,
+  // we disable parallel tool calling to avoid repeating any
+  // tool invocations when we resume.
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported for this example");
+  }
+  return { messages: [message] };
+};
+
+const graphBuilder = new StateGraph(MessagesAnnotation)
+  .addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+graphBuilder.addConditionalEdges(
+  "chatbot",
+  toolsCondition,
+);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+```
+:::
 
 !!! tip
 
@@ -84,16 +151,27 @@ graph_builder.add_edge(START, "chatbot")
 
 We compile the graph with a checkpointer, as before:
 
+:::python
 ```python
 memory = MemorySaver()
 
 graph = graph_builder.compile(checkpointer=memory)
 ```
+:::
+
+:::js
+```typescript
+const memory = new MemorySaver();
+
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+:::
 
 ## 3. Visualize the graph (optional)
 
 Visualizing the graph, you get the same layout as before – just with the added tool!
 
+:::python
 ``` python
 from IPython.display import Image, display
 
@@ -103,6 +181,19 @@ except Exception:
     # This requires some extra dependencies and is optional
     pass
 ```
+:::
+
+:::js
+```typescript
+import * as tslab from "tslab";
+
+const drawableGraph = graph.getGraph();
+const image = await drawableGraph.drawMermaidPng();
+const arrayBuffer = await image.arrayBuffer();
+
+await tslab.display.png(new Uint8Array(arrayBuffer));
+```
+:::
 
 ![chatbot-with-tools-diagram](chatbot-with-tools.png)
 
@@ -110,6 +201,7 @@ except Exception:
 
 Now, prompt the chatbot with a question that will engage the new `human_assistance` tool:
 
+:::python
 ```python
 user_input = "I need some expert guidance for building an AI agent. Could you request assistance for me?"
 config = {"configurable": {"thread_id": "1"}}
@@ -137,9 +229,49 @@ Tool Calls:
   Args:
     query: A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?
 ```
+:::
+
+:::js
+```typescript
+const userInput = "I need some expert guidance for building an AI agent. Could you request assistance for me?";
+const config = { configurable: { thread_id: "1" }, streamMode: "values" as const };
+
+const events = graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  config,
+);
+
+for await (const event of events) {
+  if (event.messages) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage.getType()} Message =================================`);
+    console.log(lastMessage.content);
+    if (lastMessage.tool_calls?.length) {
+      console.log("Tool Calls:");
+      lastMessage.tool_calls.forEach((call) => {
+        console.log(`  ${call.name} (${call.id})`);
+        console.log(`  Args: ${JSON.stringify(call.args)}`);
+      });
+    }
+  }
+}
+```
+
+```
+================================ Human Message =================================
+I need some expert guidance for building an AI agent. Could you request assistance for me?
+================================== Ai Message ==================================
+I'd be happy to request expert assistance for you regarding building an AI agent. Let me use the human assistance function to get you some expert guidance.
+
+Tool Calls:
+  human_assistance (toolu_01ABUqneqnuHNuo1vhfDFQCW)
+  Args: {"query":"A user is requesting expert guidance for building an AI agent. Could you please provide some expert advice or resources on this topic?"}
+```
+:::
 
 The chatbot generated a tool call, but then execution has been interrupted. If you inspect the graph state, you see that it stopped at the tools node:
 
+:::python
 ```python
 snapshot = graph.get_state(config)
 snapshot.next
@@ -148,7 +280,20 @@ snapshot.next
 ```
 ('tools',)
 ```
+:::
 
+:::js
+```typescript
+const snapshot = await graph.getState(config);
+console.log(snapshot.next);
+```
+
+```
+['tools']
+```
+:::
+
+:::python
 !!! info Additional information
 
     Take a closer look at the `human_assistance` tool:
@@ -162,11 +307,34 @@ snapshot.next
     ```
 
     Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the Python kernel is running.
+:::
+
+:::js
+!!! info Additional information
+
+    Take a closer look at the `human_assistance` tool:
+
+    ```typescript
+    const humanAssistance = tool(async ({ query }) => {
+      const humanResponse = interrupt({ query });
+      return humanResponse.data;
+    }, {
+      name: "human_assistance",
+      description: "Request assistance from a human.",
+      schema: z.object({
+        query: z.string().describe("Human readable question for the human")
+      })
+    });
+    ```
+
+    Similar to Python's built-in `input()` function, calling `interrupt` inside the tool will pause execution. Progress is persisted based on the [checkpointer](../../concepts/persistence.md#checkpointer-libraries); so if it is persisting with Postgres, it can resume at any time as long as the database is alive. In this example, it is persisting with the in-memory checkpointer and can resume any time if the JavaScript runtime is running.
+:::
 
 ## 5. Resume execution
 
 To resume execution, pass a [`Command`](../../concepts/low_level.md#command) object containing data expected by the tool. The format of this data can be customized based on needs. For this example, use a dict with a key `"data"`:
 
+:::python
 ``` python
 human_response = (
     "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent."
@@ -214,6 +382,47 @@ LangGraph is likely a framework or library designed specifically for creating AI
 If you'd like more specific information about LangGraph or have any questions about this recommendation, please feel free to ask, and I can request further assistance from the experts.
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
+:::
+
+:::js
+```typescript
+const humanResponse = 
+  "We, the experts are here to help! We'd recommend you check out LangGraph to build your agent." +
+  " It's much more reliable and extensible than simple autonomous agents.";
+
+const humanCommand = new Command({ resume: { data: humanResponse } });
+
+const resumeEvents = graph.stream(humanCommand, config);
+
+for await (const event of resumeEvents) {
+  if (event.messages) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage.getType()} Message =================================`);
+    console.log(lastMessage.content);
+  }
+}
+```
+
+```
+================================== Ai Message ==================================
+I'd be happy to request expert assistance for you regarding building an AI agent. Let me use the human assistance function to get you some expert guidance.
+================================= Tool Message =================================
+We, the experts are here to help! We'd recommend you check out LangGraph to build your agent. It's much more reliable and extensible than simple autonomous agents.
+================================== Ai Message ==================================
+Thank you for your patience. I've received some expert advice regarding your request for guidance on building an AI agent. Here's what the experts have suggested:
+
+The experts recommend that you look into LangGraph for building your AI agent. They mention that LangGraph is a more reliable and extensible option compared to simple autonomous agents.
+
+LangGraph is likely a framework or library designed specifically for creating AI agents with advanced capabilities. Here are a few points to consider based on this recommendation:
+
+1. Reliability: The experts emphasize that LangGraph is more reliable than simpler autonomous agent approaches. This could mean it has better stability, error handling, or consistent performance.
+
+2. Extensibility: LangGraph is described as more extensible, which suggests that it probably offers a flexible architecture that allows you to easily add new features or modify existing ones as your agent's requirements evolve.
+
+3. Advanced capabilities: Given that it's recommended over "simple autonomous agents," LangGraph likely provides more sophisticated tools and techniques for building complex AI agents.
+...
+```
+:::
 
 The input has been received and processed as a tool message. Review this call's [LangSmith trace](https://smith.langchain.com/public/9f0f87e3-56a7-4dde-9c76-b71675624e91/r) to see the exact work that was done in the above call. Notice that the state is loaded in the first step so that our chatbot can continue where it left off.
 
@@ -221,6 +430,7 @@ The input has been received and processed as a tool message. Review this call's
 
 Check out the code snippet below to review the graph from this tutorial:
 
+:::python
 {!snippets/chat_model_tabs.md!}
 
 ```python
@@ -271,6 +481,64 @@ graph_builder.add_edge(START, "chatbot")
 memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
+:::
+
+:::js
+```typescript
+import { tool } from "@langchain/core/tools";
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { z } from "zod";
+import { ChatAnthropic } from "@langchain/anthropic";
+
+import { MemorySaver } from "@langchain/langgraph";
+import { StateGraph, START, END, MessagesAnnotation } from "@langchain/langgraph";
+import { ToolNode, toolsCondition } from "@langchain/langgraph/prebuilt";
+import { interrupt, Command } from "@langchain/langgraph";
+
+const llm = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+});
+
+const humanAssistance = tool(async ({ query }) => {
+  const humanResponse = interrupt({ query });
+  return humanResponse.data;
+}, {
+  name: "human_assistance",
+  description: "Request assistance from a human.",
+  schema: z.object({
+    query: z.string().describe("Human readable question for the human")
+  })
+});
+
+const searchTool = new TavilySearchResults({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof MessagesAnnotation.State) => {
+  const message = await llmWithTools.invoke(state.messages);
+  if (message.tool_calls && message.tool_calls.length > 1) {
+    throw new Error("Multiple tool calls not supported for this example");
+  }
+  return { messages: [message] };
+};
+
+const graphBuilder = new StateGraph(MessagesAnnotation)
+  .addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+graphBuilder.addConditionalEdges(
+  "chatbot",
+  toolsCondition,
+);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+
+const memory = new MemorySaver();
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+:::
 
 ## Next steps
 

docs/docs/tutorials/get-started/5-customize-state.md

@@ -10,6 +10,7 @@ In this tutorial, you will add additional fields to the state to define complex
 
 Update the chatbot to research the birthday of an entity by adding `name` and `birthday` keys to the state:
 
+:::python
 ```python
 from typing import Annotated
 
@@ -25,11 +26,30 @@ class State(TypedDict):
     # highlight-next-line
     birthday: str
 ```
+:::
+
+:::js
+```typescript
+import { Annotation } from "@langchain/langgraph";
+import { BaseMessage } from "@langchain/core/messages";
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+  // highlight-next-line
+  name: Annotation<string>,
+  // highlight-next-line
+  birthday: Annotation<string>,
+});
+```
+:::
 
 Adding this information to the state makes it easily accessible by other graph nodes (like a downstream node that stores or processes the information), as well as the graph's persistence layer.
 
 ## 2. Update the state inside the tool
 
+:::python
 Now, populate the state keys inside of the `human_assistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
 
 ``` python
@@ -75,11 +95,73 @@ def human_assistance(
     # We return a Command object in the tool to update our state.
     return Command(update=state_update)
 ```
+:::
+
+:::js
+Now, populate the state keys inside of the `humanAssistance` tool. This allows a human to review the information before it is stored in the state. Use [`Command`](../../concepts/low_level.md#using-inside-tools) to issue a state update from inside the tool.
+
+```typescript
+import { tool } from "@langchain/core/tools";
+import { ToolMessage } from "@langchain/core/messages";
+import { z } from "zod";
+import { Command, interrupt } from "@langchain/langgraph";
+
+const humanAssistance = tool(async (input, config) => {
+  const { name, birthday } = input;
+  // Note that because we are generating a ToolMessage for a state update, we
+  // generally require the ID of the corresponding tool call. We can access this
+  // from the tool's config when it's called by a model.
+  const toolCallId = config?.toolCall?.id;
+  
+  const humanResponse = interrupt({
+    question: "Is this correct?",
+    name: name,
+    birthday: birthday,
+  });
+  
+  let verifiedName, verifiedBirthday, response;
+  
+  // If the information is correct, update the state as-is.
+  if (humanResponse?.correct?.toLowerCase().startsWith("y")) {
+    verifiedName = name;
+    verifiedBirthday = birthday;
+    response = "Correct";
+  } else {
+    // Otherwise, receive information from the human reviewer.
+    verifiedName = humanResponse?.name || name;
+    verifiedBirthday = humanResponse?.birthday || birthday;
+    response = `Made a correction: ${JSON.stringify(humanResponse)}`;
+  }
+  
+  // This time we explicitly update the state with a ToolMessage inside
+  // the tool.
+  const stateUpdate = {
+    name: verifiedName,
+    birthday: verifiedBirthday,
+    messages: [new ToolMessage({
+      content: response,
+      tool_call_id: toolCallId!
+    })],
+  };
+  
+  // We return a Command object in the tool to update our state.
+  return new Command({ update: stateUpdate });
+}, {
+  name: "humanAssistance",
+  description: "Request assistance from a human.",
+  schema: z.object({
+    name: z.string(),
+    birthday: z.string(),
+  }),
+});
+```
+:::
 
 The rest of the graph stays the same.
 
 ## 3. Prompt the chatbot
 
+:::python
 Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `human_assistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
 
 ```python
@@ -98,6 +180,30 @@ for event in events:
     if "messages" in event:
         event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+Prompt the chatbot to look up the "birthday" of the LangGraph library and direct the chatbot to reach out to the `humanAssistance` tool once it has the required information. By setting `name` and `birthday` in the arguments for the tool, you force the chatbot to generate proposals for these fields.
+
+```typescript
+const userInput = "Can you look up when LangGraph was released? " +
+  "When you have the answer, use the humanAssistance tool for review.";
+const config = { configurable: { thread_id: "1" } };
+
+const events = graph.stream(
+  { messages: [{ role: "user", content: userInput }] },
+  { ...config, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if (event.messages) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage._getType()} Message =================================`);
+    console.log(lastMessage.content);
+  }
+}
+```
+:::
 
 ```
 ================================ Human Message =================================
@@ -130,6 +236,7 @@ We've hit the `interrupt` in the `human_assistance` tool again.
 
 ## 4. Add human assistance
 
+:::python
 The chatbot failed to identify the correct date, so supply it with information:
 
 ```python
@@ -145,6 +252,32 @@ for event in events:
     if "messages" in event:
         event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+The chatbot failed to identify the correct date, so supply it with information:
+
+```typescript
+import { Command } from "@langchain/langgraph";
+
+const humanCommand = new Command({
+  resume: {
+    name: "LangGraph",
+    birthday: "Jan 17, 2024",
+  },
+});
+
+const resumeEvents = graph.stream(humanCommand, { ...config, streamMode: "values" });
+
+for await (const event of resumeEvents) {
+  if (event.messages) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage._getType()} Message =================================`);
+    console.log(lastMessage.content);
+  }
+}
+```
+:::
 
 ```
 ================================== Ai Message ==================================
@@ -175,11 +308,25 @@ It's worth noting that LangGraph had been in development and use for some time b
 
 Note that these fields are now reflected in the state:
 
+:::python
 ```python
 snapshot = graph.get_state(config)
 
 {k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
 ```
+:::
+
+:::js
+```typescript
+const snapshot = await graph.getState(config);
+
+const relevantState = {
+  name: snapshot.values.name,
+  birthday: snapshot.values.birthday
+};
+console.log(relevantState);
+```
+:::
 
 ```
 {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
@@ -189,11 +336,21 @@ This makes them easily accessible to downstream nodes (e.g., a node that further
 
 ## 5. Manually update the state
 
+:::python
 LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.update_state`:
 
 ``` python
 graph.update_state(config, {"name": "LangGraph (library)"})
 ```
+:::
+
+:::js
+LangGraph gives a high degree of control over the application state. For instance, at any point (including when interrupted), you can manually override a key using `graph.updateState`:
+
+```typescript
+await graph.updateState(config, { name: "LangGraph (library)" });
+```
+:::
 
 ```
 {'configurable': {'thread_id': '1',
@@ -203,6 +360,7 @@ graph.update_state(config, {"name": "LangGraph (library)"})
 
 ## 6. View the new value
 
+:::python
 If you call `graph.get_state`, you can see the new value is reflected:
 
 ``` python
@@ -210,6 +368,21 @@ snapshot = graph.get_state(config)
 
 {k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}
 ```
+:::
+
+:::js
+If you call `graph.getState`, you can see the new value is reflected:
+
+```typescript
+const updatedSnapshot = await graph.getState(config);
+
+const updatedState = {
+  name: updatedSnapshot.values.name,
+  birthday: updatedSnapshot.values.birthday
+};
+console.log(updatedState);
+```
+:::
 
 ```
 {'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}
@@ -231,6 +404,7 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->
 
+:::python
 ```python
 from typing import Annotated
 
@@ -304,8 +478,106 @@ graph_builder.add_edge(START, "chatbot")
 memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
+:::
+
+:::js
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { tool } from "@langchain/core/tools";
+import { ToolMessage, BaseMessage } from "@langchain/core/messages";
+import { z } from "zod";
+
+import { MemorySaver } from "@langchain/langgraph";
+import { StateGraph, START, Annotation } from "@langchain/langgraph";
+import { ToolNode } from "@langchain/langgraph/prebuilt";
+import { Command, interrupt } from "@langchain/langgraph";
+
+const llm = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+});
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: (x, y) => x.concat(y),
+  }),
+  name: Annotation<string>,
+  birthday: Annotation<string>,
+});
+
+const humanAssistance = tool(async (input, config) => {
+  const { name, birthday } = input;
+  const toolCallId = config?.toolCall?.id;
+  
+  const humanResponse = interrupt({
+    question: "Is this correct?",
+    name: name,
+    birthday: birthday,
+  });
+  
+  let verifiedName, verifiedBirthday, response;
+  
+  if (humanResponse?.correct?.toLowerCase().startsWith("y")) {
+    verifiedName = name;
+    verifiedBirthday = birthday;
+    response = "Correct";
+  } else {
+    verifiedName = humanResponse?.name || name;
+    verifiedBirthday = humanResponse?.birthday || birthday;
+    response = `Made a correction: ${JSON.stringify(humanResponse)}`;
+  }
+  
+  const stateUpdate = {
+    name: verifiedName,
+    birthday: verifiedBirthday,
+    messages: [new ToolMessage({
+      content: response,
+      tool_call_id: toolCallId!
+    })],
+  };
+  
+  return new Command({ update: stateUpdate });
+}, {
+  name: "humanAssistance",
+  description: "Request assistance from a human.",
+  schema: z.object({
+    name: z.string(),
+    birthday: z.string(),
+  }),
+});
+
+const searchTool = new TavilySearchResults({ maxResults: 2 });
+const tools = [searchTool, humanAssistance];
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  const message = await llmWithTools.invoke(state.messages);
+  return { messages: [message] };
+};
+
+const shouldContinue = (state: typeof StateAnnotation.State) => {
+  const lastMessage = state.messages[state.messages.length - 1];
+  if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+    return "tools";
+  }
+  return "__end__";
+};
+
+const graphBuilder = new StateGraph(StateAnnotation);
+graphBuilder.addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+graphBuilder.addConditionalEdges("chatbot", shouldContinue);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+
+const memory = new MemorySaver();
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+:::
 
 ## Next steps
 
-There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md). 
-
+There's one more concept to review before finishing the LangGraph basics tutorials: connecting `checkpointing` and `state updates` to [time travel](./6-time-travel.md).

docs/docs/tutorials/get-started/6-time-travel.md

@@ -12,18 +12,35 @@ You can create these types of experiences using LangGraph's built-in **time trav
 
 ## 1. Rewind your graph
 
+:::python
 Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
+:::
+
+:::js
+Rewind your graph by fetching a checkpoint using the graph's `getStateHistory` method. You can then resume execution at this previous point in time.
+:::
 
 {!snippets/chat_model_tabs.md!}
 
 <!---
+:::python
 ```python
 from langchain.chat_models import init_chat_model
 
 llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
+:::
+
+:::js
+```typescript
+import { initChatModel } from "langchain/chat_models/init";
+
+const llm = initChatModel("anthropic:claude-3-5-sonnet-latest");
+```
+:::
 -->
 
+:::python
 ```python
 from typing import Annotated
 
@@ -63,11 +80,62 @@ graph_builder.add_edge(START, "chatbot")
 memory = MemorySaver()
 graph = graph_builder.compile(checkpointer=memory)
 ```
+:::
+
+:::js
+```typescript
+import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+import { ChatAnthropic } from "@langchain/anthropic";
+import { BaseMessage } from "@langchain/core/messages";
+import { Annotation, StateGraph, START, END } from "@langchain/langgraph";
+import { MemorySaver } from "@langchain/langgraph";
+import { ToolNode } from "@langchain/langgraph/prebuilt";
+import { messagesStateReducer } from "@langchain/langgraph";
+
+const StateAnnotation = Annotation.Root({
+  messages: Annotation<BaseMessage[]>({
+    reducer: messagesStateReducer,
+  }),
+});
+
+const graphBuilder = new StateGraph(StateAnnotation);
+
+const tool = new TavilySearchResults({ maxResults: 2 });
+const tools = [tool];
+const llm = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+const llmWithTools = llm.bindTools(tools);
+
+const chatbot = async (state: typeof StateAnnotation.State) => {
+  return { messages: [await llmWithTools.invoke(state.messages)] };
+};
+
+graphBuilder.addNode("chatbot", chatbot);
+
+const toolNode = new ToolNode(tools);
+graphBuilder.addNode("tools", toolNode);
+
+const toolsCondition = (state: typeof StateAnnotation.State) => {
+  const lastMessage = state.messages[state.messages.length - 1];
+  if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+    return "tools";
+  }
+  return END;
+};
+
+graphBuilder.addConditionalEdges("chatbot", toolsCondition);
+graphBuilder.addEdge("tools", "chatbot");
+graphBuilder.addEdge(START, "chatbot");
+
+const memory = new MemorySaver();
+const graph = graphBuilder.compile({ checkpointer: memory });
+```
+:::
 
 ## 2. Add steps
 
 Add steps to your graph. Every step will be checkpointed in its state history:
 
+:::python
 ``` python
 config = {"configurable": {"thread_id": "1"}}
 events = graph.stream(
@@ -89,6 +157,42 @@ for event in events:
     if "messages" in event:
         event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+```typescript
+const config = { configurable: { thread_id: "1" } };
+const events = await graph.stream(
+  {
+    messages: [
+      {
+        role: "user",
+        content: (
+          "I'm learning LangGraph. " +
+          "Could you do some research on it for me?"
+        ),
+      },
+    ],
+  },
+  { ...config, streamMode: "values" }
+);
+
+for await (const event of events) {
+  if ("messages" in event) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage._getType()} Message =================================`);
+    console.log(lastMessage.content);
+    if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+      console.log("Tool Calls:");
+      for (const toolCall of lastMessage.tool_calls) {
+        console.log(`  ${toolCall.name} (${toolCall.id})`);
+        console.log(`  Args: ${JSON.stringify(toolCall.args)}`);
+      }
+    }
+  }
+}
+```
+:::
 
 ```
 ================================ Human Message =================================
@@ -123,6 +227,7 @@ Is there any specific aspect of LangGraph you'd like to know more about? I'd be
 Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
 ```
 
+:::python
 ```python
 events = graph.stream(
     {
@@ -143,6 +248,41 @@ for event in events:
     if "messages" in event:
         event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+```typescript
+const events2 = await graph.stream(
+  {
+    messages: [
+      {
+        role: "user",
+        content: (
+          "Ya that's helpful. Maybe I'll " +
+          "build an autonomous agent with it!"
+        ),
+      },
+    ],
+  },
+  { ...config, streamMode: "values" }
+);
+
+for await (const event of events2) {
+  if ("messages" in event) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage._getType()} Message =================================`);
+    console.log(lastMessage.content);
+    if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+      console.log("Tool Calls:");
+      for (const toolCall of lastMessage.tool_calls) {
+        console.log(`  ${toolCall.name} (${toolCall.id})`);
+        console.log(`  Args: ${JSON.stringify(toolCall.args)}`);
+      }
+    }
+  }
+}
+```
+:::
 
 ```
 ================================ Human Message =================================
@@ -159,7 +299,7 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
 Great idea! Building an autonomous agent with LangGraph is definitely an exciting project. Based on the latest information I've found, here are some insights and tips for building autonomous agents with LangGraph:
@@ -181,6 +321,7 @@ Output is truncated. View as a scrollable element or open in a text editor. Adju
 
 Now that you have added steps to the chatbot, you can `replay` the full state history to see everything that occurred.
 
+:::python
 ``` python
 to_replay = None
 for state in graph.get_state_history(config):
@@ -190,7 +331,24 @@ for state in graph.get_state_history(config):
         # We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
         to_replay = state
 ```
+:::
 
+:::js
+```typescript
+let toReplay = null;
+const stateHistory = await graph.getStateHistory(config);
+for await (const state of stateHistory) {
+  console.log("Num Messages: ", state.values.messages.length, "Next: ", state.next);
+  console.log("-".repeat(80));
+  if (state.values.messages.length === 6) {
+    // We are somewhat arbitrarily selecting a specific state based on the number of chat messages in the state.
+    toReplay = state;
+  }
+}
+```
+:::
+
+:::python
 ```
 Num Messages:  8 Next:  ()
 --------------------------------------------------------------------------------
@@ -213,6 +371,32 @@ Num Messages:  1 Next:  ('chatbot',)
 Num Messages:  0 Next:  ('__start__',)
 --------------------------------------------------------------------------------
 ```
+:::
+
+:::js
+```
+Num Messages:  8 Next:  []
+--------------------------------------------------------------------------------
+Num Messages:  7 Next:  ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages:  6 Next:  ["tools"]
+--------------------------------------------------------------------------------
+Num Messages:  5 Next:  ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages:  4 Next:  ["__start__"]
+--------------------------------------------------------------------------------
+Num Messages:  4 Next:  []
+--------------------------------------------------------------------------------
+Num Messages:  3 Next:  ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages:  2 Next:  ["tools"]
+--------------------------------------------------------------------------------
+Num Messages:  1 Next:  ["chatbot"]
+--------------------------------------------------------------------------------
+Num Messages:  0 Next:  ["__start__"]
+--------------------------------------------------------------------------------
+```
+:::
 
 Checkpoints are saved for every step of the graph. This __spans invocations__ so you can rewind across a full thread's history.
 
@@ -220,27 +404,74 @@ Checkpoints are saved for every step of the graph. This __spans invocations__ so
 
 Resume from the `to_replay` state, which is after the `chatbot` node in the second graph invocation. Resuming from this point will call the **action** node next.
 
+:::python
 ```python
 print(to_replay.next)
 print(to_replay.config)
 ```
+:::
 
+:::js
+```typescript
+console.log(toReplay.next);
+console.log(toReplay.config);
+```
+:::
+
+:::python
 ```
 ('tools',)
 {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1efd43e3-0c1f-6c4e-8006-891877d65740'}}
 ```
+:::
+
+:::js
+```
+["tools"]
+{
+  "configurable": {
+    "thread_id": "1",
+    "checkpoint_ns": "",
+    "checkpoint_id": "1efd43e3-0c1f-6c4e-8006-891877d65740"
+  }
+}
+```
+:::
 
 ## 4. Load a state from a moment-in-time
 
 The checkpoint's `to_replay.config` contains a `checkpoint_id` timestamp. Providing this `checkpoint_id` value tells LangGraph's checkpointer to **load** the state from that moment in time.
 
-
+:::python
 ``` python
 # The `checkpoint_id` in the `to_replay.config` corresponds to a state we've persisted to our checkpointer.
 for event in graph.stream(None, to_replay.config, stream_mode="values"):
     if "messages" in event:
         event["messages"][-1].pretty_print()
 ```
+:::
+
+:::js
+```typescript
+// The `checkpoint_id` in the `toReplay.config` corresponds to a state we've persisted to our checkpointer.
+const timeTravel = await graph.stream(null, { ...toReplay.config, streamMode: "values" });
+
+for await (const event of timeTravel) {
+  if ("messages" in event) {
+    const lastMessage = event.messages[event.messages.length - 1];
+    console.log(`================================ ${lastMessage._getType()} Message =================================`);
+    console.log(lastMessage.content);
+    if ("tool_calls" in lastMessage && lastMessage.tool_calls?.length) {
+      console.log("Tool Calls:");
+      for (const toolCall of lastMessage.tool_calls) {
+        console.log(`  ${toolCall.name} (${toolCall.id})`);
+        console.log(`  Args: ${JSON.stringify(toolCall.args)}`);
+      }
+    }
+  }
+}
+```
+:::
 
 ```
 ================================== Ai Message ==================================
@@ -254,7 +485,7 @@ Tool Calls:
 ================================= Tool Message =================================
 Name: tavily_search_results_json
 
-[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user’s question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
+[{"url": "https://towardsdatascience.com/building-autonomous-multi-tool-agents-with-gemini-2-0-and-langgraph-ad3d7bd5e79d", "content": "Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph | by Youness Mansar | Jan, 2025 | Towards Data Science Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Towards Data Science LLMs are remarkable — they can memorize vast amounts of information, answer general knowledge questions, write code, generate stories, and even fix your grammar. In this tutorial, we are going to build a simple LLM agent that is equipped with four tools that it can use to answer a user's question. This Agent will have the following specifications: Follow Published in Towards Data Science --------------------------------- Your home for data science and AI. Follow Follow Follow"}, {"url": "https://github.com/anmolaman20/Tools_and_Agents", "content": "GitHub - anmolaman20/Tools_and_Agents: This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository provides resources for building AI agents using Langchain and Langgraph. This repository serves as a comprehensive guide for building AI-powered agents using Langchain and Langgraph. It provides hands-on examples, practical tutorials, and resources for developers and AI enthusiasts to master building intelligent systems and workflows. AI Agent Development: Gain insights into creating intelligent systems that think, reason, and adapt in real time. This repository is ideal for AI practitioners, developers exploring language models, or anyone interested in building intelligent systems. This repository provides resources for building AI agents using Langchain and Langgraph."}]
 ================================== Ai Message ==================================
 
 Great idea! Building an autonomous agent with LangGraph is indeed an excellent way to apply and deepen your understanding of the technology. Based on the search results, I can provide you with some insights and resources to help you get started:

docs/mkdocs.yml

@@ -89,145 +89,162 @@ plugins:
                 - "!^_"
 
 nav:
-  - Get started:
-    - index.md
-    - Quickstarts:
-      - Agent: agents/agents.md
-      - Local server: tutorials/langgraph-platform/local-server.md
-      - Deployment: cloud/quick_start.md
-    - General concepts:
-      - Common patterns:
-        - Agent architectures: concepts/agentic_concepts.md
-        - Workflows & agents: tutorials/workflows.md
-      - Agent development: agents/overview.md
-      - Workflow orchestration:
-        - Graphs: concepts/low_level.md
-        - Subgraphs: concepts/subgraphs.md
-        - Runtime: concepts/pregel.md
-        - Functional API: concepts/functional_api.md
-    - Core capabilities:
-      - Streaming: concepts/streaming.md
-      - Persistence: concepts/persistence.md
-      - Durable execution: concepts/durable_execution.md
-      - Memory: concepts/memory.md
-      - Tools: concepts/tools.md
-      - Human-in-the-loop: concepts/human_in_the_loop.md
-      - Breakpoints: concepts/breakpoints.md
-      - Time travel: concepts/time-travel.md
-      - Multi-agent: concepts/multi_agent.md
-    - Platform capabilities:
-      - LangGraph Platform:
-          - Overview: concepts/langgraph_platform.md
-          - Components:
-            - Overview: concepts/langgraph_components.md
-            - LangGraph Server:
-              - Overview: concepts/langgraph_server.md
-              - Data plane: concepts/langgraph_data_plane.md
-              - Control plane: concepts/langgraph_control_plane.md
-            - LangGraph CLI: concepts/langgraph_cli.md
-            - LangGraph Studio: concepts/langgraph_studio.md
-            - LangGraph SDK: concepts/sdk.md
-          - Plans & pricing: concepts/plans.md
-          - Application structure: concepts/application_structure.md
-          - Scalability & resilience: concepts/scalability_and_resilience.md
-      - Authentication & access control: concepts/auth.md
-      - Assistants: concepts/assistants.md
-      - Double-texting: concepts/double_texting.md
-      - Webhooks: cloud/concepts/webhooks.md
-      - Cron jobs: cloud/concepts/cron_jobs.md
-      - Deployment:
-        - Overview: concepts/deployment_options.md
-        - Deployment options:
-          - Cloud SaaS: concepts/langgraph_cloud.md
-          - Self-Hosted Data Plane: concepts/langgraph_self_hosted_data_plane.md
-          - Self-Hosted Control Plane: concepts/langgraph_self_hosted_control_plane.md
-          - Standalone Container: concepts/langgraph_standalone_container.md
-    
   - Guides:
-    - LangGraph APIs:
-      - Use the Graph API: how-tos/graph-api.ipynb
-      - Use the Functional API: how-tos/use-functional-api.md
-    - Models:
-      - Configure model: agents/models.md
-    - Streaming:
-      - Stream outputs: how-tos/streaming.md
-      - Use Server API: cloud/how-tos/streaming.md
-    - Context:
-      - Use in agent: agents/context.md
-    - Memory:
-      - Basic implementation: agents/memory.md
-      - Persistence: how-tos/persistence.ipynb # MERGE
-      - Custom implementation: how-tos/memory.ipynb
-    - Human-in-the-loop:
-      - Add to agent: agents/human-in-the-loop.md
-      - Add to workflow: how-tos/human_in_the_loop/add-human-in-the-loop.md
-      - Use Server API: cloud/how-tos/add-human-in-the-loop.md
-    - Time travel:
-      - Use Server API: cloud/how-tos/human_in_the_loop_time_travel.md
-    - Breakpoints:
-      - Set breakpoints: how-tos/human_in_the_loop/breakpoints.ipynb
-      - Use Server API: cloud/how-tos/human_in_the_loop_breakpoint.md
-    - Tools:
-      - Call tools: how-tos/tool-calling.md
-    - Subgraphs:
-      - Use subgraphs: how-tos/subgraph.ipynb
-    - Multi-agent:
-      - Prebuilt implementation: agents/multi-agent.md
-      - Custom implementation: how-tos/multi_agent.ipynb
-    - MCP:
-      - Use MCP tools: agents/mcp.md
-      - Server deployment via MCP: concepts/server-mcp.md
-    - Deployment:
-      - Basic deployment: agents/deployment.md
-      - Set up your application:
-        - Use requirements.txt: cloud/deployment/setup.md
-        - Use pyproject.toml: cloud/deployment/setup_pyproject.md
-        - Use JavaScript: cloud/deployment/setup_javascript.md
-        - Use custom Docker: cloud/deployment/custom_docker.md
-      - Deploy to production:
-         - Cloud SaaS: cloud/deployment/cloud.md
-         - Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
-         - Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
-         - Standalone Container: cloud/deployment/standalone_container.md
-    - Evaluation:
-      - Basic implementation: agents/evals.md
-    - Platform capabilities:
-      - LangGraph Studio:
-        - Quickstart: cloud/how-tos/studio/quick_start.md
-        - cloud/how-tos/invoke_studio.md
-        - cloud/how-tos/studio/manage_assistants.md
-        - cloud/how-tos/threads_studio.md
-        - cloud/how-tos/iterate_graph_studio.md
-        - cloud/how-tos/studio/run_evals.md
-        - cloud/how-tos/clone_traces_studio.md
-        - cloud/how-tos/datasets_studio.md
+    - index.md
+    - Get started:
+      - Quickstart: agents/agents.md
+      - LangGraph basics:
+        -  concepts/why-langgraph.md
+        -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
+        -  tutorials/get-started/2-add-tools.md
+        -  tutorials/get-started/3-add-memory.md
+        -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
+        -  tutorials/get-started/5-customize-state.md
+        -  tutorials/get-started/6-time-travel.md
+      - Deployment: tutorials/deployment.md
+    - Prebuilt agents:
+      - Overview: agents/overview.md
+      - agents/run_agents.md
+      - agents/streaming.md
+      - agents/models.md
+      - agents/tools.md
+      - agents/mcp.md
+      - agents/context.md
+      - agents/memory.md
+      - agents/human-in-the-loop.md
+      - agents/multi-agent.md
+      - agents/evals.md
+      - agents/deployment.md
+      - agents/ui.md
+    - LangGraph framework:
+      - Agent architectures:
+        - Overview: concepts/agentic_concepts.md
+        - Workflows & agents: tutorials/workflows.md
+      - Graphs:
+        - Overview: concepts/low_level.md
+        - Runtime overview: concepts/pregel.md
+        - Use the Graph API: how-tos/graph-api.ipynb
+      - Streaming:
+          - Overview: concepts/streaming.md
+          - "Stream outputs": how-tos/streaming.md
+      - Persistence:
+        - Overview: concepts/persistence.md
+        - concepts/durable_execution.md
+        - how-tos/persistence.ipynb
+      - Memory:
+        - Overview: concepts/memory.md
+        - Manage memory: how-tos/memory.ipynb
+      - Human-in-the-loop:
+        - Overview: concepts/human_in_the_loop.md
+        - how-tos/human_in_the_loop/add-human-in-the-loop.md
+      - Breakpoints:
+        - Overview: concepts/breakpoints.md
+        - how-tos/human_in_the_loop/breakpoints.ipynb
+      - Time travel:
+        - Overview: concepts/time-travel.md
+        - how-tos/human_in_the_loop/time-travel.ipynb
+      - Tools:
+        - Overview: concepts/tools.md
+        - how-tos/tool-calling.ipynb
+      - Subgraphs:
+        - Overview: concepts/subgraphs.md
+        - how-tos/subgraph.ipynb
+      - Multi-agent:
+        - Overview: concepts/multi_agent.md
+        - how-tos/multi_agent.ipynb
+      - Functional API:
+        - Overview: concepts/functional_api.md
+        - how-tos/use-functional-api.md
+
+    - LangGraph Platform:
+      - Overview: concepts/langgraph_platform.md
+      - Get started:
+        - Quickstart: tutorials/langgraph-platform/local-server.md
+        - Deployment quickstart: cloud/quick_start.md
+      - Components:
+        - Overview: concepts/langgraph_components.md
+        - LangGraph Server:
+          - Overview: concepts/langgraph_server.md
+          - Application structure:
+            - Overview: concepts/application_structure.md
+            - cloud/deployment/setup.md
+            - cloud/deployment/setup_pyproject.md
+            - cloud/deployment/setup_javascript.md
+            - cloud/deployment/custom_docker.md
+        - LangGraph CLI: concepts/langgraph_cli.md
+        - LangGraph Studio:
+          - Overview: concepts/langgraph_studio.md
+          - Quickstart: cloud/how-tos/studio/quick_start.md
+          - cloud/how-tos/invoke_studio.md
+          - cloud/how-tos/studio/manage_assistants.md
+          - cloud/how-tos/threads_studio.md
+          - cloud/how-tos/iterate_graph_studio.md
+          - cloud/how-tos/clone_traces_studio.md
+          - cloud/how-tos/datasets_studio.md
+        - LangGraph SDK: concepts/sdk.md
+      - Data management:
+        - Add semantic search: cloud/deployment/semantic_search.md
+        - Add TTLs: how-tos/ttl/configure_ttl.md
       - Authentication & access control:
+        - Overview: concepts/auth.md
         - how-tos/auth/custom_auth.md
         - how-tos/auth/openapi_security.md
       - Assistants:
+        - Overview: concepts/assistants.md
         - cloud/how-tos/configuration_cloud.md
-        - Threads: cloud/how-tos/use_threads.md
-        - Runs:
-          - cloud/how-tos/background_run.md
-          - cloud/how-tos/same-thread.md
-          - cloud/how-tos/cron_jobs.md
-          - cloud/how-tos/stateless_runs.md
-          - cloud/how-tos/configurable_headers.md
+      - Threads:
+        - Overview: cloud/concepts/threads.md
+        - cloud/how-tos/use_threads.md
+      - Runs:
+        - Overview: cloud/concepts/runs.md
+        - cloud/how-tos/background_run.md
+        - cloud/how-tos/same-thread.md
+        - cloud/how-tos/cron_jobs.md
+        - cloud/how-tos/stateless_runs.md
+        - cloud/how-tos/configurable_headers.md
+      - Streaming:
+        - Overview: cloud/concepts/streaming.md
+        - cloud/how-tos/streaming.md
+      - Human-in-the-loop: cloud/how-tos/add-human-in-the-loop.md
+      - Breakpoints: cloud/how-tos/human_in_the_loop_breakpoint.md
+      - Time travel: cloud/how-tos/human_in_the_loop_time_travel.md
+      - MCP: concepts/server-mcp.md
       - Double-texting:
+        - Overview: concepts/double_texting.md
         - cloud/how-tos/interrupt_concurrent.md
         - cloud/how-tos/rollback_concurrent.md
         - cloud/how-tos/reject_concurrent.md
         - cloud/how-tos/enqueue_concurrent.md
-      - Webhooks: cloud/how-tos/webhooks.md
-      - Cron jobs: cloud/how-tos/cron_jobs.md
+      - Webhooks:
+        - Overview: cloud/concepts/webhooks.md
+        - cloud/how-tos/webhooks.md
+      - Cron jobs:
+        - Overview: cloud/concepts/cron_jobs.md
+        - cloud/how-tos/cron_jobs.md
       - Server customization:
         - how-tos/http/custom_lifespan.md
         - how-tos/http/custom_middleware.md
         - how-tos/http/custom_routes.md
-      - Data management:
-        - Add semantic search: cloud/deployment/semantic_search.md
-        - Add TTLs: how-tos/ttl/configure_ttl.md
-
+      - Deployment:
+        - Overview: concepts/deployment_options.md
+        - Data plane: concepts/langgraph_data_plane.md
+        - Control plane: concepts/langgraph_control_plane.md
+        - Deployment options:
+          - Cloud SaaS:
+            - Overview: concepts/langgraph_cloud.md
+            - Deploy Cloud SaaS: cloud/deployment/cloud.md
+          - Self-Hosted Data Plane:
+            - Overview: concepts/langgraph_self_hosted_data_plane.md
+            - Deploy Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
+          - Self-Hosted Control Plane:
+            - Overview: concepts/langgraph_self_hosted_control_plane.md
+            - Deploy Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
+          - Standalone Container:
+            - Overview: concepts/langgraph_standalone_container.md
+            - Deploy Standalone Container: cloud/deployment/standalone_container.md
+        - Scalability & resilience: concepts/scalability_and_resilience.md
+        - Plans & pricing: concepts/plans.md
+      
   - Reference:
     - reference/index.md
     - LangGraph:
@@ -256,20 +273,9 @@ nav:
       - Environment variables: cloud/reference/env_var.md
 
   - Examples:
-    - agents/run_agents.md
-    - LangGraph basics:
-      -  concepts/why-langgraph.md
-      -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
-      -  tutorials/get-started/2-add-tools.md
-      -  tutorials/get-started/3-add-memory.md
-      -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
-      -  tutorials/get-started/5-customize-state.md
-      -  tutorials/get-started/6-time-travel.md
-    - Template applications: concepts/template_applications.md  # TODO: make tutorial
     - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
     - Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
     - SQL agent: tutorials/sql-agent.ipynb
-    - Prebuilt chat UI: agents/ui.md
     - Graph runs in LangSmith: how-tos/run-id-langsmith.ipynb
     - LangGraph Platform:
       - Authentication:
@@ -284,12 +290,11 @@ nav:
         - Integrate LangGraph into a React app: cloud/how-tos/use_stream_react.md
         - Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md
 
-  - Additional resources:
-    - agents/prebuilt.md # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
-    - LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph
-    - Case studies: adopters.md
+  - Resources:
     - concepts/faq.md
+    - Template applications: concepts/template_applications.md  # TODO: make tutorial
     - llms.txt: llms-txt-overview.md
+    - agents/prebuilt.md # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
     - Troubleshooting:
         - Errors:
           - troubleshooting/errors/index.md
@@ -300,7 +305,9 @@ nav:
           - troubleshooting/errors/INVALID_CHAT_HISTORY.md
           - troubleshooting/errors/INVALID_LICENSE.md
         - LangGraph Studio: troubleshooting/studio.md
-    
+    - Learn:
+      - LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph
+      - Case studies: adopters.md
 
 markdown_extensions:
   - abbr
@@ -357,6 +364,16 @@ markdown_extensions:
 hooks:
   - _scripts/notebook_hooks.py
 extra:
+  consent:
+    title: Cookie consent
+    actions:
+      - accept
+      - reject
+    description: >- 
+      We use cookies to recognize your repeated visits and preferences, as well
+      as to measure the effectiveness of our documentation and whether users
+      find what they're searching for. <strong>Clicking "Accept" makes our
+      documentation better. Thank you!</strong> ❤️
   social:
     - icon: fontawesome/brands/js
       link: https://langchain-ai.github.io/langgraphjs/

docs/tests/unit_tests/test_conditional_rendering.py

@@ -0,0 +1,22 @@
+from _scripts.notebook_hooks import _apply_conditional_rendering
+
+
+CONDITIONAL_RENDERING = """
+above
+:::js
+js-content
+:::
+between
+:::python
+python-content
+:::
+below
+"""
+
+
+def test_conditional_rendering() -> None:
+    """Test logic for conditional rendering of content."""
+    output = _apply_conditional_rendering(CONDITIONAL_RENDERING, "js")
+    assert output.strip() == "above\njs-content\n\nbetween\n\nbelow"
+    output = _apply_conditional_rendering(CONDITIONAL_RENDERING, "python")
+    assert output.strip() == "above\n\nbetween\npython-content\n\nbelow"

docs/uv.lock

@@ -2590,7 +2590,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.4.7"
 source = { editable = "../libs/langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -2641,7 +2641,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.0.26"
 source = { editable = "../libs/checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -2660,8 +2660,6 @@ dev = [
     { name = "dataclasses-json" },
     { name = "mypy" },
     { name = "numpy" },
-    { name = "pandas" },
-    { name = "pandas-stubs", specifier = ">=2.2.2.240807" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
@@ -2894,7 +2892,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.2.2"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
     { name = "langchain-core" },

libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py

@@ -23,7 +23,6 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.postgres import _internal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
-from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol
 
 Conn = _internal.Conn  # For backward compatibility
@@ -457,4 +456,4 @@ class PostgresSaver(BasePostgresSaver):
         )
 
 
-__all__ = ["PostgresSaver", "BasePostgresSaver", "ShallowPostgresSaver", "Conn"]
+__all__ = ["PostgresSaver", "BasePostgresSaver", "Conn"]

libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py

@@ -23,7 +23,6 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.postgres import _ainternal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
-from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol
 
 Conn = _ainternal.Conn  # For backward compatibility
@@ -560,4 +559,4 @@ class AsyncPostgresSaver(BasePostgresSaver):
         ).result()
 
 
-__all__ = ["AsyncPostgresSaver", "AsyncShallowPostgresSaver", "Conn"]
+__all__ = ["AsyncPostgresSaver", "Conn"]

libs/checkpoint-postgres/langgraph/checkpoint/postgres/base.py

@@ -168,7 +168,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         checkpoint["channel_versions"][TASKS] = (
             max(checkpoint["channel_versions"].values())
             if checkpoint["channel_versions"]
-            else self.get_next_version(None, None)
+            else self.get_next_version(None)
         )
 
     def _load_blobs(
@@ -246,7 +246,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
             for idx, (channel, value) in enumerate(writes)
         ]
 
-    def get_next_version(self, current: str | None, channel: None) -> str:
+    def get_next_version(self, current: str | None) -> str:
         if current is None:
             current_v = 0
         elif isinstance(current, int):

libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py

@@ -1,959 +0,0 @@
-import asyncio
-import threading
-import warnings
-from collections.abc import AsyncIterator, Iterator, Sequence
-from contextlib import asynccontextmanager, contextmanager
-from typing import Any, Optional
-
-from langchain_core.runnables import RunnableConfig
-from psycopg import (
-    AsyncConnection,
-    AsyncCursor,
-    AsyncPipeline,
-    Capabilities,
-    Connection,
-    Cursor,
-    Pipeline,
-)
-from psycopg.rows import DictRow, dict_row
-from psycopg.types.json import Jsonb
-from psycopg_pool import AsyncConnectionPool, ConnectionPool
-
-from langgraph.checkpoint.base import (
-    WRITES_IDX_MAP,
-    ChannelVersions,
-    Checkpoint,
-    CheckpointMetadata,
-    CheckpointTuple,
-    get_checkpoint_metadata,
-)
-from langgraph.checkpoint.postgres import _ainternal, _internal
-from langgraph.checkpoint.postgres.base import BasePostgresSaver
-from langgraph.checkpoint.serde.base import SerializerProtocol
-from langgraph.checkpoint.serde.types import TASKS
-
-"""
-To add a new migration, add a new string to the MIGRATIONS list.
-The position of the migration in the list is the version number.
-"""
-MIGRATIONS = [
-    """CREATE TABLE IF NOT EXISTS checkpoint_migrations (
-    v INTEGER PRIMARY KEY
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoints (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    type TEXT,
-    checkpoint JSONB NOT NULL,
-    metadata JSONB NOT NULL DEFAULT '{}',
-    PRIMARY KEY (thread_id, checkpoint_ns)
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoint_blobs (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    channel TEXT NOT NULL,
-    type TEXT NOT NULL,
-    blob BYTEA,
-    PRIMARY KEY (thread_id, checkpoint_ns, channel)
-);""",
-    """CREATE TABLE IF NOT EXISTS checkpoint_writes (
-    thread_id TEXT NOT NULL,
-    checkpoint_ns TEXT NOT NULL DEFAULT '',
-    checkpoint_id TEXT NOT NULL,
-    task_id TEXT NOT NULL,
-    idx INTEGER NOT NULL,
-    channel TEXT NOT NULL,
-    type TEXT,
-    blob BYTEA NOT NULL,
-    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
-);""",
-    """
-    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
-    """,
-    """
-    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_blobs_thread_id_idx ON checkpoint_blobs(thread_id);
-    """,
-    """
-    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
-    """,
-    """
-    ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';
-    """,
-]
-
-SELECT_SQL = f"""
-select
-    thread_id,
-    checkpoint,
-    checkpoint_ns,
-    metadata,
-    (
-        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
-        from jsonb_each_text(checkpoint -> 'channel_versions')
-        inner join checkpoint_blobs bl
-            on bl.thread_id = checkpoints.thread_id
-            and bl.checkpoint_ns = checkpoints.checkpoint_ns
-            and bl.channel = jsonb_each_text.key
-    ) as channel_values,
-    (
-        select
-        array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
-        from checkpoint_writes cw
-        where cw.thread_id = checkpoints.thread_id
-            and cw.checkpoint_ns = checkpoints.checkpoint_ns
-            and cw.checkpoint_id = (checkpoint->>'id')
-    ) as pending_writes,
-    (
-        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_path, cw.task_id, cw.idx)
-        from checkpoint_writes cw
-        where cw.thread_id = checkpoints.thread_id
-            and cw.checkpoint_ns = checkpoints.checkpoint_ns
-            and cw.channel = '{TASKS}'
-    ) as pending_sends
-from checkpoints """
-
-UPSERT_CHECKPOINT_BLOBS_SQL = """
-    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s)
-    ON CONFLICT (thread_id, checkpoint_ns, channel) DO UPDATE SET
-        type = EXCLUDED.type,
-        blob = EXCLUDED.blob;
-"""
-
-UPSERT_CHECKPOINTS_SQL = """
-    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint, metadata)
-    VALUES (%s, %s, %s, %s)
-    ON CONFLICT (thread_id, checkpoint_ns)
-    DO UPDATE SET
-        checkpoint = EXCLUDED.checkpoint,
-        metadata = EXCLUDED.metadata;
-"""
-
-UPSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
-    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
-        channel = EXCLUDED.channel,
-        type = EXCLUDED.type,
-        blob = EXCLUDED.blob;
-"""
-
-INSERT_CHECKPOINT_WRITES_SQL = """
-    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
-    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
-    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
-"""
-
-
-def _dump_blobs(
-    serde: SerializerProtocol,
-    thread_id: str,
-    checkpoint_ns: str,
-    values: dict[str, Any],
-    versions: ChannelVersions,
-) -> list[tuple[str, str, str, str, Optional[bytes]]]:
-    if not versions:
-        return []
-
-    return [
-        (
-            thread_id,
-            checkpoint_ns,
-            k,
-            *(serde.dumps_typed(values[k]) if k in values else ("empty", None)),
-        )
-        for k in versions
-    ]
-
-
-class ShallowPostgresSaver(BasePostgresSaver):
-    """A checkpoint saver that uses Postgres to store checkpoints.
-
-    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
-    It is meant to be a light-weight drop-in replacement for the PostgresSaver that
-    supports most of the LangGraph persistence functionality with the exception of time travel.
-    """
-
-    SELECT_SQL = SELECT_SQL
-    MIGRATIONS = MIGRATIONS
-    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
-    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
-    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
-    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
-
-    lock: threading.Lock
-
-    def __init__(
-        self,
-        conn: _internal.Conn,
-        pipe: Optional[Pipeline] = None,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> None:
-        warnings.warn(
-            "ShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
-            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., checkpoint_during=False)`.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        super().__init__(serde=serde)
-        if isinstance(conn, ConnectionPool) and pipe is not None:
-            raise ValueError(
-                "Pipeline should be used only with a single Connection, not ConnectionPool."
-            )
-
-        self.conn = conn
-        self.pipe = pipe
-        self.lock = threading.Lock()
-        self.supports_pipeline = Capabilities().has_pipeline()
-
-    @classmethod
-    @contextmanager
-    def from_conn_string(
-        cls, conn_string: str, *, pipeline: bool = False
-    ) -> Iterator["ShallowPostgresSaver"]:
-        """Create a new ShallowPostgresSaver instance from a connection string.
-
-        Args:
-            conn_string: The Postgres connection info string.
-            pipeline: whether to use Pipeline
-
-        Returns:
-            ShallowPostgresSaver: A new ShallowPostgresSaver instance.
-        """
-        with Connection.connect(
-            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
-        ) as conn:
-            if pipeline:
-                with conn.pipeline() as pipe:
-                    yield cls(conn, pipe)
-            else:
-                yield cls(conn)
-
-    def setup(self) -> None:
-        """Set up the checkpoint database asynchronously.
-
-        This method creates the necessary tables in the Postgres database if they don't
-        already exist and runs database migrations. It MUST be called directly by the user
-        the first time checkpointer is used.
-        """
-        with self._cursor() as cur:
-            cur.execute(self.MIGRATIONS[0])
-            results = cur.execute(
-                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-            )
-            row = results.fetchone()
-            if row is None:
-                version = -1
-            else:
-                version = row["v"]
-            for v, migration in zip(
-                range(version + 1, len(self.MIGRATIONS)),
-                self.MIGRATIONS[version + 1 :],
-            ):
-                cur.execute(migration)
-                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
-        if self.pipe:
-            self.pipe.sync()
-
-    def list(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> Iterator[CheckpointTuple]:
-        """List checkpoints from the database.
-
-        This method retrieves a list of checkpoint tuples from the Postgres database based
-        on the provided config. For ShallowPostgresSaver, this method returns a list with
-        ONLY the most recent checkpoint.
-        """
-        where, args = self._search_where(config, filter, before)
-        query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
-        with self._cursor() as cur:
-            cur.execute(self.SELECT_SQL + where, args, binary=True)
-            for value in cur:
-                checkpoint: Checkpoint = {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                    "pending_sends": [
-                        self.serde.loads_typed((t.decode(), v))
-                        for t, v in value["pending_sends"]
-                    ]
-                    if value["pending_sends"]
-                    else [],
-                }
-                yield CheckpointTuple(
-                    config={
-                        "configurable": {
-                            "thread_id": value["thread_id"],
-                            "checkpoint_ns": value["checkpoint_ns"],
-                            "checkpoint_id": checkpoint["id"],
-                        }
-                    },
-                    checkpoint=checkpoint,
-                    metadata=value["metadata"],
-                    pending_writes=self._load_writes(value["pending_writes"]),
-                )
-
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database.
-
-        This method retrieves a checkpoint tuple from the Postgres database based on the
-        provided config (matching the thread ID in the config).
-
-        Args:
-            config: The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-
-        Examples:
-
-            Basic:
-            >>> config = {"configurable": {"thread_id": "1"}}
-            >>> checkpoint_tuple = memory.get_tuple(config)
-            >>> print(checkpoint_tuple)
-            CheckpointTuple(...)
-
-            With timestamp:
-
-            >>> config = {
-            ...    "configurable": {
-            ...        "thread_id": "1",
-            ...        "checkpoint_ns": "",
-            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
-            ...    }
-            ... }
-            >>> checkpoint_tuple = memory.get_tuple(config)
-            >>> print(checkpoint_tuple)
-            CheckpointTuple(...)
-        """  # noqa
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        args = (thread_id, checkpoint_ns)
-        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
-
-        with self._cursor() as cur:
-            cur.execute(
-                self.SELECT_SQL + where,
-                args,
-                binary=True,
-            )
-
-            for value in cur:
-                checkpoint: Checkpoint = {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                    "pending_sends": [
-                        self.serde.loads_typed((t.decode(), v))
-                        for t, v in value["pending_sends"]
-                    ]
-                    if value["pending_sends"]
-                    else [],
-                }
-                return CheckpointTuple(
-                    config={
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint["id"],
-                        }
-                    },
-                    checkpoint=checkpoint,
-                    metadata=value["metadata"],
-                    pending_writes=self._load_writes(value["pending_writes"]),
-                )
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database.
-
-        This method saves a checkpoint to the Postgres database. The checkpoint is associated
-        with the provided config. For ShallowPostgresSaver, this method saves ONLY the most recent
-        checkpoint and overwrites a previous checkpoint, if it exists.
-
-        Args:
-            config: The config to associate with the checkpoint.
-            checkpoint: The checkpoint to save.
-            metadata: Additional metadata to save with the checkpoint.
-            new_versions: New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-
-        Examples:
-
-            >>> from langgraph.checkpoint.postgres import ShallowPostgresSaver
-            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
-            >>> with ShallowPostgresSaver.from_conn_string(DB_URI) as memory:
-            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
-            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
-            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
-            >>> print(saved_config)
-            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
-        """
-        configurable = config["configurable"].copy()
-        thread_id = configurable.pop("thread_id")
-        checkpoint_ns = configurable.pop("checkpoint_ns")
-
-        copy = checkpoint.copy()
-        next_config = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": checkpoint_ns,
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-        with self._cursor(pipeline=True) as cur:
-            cur.execute(
-                """DELETE FROM checkpoint_writes
-                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint["id"],
-                    configurable.get("checkpoint_id", ""),
-                ),
-            )
-            cur.executemany(
-                self.UPSERT_CHECKPOINT_BLOBS_SQL,
-                _dump_blobs(
-                    self.serde,
-                    thread_id,
-                    checkpoint_ns,
-                    copy.pop("channel_values"),  # type: ignore[misc]
-                    new_versions,
-                ),
-            )
-            cur.execute(
-                self.UPSERT_CHECKPOINTS_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    Jsonb(copy),
-                    Jsonb(get_checkpoint_metadata(config, metadata)),
-                ),
-            )
-        return next_config
-
-    def put_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-        task_path: str = "",
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint.
-
-        This method saves intermediate writes associated with a checkpoint to the Postgres database.
-
-        Args:
-            config: Configuration of the related checkpoint.
-            writes: List of writes to store.
-            task_id: Identifier for the task creating the writes.
-        """
-        query = (
-            self.UPSERT_CHECKPOINT_WRITES_SQL
-            if all(w[0] in WRITES_IDX_MAP for w in writes)
-            else self.INSERT_CHECKPOINT_WRITES_SQL
-        )
-        with self._cursor(pipeline=True) as cur:
-            cur.executemany(
-                query,
-                self._dump_writes(
-                    config["configurable"]["thread_id"],
-                    config["configurable"]["checkpoint_ns"],
-                    config["configurable"]["checkpoint_id"],
-                    task_id,
-                    task_path,
-                    writes,
-                ),
-            )
-
-    @contextmanager
-    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
-        """Create a database cursor as a context manager.
-
-        Args:
-            pipeline: whether to use pipeline for the DB operations inside the context manager.
-                Will be applied regardless of whether the ShallowPostgresSaver instance was initialized with a pipeline.
-                If pipeline mode is not supported, will fall back to using transaction context manager.
-        """
-        with _internal.get_connection(self.conn) as conn:
-            if self.pipe:
-                # a connection in pipeline mode can be used concurrently
-                # in multiple threads/coroutines, but only one cursor can be
-                # used at a time
-                try:
-                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
-                        yield cur
-                finally:
-                    if pipeline:
-                        self.pipe.sync()
-            elif pipeline:
-                # a connection not in pipeline mode can only be used by one
-                # thread/coroutine at a time, so we acquire a lock
-                if self.supports_pipeline:
-                    with (
-                        self.lock,
-                        conn.pipeline(),
-                        conn.cursor(binary=True, row_factory=dict_row) as cur,
-                    ):
-                        yield cur
-                else:
-                    # Use connection's transaction context manager when pipeline mode not supported
-                    with (
-                        self.lock,
-                        conn.transaction(),
-                        conn.cursor(binary=True, row_factory=dict_row) as cur,
-                    ):
-                        yield cur
-            else:
-                with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
-                    yield cur
-
-
-class AsyncShallowPostgresSaver(BasePostgresSaver):
-    """A checkpoint saver that uses Postgres to store checkpoints asynchronously.
-
-    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
-    It is meant to be a light-weight drop-in replacement for the AsyncPostgresSaver that
-    supports most of the LangGraph persistence functionality with the exception of time travel.
-    """
-
-    SELECT_SQL = SELECT_SQL
-    MIGRATIONS = MIGRATIONS
-    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
-    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
-    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
-    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
-    lock: asyncio.Lock
-
-    def __init__(
-        self,
-        conn: _ainternal.Conn,
-        pipe: Optional[AsyncPipeline] = None,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> None:
-        warnings.warn(
-            "AsyncShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
-            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., checkpoint_during=False)`.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        super().__init__(serde=serde)
-        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
-            raise ValueError(
-                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
-            )
-
-        self.conn = conn
-        self.pipe = pipe
-        self.lock = asyncio.Lock()
-        self.loop = asyncio.get_running_loop()
-        self.supports_pipeline = Capabilities().has_pipeline()
-
-    @classmethod
-    @asynccontextmanager
-    async def from_conn_string(
-        cls,
-        conn_string: str,
-        *,
-        pipeline: bool = False,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> AsyncIterator["AsyncShallowPostgresSaver"]:
-        """Create a new AsyncShallowPostgresSaver instance from a connection string.
-
-        Args:
-            conn_string: The Postgres connection info string.
-            pipeline: whether to use AsyncPipeline
-
-        Returns:
-            AsyncShallowPostgresSaver: A new AsyncShallowPostgresSaver instance.
-        """
-        async with await AsyncConnection.connect(
-            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
-        ) as conn:
-            if pipeline:
-                async with conn.pipeline() as pipe:
-                    yield cls(conn=conn, pipe=pipe, serde=serde)
-            else:
-                yield cls(conn=conn, serde=serde)
-
-    async def setup(self) -> None:
-        """Set up the checkpoint database asynchronously.
-
-        This method creates the necessary tables in the Postgres database if they don't
-        already exist and runs database migrations. It MUST be called directly by the user
-        the first time checkpointer is used.
-        """
-        async with self._cursor() as cur:
-            await cur.execute(self.MIGRATIONS[0])
-            results = await cur.execute(
-                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
-            )
-            row = await results.fetchone()
-            if row is None:
-                version = -1
-            else:
-                version = row["v"]
-            for v, migration in zip(
-                range(version + 1, len(self.MIGRATIONS)),
-                self.MIGRATIONS[version + 1 :],
-            ):
-                await cur.execute(migration)
-                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
-        if self.pipe:
-            await self.pipe.sync()
-
-    async def alist(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> AsyncIterator[CheckpointTuple]:
-        """List checkpoints from the database asynchronously.
-
-        This method retrieves a list of checkpoint tuples from the Postgres database based
-        on the provided config. For ShallowPostgresSaver, this method returns a list with
-        ONLY the most recent checkpoint.
-        """
-        where, args = self._search_where(config, filter, before)
-        query = self.SELECT_SQL + where
-        if limit:
-            query += f" LIMIT {limit}"
-        async with self._cursor() as cur:
-            await cur.execute(self.SELECT_SQL + where, args, binary=True)
-            async for value in cur:
-                checkpoint: Checkpoint = {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                    "pending_sends": [
-                        self.serde.loads_typed((t.decode(), v))
-                        for t, v in value["pending_sends"]
-                    ]
-                    if value["pending_sends"]
-                    else [],
-                }
-                yield CheckpointTuple(
-                    config={
-                        "configurable": {
-                            "thread_id": value["thread_id"],
-                            "checkpoint_ns": value["checkpoint_ns"],
-                            "checkpoint_id": checkpoint["id"],
-                        }
-                    },
-                    checkpoint=checkpoint,
-                    metadata=value["metadata"],
-                    pending_writes=await asyncio.to_thread(
-                        self._load_writes, value["pending_writes"]
-                    ),
-                )
-
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database asynchronously.
-
-        This method retrieves a checkpoint tuple from the Postgres database based on the
-        provided config (matching the thread ID in the config).
-
-        Args:
-            config: The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-        """
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
-        args = (thread_id, checkpoint_ns)
-        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
-
-        async with self._cursor() as cur:
-            await cur.execute(
-                self.SELECT_SQL + where,
-                args,
-                binary=True,
-            )
-
-            async for value in cur:
-                checkpoint: Checkpoint = {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                    "pending_sends": [
-                        self.serde.loads_typed((t.decode(), v))
-                        for t, v in value["pending_sends"]
-                    ]
-                    if value["pending_sends"]
-                    else [],
-                }
-                return CheckpointTuple(
-                    config={
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": checkpoint["id"],
-                        }
-                    },
-                    checkpoint=checkpoint,
-                    metadata=value["metadata"],
-                    pending_writes=await asyncio.to_thread(
-                        self._load_writes, value["pending_writes"]
-                    ),
-                )
-
-    async def aput(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database asynchronously.
-
-        This method saves a checkpoint to the Postgres database. The checkpoint is associated
-        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
-        checkpoint and overwrites a previous checkpoint, if it exists.
-
-        Args:
-            config: The config to associate with the checkpoint.
-            checkpoint: The checkpoint to save.
-            metadata: Additional metadata to save with the checkpoint.
-            new_versions: New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-        """
-        configurable = config["configurable"].copy()
-        thread_id = configurable.pop("thread_id")
-        checkpoint_ns = configurable.pop("checkpoint_ns")
-
-        copy = checkpoint.copy()
-        next_config = {
-            "configurable": {
-                "thread_id": thread_id,
-                "checkpoint_ns": checkpoint_ns,
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-        async with self._cursor(pipeline=True) as cur:
-            await cur.execute(
-                """DELETE FROM checkpoint_writes
-                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    checkpoint["id"],
-                    configurable.get("checkpoint_id", ""),
-                ),
-            )
-            await cur.executemany(
-                self.UPSERT_CHECKPOINT_BLOBS_SQL,
-                _dump_blobs(
-                    self.serde,
-                    thread_id,
-                    checkpoint_ns,
-                    copy.pop("channel_values"),  # type: ignore[misc]
-                    new_versions,
-                ),
-            )
-            await cur.execute(
-                self.UPSERT_CHECKPOINTS_SQL,
-                (
-                    thread_id,
-                    checkpoint_ns,
-                    Jsonb(copy),
-                    Jsonb(get_checkpoint_metadata(config, metadata)),
-                ),
-            )
-        return next_config
-
-    async def aput_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-        task_path: str = "",
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint asynchronously.
-
-        This method saves intermediate writes associated with a checkpoint to the database.
-
-        Args:
-            config: Configuration of the related checkpoint.
-            writes: List of writes to store, each as (channel, value) pair.
-            task_id: Identifier for the task creating the writes.
-        """
-        query = (
-            self.UPSERT_CHECKPOINT_WRITES_SQL
-            if all(w[0] in WRITES_IDX_MAP for w in writes)
-            else self.INSERT_CHECKPOINT_WRITES_SQL
-        )
-        params = await asyncio.to_thread(
-            self._dump_writes,
-            config["configurable"]["thread_id"],
-            config["configurable"]["checkpoint_ns"],
-            config["configurable"]["checkpoint_id"],
-            task_id,
-            task_path,
-            writes,
-        )
-        async with self._cursor(pipeline=True) as cur:
-            await cur.executemany(query, params)
-
-    @asynccontextmanager
-    async def _cursor(
-        self, *, pipeline: bool = False
-    ) -> AsyncIterator[AsyncCursor[DictRow]]:
-        """Create a database cursor as a context manager.
-
-        Args:
-            pipeline: whether to use pipeline for the DB operations inside the context manager.
-                Will be applied regardless of whether the AsyncShallowPostgresSaver instance was initialized with a pipeline.
-                If pipeline mode is not supported, will fall back to using transaction context manager.
-        """
-        async with _ainternal.get_connection(self.conn) as conn:
-            if self.pipe:
-                # a connection in pipeline mode can be used concurrently
-                # in multiple threads/coroutines, but only one cursor can be
-                # used at a time
-                try:
-                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
-                        yield cur
-                finally:
-                    if pipeline:
-                        await self.pipe.sync()
-            elif pipeline:
-                # a connection not in pipeline mode can only be used by one
-                # thread/coroutine at a time, so we acquire a lock
-                if self.supports_pipeline:
-                    async with (
-                        self.lock,
-                        conn.pipeline(),
-                        conn.cursor(binary=True, row_factory=dict_row) as cur,
-                    ):
-                        yield cur
-                else:
-                    # Use connection's transaction context manager when pipeline mode not supported
-                    async with (
-                        self.lock,
-                        conn.transaction(),
-                        conn.cursor(binary=True, row_factory=dict_row) as cur,
-                    ):
-                        yield cur
-            else:
-                async with (
-                    self.lock,
-                    conn.cursor(binary=True, row_factory=dict_row) as cur,
-                ):
-                    yield cur
-
-    def list(
-        self,
-        config: Optional[RunnableConfig],
-        *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
-    ) -> Iterator[CheckpointTuple]:
-        """List checkpoints from the database.
-
-        This method retrieves a list of checkpoint tuples from the Postgres database based
-        on the provided config. For ShallowPostgresSaver, this method returns a list with
-        ONLY the most recent checkpoint.
-        """
-        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
-        while True:
-            try:
-                yield asyncio.run_coroutine_threadsafe(
-                    anext(aiter_),  # noqa: F821
-                    self.loop,
-                ).result()
-            except StopAsyncIteration:
-                break
-
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        """Get a checkpoint tuple from the database.
-
-        This method retrieves a checkpoint tuple from the Postgres database based on the
-        provided config (matching the thread ID in the config).
-
-        Args:
-            config: The config to use for retrieving the checkpoint.
-
-        Returns:
-            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
-        """
-        try:
-            # check if we are in the main thread, only bg threads can block
-            # we don't check in other methods to avoid the overhead
-            if asyncio.get_running_loop() is self.loop:
-                raise asyncio.InvalidStateError(
-                    "Synchronous calls to AsyncShallowPostgresSaver are only allowed from a "
-                    "different thread. From the main thread, use the async interface."
-                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
-                    "graph.ainvoke(...)`."
-                )
-        except RuntimeError:
-            pass
-        return asyncio.run_coroutine_threadsafe(
-            self.aget_tuple(config), self.loop
-        ).result()
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        """Save a checkpoint to the database.
-
-        This method saves a checkpoint to the Postgres database. The checkpoint is associated
-        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
-        checkpoint and overwrites a previous checkpoint, if it exists.
-
-        Args:
-            config: The config to associate with the checkpoint.
-            checkpoint: The checkpoint to save.
-            metadata: Additional metadata to save with the checkpoint.
-            new_versions: New channel versions as of this write.
-
-        Returns:
-            RunnableConfig: Updated configuration after storing the checkpoint.
-        """
-        return asyncio.run_coroutine_threadsafe(
-            self.aput(config, checkpoint, metadata, new_versions), self.loop
-        ).result()
-
-    def put_writes(
-        self,
-        config: RunnableConfig,
-        writes: Sequence[tuple[str, Any]],
-        task_id: str,
-        task_path: str = "",
-    ) -> None:
-        """Store intermediate writes linked to a checkpoint.
-
-        This method saves intermediate writes associated with a checkpoint to the database.
-
-        Args:
-            config: Configuration of the related checkpoint.
-            writes: List of writes to store, each as (channel, value) pair.
-            task_id: Identifier for the task creating the writes.
-            task_path: Path of the task creating the writes.
-        """
-        return asyncio.run_coroutine_threadsafe(
-            self.aput_writes(config, writes, task_id, task_path), self.loop
-        ).result()

libs/checkpoint-postgres/tests/checkpoint_utils.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from datetime import datetime, timezone
+from typing import Any, Protocol
+
+from langgraph.checkpoint.base import Checkpoint, EmptyChannelError
+from langgraph.checkpoint.base.id import uuid6
+
+
+class ChannelProtocol(Protocol):
+    def checkpoint(self) -> Any | None: ...
+
+
+def empty_checkpoint() -> Checkpoint:
+    return Checkpoint(
+        v=1,
+        id=str(uuid6(clock_seq=-2)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values={},
+        channel_versions={},
+        versions_seen={},
+    )
+
+
+def create_checkpoint(
+    checkpoint: Checkpoint,
+    channels: Mapping[str, ChannelProtocol] | None,
+    step: int,
+    *,
+    id: str | None = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values = {}
+        for k, v in channels.items():
+            if k not in checkpoint["channel_versions"]:
+                continue
+            try:
+                values[k] = v.checkpoint()
+            except EmptyChannelError:
+                pass
+    return Checkpoint(
+        v=1,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+    )

libs/checkpoint-postgres/tests/test_async.py

@@ -14,14 +14,10 @@ from langgraph.checkpoint.base import (
     EXCLUDED_METADATA_KEYS,
     Checkpoint,
     CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
-)
-from langgraph.checkpoint.postgres.aio import (
-    AsyncPostgresSaver,
-    AsyncShallowPostgresSaver,
 )
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 from langgraph.checkpoint.serde.types import TASKS
+from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 from tests.conftest import DEFAULT_POSTGRES_URI
 
 
@@ -112,41 +108,11 @@ async def _base_saver():
             await conn.execute(f"DROP DATABASE {database}")
 
 
-@asynccontextmanager
-async def _shallow_saver():
-    """Fixture for shallow connection mode testing."""
-    database = f"test_{uuid4().hex[:16]}"
-    # create unique db
-    async with await AsyncConnection.connect(
-        DEFAULT_POSTGRES_URI, autocommit=True
-    ) as conn:
-        await conn.execute(f"CREATE DATABASE {database}")
-    try:
-        async with await AsyncConnection.connect(
-            DEFAULT_POSTGRES_URI + database,
-            autocommit=True,
-            prepare_threshold=0,
-            row_factory=dict_row,
-        ) as conn:
-            checkpointer = AsyncShallowPostgresSaver(conn)
-            await checkpointer.setup()
-            yield checkpointer
-    finally:
-        # drop unique db
-        async with await AsyncConnection.connect(
-            DEFAULT_POSTGRES_URI, autocommit=True
-        ) as conn:
-            await conn.execute(f"DROP DATABASE {database}")
-
-
 @asynccontextmanager
 async def _saver(name: str):
     if name == "base":
         async with _base_saver() as saver:
             yield saver
-    elif name == "shallow":
-        async with _shallow_saver() as saver:
-            yield saver
     elif name == "pool":
         async with _pool_saver() as saver:
             yield saver
@@ -206,7 +172,7 @@ def test_data():
     }
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 async def test_combined_metadata(saver_name: str, test_data) -> None:
     async with _saver(saver_name) as saver:
         config = {
@@ -233,7 +199,7 @@ async def test_combined_metadata(saver_name: str, test_data) -> None:
         }
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 async def test_asearch(saver_name: str, test_data) -> None:
     async with _saver(saver_name) as saver:
         configs = test_data["configs"]
@@ -284,7 +250,7 @@ async def test_asearch(saver_name: str, test_data) -> None:
         } == {"", "inner"}
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 async def test_null_chars(saver_name: str, test_data) -> None:
     async with _saver(saver_name) as saver:
         config = await saver.aput(

libs/checkpoint-postgres/tests/test_sync.py

@@ -15,11 +15,10 @@ from langgraph.checkpoint.base import (
     EXCLUDED_METADATA_KEYS,
     Checkpoint,
     CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
 )
-from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
+from langgraph.checkpoint.postgres import PostgresSaver
 from langgraph.checkpoint.serde.types import TASKS
+from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 from tests.conftest import DEFAULT_POSTGRES_URI
 
 
@@ -98,37 +97,11 @@ def _base_saver():
             conn.execute(f"DROP DATABASE {database}")
 
 
-@contextmanager
-def _shallow_saver():
-    """Fixture for regular connection mode testing with a shallow checkpointer."""
-    database = f"test_{uuid4().hex[:16]}"
-    # create unique db
-    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
-        conn.execute(f"CREATE DATABASE {database}")
-    try:
-        with Connection.connect(
-            DEFAULT_POSTGRES_URI + database,
-            autocommit=True,
-            prepare_threshold=0,
-            row_factory=dict_row,
-        ) as conn:
-            checkpointer = ShallowPostgresSaver(conn)
-            checkpointer.setup()
-            yield checkpointer
-    finally:
-        # drop unique db
-        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
-            conn.execute(f"DROP DATABASE {database}")
-
-
 @contextmanager
 def _saver(name: str):
     if name == "base":
         with _base_saver() as saver:
             yield saver
-    elif name == "shallow":
-        with _shallow_saver() as saver:
-            yield saver
     elif name == "pool":
         with _pool_saver() as saver:
             yield saver
@@ -188,7 +161,7 @@ def test_data():
     }
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 def test_combined_metadata(saver_name: str, test_data) -> None:
     with _saver(saver_name) as saver:
         config = {
@@ -215,7 +188,7 @@ def test_combined_metadata(saver_name: str, test_data) -> None:
         }
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 def test_search(saver_name: str, test_data) -> None:
     with _saver(saver_name) as saver:
         configs = test_data["configs"]
@@ -264,7 +237,7 @@ def test_search(saver_name: str, test_data) -> None:
         } == {"", "inner"}
 
 
-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
 def test_null_chars(saver_name: str, test_data) -> None:
     with _saver(saver_name) as saver:
         config = saver.put(

libs/checkpoint-postgres/uv.lock

@@ -328,7 +328,6 @@ dev = [
     { name = "mypy" },
     { name = "numpy" },
     { name = "pandas" },
-    { name = "pandas-stubs", specifier = ">=2.2.2.240807" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/__init__.py

@@ -536,7 +536,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         """
         raise NotImplementedError(_AIO_ERROR_MSG)
 
-    def get_next_version(self, current: str | None, channel: None) -> str:
+    def get_next_version(self, current: str | None) -> str:
         """Generate the next version ID for a channel.
 
         This method creates a new version identifier for a channel based on its current version.

libs/checkpoint-sqlite/langgraph/checkpoint/sqlite/aio.py

@@ -591,7 +591,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
             )
             await self.conn.commit()
 
-    def get_next_version(self, current: str | None, channel: None) -> str:
+    def get_next_version(self, current: str | None) -> str:
         """Generate the next version ID for a channel.
 
         This method creates a new version identifier for a channel based on its current version.

libs/checkpoint-sqlite/tests/checkpoint_utils.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from datetime import datetime, timezone
+from typing import Any, Protocol
+
+from langgraph.checkpoint.base import Checkpoint, EmptyChannelError
+from langgraph.checkpoint.base.id import uuid6
+
+
+class ChannelProtocol(Protocol):
+    def checkpoint(self) -> Any | None: ...
+
+
+def empty_checkpoint() -> Checkpoint:
+    return Checkpoint(
+        v=1,
+        id=str(uuid6(clock_seq=-2)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values={},
+        channel_versions={},
+        versions_seen={},
+    )
+
+
+def create_checkpoint(
+    checkpoint: Checkpoint,
+    channels: Mapping[str, ChannelProtocol] | None,
+    step: int,
+    *,
+    id: str | None = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values = {}
+        for k, v in channels.items():
+            if k not in checkpoint["channel_versions"]:
+                continue
+            try:
+                values[k] = v.checkpoint()
+            except EmptyChannelError:
+                pass
+    return Checkpoint(
+        v=1,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+    )

libs/checkpoint-sqlite/tests/test_aiosqlite.py

@@ -6,10 +6,9 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 
 
 class TestAsyncSqliteSaver:

libs/checkpoint-sqlite/tests/test_sqlite.py

@@ -6,11 +6,10 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
-    create_checkpoint,
-    empty_checkpoint,
 )
 from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.checkpoint.sqlite.utils import _metadata_predicate, search_where
+from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 
 
 class TestSqliteSaver:

libs/checkpoint-sqlite/uv.lock

@@ -340,7 +340,6 @@ dev = [
     { name = "mypy" },
     { name = "numpy" },
     { name = "pandas" },
-    { name = "pandas-stubs", specifier = ">=2.2.2.240807" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },

libs/checkpoint/langgraph/checkpoint/base/__init__.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
+from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import (  # noqa: UP035
     Any,
     Generic,
@@ -13,7 +13,6 @@ from typing import (  # noqa: UP035
 
 from langchain_core.runnables import RunnableConfig
 
-from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.checkpoint.serde.types import (
@@ -21,7 +20,6 @@ from langgraph.checkpoint.serde.types import (
     INTERRUPT,
     RESUME,
     SCHEDULED,
-    ChannelProtocol,
 )
 
 V = TypeVar("V", int, float, str)
@@ -91,7 +89,6 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
         channel_values=checkpoint["channel_values"].copy(),
         channel_versions=checkpoint["channel_versions"].copy(),
         versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
-        pending_sends=checkpoint.get("pending_sends", []).copy(),
     )
 
 
@@ -128,15 +125,6 @@ class BaseCheckpointSaver(Generic[V]):
     ) -> None:
         self.serde = maybe_add_typed_methods(serde or self.serde)
 
-    @property
-    def config_specs(self) -> list:
-        """Define the configuration options for the checkpoint saver.
-
-        Returns:
-            list: List of configuration field specs.
-        """
-        return []
-
     def get(self, config: RunnableConfig) -> Checkpoint | None:
         """Fetch a checkpoint using the given configuration.
 
@@ -346,7 +334,7 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
-    def get_next_version(self, current: V | None, channel: None) -> V:
+    def get_next_version(self, current: V | None) -> V:
         """Generate the next version ID for a channel.
 
         Default is to use integer versions, incrementing by 1. If you override, you can use str/int/float versions,
@@ -354,7 +342,6 @@ class BaseCheckpointSaver(Generic[V]):
 
         Args:
             current: The current version identifier (int, float, or str).
-            channel: Deprecated argument, kept for backwards compatibility.
 
         Returns:
             V: The next version identifier, which must be increasing.
@@ -417,54 +404,3 @@ EXCLUDED_METADATA_KEYS = {
     "checkpoint_ns",
     "checkpoint_map",
 }
-
-# --- below are deprecated utilities used by past versions of LangGraph ---
-
-LATEST_VERSION = 2
-
-
-def empty_checkpoint() -> Checkpoint:
-    from datetime import datetime, timezone
-
-    return Checkpoint(
-        v=LATEST_VERSION,
-        id=str(uuid6(clock_seq=-2)),
-        ts=datetime.now(timezone.utc).isoformat(),
-        channel_values={},
-        channel_versions={},
-        versions_seen={},
-        pending_sends=[],
-    )
-
-
-def create_checkpoint(
-    checkpoint: Checkpoint,
-    channels: Mapping[str, ChannelProtocol] | None,
-    step: int,
-    *,
-    id: str | None = None,
-) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
-    from datetime import datetime, timezone
-
-    ts = datetime.now(timezone.utc).isoformat()
-    if channels is None:
-        values = checkpoint["channel_values"]
-    else:
-        values = {}
-        for k, v in channels.items():
-            if k not in checkpoint["channel_versions"]:
-                continue
-            try:
-                values[k] = v.checkpoint()
-            except EmptyChannelError:
-                pass
-    return Checkpoint(
-        v=LATEST_VERSION,
-        ts=ts,
-        id=id or str(uuid6(clock_seq=step)),
-        channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
-        versions_seen=checkpoint["versions_seen"],
-        pending_sends=checkpoint.get("pending_sends", []),
-    )

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -512,7 +512,7 @@ class InMemorySaver(
         """
         return self.delete_thread(thread_id)
 
-    def get_next_version(self, current: str | None, channel: None) -> str:
+    def get_next_version(self, current: str | None) -> str:
         if current is None:
             current_v = 0
         elif isinstance(current, int):

libs/checkpoint/langgraph/checkpoint/serde/types.py

@@ -1,13 +1,4 @@
-from collections.abc import Sequence
-from typing import (
-    Any,
-    Optional,
-    Protocol,
-    TypeVar,
-    runtime_checkable,
-)
-
-from typing_extensions import Self
+from typing import Any, Protocol, TypeVar, runtime_checkable
 
 ERROR = "__error__"
 SCHEDULED = "__scheduled__"
@@ -20,25 +11,6 @@ Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")
 
 
-class ChannelProtocol(Protocol[Value, Update, C]):
-    # Mirrors langgraph.channels.base.BaseChannel
-    @property
-    def ValueType(self) -> Any: ...
-
-    @property
-    def UpdateType(self) -> Any: ...
-
-    def checkpoint(self) -> Optional[C]: ...
-
-    def from_checkpoint(self, checkpoint: Optional[C]) -> Self: ...
-
-    def update(self, values: Sequence[Update]) -> bool: ...
-
-    def get(self) -> Value: ...
-
-    def consume(self) -> bool: ...
-
-
 @runtime_checkable
 class SendProtocol(Protocol):
     # Mirrors langgraph.constants.Send

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.0.26"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.9"
@@ -31,7 +31,6 @@ dev = [
   "dataclasses-json",
   "numpy",
   "pandas",
-  "pandas-stubs>=2.2.2.240807",
 ]
 
 [tool.hatch.build.targets.wheel]

libs/checkpoint/tests/checkpoint_utils.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+from datetime import datetime, timezone
+from typing import Any, Protocol
+
+from langgraph.checkpoint.base import Checkpoint, EmptyChannelError
+from langgraph.checkpoint.base.id import uuid6
+
+
+class ChannelProtocol(Protocol):
+    def checkpoint(self) -> Any | None: ...
+
+
+def empty_checkpoint() -> Checkpoint:
+    return Checkpoint(
+        v=1,
+        id=str(uuid6(clock_seq=-2)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values={},
+        channel_versions={},
+        versions_seen={},
+    )
+
+
+def create_checkpoint(
+    checkpoint: Checkpoint,
+    channels: Mapping[str, ChannelProtocol] | None,
+    step: int,
+    *,
+    id: str | None = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values = {}
+        for k, v in channels.items():
+            if k not in checkpoint["channel_versions"]:
+                continue
+            try:
+                values[k] = v.checkpoint()
+            except EmptyChannelError:
+                pass
+    return Checkpoint(
+        v=1,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+    )

libs/checkpoint/tests/test_memory.py

@@ -6,10 +6,12 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
     Checkpoint,
     CheckpointMetadata,
+)
+from langgraph.checkpoint.memory import InMemorySaver
+from tests.checkpoint_utils import (
     create_checkpoint,
     empty_checkpoint,
 )
-from langgraph.checkpoint.memory import InMemorySaver
 
 
 class TestMemorySaver:

libs/cli/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-cli"
-version = "0.3.3"
+version = "0.3.2"
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/cli/uv.lock

@@ -501,7 +501,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-cli"
-version = "0.3.3"
+version = "0.3.2"
 source = { editable = "." }
 dependencies = [
     { name = "click" },

libs/langgraph/langgraph/channels/__init__.py

@@ -1,15 +1,14 @@
 from langgraph.channels.any_value import AnyValue
 from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.ephemeral_value import EphemeralValue
-from langgraph.channels.last_value import LastValue
+from langgraph.channels.last_value import LastValue, LastValueAfterFinish
 from langgraph.channels.topic import Topic
-from langgraph.channels.untracked_value import UntrackedValue
 
 __all__ = [
     "LastValue",
+    "LastValueAfterFinish",
     "Topic",
     "BinaryOperatorAggregate",
-    "UntrackedValue",
     "EphemeralValue",
     "AnyValue",
 ]

libs/langgraph/langgraph/channels/untracked_value.py

@@ -1,66 +0,0 @@
-from collections.abc import Sequence
-from typing import Generic
-
-from typing_extensions import Self
-
-from langgraph.channels.base import BaseChannel, Value
-from langgraph.constants import MISSING
-from langgraph.errors import EmptyChannelError, InvalidUpdateError
-
-
-class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
-    """Stores the last value received, never checkpointed."""
-
-    __slots__ = ("value", "guard")
-
-    def __init__(self, typ: type[Value], guard: bool = True) -> None:
-        super().__init__(typ)
-        self.guard = guard
-        self.value = MISSING
-
-    def __eq__(self, value: object) -> bool:
-        return isinstance(value, UntrackedValue) and value.guard == self.guard
-
-    @property
-    def ValueType(self) -> type[Value]:
-        """The type of the value stored in the channel."""
-        return self.typ
-
-    @property
-    def UpdateType(self) -> type[Value]:
-        """The type of the update received by the channel."""
-        return self.typ
-
-    def copy(self) -> Self:
-        """Return a copy of the channel."""
-        empty = self.__class__(self.typ, self.guard)
-        empty.key = self.key
-        empty.value = self.value
-        return empty
-
-    def checkpoint(self) -> Value:
-        return MISSING
-
-    def from_checkpoint(self, checkpoint: Value) -> Self:
-        empty = self.__class__(self.typ, self.guard)
-        empty.key = self.key
-        return empty
-
-    def update(self, values: Sequence[Value]) -> bool:
-        if len(values) == 0:
-            return False
-        if len(values) != 1 and self.guard:
-            raise InvalidUpdateError(
-                f"At key '{self.key}': UntrackedValue(guard=True) can receive only one value per step. Use guard=False if you want to store any one of multiple values."
-            )
-
-        self.value = values[-1]
-        return True
-
-    def get(self) -> Value:
-        if self.value is MISSING:
-            raise EmptyChannelError()
-        return self.value
-
-    def is_available(self) -> bool:
-        return self.value is not MISSING

libs/langgraph/langgraph/func/__init__.py

@@ -38,7 +38,7 @@ from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.store.base import BaseStore
 from langgraph.types import _DC_KWARGS, CachePolicy, RetryPolicy, StreamMode
-from langgraph.warnings import LangGraphDeprecatedSinceV05
+from langgraph.warnings import LangGraphDeprecatedSinceV10
 
 
 class TaskFunction(Generic[P, T]):
@@ -179,7 +179,7 @@ def task(
     if (retry := kwargs.get("retry", UNSET)) is not UNSET:
         warnings.warn(
             "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
-            category=LangGraphDeprecatedSinceV05,
+            category=LangGraphDeprecatedSinceV10,
         )
         if retry_policy is None:
             retry_policy = retry  # type: ignore[assignment]
@@ -383,7 +383,7 @@ class entrypoint:
         if (retry := kwargs.get("retry", UNSET)) is not UNSET:
             warnings.warn(
                 "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
-                category=LangGraphDeprecatedSinceV05,
+                category=LangGraphDeprecatedSinceV10,
             )
             if retry_policy is None:
                 retry_policy = retry  # type: ignore[assignment]

libs/langgraph/langgraph/graph/__init__.py

@@ -1,12 +1,11 @@
 from langgraph.constants import END, START
-from langgraph.graph.message import MessageGraph, MessagesState, add_messages
+from langgraph.graph.message import MessagesState, add_messages
 from langgraph.graph.state import StateGraph
 
 __all__ = [
     "END",
     "START",
     "StateGraph",
-    "MessageGraph",
     "add_messages",
     "MessagesState",
 ]

libs/langgraph/langgraph/graph/message.py

@@ -25,7 +25,6 @@ from langchain_core.messages import (
 from typing_extensions import TypedDict
 
 from langgraph.constants import CONF, CONFIG_KEY_SEND
-from langgraph.graph.state import StateGraph
 
 Messages = Union[list[MessageLikeRepresentation], MessageLikeRepresentation]
 
@@ -227,57 +226,6 @@ def add_messages(
     return merged
 
 
-class MessageGraph(StateGraph):
-    """A StateGraph where every node receives a list of messages as input and returns one or more messages as output.
-
-    MessageGraph is a subclass of StateGraph whose entire state is a single, append-only* list of messages.
-    Each node in a MessageGraph takes a list of messages as input and returns zero or more
-    messages as output. The `add_messages` function is used to merge the output messages from each node
-    into the existing list of messages in the graph's state.
-
-    Examples:
-        ```pycon
-        >>> from langgraph.graph.message import MessageGraph
-        ...
-        >>> builder = MessageGraph()
-        >>> builder.add_node("chatbot", lambda state: [("assistant", "Hello!")])
-        >>> builder.set_entry_point("chatbot")
-        >>> builder.set_finish_point("chatbot")
-        >>> builder.compile().invoke([("user", "Hi there.")])
-        [HumanMessage(content="Hi there.", id='...'), AIMessage(content="Hello!", id='...')]
-        ```
-
-        ```pycon
-        >>> from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
-        >>> from langgraph.graph.message import MessageGraph
-        ...
-        >>> builder = MessageGraph()
-        >>> builder.add_node(
-        ...     "chatbot",
-        ...     lambda state: [
-        ...         AIMessage(
-        ...             content="Hello!",
-        ...             tool_calls=[{"name": "search", "id": "123", "args": {"query": "X"}}],
-        ...         )
-        ...     ],
-        ... )
-        >>> builder.add_node(
-        ...     "search", lambda state: [ToolMessage(content="Searching...", tool_call_id="123")]
-        ... )
-        >>> builder.set_entry_point("chatbot")
-        >>> builder.add_edge("chatbot", "search")
-        >>> builder.set_finish_point("search")
-        >>> builder.compile().invoke([HumanMessage(content="Hi there. Can you search for X?")])
-        {'messages': [HumanMessage(content="Hi there. Can you search for X?", id='b8b7d8f4-7f4d-4f4d-9c1d-f8b8d8f4d9c1'),
-                     AIMessage(content="Hello!", id='f4d9c1d8-8d8f-4d9c-b8b7-d8f4f4d9c1d8'),
-                     ToolMessage(content="Searching...", id='d8f4f4d9-c1d8-4f4d-b8b7-d8f4f4d9c1d8', tool_call_id="123")]}
-        ```
-    """
-
-    def __init__(self) -> None:
-        super().__init__(Annotated[list[AnyMessage], add_messages])  # type: ignore[arg-type]
-
-
 class MessagesState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 

libs/langgraph/langgraph/graph/state.py

@@ -86,7 +86,7 @@ from langgraph.utils.fields import (
 )
 from langgraph.utils.pydantic import create_model
 from langgraph.utils.runnable import coerce_to_runnable
-from langgraph.warnings import LangGraphDeprecatedSinceV05
+from langgraph.warnings import LangGraphDeprecatedSinceV10
 
 logger = logging.getLogger(__name__)
 
@@ -160,7 +160,6 @@ StateNode: TypeAlias = Union[
     _NodeWithConfigWriter[StateT_contra],
     _NodeWithConfigStore[StateT_contra],
     _NodeWithConfigWriterStore[StateT_contra],
-    Runnable[StateT_contra, Any],
 ]
 
 
@@ -262,7 +261,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         if (input_ := kwargs.get("input", UNSET)) is not UNSET:
             warnings.warn(
                 "`input` is deprecated and will be removed. Please use `input_schema` instead.",
-                category=LangGraphDeprecatedSinceV05,
+                category=LangGraphDeprecatedSinceV10,
                 stacklevel=2,
             )
             if input_schema is None:
@@ -271,7 +270,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         if (output := kwargs.get("output", UNSET)) is not UNSET:
             warnings.warn(
                 "`output` is deprecated and will be removed. Please use `output_schema` instead.",
-                category=LangGraphDeprecatedSinceV05,
+                category=LangGraphDeprecatedSinceV10,
                 stacklevel=2,
             )
             if output_schema is None:
@@ -437,7 +436,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         if (retry := kwargs.get("retry", UNSET)) is not UNSET:
             warnings.warn(
                 "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
-                category=LangGraphDeprecatedSinceV05,
+                category=LangGraphDeprecatedSinceV10,
             )
             if retry_policy is None:
                 retry_policy = retry  # type: ignore[assignment]
@@ -445,7 +444,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         if (input_ := kwargs.get("input", UNSET)) is not UNSET:
             warnings.warn(
                 "`input` is deprecated and will be removed. Please use `input_schema` instead.",
-                category=LangGraphDeprecatedSinceV05,
+                category=LangGraphDeprecatedSinceV10,
             )
             if input_schema is None:
                 input_schema = cast(Union[type[InputT], None], input_)
@@ -536,7 +535,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         if input_schema is not None:
             self._add_schema(input_schema)
         self.nodes[node] = StateNodeSpec(
-            coerce_to_runnable(action, name=node, trace=False),
+            coerce_to_runnable(action, name=node, trace=False),  # type: ignore
             metadata,
             input=input_schema or self.state_schema,
             retry_policy=retry_policy,
@@ -1102,7 +1101,6 @@ class CompiledStateGraph(
 
     def _migrate_checkpoint(self, checkpoint: Checkpoint) -> None:
         """Migrate a checkpoint to new channel layout."""
-        super()._migrate_checkpoint(checkpoint)
 
         values = checkpoint["channel_values"]
         versions = checkpoint["channel_versions"]

libs/langgraph/langgraph/pregel/__init__.py

@@ -32,6 +32,7 @@ from langgraph.checkpoint.base import (
     BaseCheckpointSaver,
     Checkpoint,
     CheckpointTuple,
+    copy_checkpoint,
 )
 from langgraph.config import get_config
 from langgraph.constants import (
@@ -78,7 +79,6 @@ from langgraph.pregel.algo import (
 from langgraph.pregel.call import identifier
 from langgraph.pregel.checkpoint import (
     channels_from_checkpoint,
-    copy_checkpoint,
     create_checkpoint,
     empty_checkpoint,
 )
@@ -908,12 +908,7 @@ class Pregel(PregelProtocol[StateT, InputT, OutputT], Generic[StateT, InputT, Ou
 
     def _migrate_checkpoint(self, checkpoint: Checkpoint) -> None:
         """Migrate a saved checkpoint to new channel layout."""
-        if checkpoint["v"] < 4 and checkpoint.get("pending_sends"):
-            pending_sends: list[Send] = checkpoint.pop("pending_sends")
-            checkpoint["channel_values"][TASKS] = pending_sends
-            checkpoint["channel_versions"][TASKS] = max(
-                checkpoint["channel_versions"].values()
-            )
+        pass
 
     def _prepare_state_snapshot(
         self,
@@ -2303,8 +2298,7 @@ class Pregel(PregelProtocol[StateT, InputT, OutputT], Generic[StateT, InputT, Ou
                 - `"custom"`: Emit custom data from inside nodes or tasks using `StreamWriter`.
                 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
                     Will be emitted as 2-tuples `(LLM token, metadata)`.
-                - `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by get_state().
-                - `"tasks"`: Emit events when tasks start and finish, including their results and errors.
+                - `"debug"`: Emit debug events with as much information as possible for each step.
 
                 You can pass a list as the `stream_mode` parameter to stream multiple modes at once.
                 The streamed outputs will be tuples of `(mode, data)`.

libs/langgraph/langgraph/pregel/algo.py

@@ -83,7 +83,7 @@ from langgraph.types import (
 )
 from langgraph.utils.config import merge_configs, patch_config
 
-GetNextVersion = Callable[[Optional[V], None], V]
+GetNextVersion = Callable[[Optional[V]], V]
 SUPPORTS_EXC_NOTES = sys.version_info >= (3, 11)
 
 
@@ -214,7 +214,7 @@ def local_read(
     return values
 
 
-def increment(current: int | None, channel: None) -> int:
+def increment(current: int | None) -> int:
     """Default channel versioning function, increments the current int version."""
     return current + 1 if current is not None else 1
 
@@ -265,8 +265,7 @@ def apply_writes(
         next_version = get_next_version(
             max(checkpoint["channel_versions"].values())
             if checkpoint["channel_versions"]
-            else None,
-            None,
+            else None
         )
 
     # Consume all channels that were read

libs/langgraph/langgraph/pregel/checkpoint.py

@@ -71,14 +71,3 @@ def channels_from_checkpoint(
         },
         managed_specs,
     )
-
-
-def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
-    return Checkpoint(
-        v=checkpoint["v"],
-        ts=checkpoint["ts"],
-        id=checkpoint["id"],
-        channel_values=checkpoint["channel_values"].copy(),
-        channel_versions=checkpoint["channel_versions"].copy(),
-        versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
-    )

libs/langgraph/langgraph/pregel/debug.py

@@ -3,8 +3,13 @@ from __future__ import annotations
 from collections import defaultdict
 from collections.abc import Iterable, Iterator, Mapping, Sequence
 from dataclasses import asdict
+from datetime import datetime, timezone
 from pprint import pformat
-from typing import Any
+from typing import (
+    Any,
+    Literal,
+    Union,
+)
 from uuid import UUID
 
 from langchain_core.runnables.config import RunnableConfig
@@ -12,7 +17,7 @@ from langchain_core.utils.input import get_bolded_text, get_colored_text
 from typing_extensions import TypedDict
 
 from langgraph.channels.base import BaseChannel
-from langgraph.checkpoint.base import CheckpointMetadata, PendingWrite
+from langgraph.checkpoint.base import Checkpoint, CheckpointMetadata, PendingWrite
 from langgraph.constants import (
     CONF,
     CONFIG_KEY_CHECKPOINT_NS,
@@ -61,43 +66,82 @@ class CheckpointPayload(TypedDict):
     tasks: list[CheckpointTask]
 
 
+class DebugOutputBase(TypedDict):
+    timestamp: str
+    step: int
+
+
+class DebugOutputTask(DebugOutputBase):
+    type: Literal["task"]
+    payload: TaskPayload
+
+
+class DebugOutputTaskResult(DebugOutputBase):
+    type: Literal["task_result"]
+    payload: TaskResultPayload
+
+
+class DebugOutputCheckpoint(DebugOutputBase):
+    type: Literal["checkpoint"]
+    payload: CheckpointPayload
+
+
+DebugOutput = Union[DebugOutputTask, DebugOutputTaskResult, DebugOutputCheckpoint]
+
+
 TASK_NAMESPACE = UUID("6ba7b831-9dad-11d1-80b4-00c04fd430c8")
 
 
-def map_debug_tasks(tasks: Iterable[PregelExecutableTask]) -> Iterator[TaskPayload]:
+def map_debug_tasks(
+    step: int, tasks: Iterable[PregelExecutableTask]
+) -> Iterator[DebugOutputTask]:
     """Produce "task" events for stream_mode=debug."""
+    ts = datetime.now(timezone.utc).isoformat()
     for task in tasks:
         if task.config is not None and TAG_HIDDEN in task.config.get("tags", []):
             continue
 
         yield {
-            "id": task.id,
-            "name": task.name,
-            "input": task.input,
-            "triggers": task.triggers,
+            "type": "task",
+            "timestamp": ts,
+            "step": step,
+            "payload": {
+                "id": task.id,
+                "name": task.name,
+                "input": task.input,
+                "triggers": task.triggers,
+            },
         }
 
 
 def map_debug_task_results(
+    step: int,
     task_tup: tuple[PregelExecutableTask, Sequence[tuple[str, Any]]],
     stream_keys: str | Sequence[str],
-) -> Iterator[TaskResultPayload]:
+) -> Iterator[DebugOutputTaskResult]:
     """Produce "task_result" events for stream_mode=debug."""
     stream_channels_list = (
         [stream_keys] if isinstance(stream_keys, str) else stream_keys
     )
     task, writes = task_tup
     yield {
-        "id": task.id,
-        "name": task.name,
-        "error": next((w[1] for w in writes if w[0] == ERROR), None),
-        "result": [w for w in writes if w[0] in stream_channels_list or w[0] == RETURN],
-        "interrupts": [
-            asdict(v)
-            for w in writes
-            if w[0] == INTERRUPT
-            for v in (w[1] if isinstance(w[1], Sequence) else [w[1]])
-        ],
+        "type": "task_result",
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "step": step,
+        "payload": {
+            "id": task.id,
+            "name": task.name,
+            "error": next((w[1] for w in writes if w[0] == ERROR), None),
+            "result": [
+                w for w in writes if w[0] in stream_channels_list or w[0] == RETURN
+            ],
+            "interrupts": [
+                asdict(v)
+                for w in writes
+                if w[0] == INTERRUPT
+                for v in (w[1] if isinstance(w[1], Sequence) else [w[1]])
+            ],
+        },
     }
 
 
@@ -115,15 +159,17 @@ def rm_pregel_keys(config: RunnableConfig | None) -> RunnableConfig | None:
 
 
 def map_debug_checkpoint(
+    step: int,
     config: RunnableConfig,
     channels: Mapping[str, BaseChannel],
     stream_channels: str | Sequence[str],
     metadata: CheckpointMetadata,
+    checkpoint: Checkpoint,
     tasks: Iterable[PregelExecutableTask],
     pending_writes: list[PendingWrite],
     parent_config: RunnableConfig | None,
     output_keys: str | Sequence[str],
-) -> Iterator[CheckpointPayload]:
+) -> Iterator[DebugOutputCheckpoint]:
     """Produce "checkpoint" events for stream_mode=debug."""
 
     parent_ns = config[CONF].get(CONFIG_KEY_CHECKPOINT_NS, "")
@@ -147,35 +193,42 @@ def map_debug_checkpoint(
         }
 
     yield {
-        "config": rm_pregel_keys(patch_checkpoint_map(config, metadata)),
-        "parent_config": rm_pregel_keys(patch_checkpoint_map(parent_config, metadata)),
-        "values": read_channels(channels, stream_channels),
-        "metadata": metadata,
-        "next": [t.name for t in tasks],
-        "tasks": [
-            {
-                "id": t.id,
-                "name": t.name,
-                "error": t.error,
-                "state": t.state,
-            }
-            if t.error
-            else {
-                "id": t.id,
-                "name": t.name,
-                "result": t.result,
-                "interrupts": tuple(asdict(i) for i in t.interrupts),
-                "state": t.state,
-            }
-            if t.result
-            else {
-                "id": t.id,
-                "name": t.name,
-                "interrupts": tuple(asdict(i) for i in t.interrupts),
-                "state": t.state,
-            }
-            for t in tasks_w_writes(tasks, pending_writes, task_states, output_keys)
-        ],
+        "type": "checkpoint",
+        "timestamp": checkpoint["ts"],
+        "step": step,
+        "payload": {
+            "config": rm_pregel_keys(patch_checkpoint_map(config, metadata)),
+            "parent_config": rm_pregel_keys(
+                patch_checkpoint_map(parent_config, metadata)
+            ),
+            "values": read_channels(channels, stream_channels),
+            "metadata": metadata,
+            "next": [t.name for t in tasks],
+            "tasks": [
+                {
+                    "id": t.id,
+                    "name": t.name,
+                    "error": t.error,
+                    "state": t.state,
+                }
+                if t.error
+                else {
+                    "id": t.id,
+                    "name": t.name,
+                    "result": t.result,
+                    "interrupts": tuple(asdict(i) for i in t.interrupts),
+                    "state": t.state,
+                }
+                if t.result
+                else {
+                    "id": t.id,
+                    "name": t.name,
+                    "interrupts": tuple(asdict(i) for i in t.interrupts),
+                    "state": t.state,
+                }
+                for t in tasks_w_writes(tasks, pending_writes, task_states, output_keys)
+            ],
+        },
     }
 
 

libs/langgraph/langgraph/pregel/loop.py

@@ -11,7 +11,6 @@ from contextlib import (
     AsyncExitStack,
     ExitStack,
 )
-from datetime import datetime, timezone
 from inspect import signature
 from types import TracebackType
 from typing import (
@@ -38,6 +37,7 @@ from langgraph.checkpoint.base import (
     CheckpointMetadata,
     CheckpointTuple,
     PendingWrite,
+    copy_checkpoint,
 )
 from langgraph.constants import (
     CONF,
@@ -84,7 +84,6 @@ from langgraph.pregel.algo import (
 )
 from langgraph.pregel.checkpoint import (
     channels_from_checkpoint,
-    copy_checkpoint,
     create_checkpoint,
     empty_checkpoint,
 )
@@ -119,7 +118,6 @@ from langgraph.types import (
     PregelScratchpad,
     RetryPolicy,
     StreamChunk,
-    StreamMode,
     StreamProtocol,
 )
 from langgraph.utils.config import patch_configurable
@@ -423,7 +421,7 @@ class PregelLoop:
             ),
         ):
             # produce debug output
-            self._emit("tasks", map_debug_tasks, [pushed])
+            self._emit("debug", map_debug_tasks, self.step, [pushed])
             # debug flag
             if self.debug:
                 print_step_tasks(self.step, [pushed])
@@ -473,8 +471,9 @@ class PregelLoop:
         # produce debug output
         if self._checkpointer_put_after_previous is not None:
             self._emit(
-                "checkpoints",
+                "debug",
                 map_debug_checkpoint,
+                self.step - 1,  # printing checkpoint for previous step
                 {
                     **self.checkpoint_config,
                     CONF: {
@@ -485,6 +484,7 @@ class PregelLoop:
                 self.channels,
                 self.stream_keys,
                 self.checkpoint_metadata,
+                self.checkpoint,
                 self.tasks.values(),
                 self.checkpoint_pending_writes,
                 self.prev_checkpoint_config,
@@ -508,7 +508,7 @@ class PregelLoop:
             raise GraphInterrupt()
 
         # produce debug output
-        self._emit("tasks", map_debug_tasks, self.tasks.values())
+        self._emit("debug", map_debug_tasks, self.step, self.tasks.values())
 
         # debug flag
         if self.debug:
@@ -833,39 +833,17 @@ class PregelLoop:
 
     def _emit(
         self,
-        mode: StreamMode,
+        mode: str,
         values: Callable[P, Iterator[Any]],
         *args: P.args,
         **kwargs: P.kwargs,
     ) -> None:
         if self.stream is None:
             return
-        debug_remap = mode in ("checkpoints", "tasks") and "debug" in self.stream.modes
-        if mode not in self.stream.modes and not debug_remap:
+        if mode not in self.stream.modes:
             return
         for v in values(*args, **kwargs):
-            if mode in self.stream.modes:
-                self.stream((self.checkpoint_ns, mode, v))
-            # "debug" mode is "checkpoints" or "tasks" with a wrapper dict
-            if debug_remap:
-                self.stream(
-                    (
-                        self.checkpoint_ns,
-                        "debug",
-                        {
-                            "step": self.step - 1
-                            if mode == "checkpoints"
-                            else self.step,
-                            "timestamp": datetime.now(timezone.utc).isoformat(),
-                            "type": "checkpoint"
-                            if mode == "checkpoints"
-                            else "task_result"
-                            if "result" in v
-                            else "task",
-                            "payload": v,
-                        },
-                    )
-                )
+            self.stream((self.checkpoint_ns, mode, v))
 
     def output_writes(
         self, task_id: str, writes: WritesT, *, cached: bool = False
@@ -906,8 +884,9 @@ class PregelLoop:
                 )
             if not cached:
                 self._emit(
-                    "tasks",
+                    "debug",
                     map_debug_task_results,
+                    self.step,
                     (task, writes),
                     self.stream_keys,
                 )

libs/langgraph/langgraph/types.py

@@ -46,9 +46,7 @@ Checkpointer = Union[None, bool, BaseCheckpointSaver]
 - False disables checkpointing, even if the parent graph has a checkpointer.
 - None inherits checkpointer from the parent graph."""
 
-StreamMode = Literal[
-    "values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"
-]
+StreamMode = Literal["values", "updates", "debug", "messages", "custom"]
 """How the stream method should emit outputs.
 
 - `"values"`: Emit all values in the state after each step, including interrupts.
@@ -57,9 +55,7 @@ StreamMode = Literal[
     If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
 - `"custom"`: Emit custom data using from inside nodes or tasks using `StreamWriter`.
 - `"messages"`: Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes or tasks.
-- `"checkpoints"`: Emit an event when a checkpoint is created, in the same format as returned by get_state().
-- `"tasks"`: Emit events when tasks start and finish, including their results and errors.
-- `"debug"`: Emit "checlkpoints" and "tasks" events, for debugging purposes.
+- `"debug"`: Emit debug events with as much information as possible for each step.
 """
 
 StreamWriter = Callable[[Any], None]

libs/langgraph/langgraph/warnings.py

@@ -41,8 +41,8 @@ class LangGraphDeprecationWarning(DeprecationWarning):
         return message
 
 
-class LangGraphDeprecatedSinceV05(LangGraphDeprecationWarning):
-    """A specific `LangGraphDeprecationWarning` subclass defining functionality deprecated since LangGraph v0.5.0"""
+class LangGraphDeprecatedSinceV10(LangGraphDeprecationWarning):
+    """A specific `LangGraphDeprecationWarning` subclass defining functionality deprecated since LangGraph v1.0.0"""
 
     def __init__(self, message: str, *args: object) -> None:
-        super().__init__(message, *args, since=(0, 5), expected_removal=(2, 0))
+        super().__init__(message, *args, since=(1, 0), expected_removal=(2, 0))

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.4.7"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -13,9 +13,9 @@ license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
     "langchain-core>=0.1",
-    "langgraph-checkpoint>=2.1.0",
+    "langgraph-checkpoint>=2.0.26",
     "langgraph-sdk>=0.1.42",
-    "langgraph-prebuilt>=0.5.0rc0",
+    "langgraph-prebuilt>=0.2.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]

libs/langgraph/tests/conftest.py

@@ -12,7 +12,6 @@ from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.store.base import BaseStore
 from tests.conftest_checkpointer import (
     _checkpointer_memory,
-    _checkpointer_memory_migrate_sends,
     _checkpointer_postgres,
     _checkpointer_postgres_aio,
     _checkpointer_postgres_aio_pipe,
@@ -126,7 +125,6 @@ async def async_store(request: pytest.FixtureRequest) -> AsyncIterator[BaseStore
     if NO_DOCKER
     else [
         "memory",
-        "memory_migrate_sends",
         "sqlite",
         "sqlite_aes",
         "postgres",
@@ -141,9 +139,6 @@ def sync_checkpointer(
     if checkpointer_name == "memory":
         with _checkpointer_memory() as checkpointer:
             yield checkpointer
-    elif checkpointer_name == "memory_migrate_sends":
-        with _checkpointer_memory_migrate_sends() as checkpointer:
-            yield checkpointer
     elif checkpointer_name == "sqlite":
         with _checkpointer_sqlite() as checkpointer:
             yield checkpointer

libs/langgraph/tests/conftest_checkpointer.py

@@ -14,10 +14,7 @@ from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
 
 pytest.register_assert_rewrite("tests.memory_assert")
 
-from tests.memory_assert import (  # noqa: E402
-    MemorySaverAssertImmutable,
-    MemorySaverNeedsPendingSendsMigration,
-)
+from tests.memory_assert import MemorySaverAssertImmutable  # noqa: E402
 
 DEFAULT_POSTGRES_URI = "postgres://postgres:postgres@localhost:5442/"
 
@@ -27,11 +24,6 @@ def _checkpointer_memory():
     yield MemorySaverAssertImmutable()
 
 
-@contextmanager
-def _checkpointer_memory_migrate_sends():
-    yield MemorySaverNeedsPendingSendsMigration()
-
-
 @contextmanager
 def _checkpointer_sqlite():
     with SqliteSaver.from_conn_string(":memory:") as checkpointer:
@@ -195,7 +187,6 @@ async def _checkpointer_postgres_aio_pool():
 
 __all__ = [
     "_checkpointer_memory",
-    "_checkpointer_memory_migrate_sends",
     "_checkpointer_sqlite",
     "_checkpointer_sqlite_aes",
     "_checkpointer_postgres",

libs/langgraph/tests/memory_assert.py

@@ -7,7 +7,6 @@ from typing import Any, Optional
 from langchain_core.runnables import RunnableConfig
 
 from langgraph.checkpoint.base import (
-    BaseCheckpointSaver,
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
@@ -15,7 +14,6 @@ from langgraph.checkpoint.base import (
     SerializerProtocol,
 )
 from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
-from langgraph.constants import TASKS
 
 
 class NoopSerializer(SerializerProtocol):
@@ -26,28 +24,6 @@ class NoopSerializer(SerializerProtocol):
         return "type", obj
 
 
-class MemorySaverNeedsPendingSendsMigration(BaseCheckpointSaver):
-    def __init__(self) -> None:
-        self.saver = InMemorySaver()
-
-    def __getattribute__(self, name):
-        if name in ("saver", "__class__", "get_tuple"):
-            return object.__getattribute__(self, name)
-        return getattr(self.saver, name)
-
-    def get_tuple(self, config):
-        if tup := self.saver.get_tuple(config):
-            if tup.checkpoint["v"] == 4 and tup.checkpoint["channel_values"].get(TASKS):
-                tup.checkpoint["v"] = 3
-                tup.checkpoint["pending_sends"] = tup.checkpoint["channel_values"].pop(
-                    TASKS
-                )
-                tup.checkpoint["channel_versions"].pop(TASKS)
-                for seen in tup.checkpoint["versions_seen"].values():
-                    seen.pop(TASKS, None)
-        return tup
-
-
 class MemorySaverAssertImmutable(InMemorySaver):
     storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
 

libs/langgraph/tests/test_checkpoint_migration.py

@@ -7,9 +7,12 @@ from typing import Annotated, Literal, Optional, Union
 import pytest
 from typing_extensions import TypedDict
 
-from langgraph.checkpoint.base import BaseCheckpointSaver, CheckpointTuple
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    CheckpointTuple,
+    copy_checkpoint,
+)
 from langgraph.graph.state import StateGraph
-from langgraph.pregel.checkpoint import copy_checkpoint
 from langgraph.types import Command, Interrupt, PregelTask, StateSnapshot, interrupt
 from langgraph.utils.config import patch_configurable
 from tests.any_int import AnyInt

libs/langgraph/tests/test_deprecation.py

@@ -4,7 +4,7 @@ from typing_extensions import TypedDict
 from langgraph.func import entrypoint, task
 from langgraph.graph import StateGraph
 from langgraph.types import RetryPolicy
-from langgraph.warnings import LangGraphDeprecatedSinceV05
+from langgraph.warnings import LangGraphDeprecatedSinceV10
 
 
 class PlainState(TypedDict): ...
@@ -14,7 +14,7 @@ def test_add_node_retry_arg() -> None:
     builder = StateGraph(PlainState)
 
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
     ):
         builder.add_node("test_node", lambda state: state, retry=RetryPolicy())  # type: ignore[arg-type]
@@ -22,7 +22,7 @@ def test_add_node_retry_arg() -> None:
 
 def test_task_retry_arg() -> None:
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
     ):
 
@@ -33,7 +33,7 @@ def test_task_retry_arg() -> None:
 
 def test_entrypoint_retry_arg() -> None:
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
     ):
 
@@ -44,7 +44,7 @@ def test_entrypoint_retry_arg() -> None:
 
 def test_state_graph_input_schema() -> None:
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`input` is deprecated and will be removed. Please use `input_schema` instead.",
     ):
         StateGraph(PlainState, input=PlainState)  # type: ignore[arg-type]
@@ -52,7 +52,7 @@ def test_state_graph_input_schema() -> None:
 
 def test_state_graph_output_schema() -> None:
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`output` is deprecated and will be removed. Please use `output_schema` instead.",
     ):
         StateGraph(PlainState, output=PlainState)  # type: ignore[arg-type]
@@ -62,7 +62,7 @@ def test_add_node_input_schema() -> None:
     builder = StateGraph(PlainState)
 
     with pytest.warns(
-        LangGraphDeprecatedSinceV05,
+        LangGraphDeprecatedSinceV10,
         match="`input` is deprecated and will be removed. Please use `input_schema` instead.",
     ):
         builder.add_node("test_node", lambda state: state, input=PlainState)  # type: ignore[arg-type]

libs/langgraph/tests/test_large_cases_async.py

@@ -16,14 +16,13 @@ from langchain_core.runnables import RunnableConfig, RunnablePick
 from pytest_mock import MockerFixture
 from typing_extensions import TypedDict
 
+from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
-from langgraph.channels.untracked_value import UntrackedValue
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, PULL, PUSH, START
-from langgraph.graph.message import MessageGraph, add_messages
+from langgraph.graph.message import add_messages
 from langgraph.graph.state import StateGraph
 from langgraph.prebuilt.chat_agent_executor import create_react_agent
-from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import NodeBuilder, Pregel
 from langgraph.types import PregelTask, Send, StateSnapshot, StreamWriter
 from tests.any_int import AnyInt
@@ -500,7 +499,7 @@ async def test_conditional_graph_state(async_checkpointer: BaseCheckpointSaver)
     from langchain_core.tools import tool
 
     class AgentState(TypedDict):
-        input: Annotated[str, UntrackedValue]
+        input: Annotated[str, EphemeralValue]
         agent_outcome: Optional[Union[AgentAction, AgentFinish]]
         intermediate_steps: Annotated[list[tuple[AgentAction, str]], operator.add]
 
@@ -575,7 +574,6 @@ async def test_conditional_graph_state(async_checkpointer: BaseCheckpointSaver)
     app = workflow.compile()
 
     assert await app.ainvoke({"input": "what is weather in sf"}) == {
-        "input": "what is weather in sf",
         "intermediate_steps": [
             [
                 AgentAction(
@@ -2084,417 +2082,7 @@ async def test_state_graph_packets(async_checkpointer: BaseCheckpointSaver) -> N
     )
 
 
-async def test_message_graph(async_checkpointer: BaseCheckpointSaver) -> None:
-    from langchain_core.language_models.fake_chat_models import (
-        FakeMessagesListChatModel,
-    )
-    from langchain_core.messages import AIMessage, HumanMessage
-    from langchain_core.tools import tool
-
-    class FakeFuntionChatModel(FakeMessagesListChatModel):
-        def bind_functions(self, functions: list):
-            return self
-
-    @tool()
-    def search_api(query: str) -> str:
-        """Searches the API for the query."""
-        return f"result for {query}"
-
-    tools = [search_api]
-
-    model = FakeFuntionChatModel(
-        responses=[
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    }
-                ],
-                id="ai1",
-            ),
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call456",
-                        "name": "search_api",
-                        "args": {"query": "another"},
-                    }
-                ],
-                id="ai2",
-            ),
-            AIMessage(content="answer", id="ai3"),
-        ]
-    )
-
-    # Define the function that determines whether to continue or not
-    def should_continue(messages):
-        last_message = messages[-1]
-        # If there is no function call, then we finish
-        if not last_message.tool_calls:
-            return "end"
-        # Otherwise if there is, we continue
-        else:
-            return "continue"
-
-    # Define a new graph
-    workflow = MessageGraph()
-
-    # Define the two nodes we will cycle between
-    workflow.add_node("agent", model)
-    workflow.add_node("tools", ToolNode(tools))
-
-    # Set the entrypoint as `agent`
-    # This means that this node is the first one called
-    workflow.set_entry_point("agent")
-
-    # We now add a conditional edge
-    workflow.add_conditional_edges(
-        # First, we define the start node. We use `agent`.
-        # This means these are the edges taken after the `agent` node is called.
-        "agent",
-        # Next, we pass in the function that will determine which node is called next.
-        should_continue,
-        # Finally we pass in a mapping.
-        # The keys are strings, and the values are other nodes.
-        # END is a special node marking that the graph should finish.
-        # What will happen is we will call `should_continue`, and then the output of that
-        # will be matched against the keys in this mapping.
-        # Based on which one it matches, that node will then be called.
-        {
-            # If `tools`, then we call the tool node.
-            "continue": "tools",
-            # Otherwise we finish.
-            "end": END,
-        },
-    )
-
-    # We now add a normal edge from `tools` to `agent`.
-    # This means that after `tools` is called, `agent` node is called next.
-    workflow.add_edge("tools", "agent")
-
-    # Finally, we compile it!
-    # This compiles it into a LangChain Runnable,
-    # meaning you can use it as you would any other runnable
-    app = workflow.compile()
-
-    assert await app.ainvoke(HumanMessage(content="what is weather in sf")) == [
-        _AnyIdHumanMessage(
-            content="what is weather in sf",
-        ),
-        AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "id": "tool_call123",
-                    "name": "search_api",
-                    "args": {"query": "query"},
-                }
-            ],
-            id="ai1",  # respects ids passed in
-        ),
-        _AnyIdToolMessage(
-            content="result for query",
-            name="search_api",
-            tool_call_id="tool_call123",
-        ),
-        AIMessage(
-            content="",
-            tool_calls=[
-                {
-                    "id": "tool_call456",
-                    "name": "search_api",
-                    "args": {"query": "another"},
-                }
-            ],
-            id="ai2",
-        ),
-        _AnyIdToolMessage(
-            content="result for another",
-            name="search_api",
-            tool_call_id="tool_call456",
-        ),
-        AIMessage(content="answer", id="ai3"),
-    ]
-
-    assert [
-        c async for c in app.astream([HumanMessage(content="what is weather in sf")])
-    ] == [
-        {
-            "agent": AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    }
-                ],
-                id="ai1",
-            )
-        },
-        {
-            "tools": [
-                _AnyIdToolMessage(
-                    content="result for query",
-                    name="search_api",
-                    tool_call_id="tool_call123",
-                )
-            ]
-        },
-        {
-            "agent": AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call456",
-                        "name": "search_api",
-                        "args": {"query": "another"},
-                    }
-                ],
-                id="ai2",
-            )
-        },
-        {
-            "tools": [
-                _AnyIdToolMessage(
-                    content="result for another",
-                    name="search_api",
-                    tool_call_id="tool_call456",
-                )
-            ]
-        },
-        {"agent": AIMessage(content="answer", id="ai3")},
-    ]
-
-    app_w_interrupt = workflow.compile(
-        checkpointer=async_checkpointer,
-        interrupt_after=["agent"],
-    )
-    config = {"configurable": {"thread_id": "1"}}
-
-    assert [
-        c
-        async for c in app_w_interrupt.astream(
-            HumanMessage(content="what is weather in sf"), config
-        )
-    ] == [
-        {
-            "agent": AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    }
-                ],
-                id="ai1",
-            )
-        },
-        {"__interrupt__": ()},
-    ]
-
-    tup = await app_w_interrupt.checkpointer.aget_tuple(config)
-    assert await app_w_interrupt.aget_state(config) == StateSnapshot(
-        values=[
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "query"},
-                    }
-                ],
-                id="ai1",
-            ),
-        ],
-        tasks=(PregelTask(AnyStr(), "tools", (PULL, "tools")),),
-        next=("tools",),
-        config=tup.config,
-        created_at=tup.checkpoint["ts"],
-        metadata={
-            "parents": {},
-            "source": "loop",
-            "step": 1,
-            "thread_id": "1",
-        },
-        parent_config=None,
-        interrupts=(),
-    )
-
-    # modify ai message
-    last_message = (await app_w_interrupt.aget_state(config)).values[-1]
-    last_message.tool_calls[0]["args"] = {"query": "a different query"}
-    await app_w_interrupt.aupdate_state(config, last_message)
-
-    # message was replaced instead of appended
-    tup = await app_w_interrupt.checkpointer.aget_tuple(config)
-    assert await app_w_interrupt.aget_state(config) == StateSnapshot(
-        values=[
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                id="ai1",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "a different query"},
-                    }
-                ],
-            ),
-        ],
-        tasks=(PregelTask(AnyStr(), "tools", (PULL, "tools")),),
-        next=("tools",),
-        config=tup.config,
-        created_at=tup.checkpoint["ts"],
-        metadata={
-            "parents": {},
-            "source": "update",
-            "step": 2,
-            "thread_id": "1",
-        },
-        parent_config=(
-            [c async for c in app_w_interrupt.checkpointer.alist(config, limit=2)][
-                -1
-            ].config
-        ),
-        interrupts=(),
-    )
-
-    assert [c async for c in app_w_interrupt.astream(None, config)] == [
-        {
-            "tools": [
-                _AnyIdToolMessage(
-                    content="result for a different query",
-                    name="search_api",
-                    tool_call_id="tool_call123",
-                )
-            ]
-        },
-        {
-            "agent": AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call456",
-                        "name": "search_api",
-                        "args": {"query": "another"},
-                    }
-                ],
-                id="ai2",
-            )
-        },
-        {"__interrupt__": ()},
-    ]
-
-    tup = await app_w_interrupt.checkpointer.aget_tuple(config)
-    assert await app_w_interrupt.aget_state(config) == StateSnapshot(
-        values=[
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                id="ai1",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "a different query"},
-                    }
-                ],
-            ),
-            _AnyIdToolMessage(
-                content="result for a different query",
-                name="search_api",
-                tool_call_id="tool_call123",
-            ),
-            AIMessage(
-                content="",
-                tool_calls=[
-                    {
-                        "id": "tool_call456",
-                        "name": "search_api",
-                        "args": {"query": "another"},
-                    }
-                ],
-                id="ai2",
-            ),
-        ],
-        tasks=(PregelTask(AnyStr(), "tools", (PULL, "tools")),),
-        next=("tools",),
-        config=tup.config,
-        created_at=tup.checkpoint["ts"],
-        metadata={
-            "parents": {},
-            "source": "loop",
-            "step": 4,
-            "thread_id": "1",
-        },
-        parent_config=(
-            [c async for c in app_w_interrupt.checkpointer.alist(config, limit=2)][
-                -1
-            ].config
-        ),
-        interrupts=(),
-    )
-
-    await app_w_interrupt.aupdate_state(
-        config,
-        AIMessage(content="answer", id="ai2"),
-    )
-
-    # replaces message even if object identity is different, as long as id is the same
-    tup = await app_w_interrupt.checkpointer.aget_tuple(config)
-    assert await app_w_interrupt.aget_state(config) == StateSnapshot(
-        values=[
-            _AnyIdHumanMessage(content="what is weather in sf"),
-            AIMessage(
-                content="",
-                id="ai1",
-                tool_calls=[
-                    {
-                        "id": "tool_call123",
-                        "name": "search_api",
-                        "args": {"query": "a different query"},
-                    }
-                ],
-            ),
-            _AnyIdToolMessage(
-                content="result for a different query",
-                name="search_api",
-                tool_call_id="tool_call123",
-            ),
-            AIMessage(content="answer", id="ai2"),
-        ],
-        tasks=(),
-        next=(),
-        config=tup.config,
-        created_at=tup.checkpoint["ts"],
-        metadata={
-            "parents": {},
-            "source": "update",
-            "step": 5,
-            "thread_id": "1",
-        },
-        parent_config=(
-            [c async for c in app_w_interrupt.checkpointer.alist(config, limit=2)][
-                -1
-            ].config
-        ),
-        interrupts=(),
-    )
-
-
 async def test_in_one_fan_out_out_one_graph_state() -> None:
-    def sorted_add(x: list[str], y: list[str]) -> list[str]:
-        return sorted(operator.add(x, y))
-
     class State(TypedDict, total=False):
         query: str
         answer: str

libs/langgraph/tests/test_pregel.py

@@ -46,7 +46,7 @@ from langgraph.constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL, START
 from langgraph.errors import InvalidUpdateError, ParentCommand
 from langgraph.func import entrypoint, task
 from langgraph.graph import END, StateGraph
-from langgraph.graph.message import MessageGraph, MessagesState, add_messages
+from langgraph.graph.message import MessagesState, add_messages
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.pregel import (
     GraphRecursionError,
@@ -159,7 +159,7 @@ def test_checkpoint_errors() -> None:
             raise ValueError("Faulty put_writes")
 
     class FaultyVersionCheckpointer(InMemorySaver):
-        def get_next_version(self, current: Optional[int], channel: None) -> int:
+        def get_next_version(self, current: Optional[int]) -> int:
             raise ValueError("Faulty get_next_version")
 
     def logic(inp: str) -> str:
@@ -3994,9 +3994,14 @@ def test_checkpoint_metadata(sync_checkpointer: BaseCheckpointSaver) -> None:
 def test_remove_message_via_state_update(
     sync_checkpointer: BaseCheckpointSaver,
 ) -> None:
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+    from langchain_core.messages import (
+        AIMessage,
+        AnyMessage,
+        HumanMessage,
+        RemoveMessage,
+    )
 
-    workflow = MessageGraph()
+    workflow = StateGraph(Annotated[list[AnyMessage], add_messages])
     workflow.add_node(
         "chatbot",
         lambda state: [
@@ -4027,9 +4032,14 @@ def test_remove_message_via_state_update(
 
 
 def test_remove_message_from_node():
-    from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
+    from langchain_core.messages import (
+        AIMessage,
+        AnyMessage,
+        HumanMessage,
+        RemoveMessage,
+    )
 
-    workflow = MessageGraph()
+    workflow = StateGraph(Annotated[list[AnyMessage], add_messages])
     workflow.add_node(
         "chatbot",
         lambda state: [

libs/langgraph/tests/test_pregel_async.py

@@ -103,7 +103,7 @@ async def test_checkpoint_errors() -> None:
             raise ValueError("Faulty put_writes")
 
     class FaultyVersionCheckpointer(InMemorySaver):
-        def get_next_version(self, current: Optional[int], channel: None) -> int:
+        def get_next_version(self, current: Optional[int]) -> int:
             raise ValueError("Faulty get_next_version")
 
     def logic(inp: str) -> str:

libs/langgraph/uv.lock

@@ -1201,7 +1201,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.0rc1"
+version = "0.4.7"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -1310,7 +1310,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.0.26"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -1330,7 +1330,6 @@ dev = [
     { name = "mypy" },
     { name = "numpy" },
     { name = "pandas" },
-    { name = "pandas-stubs", specifier = ">=2.2.2.240807" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
@@ -1423,7 +1422,7 @@ inmem = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.2.2"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -591,7 +591,7 @@ def create_react_agent(
         workflow = StateGraph(state_schema, config_schema=config_schema)
         workflow.add_node(
             "agent",
-            RunnableCallable(call_model, acall_model),
+            RunnableCallable(call_model, acall_model),  # type: ignore[call-overload]
             input_schema=input_schema,
         )
         if pre_model_hook is not None:
@@ -610,7 +610,7 @@ def create_react_agent(
         if response_format is not None:
             workflow.add_node(
                 "generate_structured_response",
-                RunnableCallable(
+                RunnableCallable(  # type: ignore[call-overload]
                     generate_structured_response,
                     agenerate_structured_response,
                 ),
@@ -660,10 +660,10 @@ def create_react_agent(
     # Define the two nodes we will cycle between
     workflow.add_node(
         "agent",
-        RunnableCallable(call_model, acall_model),
+        RunnableCallable(call_model, acall_model),  # type: ignore[call-overload]
         input_schema=input_schema,
     )
-    workflow.add_node("tools", tool_node)
+    workflow.add_node("tools", tool_node)  # type: ignore[call-overload]
 
     # Optionally add a pre-model hook node that will be called
     # every time before the "agent" (LLM-calling node)
@@ -693,7 +693,7 @@ def create_react_agent(
     if response_format is not None:
         workflow.add_node(
             "generate_structured_response",
-            RunnableCallable(
+            RunnableCallable(  # type: ignore[call-overload]
                 generate_structured_response,
                 agenerate_structured_response,
             ),

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -629,7 +629,7 @@ def tools_condition(
 
     Args:
         state: The state to check for
-            tool calls. Must have a list of messages (MessageGraph) or have the
+            tool calls. Must have a list of messages or have the
             "messages" key (StateGraph).
 
     Returns:

libs/prebuilt/langgraph/prebuilt/tool_validator.py

@@ -2,7 +2,7 @@
 in a langchain graph. It applies a pydantic schema to tool_calls in the models' outputs,
 and returns a ToolMessage with the validated content. If the schema is not valid, it
 returns a ToolMessage with the error message. The ValidationNode can be used in a
-StateGraph with a "messages" key or in a MessageGraph. If multiple tool calls are
+StateGraph with a "messages" key. If multiple tool calls are
 requested, they will be run in parallel.
 """
 
@@ -49,7 +49,7 @@ def _default_format_error(
 class ValidationNode(RunnableCallable):
     """A node that validates all tools requests from the last AIMessage.
 
-    It can be used either in StateGraph with a "messages" key or in MessageGraph.
+    It can be used in StateGraph with a "messages" key.
 
     !!! note
 

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.5.0rc0"
+version = "0.2.2"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"
@@ -12,7 +12,7 @@ readme = "README.md"
 license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
-    "langgraph-checkpoint>=2.1.0",
+    "langgraph-checkpoint>=2.0.10",
     "langchain-core>=0.3.22",
 ]