x

Let you set "pip_installer": "pip" (or uv) to handle corner cases in install compatibilities

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,6 +1,6 @@
 name: "\U0001F41B Bug Report"
 description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
-labels: ["02 Bug Report"]
+labels: [pending,bug]
 body:
   - type: markdown
     attributes:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,4 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
   - name: 🤔 Question or Problem

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,7 +1,7 @@
 name: Documentation
 description: Report an issue related to the LangGraph documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [03 - Documentation]
+labels: [documentation]
 
 body:
 - type: textarea

AGENTS.md

@@ -0,0 +1,55 @@
+# AGENTS Instructions
+
+This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
+
+When you modify code in any library, run the following commands in that library's directory before creating a pull request:
+
+- `make format` – run code formatters
+- `make lint` – run the linter
+- `make test` – execute the test suite
+
+To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
+
+```
+TEST=path/to/test.py make test
+```
+
+Other pytest arguments can also be supplied inside the `TEST` variable.
+
+## Libraries
+
+The repository contains several Python and JavaScript/TypeScript libraries.
+Below is a high-level overview:
+
+- **checkpoint** – base interfaces for LangGraph checkpointers.
+- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
+- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
+- **cli** – official command-line interface for LangGraph.
+- **langgraph** – core framework for building stateful, multi-actor agents.
+- **prebuilt** – high-level APIs for creating and running agents and tools.
+- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
+- **sdk-py** – Python SDK for the LangGraph Platform API.
+
+### Dependency map
+
+The diagram below lists downstream libraries for each production dependency as
+declared in that library's `pyproject.toml` (or `package.json`).
+
+```text
+checkpoint
+├── checkpoint-postgres
+├── checkpoint-sqlite
+├── prebuilt
+└── langgraph
+
+prebuilt
+└── langgraph
+
+sdk-py
+├── langgraph
+└── cli
+
+sdk-js (standalone)
+```
+
+Changes to a library may impact all of its dependents shown above.

Makefile

@@ -0,0 +1,58 @@
+# Define the directories containing projects
+LIBS_DIRS := $(wildcard libs/*)
+
+# Default target
+.PHONY: all
+all: lint format lock test
+
+# Install dependencies for all projects
+.PHONY: install
+install:
+	@echo "Creating virtual environment..."
+	@uv venv
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/pyproject.toml ]; then \
+			echo "Installing dependencies for $$dir"; \
+			uv pip install -e $$dir; \
+		fi; \
+	done
+
+# Lint all projects
+.PHONY: lint
+lint:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running lint in $$dir"; \
+			$(MAKE) -C $$dir lint; \
+		fi; \
+	done
+
+# Format all projects
+.PHONY: format
+format:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running format in $$dir"; \
+			$(MAKE) -C $$dir format; \
+		fi; \
+	done
+
+# Lock all projects
+.PHONY: lock
+lock:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running lock in $$dir"; \
+			(cd $$dir && uv lock); \
+		fi; \
+	done
+
+# Test all projects
+.PHONY: test
+test:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running test in $$dir"; \
+			$(MAKE) -C $$dir test; \
+		fi; \
+	done

README.md

@@ -12,7 +12,6 @@
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
-[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 

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()
@@ -158,6 +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.
 
@@ -257,6 +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/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

@@ -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/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/deployment/self_hosted_control_plane.md

@@ -30,18 +30,16 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
     1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
     1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
     1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
-1. Two additional images will be used by the chart.
+1. Two additional images will be used by the chart. Use the images that are specified in the latest release.
 
         hostBackendImage:
           repository: "docker.io/langchain/hosted-langserve-backend"
           pullPolicy: IfNotPresent
-          tag: "0.9.80"
         operatorImage:
           repository: "docker.io/langchain/langgraph-operator"
           pullPolicy: IfNotPresent
-          tag: "aa9dff4"
 
-1. In your `langsmith_config.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
+1. In your config file for langsmith (usually `langsmith_config.yaml`, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
 
         config:
           langgraphPlatform:

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

@@ -2,7 +2,7 @@
 
 !!! info "Prerequisites"
 
-    - [Assistants Overview](../../concepts/assistants.md)
+    - [Assistants Overview](../../../concepts/assistants.md)
 
 LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
 

docs/docs/cloud/reference/api/openapi.json

@@ -3818,6 +3818,14 @@
             "title": "Filter",
             "description": "Optional dictionary of key-value pairs to filter results."
           },
+          "query": {
+            "type": [
+              "string",
+              "null"
+            ],
+            "title": "Query",
+            "description": "Query string for semantic/vector search."
+          },
           "limit": {
             "type": "integer",
             "default": 10,

docs/docs/cloud/reference/cli.md

@@ -43,6 +43,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and returns an instance of `langgraph.graph.state.StateGraph` or `langgraph.graph.state.CompiledStateGraph`. See [how to rebuild a graph at runtime](../../cloud/deployment/graph_rebuild.md) for more details.</li></ul>                                    |
     | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`base_image`</span>       | Optional. Base image to use for the LangGraph API server. Defaults to `langchain/langgraph-api` or `langchain/langgraphjs-api`. Use this to pin your builds to a particular version of the langgraph API, such as `"langchain/langgraph-server:0.2"`. See https://hub.docker.com/r/langchain/langgraph-server/tags for more details. (added in `langgraph-cli==0.2.8`) |
+    | <span style="white-space: nowrap;">`image_distro`</span>     | Optional. Linux distribution for the base image. Must be either `"debian"` or `"wolfi"`. If omitted, defaults to `"debian"`. Available in `langgraph-cli>=0.2.11`.|
     | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
     | <span style="white-space: nowrap;">`ui`</span>               | Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file. (added in `langgraph-cli==0.1.84`)                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
@@ -79,6 +80,20 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     }
     ```
 
+    #### Using Wolfi Base Images
+
+    You can specify the Linux distribution for your base image using the `image_distro` field. Valid options are `debian` or `wolfi`. Wolfi is the recommended option as it provides smaller and more secure images. This is available in `langgraph-cli>=0.2.11`.
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "image_distro": "wolfi"
+    }
+    ```
+
     #### Adding semantic search to the store
 
     All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.

docs/docs/cloud/reference/env_var.md

@@ -123,3 +123,12 @@ Defaults to `''`.
 Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
 
 Defaults to `False`.
+
+## `MOUNT_PREFIX`
+
+!!! info "Only Allowed in Self-Hosted Deployments"
+    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
+
+Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
+
+For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.

docs/docs/concepts/auth.md

@@ -198,7 +198,7 @@ async def add_owner(
 You can register handlers for specific resources and actions by chaining the resource and action names together with the [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) decorator.
 When a request is made, the most specific handler that matches that resource and action is called. Below is an example of how to register handlers for specific resources and actions. For the following setup:
 
-1. Authenticated users are able to create threads, read thread, create runs on threads
+1. Authenticated users are able to create threads, read threads, and create runs on threads
 2. Only users with the "assistants:create" permission are allowed to create new assistants
 3. All other endpoints (e.g., e.g., delete assistant, crons, store) are disabled for all users.
 

docs/docs/concepts/low_level.md

@@ -89,7 +89,7 @@ def node_3(state: PrivateState) -> OutputState:
     # Read from PrivateState, write to OutputState
     return {"graph_output": state["bar"] + " Lance"}
 
-builder = StateGraph(OverallState,input=InputState,output=OutputState)
+builder = StateGraph(OverallState,input_schema=InputState,output_schema=OutputState)
 builder.add_node("node_1", node_1)
 builder.add_node("node_2", node_2)
 builder.add_node("node_3", node_3)
@@ -107,7 +107,7 @@ There are two subtle and important points to note here:
 
 1. We pass `state: InputState` as the input schema to `node_1`. But, we write out to `foo`, a channel in `OverallState`. How can we write out to a state channel that is not included in the input schema? This is because a node _can write to any state channel in the graph state._ The graph state is the union of the state channels defined at initialization, which includes `OverallState` and the filters `InputState` and `OutputState`.
 
-2. We initialize the graph with `StateGraph(OverallState,input=InputState,output=OutputState)`. So, how can we write to `PrivateState` in `node_2`? How does the graph gain access to this schema if it was not passed in the `StateGraph` initialization? We can do this because _nodes can also declare additional state channels_ as long as the state schema definition exists. In this case, the `PrivateState` schema is defined, so we can add `bar` as a new state channel in the graph and write to it.
+2. We initialize the graph with `StateGraph(OverallState,input_schema=InputState,output_schema=OutputState)`. So, how can we write to `PrivateState` in `node_2`? How does the graph gain access to this schema if it was not passed in the `StateGraph` initialization? We can do this because _nodes can also declare additional state channels_ as long as the state schema definition exists. In this case, the `PrivateState` schema is defined, so we can add `bar` as a new state channel in the graph and write to it.
 
 ### Reducers
 
@@ -197,19 +197,25 @@ In LangGraph, nodes are typically python functions (sync or async) where the **f
 Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:
 
 ```python
+from typing_extensions import TypedDict
+
 from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph
 
-builder = StateGraph(dict)
+class State(TypedDict):
+    input: str
+    results: str
+
+builder = StateGraph(State)
 
 
-def my_node(state: dict, config: RunnableConfig):
+def my_node(state: State, config: RunnableConfig):
     print("In node: ", config["configurable"]["user_id"])
     return {"results": f"Hello, {state['input']}!"}
 
 
 # The second argument is optional
-def my_other_node(state: dict):
+def my_other_node(state: State):
     return state
 
 

docs/docs/concepts/persistence.md

@@ -470,9 +470,51 @@ If the checkpointer is used with asynchronous graph execution (i.e. executing th
 
 ### Serializer
 
-When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects. 
+When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects.
 `langgraph_checkpoint` defines [protocol][langgraph.checkpoint.serde.base.SerializerProtocol] for implementing serializers provides a default implementation ([JsonPlusSerializer][langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer]) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
 
+#### Serialization with `pickle`
+
+The default serializer, [`JsonPlusSerializer`][langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer], uses ormsgpack and JSON under the hood, which is not suitable for all types of objects.
+
+If you want to fallback to pickle for objects not currently supported by our msgpack encoder (such as Pandas dataframes),
+you can use the `pickle_fallback` argument of the `JsonPlusSerializer`:
+
+```python
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
+
+# ... Define the graph ...
+graph.compile(
+    checkpointer=MemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
+)
+```
+
+#### Encryption
+
+Checkpointers can optionally encrypt all persisted state. To enable this, pass an instance of [`EncryptedSerializer`][langgraph.checkpoint.serde.encrypted.EncryptedSerializer] to the `serde` argument of any `BaseCheckpointSaver` implementation. The easiest way to create an encrypted serializer is via [`from_pycryptodome_aes`][langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes], which reads the AES key from the `LANGGRAPH_AES_KEY` environment variable (or accepts a `key` argument):
+
+```python
+import sqlite3
+
+from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+serde = EncryptedSerializer.from_pycryptodome_aes()  # reads LANGGRAPH_AES_KEY
+checkpointer = SqliteSaver(sqlite3.connect("checkpoint.db"), serde=serde)
+```
+
+```python
+from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
+from langgraph.checkpoint.postgres import PostgresSaver
+
+serde = EncryptedSerializer.from_pycryptodome_aes()
+checkpointer = PostgresSaver.from_conn_string("postgresql://...", serde=serde)
+checkpointer.setup()
+```
+
+When running on LangGraph Platform, encryption is automatically enabled whenever `LANGGRAPH_AES_KEY` is present, so you only need to provide the environment variable. Other encryption schemes can be used by implementing [`CipherProtocol`][langgraph.checkpoint.serde.base.CipherProtocol] and supplying it to `EncryptedSerializer`.
+
 ## Capabilities
 
 ### Human-in-the-loop

docs/docs/concepts/server-mcp.md

@@ -94,7 +94,7 @@ def answer_node(state: InputState):
     return {"answer": "bye", "question": state["question"]}
 
 # Build the graph with explicit schemas
-builder = StateGraph(OverallState, input=InputState, output=OutputState)
+builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
 builder.add_node(answer_node)
 builder.add_edge(START, "answer_node")
 builder.add_edge("answer_node", END)

docs/docs/concepts/subgraphs.md

@@ -59,8 +59,9 @@ The main question when adding subgraphs is how the parent graph and subgraph com
         response = model.invoke(state["subgraph_messages"])
         return {"subgraph_messages": response}
 
-    subgraph_builder = StateGraph(State)
-    subgraph_builder.add_node(call_model)
+    subgraph_builder = StateGraph(SubgraphMessagesState)
+    subgraph_builder.add_node("call_model_from_subgraph", call_model)
+    subgraph_builder.add_edge(START, "call_model_from_subgraph")
     ...
     # highlight-next-line
     subgraph = subgraph_builder.compile()

docs/docs/how-tos/graph-api.ipynb

@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "6ec0eb77-874e-443e-8c73-93125b515106",
    "metadata": {},
    "outputs": [
@@ -478,7 +478,7 @@
     "\n",
     "\n",
     "# Build the graph with input and output schemas specified\n",
-    "builder = StateGraph(OverallState, input=InputState, output=OutputState)\n",
+    "builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)\n",
     "builder.add_node(answer_node)  # Add the answer node\n",
     "builder.add_edge(START, \"answer_node\")  # Define the starting edge\n",
     "builder.add_edge(\"answer_node\", END)  # Define the ending edge\n",
@@ -3430,7 +3430,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/persistence.ipynb

@@ -1107,10 +1107,10 @@
    "source": [
     "### Use in production\n",
     "\n",
-    "In production, you would want to use a checkpointer backed by a database:\n",
+    "In production, you would want to use a store backed by a database:\n",
     "\n",
     "```python\n",
-    "from langgraph.checkpoint.postgres import PostgresSaver\n",
+    "from langgraph.store.postgres import PostgresStore\n",
     "\n",
     "DB_URI = \"postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable\"\n",
     "# highlight-next-line\n",

docs/docs/reference/checkpoints.md

@@ -12,12 +12,18 @@
     options:
       members:
         - SerializerProtocol
+        - CipherProtocol
 
 ::: langgraph.checkpoint.serde.jsonplus
     options:
       members:
         - JsonPlusSerializer
 
+::: langgraph.checkpoint.serde.encrypted
+    options:
+      members:
+        - EncryptedSerializer
+
 ::: langgraph.checkpoint.memory
 
 ::: langgraph.checkpoint.sqlite
@@ -32,4 +38,4 @@
 ::: langgraph.checkpoint.postgres.aio
     options:
       members:
-        - AsyncPostgresSaver
+        - AsyncPostgresSaver

docs/docs/reference/index.md

@@ -22,7 +22,7 @@ Welcome to the LangGraph reference docs! These pages detail the core interfaces
 
 ## LangGraph
 
-The core APIs for the LangGraph opens source library.
+The core APIs for the LangGraph open source library.
 
 - [Graphs](graphs.md): Main graph abstraction and usage.
 - [Functional API](func.md): Functional programming interface for graphs.

docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb

@@ -580,9 +580,7 @@
     "    ]\n",
     ")\n",
     "\n",
-    "evaluator = prompt | ChatOpenAI(model=\"gpt-4-turbo-preview\").with_structured_output(\n",
-    "    RedTeamingResult, method=\"function_calling\"\n",
-    ")\n",
+    "evaluator = prompt | ChatOpenAI(model=\"gpt-4o\").with_structured_output(RedTeamingResult)\n",
     "\n",
     "\n",
     "def did_resist(run, example):\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
 
@@ -27,12 +35,13 @@ 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
 
 from typing_extensions import TypedDict
 
-from langgraph.graph import StateGraph, START
+from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 
 
@@ -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):
@@ -87,32 +136,87 @@ 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")
 ```
+:::
 
-## 5. Compile the graph
+:::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
 
 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()
 ```
+:::
 
-## 6. Visualize the graph (optional)
+:::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
 
@@ -122,14 +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)
 
-
-## 7. Run the chatbot
+## 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`.
@@ -160,18 +281,48 @@ 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
 
 from langchain.chat_models import init_chat_model
 from typing_extensions import TypedDict
 
-from langgraph.graph import StateGraph, START
+from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
 
 
@@ -194,11 +345,44 @@ def chatbot(state: State):
 # the node is used.
 graph_builder.add_node("chatbot", chatbot)
 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.
 
@@ -157,14 +337,25 @@ 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
+```python hl_lines="36 37"
 from typing import Annotated
 
 from langchain.chat_models import init_chat_model
@@ -203,6 +394,50 @@ 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
 

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/docs/tutorials/llm-compiler/LLMCompiler.ipynb

@@ -471,7 +471,7 @@
     "\n",
     "_get_pass(\"TAVILY_API_KEY\")\n",
     "\n",
-    "calculate = get_math_tool(ChatOpenAI(model=\"gpt-4-turbo-preview\"))\n",
+    "calculate = get_math_tool(ChatOpenAI(model=\"gpt-4o\"))\n",
     "search = TavilySearchResults(\n",
     "    max_results=1,\n",
     "    description='tavily_search_results_json(query=\"the search query\") - a search engine.',\n",
@@ -540,11 +540,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001b[1m System Message \u001b[0m================================\n",
+      "================================\u001B[1m System Message \u001B[0m================================\n",
       "\n",
-      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
-      "\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
+      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m types:\n",
+      "\u001B[33;1m\u001B[1;3m{tool_descriptions}\u001B[0m\n",
+      "\u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m. join(): Collects and combines results from prior actions.\n",
       "\n",
       " - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
       " - join should always be the last action in the plan, and will be called in two scenarios:\n",
@@ -561,11 +561,11 @@
       " - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
       " - Never introduce new actions other than the ones provided.\n",
       "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
+      "=============================\u001B[1m Messages Placeholder \u001B[0m=============================\n",
       "\n",
-      "\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
+      "\u001B[33;1m\u001B[1;3m{messages}\u001B[0m\n",
       "\n",
-      "================================\u001b[1m System Message \u001b[0m================================\n",
+      "================================\u001B[1m System Message \u001B[0m================================\n",
       "\n",
       "Remember, ONLY respond with the task list in the correct format! E.g.:\n",
       "idx. tool(arg_name=args)\n",
@@ -1030,7 +1030,7 @@
     "joiner_prompt = hub.pull(\"wfh/llm-compiler-joiner\").partial(\n",
     "    examples=\"\"\n",
     ")  # You can optionally add examples\n",
-    "llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
+    "llm = ChatOpenAI(model=\"gpt-4o\")\n",
     "\n",
     "runnable = joiner_prompt | llm.with_structured_output(\n",
     "    JoinOutputs, method=\"function_calling\"\n",

docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb

@@ -135,7 +135,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain import hub\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "from langgraph.prebuilt import create_react_agent\n",

docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb

@@ -90,7 +90,11 @@
    "id": "9ac1c2cd-81fb-40eb-8ba1-e9197800cba6",
    "metadata": {},
    "source": [
-    "## Create Index"
+    "## Create Index\n",
+    "\n",
+    "Set up a vector database using OpenAI Embeddings and the Chroma vector database.  \n",
+    "Input URLs of blog posts related to agents, prompt engineering, and large language models (LLMs).  \n",
+    "Generate vector indices for use in Retrieval-Augmented Generation (RAG)."
    ]
   },
   {
@@ -159,6 +163,21 @@
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6cdd5ac0-fa18-4ee9-8051-062a0c56268f",
+   "metadata": {},
+   "source": [
+    "### Router for Query Analysis\n",
+    "\n",
+    "Let’s start with Routing. First, assign the query analysis to the LLM.\n",
+    "\n",
+    "Create a RouteQuery data model and specify it in a structured format for the LLM. The decision for routing should be embedded in the prompt. You need to clearly define which parts of the document should be directed to RAG based on the topic.\n",
+    "\n",
+    "While you could automate this process by having the LLM summarize the RAG documents again, it’s more cost-effective to manually manage this when dealing with large documents, as automation could become expensive.\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -219,6 +238,18 @@
     "print(question_router.invoke({\"question\": \"What are the types of agent memory?\"}))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cb248c94-0b0c-4d86-8565-32aa8d7424e4",
+   "metadata": {},
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "After performing retrieval, evaluate the results. Although you initially decided to use RAG based on the query, the retrieved documents might not be satisfactory. Assess whether the retrieved documents are sufficiently relevant to the query.\n",
+    "\n",
+    "For this, rely on the LLM to evaluate the relevance, providing a binary ‘yes’ or ‘no’ decision."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -309,6 +340,17 @@
     "print(generation)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cb0ab54a-4a4f-45fa-b1c5-cea1bf4c59d5",
+   "metadata": {},
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "Verify if the LLM produced any hallucinations by comparing its output to the retrieved facts.  \n",
+    "Provide the LLM’s evaluation in a binary ‘yes’ or ‘no’ format.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -357,6 +399,16 @@
     "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4f58502a-c25f-4d80-a402-5583b0cd3e41",
+   "metadata": {},
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "Evaluate the answer finally."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -405,6 +457,18 @@
     "answer_grader.invoke({\"question\": question, \"generation\": generation})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "af77946c-2646-4039-86b0-e2fde1ab7459",
+   "metadata": {},
+   "source": [
+    "### Question Rewriting\n",
+    "\n",
+    "The original question from user was directly used in RAG.  \n",
+    "However, the user’s question might not be in a form suitable for RAG.  \n",
+    "To improve retrieval, rephrase the question to ensure it aligns better with vector similarity search."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -450,7 +514,9 @@
    "id": "d07c0b31-b919-4498-869f-9673125c2473",
    "metadata": {},
    "source": [
-    "## Web Search Tool"
+    "## Web Search Tool\n",
+    "\n",
+    "Use Tavily Search tool to get information from the web."
    ]
   },
   {

docs/docs/tutorials/rag/langgraph_crag.ipynb

@@ -185,7 +185,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
     "\n",
     "# Prompt\n",

docs/docs/tutorials/usaco/usaco.ipynb

@@ -1758,7 +1758,7 @@
    "id": "4eb67198-c84f-458b-8baf-783d7246dddc",
    "metadata": {},
    "source": [
-    "Let's let the agent try again. Call `stream` with `None` to just use the inputs loaded from the memory. We will skip our human review for the next few attempats\n",
+    "Let's let the agent try again. Call `stream` with `None` to just use the inputs loaded from the memory. We will skip our human review for the next few attempts\n",
     "to see if it can correct itself."
    ]
   },

docs/mkdocs.yml

@@ -381,25 +381,6 @@ extra:
       link: https://github.com/langchain-ai/langgraph
     - icon: fontawesome/brands/twitter
       link: https://twitter.com/LangChainAI
-  analytics:
-    provider: google
-    property: G-G8X6ELZYE0
-    feedback:
-      title: Was this page helpful?
-      ratings:
-        - icon: material/emoticon-happy-outline
-          name: This page was helpful
-          data: 1
-          note: >-
-            Thanks for your feedback!
-        - icon: material/emoticon-sad-outline
-          name: This page could be improved
-          data: 0
-          note: >-
-            Thanks for your feedback! Please help us improve this page by adding to the discussion below.
-  shared_analytics:
-    provider: google
-    property: G-47WX3HKKY2
 validation:
   # https://www.mkdocs.org/user-guide/configuration/
   # We are still raising for omitted files because they determine the breadcrumbs for pages.

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"

examples/rag/langgraph_adaptive_rag.ipynb

@@ -184,7 +184,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
     "\n",
     "# Prompt\n",
@@ -235,7 +235,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
     "\n",
     "# Prompt\n",
@@ -328,7 +328,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
     "\n",
     "# Prompt\n",
@@ -376,7 +376,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
     "\n",
     "# Prompt\n",

examples/rag/langgraph_agentic_rag.ipynb

@@ -200,11 +200,11 @@
      "output_type": "stream",
      "text": [
       "********************Prompt[rlm/rag-prompt]********************\n",
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "================================\u001B[1m Human Message \u001B[0m=================================\n",
       "\n",
       "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
-      "Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
-      "Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
+      "Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
+      "Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
       "Answer:\n"
      ]
     }
@@ -244,7 +244,7 @@
     "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
     "\n",
     "    # LLM\n",
-    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4o\", streaming=True)\n",
     "\n",
     "    # LLM with tool and validation\n",
     "    llm_with_tool = model.with_structured_output(grade)\n",

examples/rag/langgraph_crag.ipynb

@@ -171,7 +171,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
     "\n",
     "# Prompt\n",

examples/rag/langgraph_self_rag.ipynb

@@ -191,7 +191,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
     "\n",
     "# Prompt\n",
@@ -284,7 +284,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
     "\n",
     "# Prompt\n",
@@ -332,7 +332,7 @@
     "\n",
     "\n",
     "# LLM with function call\n",
-    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
     "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
     "\n",
     "# Prompt\n",

examples/rag/langgraph_self_rag_pinecone_movies.ipynb

@@ -33,7 +33,9 @@
    "id": "a384cc48-0425-4e8f-aafc-cfb8e56025c9",
    "metadata": {},
    "outputs": [],
-   "source": ["%pip install -qU langchain-pinecone langchain-openai langchainhub langgraph"]
+   "source": [
+    "%pip install -qU langchain-pinecone langchain-openai langchainhub langgraph"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -51,7 +53,9 @@
    "id": "ccc3dae5-1df6-48ca-af8a-50f0e6128876",
    "metadata": {},
    "outputs": [],
-   "source": ["import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""]
+   "source": [
+    "import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
+   ]
   },
   {
    "cell_type": "code",
@@ -59,7 +63,9 @@
    "id": "88637820",
    "metadata": {},
    "outputs": [],
-   "source": ["import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""]
+   "source": [
+    "import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -77,7 +83,9 @@
    "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
    "metadata": {},
    "outputs": [],
-   "source": ["from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n    embedding=OpenAIEmbeddings(),\n    index_name=\"sample-movies\",\n    text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"]
+   "source": [
+    "from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n    embedding=OpenAIEmbeddings(),\n    index_name=\"sample-movies\",\n    text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
+   ]
   },
   {
    "cell_type": "code",
@@ -104,7 +112,9 @@
      ]
     }
    ],
-   "source": ["docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n    print(\"# \" + doc.metadata[\"title\"])\n    print(doc.page_content)\n    print()"]
+   "source": [
+    "docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n    print(\"# \" + doc.metadata[\"title\"])\n    print(doc.page_content)\n    print()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -120,7 +130,32 @@
    "id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
    "metadata": {},
    "outputs": [],
-   "source": ["### Retrieval Grader\n\nfrom langchain import hub\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_openai import ChatOpenAI\n\n\n# Data model\nclass GradeDocuments(BaseModel):\n    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n\n    binary_score: str = Field(\n        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n    )\n\n\n# https://smith.langchain.com/hub/efriis/self-rag-retrieval-grader\ngrade_prompt = hub.pull(\"efriis/self-rag-retrieval-grader\")\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeDocuments)\n\nretrieval_grader = grade_prompt | structured_llm_grader"]
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# https://smith.langchain.com/hub/efriis/self-rag-retrieval-grader\n",
+    "grade_prompt = hub.pull(\"efriis/self-rag-retrieval-grader\")\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader"
+   ]
   },
   {
    "cell_type": "code",
@@ -137,7 +172,9 @@
      ]
     }
    ],
-   "source": ["# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"]
+   "source": [
+    "# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -163,7 +200,9 @@
      ]
     }
    ],
-   "source": ["### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"]
+   "source": [
+    "### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
+   ]
   },
   {
    "cell_type": "code",
@@ -189,7 +228,30 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["### Hallucination Grader\n\n\n# Data model\nclass GradeHallucinations(BaseModel):\n    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n\n    binary_score: str = Field(\n        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n    )\n\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeHallucinations)\n\n# https://smith.langchain.com/hub/efriis/self-rag-hallucination-grader\nhallucination_prompt = hub.pull(\"efriis/self-rag-hallucination-grader\")\n\nhallucination_grader = hallucination_prompt | structured_llm_grader\nprint(generation)\nhallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"]
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeHallucinations(BaseModel):\n",
+    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# https://smith.langchain.com/hub/efriis/self-rag-hallucination-grader\n",
+    "hallucination_prompt = hub.pull(\"efriis/self-rag-hallucination-grader\")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "print(generation)\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
   },
   {
    "cell_type": "code",
@@ -216,7 +278,31 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["### Answer Grader\n\n\n# Data model\nclass GradeAnswer(BaseModel):\n    \"\"\"Binary score to assess answer addresses question.\"\"\"\n\n    binary_score: str = Field(\n        description=\"Answer addresses the question, 'yes' or 'no'\"\n    )\n\n\n# LLM with function call\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\nstructured_llm_grader = llm.with_structured_output(GradeAnswer)\n\n# Prompt\nanswer_prompt = hub.pull(\"efriis/self-rag-answer-grader\")\n\nanswer_grader = answer_prompt | structured_llm_grader\nprint(question)\nprint(generation)\nanswer_grader.invoke({\"question\": question, \"generation\": generation})"]
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeAnswer(BaseModel):\n",
+    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer addresses the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "answer_prompt = hub.pull(\"efriis/self-rag-answer-grader\")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "print(question)\n",
+    "print(generation)\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
   },
   {
    "cell_type": "code",
@@ -242,7 +328,9 @@
      "output_type": "execute_result"
     }
    ],
-   "source": ["### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"]
+   "source": [
+    "### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -262,7 +350,9 @@
    "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
    "metadata": {},
    "outputs": [],
-   "source": ["from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        question: question\n        generation: LLM generation\n        documents: list of documents\n    \"\"\"\n\n    question: str\n    generation: str\n    documents: List[str]"]
+   "source": [
+    "from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        question: question\n        generation: LLM generation\n        documents: list of documents\n    \"\"\"\n\n    question: str\n    generation: str\n    documents: List[str]"
+   ]
   },
   {
    "cell_type": "code",
@@ -270,7 +360,9 @@
    "id": "add509d8-6682-4127-8d95-13dd37d79702",
    "metadata": {},
    "outputs": [],
-   "source": ["### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.invoke(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}"]
+   "source": [
+    "### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.invoke(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}"
+   ]
   },
   {
    "cell_type": "code",
@@ -278,7 +370,9 @@
    "id": "09fc91b4",
    "metadata": {},
    "outputs": [],
-   "source": ["### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score.binary_score\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""]
+   "source": [
+    "### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score.binary_score\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -331,7 +425,9 @@
      ]
     }
    ],
-   "source": ["from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"]
+   "source": [
+    "from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+   ]
   },
   {
    "cell_type": "code",
@@ -339,7 +435,9 @@
    "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
    "metadata": {},
    "outputs": [],
-   "source": ["inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"]
+   "source": [
+    "inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+   ]
   },
   {
    "cell_type": "code",
@@ -347,7 +445,9 @@
    "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
    "metadata": {},
    "outputs": [],
-   "source": [""]
+   "source": [
+    ""
+   ]
   }
  ],
  "metadata": {

libs/checkpoint-postgres/README.md

@@ -13,6 +13,20 @@ By default `langgraph-checkpoint-postgres` installs `psycopg` (Psycopg 3) withou
 
 > [!IMPORTANT]
 > When manually creating Postgres connections and passing them to `PostgresSaver` or `AsyncPostgresSaver`, make sure to include `autocommit=True` and `row_factory=dict_row` (`from psycopg.rows import dict_row`). See a full example in this [how-to guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/).
+>
+> **Why these parameters are required:**
+> - `autocommit=True`: Required for the `.setup()` method to properly commit the checkpoint tables to the database. Without this, table creation may not be persisted.
+> - `row_factory=dict_row`: Required because the PostgresSaver implementation accesses database rows using dictionary-style syntax (e.g., `row["column_name"]`). The default `tuple_row` factory returns tuples that only support index-based access (e.g., `row[0]`), which will cause `TypeError` exceptions when the checkpointer tries to access columns by name.
+>
+> **Example of incorrect usage:**
+> ```python
+> # ❌ This will fail with TypeError during checkpointer operations
+> with psycopg.connect(DB_URI) as conn:  # Missing autocommit=True and row_factory=dict_row
+>     checkpointer = PostgresSaver(conn)
+>     checkpointer.setup()  # May not persist tables properly
+>     # Any operation that reads from database will fail with:
+>     # TypeError: tuple indices must be integers or slices, not str
+> ```
 
 ```python
 from langgraph.checkpoint.postgres import PostgresSaver

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

@@ -1,8 +1,10 @@
+from __future__ import annotations
+
 import threading
 from collections import defaultdict
 from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any, Optional
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from psycopg import Capabilities, Connection, Cursor, Pipeline
@@ -34,8 +36,8 @@ class PostgresSaver(BasePostgresSaver):
     def __init__(
         self,
         conn: _internal.Conn,
-        pipe: Optional[Pipeline] = None,
-        serde: Optional[SerializerProtocol] = None,
+        pipe: Pipeline | None = None,
+        serde: SerializerProtocol | None = None,
     ) -> None:
         super().__init__(serde=serde)
         if isinstance(conn, ConnectionPool) and pipe is not None:
@@ -52,7 +54,7 @@ class PostgresSaver(BasePostgresSaver):
     @contextmanager
     def from_conn_string(
         cls, conn_string: str, *, pipeline: bool = False
-    ) -> Iterator["PostgresSaver"]:
+    ) -> Iterator[PostgresSaver]:
         """Create a new PostgresSaver instance from a connection string.
 
         Args:
@@ -99,11 +101,11 @@ class PostgresSaver(BasePostgresSaver):
 
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints from the database.
 
@@ -173,34 +175,9 @@ class PostgresSaver(BasePostgresSaver):
                             value["channel_values"],
                         )
             for value in values:
-                yield CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": value["thread_id"],
-                            "checkpoint_ns": value["checkpoint_ns"],
-                            "checkpoint_id": value["checkpoint_id"],
-                        }
-                    },
-                    {
-                        **value["checkpoint"],
-                        "channel_values": self._load_blobs(value["channel_values"]),
-                    },
-                    value["metadata"],
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": value["thread_id"],
-                                "checkpoint_ns": value["checkpoint_ns"],
-                                "checkpoint_id": value["parent_checkpoint_id"],
-                            }
-                        }
-                        if value["parent_checkpoint_id"]
-                        else None
-                    ),
-                    self._load_writes(value["pending_writes"]),
-                )
+                yield self._load_checkpoint_tuple(value)
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database.
 
         This method retrieves a checkpoint tuple from the Postgres database based on the
@@ -269,32 +246,7 @@ class PostgresSaver(BasePostgresSaver):
                         value["channel_values"],
                     )
 
-            return CheckpointTuple(
-                {
-                    "configurable": {
-                        "thread_id": thread_id,
-                        "checkpoint_ns": checkpoint_ns,
-                        "checkpoint_id": value["checkpoint_id"],
-                    }
-                },
-                {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                },
-                value["metadata"],
-                (
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": value["parent_checkpoint_id"],
-                        }
-                    }
-                    if value["parent_checkpoint_id"]
-                    else None
-                ),
-                self._load_writes(value["pending_writes"]),
-            )
+            return self._load_checkpoint_tuple(value)
 
     def put(
         self,
@@ -464,5 +416,44 @@ class PostgresSaver(BasePostgresSaver):
                 with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
+        """
+        Convert a database row into a CheckpointTuple object.
+
+        Args:
+            value: A row from the database containing checkpoint data.
+
+        Returns:
+            CheckpointTuple: A structured representation of the checkpoint,
+            including its configuration, metadata, parent checkpoint (if any),
+            and pending writes.
+        """
+        return CheckpointTuple(
+            {
+                "configurable": {
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
+                    "checkpoint_id": value["checkpoint_id"],
+                }
+            },
+            {
+                **value["checkpoint"],
+                "channel_values": self._load_blobs(value["channel_values"]),
+            },
+            value["metadata"],
+            (
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None
+            ),
+            self._load_writes(value["pending_writes"]),
+        )
+
 
 __all__ = ["PostgresSaver", "BasePostgresSaver", "Conn"]

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

@@ -1,8 +1,10 @@
+from __future__ import annotations
+
 import asyncio
 from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any, Optional
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
@@ -34,8 +36,8 @@ class AsyncPostgresSaver(BasePostgresSaver):
     def __init__(
         self,
         conn: _ainternal.Conn,
-        pipe: Optional[AsyncPipeline] = None,
-        serde: Optional[SerializerProtocol] = None,
+        pipe: AsyncPipeline | None = None,
+        serde: SerializerProtocol | None = None,
     ) -> None:
         super().__init__(serde=serde)
         if isinstance(conn, AsyncConnectionPool) and pipe is not None:
@@ -56,8 +58,8 @@ class AsyncPostgresSaver(BasePostgresSaver):
         conn_string: str,
         *,
         pipeline: bool = False,
-        serde: Optional[SerializerProtocol] = None,
-    ) -> AsyncIterator["AsyncPostgresSaver"]:
+        serde: SerializerProtocol | None = None,
+    ) -> AsyncIterator[AsyncPostgresSaver]:
         """Create a new AsyncPostgresSaver instance from a connection string.
 
         Args:
@@ -104,11 +106,11 @@ class AsyncPostgresSaver(BasePostgresSaver):
 
     async def alist(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> AsyncIterator[CheckpointTuple]:
         """List checkpoints from the database asynchronously.
 
@@ -160,34 +162,9 @@ class AsyncPostgresSaver(BasePostgresSaver):
                             value["channel_values"],
                         )
             for value in values:
-                yield CheckpointTuple(
-                    {
-                        "configurable": {
-                            "thread_id": value["thread_id"],
-                            "checkpoint_ns": value["checkpoint_ns"],
-                            "checkpoint_id": value["checkpoint_id"],
-                        }
-                    },
-                    {
-                        **value["checkpoint"],
-                        "channel_values": self._load_blobs(value["channel_values"]),
-                    },
-                    value["metadata"],
-                    (
-                        {
-                            "configurable": {
-                                "thread_id": value["thread_id"],
-                                "checkpoint_ns": value["checkpoint_ns"],
-                                "checkpoint_id": value["parent_checkpoint_id"],
-                            }
-                        }
-                        if value["parent_checkpoint_id"]
-                        else None
-                    ),
-                    await asyncio.to_thread(self._load_writes, value["pending_writes"]),
-                )
+                yield await self._load_checkpoint_tuple(value)
 
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database asynchronously.
 
         This method retrieves a checkpoint tuple from the Postgres database based on the
@@ -236,32 +213,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
                         value["channel_values"],
                     )
 
-            return CheckpointTuple(
-                {
-                    "configurable": {
-                        "thread_id": thread_id,
-                        "checkpoint_ns": checkpoint_ns,
-                        "checkpoint_id": value["checkpoint_id"],
-                    }
-                },
-                {
-                    **value["checkpoint"],
-                    "channel_values": self._load_blobs(value["channel_values"]),
-                },
-                value["metadata"],
-                (
-                    {
-                        "configurable": {
-                            "thread_id": thread_id,
-                            "checkpoint_ns": checkpoint_ns,
-                            "checkpoint_id": value["parent_checkpoint_id"],
-                        }
-                    }
-                    if value["parent_checkpoint_id"]
-                    else None
-                ),
-                await asyncio.to_thread(self._load_writes, value["pending_writes"]),
-            )
+            return await self._load_checkpoint_tuple(value)
 
     async def aput(
         self,
@@ -422,13 +374,52 @@ class AsyncPostgresSaver(BasePostgresSaver):
                 async with conn.cursor(binary=True, row_factory=dict_row) as cur:
                     yield cur
 
+    async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
+        """
+        Convert a database row into a CheckpointTuple object.
+
+        Args:
+            value: A row from the database containing checkpoint data.
+
+        Returns:
+            CheckpointTuple: A structured representation of the checkpoint,
+            including its configuration, metadata, parent checkpoint (if any),
+            and pending writes.
+        """
+        return CheckpointTuple(
+            {
+                "configurable": {
+                    "thread_id": value["thread_id"],
+                    "checkpoint_ns": value["checkpoint_ns"],
+                    "checkpoint_id": value["checkpoint_id"],
+                }
+            },
+            {
+                **value["checkpoint"],
+                "channel_values": self._load_blobs(value["channel_values"]),
+            },
+            value["metadata"],
+            (
+                {
+                    "configurable": {
+                        "thread_id": value["thread_id"],
+                        "checkpoint_ns": value["checkpoint_ns"],
+                        "checkpoint_id": value["parent_checkpoint_id"],
+                    }
+                }
+                if value["parent_checkpoint_id"]
+                else None
+            ),
+            await asyncio.to_thread(self._load_writes, value["pending_writes"]),
+        )
+
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints from the database.
 
@@ -466,7 +457,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
             except StopAsyncIteration:
                 break
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database.
 
         This method retrieves a checkpoint tuple from the Postgres database based on the

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import random
 from collections.abc import Sequence
 from typing import Any, Optional, cast
@@ -186,7 +188,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
         checkpoint_ns: str,
         values: dict[str, Any],
         versions: ChannelVersions,
-    ) -> list[tuple[str, str, str, str, str, Optional[bytes]]]:
+    ) -> list[tuple[str, str, str, str, str, bytes | None]]:
         if not versions:
             return []
 
@@ -244,7 +246,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
             for idx, (channel, value) in enumerate(writes)
         ]
 
-    def get_next_version(self, current: Optional[str]) -> str:
+    def get_next_version(self, current: str | None) -> str:
         if current is None:
             current_v = 0
         elif isinstance(current, int):
@@ -257,9 +259,9 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
 
     def _search_where(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         filter: MetadataInput,
-        before: Optional[RunnableConfig] = None,
+        before: RunnableConfig | None = None,
     ) -> tuple[str, list[Any]]:
         """Return WHERE clause predicates for alist() given config, filter, before.
 

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

@@ -1,9 +1,11 @@
+from __future__ import annotations
+
 import asyncio
 import logging
 from collections.abc import AsyncIterator, Iterable, Sequence
 from contextlib import asynccontextmanager
 from types import TracebackType
-from typing import Any, Callable, Optional, Union, cast
+from typing import Any, Callable, cast
 
 import orjson
 from psycopg import AsyncConnection, AsyncCursor, AsyncPipeline, Capabilities
@@ -132,12 +134,10 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
         self,
         conn: _ainternal.Conn,
         *,
-        pipe: Optional[AsyncPipeline] = None,
-        deserializer: Optional[
-            Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]
-        ] = None,
-        index: Optional[PostgresIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
+        pipe: AsyncPipeline | None = None,
+        deserializer: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
     ) -> None:
         if isinstance(conn, AsyncConnectionPool) and pipe is not None:
             raise ValueError(
@@ -157,7 +157,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
             self.embeddings = None
 
         self.ttl_config = ttl
-        self._ttl_sweeper_task: Optional[asyncio.Task[None]] = None
+        self._ttl_sweeper_task: asyncio.Task[None] | None = None
         self._ttl_stop_event = asyncio.Event()
 
     async def abatch(self, ops: Iterable[Op]) -> list[Result]:
@@ -180,10 +180,10 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
         conn_string: str,
         *,
         pipeline: bool = False,
-        pool_config: Optional[PoolConfig] = None,
-        index: Optional[PostgresIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
-    ) -> AsyncIterator["AsyncPostgresStore"]:
+        pool_config: PoolConfig | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> AsyncIterator[AsyncPostgresStore]:
         """Create a new AsyncPostgresStore instance from a connection string.
 
         Args:
@@ -289,7 +289,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
             return deleted_count
 
     async def start_ttl_sweeper(
-        self, sweep_interval_minutes: Optional[int] = None
+        self, sweep_interval_minutes: int | None = None
     ) -> asyncio.Task[None]:
         """Periodically delete expired store items based on TTL.
 
@@ -334,7 +334,7 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
         self._ttl_sweeper_task = task
         return task
 
-    async def stop_ttl_sweeper(self, timeout: Optional[float] = None) -> bool:
+    async def stop_ttl_sweeper(self, timeout: float | None = None) -> bool:
         """Stop the TTL sweeper task if it's running.
 
         Args:
@@ -369,14 +369,14 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
 
         return success
 
-    async def __aenter__(self) -> "AsyncPostgresStore":
+    async def __aenter__(self) -> AsyncPostgresStore:
         return self
 
     async def __aexit__(
         self,
-        exc_type: Optional[type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional["TracebackType"],
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: TracebackType | None,
     ) -> None:
         # Ensure the TTL sweeper task is stopped when exiting the context
         if hasattr(self, "_ttl_sweeper_task") and self._ttl_sweeper_task is not None:

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import asyncio
 import concurrent.futures
 import json
@@ -14,7 +16,6 @@ from typing import (
     Generic,
     Literal,
     NamedTuple,
-    Optional,
     TypeVar,
     Union,
     cast,
@@ -56,8 +57,8 @@ class Migration(NamedTuple):
     """A database migration with optional conditions and parameters."""
 
     sql: str
-    params: Optional[dict[str, Any]] = None
-    condition: Optional[Callable[["BasePostgresStore"], bool]] = None
+    params: dict[str, Any] | None = None
+    condition: Callable[[BasePostgresStore], bool] | None = None
 
 
 MIGRATIONS: Sequence[str] = [
@@ -155,7 +156,7 @@ class PoolConfig(TypedDict, total=False):
     min_size: int
     """Minimum number of connections maintained in the pool. Defaults to 1."""
 
-    max_size: Optional[int]
+    max_size: int | None
     """Maximum number of connections allowed in the pool. None means unlimited."""
 
     kwargs: dict
@@ -230,8 +231,8 @@ class BasePostgresStore(Generic[C]):
     MIGRATIONS = MIGRATIONS
     VECTOR_MIGRATIONS = VECTOR_MIGRATIONS
     conn: C
-    _deserializer: Optional[Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]]
-    index_config: Optional[PostgresIndexConfig]
+    _deserializer: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None
+    index_config: PostgresIndexConfig | None
 
     def _get_batch_GET_ops_queries(
         self,
@@ -293,7 +294,7 @@ class BasePostgresStore(Generic[C]):
         put_ops: Sequence[tuple[int, PutOp]],
     ) -> tuple[
         list[tuple[str, Sequence]],
-        Optional[tuple[str, Sequence[tuple[str, str, str, str]]]],
+        tuple[str, Sequence[tuple[str, str, str, str]]] | None,
     ]:
         dedupped_ops: dict[tuple[tuple[str, ...], str], PutOp] = {}
         for _, op in put_ops:
@@ -320,9 +321,7 @@ class BasePostgresStore(Generic[C]):
                 )
                 params = (_namespace_to_text(namespace), *keys)
                 queries.append((query, params))
-        embedding_request: Optional[tuple[str, Sequence[tuple[str, str, str, str]]]] = (
-            None
-        )
+        embedding_request: tuple[str, Sequence[tuple[str, str, str, str]]] | None = None
         if inserts:
             values = []
             insertion_params = []
@@ -403,7 +402,7 @@ class BasePostgresStore(Generic[C]):
         self,
         search_ops: Sequence[tuple[int, SearchOp]],
     ) -> tuple[
-        list[tuple[str, list[Union[None, str, list[float]]]]],  # queries, params
+        list[tuple[str, list[None | str | list[float]]]],  # queries, params
         list[tuple[int, str]],  # idx, query_text pairs to embed
     ]:
         """
@@ -432,7 +431,7 @@ class BasePostgresStore(Generic[C]):
                         filter_params.extend([key, orjson.dumps(value).decode("utf-8")])
 
             ns_condition = "TRUE"
-            ns_param: Optional[Sequence[Union[str]]] = None
+            ns_param: Sequence[str] | None = None
             if op.namespace_prefix:
                 ns_condition = "store.prefix LIKE %s"
                 ns_param = (f"{_namespace_to_text(op.namespace_prefix)}%",)
@@ -719,12 +718,10 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         self,
         conn: _pg_internal.Conn,
         *,
-        pipe: Optional[Pipeline] = None,
-        deserializer: Optional[
-            Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]
-        ] = None,
-        index: Optional[PostgresIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
+        pipe: Pipeline | None = None,
+        deserializer: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
     ) -> None:
         super().__init__()
         self._deserializer = deserializer
@@ -738,7 +735,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         else:
             self.embeddings = None
         self.ttl_config = ttl
-        self._ttl_sweeper_thread: Optional[threading.Thread] = None
+        self._ttl_sweeper_thread: threading.Thread | None = None
         self._ttl_stop_event = threading.Event()
 
     @classmethod
@@ -748,10 +745,10 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         conn_string: str,
         *,
         pipeline: bool = False,
-        pool_config: Optional[PoolConfig] = None,
-        index: Optional[PostgresIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
-    ) -> Iterator["PostgresStore"]:
+        pool_config: PoolConfig | None = None,
+        index: PostgresIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> Iterator[PostgresStore]:
         """Create a new PostgresStore instance from a connection string.
 
         Args:
@@ -810,7 +807,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
             return deleted_count
 
     def start_ttl_sweeper(
-        self, sweep_interval_minutes: Optional[int] = None
+        self, sweep_interval_minutes: int | None = None
     ) -> concurrent.futures.Future[None]:
         """Periodically delete expired store items based on TTL.
 
@@ -867,7 +864,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
         )
         return future
 
-    def stop_ttl_sweeper(self, timeout: Optional[float] = None) -> bool:
+    def stop_ttl_sweeper(self, timeout: float | None = None) -> bool:
         """Stop the TTL sweeper thread if it's running.
 
         Args:
@@ -1196,7 +1193,7 @@ def _row_to_item(
     namespace: tuple[str, ...],
     row: Row,
     *,
-    loader: Optional[Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]] = None,
+    loader: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None = None,
 ) -> Item:
     """Convert a row from the database into an Item.
 
@@ -1224,7 +1221,7 @@ def _row_to_search_item(
     namespace: tuple[str, ...],
     row: Row,
     *,
-    loader: Optional[Callable[[Union[bytes, orjson.Fragment]], dict[str, Any]]] = None,
+    loader: Callable[[bytes | orjson.Fragment], dict[str, Any]] | None = None,
 ) -> SearchItem:
     """Convert a row from the database into an Item."""
     loader = loader or _json_loads
@@ -1255,7 +1252,7 @@ def _group_ops(ops: Iterable[Op]) -> tuple[dict[type, list[tuple[int, Op]]], int
     return grouped_ops, tot
 
 
-def _json_loads(content: Union[bytes, orjson.Fragment]) -> Any:
+def _json_loads(content: bytes | orjson.Fragment) -> Any:
     if isinstance(content, orjson.Fragment):
         if hasattr(content, "buf"):
             content = content.buf
@@ -1267,7 +1264,7 @@ def _json_loads(content: Union[bytes, orjson.Fragment]) -> Any:
     return orjson.loads(cast(bytes, content))
 
 
-def _decode_ns_bytes(namespace: Union[str, bytes, list]) -> tuple[str, ...]:
+def _decode_ns_bytes(namespace: str | bytes | list) -> tuple[str, ...]:
     if isinstance(namespace, list):
         return tuple(namespace)
     if isinstance(namespace, bytes):
@@ -1316,16 +1313,16 @@ def get_distance_operator(store: Any) -> tuple[str, str]:
 
 def _ensure_index_config(
     index_config: PostgresIndexConfig,
-) -> tuple[Optional["Embeddings"], PostgresIndexConfig]:
+) -> tuple[Embeddings | None, PostgresIndexConfig]:
     index_config = index_config.copy()
-    tokenized: list[tuple[str, Union[Literal["$"], list[str]]]] = []
+    tokenized: list[tuple[str, Literal["$"] | list[str]]] = []
     tot = 0
-    text_fields = index_config.get("fields") or ["$"]
-    if isinstance(text_fields, str):
-        text_fields = [text_fields]
-    if not isinstance(text_fields, list):
-        raise ValueError(f"Text fields must be a list or a string. Got {text_fields}")
-    for p in text_fields:
+    fields = index_config.get("fields") or ["$"]
+    if isinstance(fields, str):
+        fields = [fields]
+    if not isinstance(fields, list):
+        raise ValueError(f"Text fields must be a list or a string. Got {fields}")
+    for p in fields:
         if p == "$":
             tokenized.append((p, "$"))
             tot += 1

libs/checkpoint-postgres/pyproject.toml

@@ -56,7 +56,7 @@ lint.select = [
   "B",  # flake8-bugbear
   "I",  # isort
 ]
-lint.ignore = ["E501", "B008", "UP007", "UP006"]
+lint.ignore = ["E501", "B008"]
 
 [tool.mypy]
 # https://mypy.readthedocs.io/en/stable/config_file.html

libs/checkpoint-postgres/tests/checkpoint_utils.py

@@ -1,13 +1,15 @@
+from __future__ import annotations
+
 from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Any, Optional, Protocol
+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) -> Optional[Any]: ...
+    def checkpoint(self) -> Any | None: ...
 
 
 def empty_checkpoint() -> Checkpoint:
@@ -23,10 +25,10 @@ def empty_checkpoint() -> Checkpoint:
 
 def create_checkpoint(
     checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, ChannelProtocol]],
+    channels: Mapping[str, ChannelProtocol] | None,
     step: int,
     *,
-    id: Optional[str] = None,
+    id: str | None = None,
 ) -> Checkpoint:
     """Create a checkpoint for the given channels."""
     ts = datetime.now(timezone.utc).isoformat()

libs/checkpoint-postgres/tests/test_async_store.py

@@ -1,4 +1,6 @@
 # type: ignore
+from __future__ import annotations
+
 import asyncio
 import itertools
 import sys
@@ -6,7 +8,7 @@ import uuid
 from collections.abc import AsyncIterator
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager
-from typing import Any, Optional
+from typing import Any
 
 import pytest
 from langchain_core.embeddings import Embeddings
@@ -353,7 +355,7 @@ async def _create_vector_store(
     vector_type: str,
     distance_type: str,
     fake_embeddings: CharacterEmbeddings,
-    text_fields: Optional[list[str]] = None,
+    text_fields: list[str] | None = None,
 ) -> AsyncIterator[AsyncPostgresStore]:
     """Create a store with vector search enabled."""
     if sys.version_info < (3, 10):

libs/checkpoint-postgres/tests/test_store.py

@@ -1,9 +1,10 @@
 # type: ignore
+from __future__ import annotations
 
 import re
 import time
 from contextlib import contextmanager
-from typing import Any, Optional
+from typing import Any
 from uuid import uuid4
 
 import pytest
@@ -379,7 +380,7 @@ def _create_vector_store(
     vector_type: str,
     distance_type: str,
     fake_embeddings: Embeddings,
-    text_fields: Optional[list[str]] = None,
+    text_fields: list[str] | None = None,
     enable_ttl: bool = True,
 ) -> PostgresStore:
     """Create a store with vector search enabled."""

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

@@ -1,9 +1,11 @@
+from __future__ import annotations
+
 import random
 import sqlite3
 import threading
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import closing, contextmanager
-from typing import Any, Optional, cast
+from typing import Any, cast
 
 from langchain_core.runnables import RunnableConfig
 
@@ -76,7 +78,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         self,
         conn: sqlite3.Connection,
         *,
-        serde: Optional[SerializerProtocol] = None,
+        serde: SerializerProtocol | None = None,
     ) -> None:
         super().__init__(serde=serde)
         self.jsonplus_serde = JsonPlusSerializer()
@@ -86,7 +88,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
 
     @classmethod
     @contextmanager
-    def from_conn_string(cls, conn_string: str) -> Iterator["SqliteSaver"]:
+    def from_conn_string(cls, conn_string: str) -> Iterator[SqliteSaver]:
         """Create a new SqliteSaver instance from a connection string.
 
         Args:
@@ -178,7 +180,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
                     self.conn.commit()
                 cur.close()
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database.
 
         This method retrieves a checkpoint tuple from the SQLite database based on the
@@ -286,11 +288,11 @@ class SqliteSaver(BaseCheckpointSaver[str]):
 
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints from the database.
 
@@ -493,7 +495,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
                 (str(thread_id),),
             )
 
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database asynchronously.
 
         Note:
@@ -504,11 +506,11 @@ class SqliteSaver(BaseCheckpointSaver[str]):
 
     async def alist(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> AsyncIterator[CheckpointTuple]:
         """List checkpoints from the database asynchronously.
 
@@ -534,7 +536,7 @@ class SqliteSaver(BaseCheckpointSaver[str]):
         """
         raise NotImplementedError(_AIO_ERROR_MSG)
 
-    def get_next_version(self, current: Optional[str]) -> 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

@@ -1,8 +1,10 @@
+from __future__ import annotations
+
 import asyncio
 import random
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager
-from typing import Any, Callable, Optional, TypeVar, cast
+from typing import Any, Callable, TypeVar, cast
 
 import aiosqlite
 from langchain_core.runnables import RunnableConfig
@@ -108,7 +110,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         self,
         conn: aiosqlite.Connection,
         *,
-        serde: Optional[SerializerProtocol] = None,
+        serde: SerializerProtocol | None = None,
     ):
         super().__init__(serde=serde)
         self.jsonplus_serde = JsonPlusSerializer()
@@ -121,7 +123,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
     @asynccontextmanager
     async def from_conn_string(
         cls, conn_string: str
-    ) -> AsyncIterator["AsyncSqliteSaver"]:
+    ) -> AsyncIterator[AsyncSqliteSaver]:
         """Create a new AsyncSqliteSaver instance from a connection string.
 
         Args:
@@ -133,7 +135,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
         async with aiosqlite.connect(conn_string) as conn:
             yield cls(conn)
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database.
 
         This method retrieves a checkpoint tuple from the SQLite database based on the
@@ -165,11 +167,11 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
 
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints from the database asynchronously.
 
@@ -310,7 +312,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
 
             self.is_setup = True
 
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the database asynchronously.
 
         This method retrieves a checkpoint tuple from the SQLite database based on the
@@ -398,11 +400,11 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
 
     async def alist(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> AsyncIterator[CheckpointTuple]:
         """List checkpoints from the database asynchronously.
 
@@ -589,7 +591,7 @@ class AsyncSqliteSaver(BaseCheckpointSaver[str]):
             )
             await self.conn.commit()
 
-    def get_next_version(self, current: Optional[str]) -> 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/utils.py

@@ -1,6 +1,8 @@
+from __future__ import annotations
+
 import json
 from collections.abc import Sequence
-from typing import Any, Optional
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 
@@ -52,9 +54,9 @@ def _metadata_predicate(
 
 
 def search_where(
-    config: Optional[RunnableConfig],
-    filter: Optional[dict[str, Any]],
-    before: Optional[RunnableConfig] = None,
+    config: RunnableConfig | None,
+    filter: dict[str, Any] | None,
+    before: RunnableConfig | None = None,
 ) -> tuple[str, Sequence[Any]]:
     """Return WHERE clause predicates for (a)search() given metadata filter
     and `before` config.

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

@@ -1,10 +1,12 @@
+from __future__ import annotations
+
 import asyncio
 import logging
 from collections import defaultdict
 from collections.abc import AsyncIterator, Iterable, Sequence
 from contextlib import asynccontextmanager
 from types import TracebackType
-from typing import Any, Callable, Optional, Union, cast
+from typing import Any, Callable, cast
 
 import aiosqlite
 import orjson
@@ -88,11 +90,10 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
         self,
         conn: aiosqlite.Connection,
         *,
-        deserializer: Optional[
-            Callable[[Union[bytes, str, orjson.Fragment]], dict[str, Any]]
-        ] = None,
-        index: Optional[SqliteIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
+        deserializer: Callable[[bytes | str | orjson.Fragment], dict[str, Any]]
+        | None = None,
+        index: SqliteIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
     ):
         """Initialize the async SQLite store.
 
@@ -114,7 +115,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
         else:
             self.embeddings = None
         self.ttl_config = ttl
-        self._ttl_sweeper_task: Optional[asyncio.Task[None]] = None
+        self._ttl_sweeper_task: asyncio.Task[None] | None = None
         self._ttl_stop_event = asyncio.Event()
 
     @classmethod
@@ -123,9 +124,9 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
         cls,
         conn_string: str,
         *,
-        index: Optional[SqliteIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
-    ) -> AsyncIterator["AsyncSqliteStore"]:
+        index: SqliteIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> AsyncIterator[AsyncSqliteStore]:
         """Create a new AsyncSqliteStore instance from a connection string.
 
         Args:
@@ -253,7 +254,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
             return deleted_count
 
     async def start_ttl_sweeper(
-        self, sweep_interval_minutes: Optional[int] = None
+        self, sweep_interval_minutes: int | None = None
     ) -> asyncio.Task[None]:
         """Periodically delete expired store items based on TTL.
 
@@ -298,7 +299,7 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
         self._ttl_sweeper_task = task
         return task
 
-    async def stop_ttl_sweeper(self, timeout: Optional[float] = None) -> bool:
+    async def stop_ttl_sweeper(self, timeout: float | None = None) -> bool:
         """Stop the TTL sweeper task if it's running.
 
         Args:
@@ -333,14 +334,14 @@ class AsyncSqliteStore(AsyncBatchedBaseStore, BaseSqliteStore):
 
         return success
 
-    async def __aenter__(self) -> "AsyncSqliteStore":
+    async def __aenter__(self) -> AsyncSqliteStore:
         return self
 
     async def __aexit__(
         self,
-        exc_type: Optional[type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional["TracebackType"],
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: TracebackType | None,
     ) -> None:
         # Ensure the TTL sweeper task is stopped when exiting the context
         if hasattr(self, "_ttl_sweeper_task") and self._ttl_sweeper_task is not None:

libs/checkpoint-sqlite/langgraph/store/sqlite/base.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import concurrent.futures
 import datetime
 import logging
@@ -6,7 +8,7 @@ import threading
 from collections import defaultdict
 from collections.abc import Iterable, Iterator, Sequence
 from contextlib import contextmanager
-from typing import Any, Callable, Literal, NamedTuple, Optional, Union, cast
+from typing import Any, Callable, Literal, NamedTuple, cast
 
 import orjson
 import sqlite_vec  # type: ignore[import-untyped]
@@ -105,7 +107,7 @@ def _decode_ns_text(namespace: str) -> tuple[str, ...]:
     return tuple(namespace.split("."))
 
 
-def _json_loads(content: Union[bytes, str, orjson.Fragment]) -> Any:
+def _json_loads(content: bytes | str | orjson.Fragment) -> Any:
     if isinstance(content, orjson.Fragment):
         if hasattr(content, "buf"):
             content = content.buf
@@ -125,9 +127,7 @@ def _row_to_item(
     namespace: tuple[str, ...],
     row: dict[str, Any],
     *,
-    loader: Optional[
-        Callable[[Union[bytes, str, orjson.Fragment]], dict[str, Any]]
-    ] = None,
+    loader: Callable[[bytes | str | orjson.Fragment], dict[str, Any]] | None = None,
 ) -> Item:
     """Convert a row from the database into an Item."""
     val = row["value"]
@@ -149,9 +149,7 @@ def _row_to_search_item(
     namespace: tuple[str, ...],
     row: dict[str, Any],
     *,
-    loader: Optional[
-        Callable[[Union[bytes, str, orjson.Fragment]], dict[str, Any]]
-    ] = None,
+    loader: Callable[[bytes | str | orjson.Fragment], dict[str, Any]] | None = None,
 ) -> SearchItem:
     """Convert a row from the database into a SearchItem."""
     loader = loader or _json_loads
@@ -196,8 +194,8 @@ class BaseSqliteStore:
     MIGRATIONS = MIGRATIONS
     VECTOR_MIGRATIONS = VECTOR_MIGRATIONS
     supports_ttl = True
-    index_config: Optional[SqliteIndexConfig] = None
-    ttl_config: Optional[TTLConfig] = None
+    index_config: SqliteIndexConfig | None = None
+    ttl_config: TTLConfig | None = None
 
     def _get_batch_GET_ops_queries(
         self, get_ops: Sequence[tuple[int, GetOp]]
@@ -259,7 +257,7 @@ class BaseSqliteStore:
         self, put_ops: Sequence[tuple[int, PutOp]]
     ) -> tuple[
         list[tuple[str, Sequence]],
-        Optional[tuple[str, Sequence[tuple[str, str, str, str]]]],
+        tuple[str, Sequence[tuple[str, str, str, str]]] | None,
     ]:
         # Last-write wins
         dedupped_ops: dict[tuple[tuple[str, ...], str], PutOp] = {}
@@ -288,9 +286,7 @@ class BaseSqliteStore:
                 params = (_namespace_to_text(namespace), *keys)
                 queries.append((query, params))
 
-        embedding_request: Optional[tuple[str, Sequence[tuple[str, str, str, str]]]] = (
-            None
-        )
+        embedding_request: tuple[str, Sequence[tuple[str, str, str, str]]] | None = None
         if inserts:
             values = []
             insertion_params = []
@@ -358,7 +354,7 @@ class BaseSqliteStore:
     def _prepare_batch_search_queries(
         self, search_ops: Sequence[tuple[int, SearchOp]]
     ) -> tuple[
-        list[tuple[str, list[Union[None, str, list[float]]]]],  # queries, params
+        list[tuple[str, list[None | str | list[float]]]],  # queries, params
         list[tuple[int, str]],  # idx, query_text pairs to embed
     ]:
         """
@@ -785,11 +781,10 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         self,
         conn: sqlite3.Connection,
         *,
-        deserializer: Optional[
-            Callable[[Union[bytes, str, orjson.Fragment]], dict[str, Any]]
-        ] = None,
-        index: Optional[SqliteIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
+        deserializer: Callable[[bytes | str | orjson.Fragment], dict[str, Any]]
+        | None = None,
+        index: SqliteIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
     ):
         super().__init__()
         self._deserializer = deserializer
@@ -802,7 +797,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         else:
             self.embeddings = None
         self.ttl_config = ttl
-        self._ttl_sweeper_thread: Optional[threading.Thread] = None
+        self._ttl_sweeper_thread: threading.Thread | None = None
         self._ttl_stop_event = threading.Event()
 
     def _get_batch_GET_ops_queries(
@@ -956,9 +951,9 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         cls,
         conn_string: str,
         *,
-        index: Optional[SqliteIndexConfig] = None,
-        ttl: Optional[TTLConfig] = None,
-    ) -> Iterator["SqliteStore"]:
+        index: SqliteIndexConfig | None = None,
+        ttl: TTLConfig | None = None,
+    ) -> Iterator[SqliteStore]:
         """Create a new SqliteStore instance from a connection string.
 
         Args:
@@ -1087,7 +1082,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
             return deleted_count
 
     def start_ttl_sweeper(
-        self, sweep_interval_minutes: Optional[int] = None
+        self, sweep_interval_minutes: int | None = None
     ) -> concurrent.futures.Future[None]:
         """Periodically delete expired store items based on TTL.
 
@@ -1144,7 +1139,7 @@ class SqliteStore(BaseSqliteStore, BaseStore):
         )
         return future
 
-    def stop_ttl_sweeper(self, timeout: Optional[float] = None) -> bool:
+    def stop_ttl_sweeper(self, timeout: float | None = None) -> bool:
         """Stop the TTL sweeper thread if it's running.
 
         Args:
@@ -1396,7 +1391,7 @@ def _ensure_index_config(
 ) -> tuple[Any, SqliteIndexConfig]:
     """Process and validate index configuration."""
     index_config = index_config.copy()
-    tokenized: list[tuple[str, Union[Literal["$"], list[str]]]] = []
+    tokenized: list[tuple[str, Literal["$"] | list[str]]] = []
     tot = 0
     text_fields = index_config.get("text_fields") or ["$"]
     if isinstance(text_fields, str):

libs/checkpoint-sqlite/pyproject.toml

@@ -54,7 +54,7 @@ lint.select = [
   "B",  # flake8-bugbear
   "I",  # isort
 ]
-lint.ignore = ["E501", "B008", "UP007", "UP006"]
+lint.ignore = ["E501", "B008"]
 
 [tool.pytest-watcher]
 now = true

libs/checkpoint-sqlite/tests/checkpoint_utils.py

@@ -1,13 +1,15 @@
+from __future__ import annotations
+
 from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Any, Optional, Protocol
+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) -> Optional[Any]: ...
+    def checkpoint(self) -> Any | None: ...
 
 
 def empty_checkpoint() -> Checkpoint:
@@ -23,10 +25,10 @@ def empty_checkpoint() -> Checkpoint:
 
 def create_checkpoint(
     checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, ChannelProtocol]],
+    channels: Mapping[str, ChannelProtocol] | None,
     step: int,
     *,
-    id: Optional[str] = None,
+    id: str | None = None,
 ) -> Checkpoint:
     """Create a checkpoint for the given channels."""
     ts = datetime.now(timezone.utc).isoformat()

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

@@ -1,11 +1,11 @@
+from __future__ import annotations
+
 from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import (  # noqa: UP035
     Any,
     Generic,
-    List,
     Literal,
     NamedTuple,
-    Optional,
     TypedDict,
     TypeVar,
     Union,
@@ -98,8 +98,8 @@ class CheckpointTuple(NamedTuple):
     config: RunnableConfig
     checkpoint: Checkpoint
     metadata: CheckpointMetadata
-    parent_config: Optional[RunnableConfig] = None
-    pending_writes: Optional[List[PendingWrite]] = None
+    parent_config: RunnableConfig | None = None
+    pending_writes: list[PendingWrite] | None = None
 
 
 class BaseCheckpointSaver(Generic[V]):
@@ -121,11 +121,11 @@ class BaseCheckpointSaver(Generic[V]):
     def __init__(
         self,
         *,
-        serde: Optional[SerializerProtocol] = None,
+        serde: SerializerProtocol | None = None,
     ) -> None:
         self.serde = maybe_add_typed_methods(serde or self.serde)
 
-    def get(self, config: RunnableConfig) -> Optional[Checkpoint]:
+    def get(self, config: RunnableConfig) -> Checkpoint | None:
         """Fetch a checkpoint using the given configuration.
 
         Args:
@@ -137,7 +137,7 @@ class BaseCheckpointSaver(Generic[V]):
         if value := self.get_tuple(config):
             return value.checkpoint
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Fetch a checkpoint tuple using the given configuration.
 
         Args:
@@ -153,11 +153,11 @@ class BaseCheckpointSaver(Generic[V]):
 
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints that match the given criteria.
 
@@ -229,7 +229,7 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
-    async def aget(self, config: RunnableConfig) -> Optional[Checkpoint]:
+    async def aget(self, config: RunnableConfig) -> Checkpoint | None:
         """Asynchronously fetch a checkpoint using the given configuration.
 
         Args:
@@ -241,7 +241,7 @@ class BaseCheckpointSaver(Generic[V]):
         if value := await self.aget_tuple(config):
             return value.checkpoint
 
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Asynchronously fetch a checkpoint tuple using the given configuration.
 
         Args:
@@ -257,11 +257,11 @@ class BaseCheckpointSaver(Generic[V]):
 
     async def alist(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> AsyncIterator[CheckpointTuple]:
         """Asynchronously list checkpoints that match the given criteria.
 
@@ -334,7 +334,7 @@ class BaseCheckpointSaver(Generic[V]):
         """
         raise NotImplementedError
 
-    def get_next_version(self, current: Optional[V]) -> 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,
@@ -361,7 +361,7 @@ class EmptyChannelError(Exception):
     pass
 
 
-def get_checkpoint_id(config: RunnableConfig) -> Optional[str]:
+def get_checkpoint_id(config: RunnableConfig) -> str | None:
     """Get checkpoint ID in a backwards-compatible manner (fallback on thread_ts)."""
     return config["configurable"].get(
         "checkpoint_id", config["configurable"].get("thread_ts")

libs/checkpoint/langgraph/checkpoint/base/id.py

@@ -3,10 +3,11 @@ https://github.com/oittaa/uuid6-python/blob/main/src/uuid6/__init__.py#L95
 Bundled in to avoid install issues with uuid6 package
 """
 
+from __future__ import annotations
+
 import random
 import time
 import uuid
-from typing import Optional
 
 _last_v6_timestamp = None
 
@@ -18,12 +19,12 @@ class UUID(uuid.UUID):
 
     def __init__(
         self,
-        hex: Optional[str] = None,
-        bytes: Optional[bytes] = None,
-        bytes_le: Optional[bytes] = None,
-        fields: Optional[tuple[int, int, int, int, int, int]] = None,
-        int: Optional[int] = None,
-        version: Optional[int] = None,
+        hex: str | None = None,
+        bytes: bytes | None = None,
+        bytes_le: bytes | None = None,
+        fields: tuple[int, int, int, int, int, int] | None = None,
+        int: int | None = None,
+        version: int | None = None,
         *,
         is_safe: uuid.SafeUUID = uuid.SafeUUID.unknown,
     ) -> None:
@@ -75,7 +76,7 @@ def _subsec_decode(value: int) -> int:
     return -(-value * 10**6 // 2**20)
 
 
-def uuid6(node: Optional[int] = None, clock_seq: Optional[int] = None) -> UUID:
+def uuid6(node: int | None = None, clock_seq: int | None = None) -> UUID:
     r"""UUID version 6 is a field-compatible version of UUIDv1, reordered for
     improved DB locality. It is expected that UUIDv6 will primarily be
     used in contexts where there are existing v1 UUIDs. Systems that do

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

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import logging
 import os
 import pickle
@@ -7,7 +9,7 @@ from collections import defaultdict
 from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from types import TracebackType
-from typing import Any, Optional, Union
+from typing import Any
 
 from langchain_core.runnables import RunnableConfig
 
@@ -63,9 +65,7 @@ class InMemorySaver(
     # thread ID ->  checkpoint NS -> checkpoint ID -> checkpoint mapping
     storage: defaultdict[
         str,
-        dict[
-            str, dict[str, tuple[tuple[str, bytes], tuple[str, bytes], Optional[str]]]
-        ],
+        dict[str, dict[str, tuple[tuple[str, bytes], tuple[str, bytes], str | None]]],
     ]
     # (thread ID, checkpoint NS, checkpoint ID) -> (task ID, write idx)
     writes: defaultdict[
@@ -74,7 +74,7 @@ class InMemorySaver(
     ]
     blobs: dict[
         tuple[
-            str, str, str, Union[str, int, float]
+            str, str, str, str | int | float
         ],  # thread id, checkpoint ns, channel, version
         tuple[str, bytes],
     ]
@@ -82,7 +82,7 @@ class InMemorySaver(
     def __init__(
         self,
         *,
-        serde: Optional[SerializerProtocol] = None,
+        serde: SerializerProtocol | None = None,
         factory: type[defaultdict] = defaultdict,
     ) -> None:
         super().__init__(serde=serde)
@@ -95,26 +95,26 @@ class InMemorySaver(
             self.stack.enter_context(self.writes)  # type: ignore[arg-type]
             self.stack.enter_context(self.blobs)  # type: ignore[arg-type]
 
-    def __enter__(self) -> "InMemorySaver":
+    def __enter__(self) -> InMemorySaver:
         return self.stack.__enter__()
 
     def __exit__(
         self,
-        exc_type: Optional[type[BaseException]],
-        exc_value: Optional[BaseException],
-        traceback: Optional[TracebackType],
-    ) -> Optional[bool]:
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> bool | None:
         return self.stack.__exit__(exc_type, exc_value, traceback)
 
-    async def __aenter__(self) -> "InMemorySaver":
+    async def __aenter__(self) -> InMemorySaver:
         return self.stack.__enter__()
 
     async def __aexit__(
         self,
-        __exc_type: Optional[type[BaseException]],
-        __exc_value: Optional[BaseException],
-        __traceback: Optional[TracebackType],
-    ) -> Optional[bool]:
+        __exc_type: type[BaseException] | None,
+        __exc_value: BaseException | None,
+        __traceback: TracebackType | None,
+    ) -> bool | None:
         return self.stack.__exit__(__exc_type, __exc_value, __traceback)
 
     def _load_blobs(
@@ -129,7 +129,7 @@ class InMemorySaver(
                     channel_values[k] = self.serde.loads_typed(vv)
         return channel_values
 
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Get a checkpoint tuple from the in-memory storage.
 
         This method retrieves a checkpoint tuple from the in-memory storage based on the
@@ -213,11 +213,11 @@ class InMemorySaver(
 
     def list(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> Iterator[CheckpointTuple]:
         """List checkpoints from the in-memory storage.
 
@@ -422,7 +422,7 @@ class InMemorySaver(
             if k[0] == thread_id:
                 del self.blobs[k]
 
-    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+    async def aget_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
         """Asynchronous version of get_tuple.
 
         This method is an asynchronous wrapper around get_tuple that runs the synchronous
@@ -438,11 +438,11 @@ class InMemorySaver(
 
     async def alist(
         self,
-        config: Optional[RunnableConfig],
+        config: RunnableConfig | None,
         *,
-        filter: Optional[dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
     ) -> AsyncIterator[CheckpointTuple]:
         """Asynchronous version of list.
 
@@ -512,7 +512,7 @@ class InMemorySaver(
         """
         return self.delete_thread(thread_id)
 
-    def get_next_version(self, current: Optional[str]) -> str:
+    def get_next_version(self, current: str | None) -> str:
         if current is None:
             current_v = 0
         elif isinstance(current, int):
@@ -571,7 +571,7 @@ class PersistentDict(defaultdict):
         self.sync()
         self.clear()
 
-    def __enter__(self) -> "PersistentDict":
+    def __enter__(self) -> PersistentDict:
         return self
 
     def __exit__(self, *exc_info: Any) -> None:

libs/checkpoint/langgraph/checkpoint/serde/jsonplus.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import dataclasses
 import decimal
 import importlib
@@ -5,6 +7,7 @@ import json
 import pathlib
 import pickle
 import re
+import sys
 from collections import deque
 from collections.abc import Sequence
 from datetime import date, datetime, time, timedelta, timezone
@@ -18,7 +21,7 @@ from ipaddress import (
     IPv6Interface,
     IPv6Network,
 )
-from typing import Any, Callable, Optional, Union, cast
+from typing import Any, Callable, cast
 from uuid import UUID
 from zoneinfo import ZoneInfo
 
@@ -41,7 +44,7 @@ class JsonPlusSerializer(SerializerProtocol):
         self,
         *,
         pickle_fallback: bool = False,
-        __unpack_ext_hook__: Optional[Callable[[int, bytes], Any]] = None,
+        __unpack_ext_hook__: Callable[[int, bytes], Any] | None = None,
     ) -> None:
         self.pickle_fallback = pickle_fallback
         self._unpack_ext_hook = (
@@ -52,11 +55,11 @@ class JsonPlusSerializer(SerializerProtocol):
 
     def _encode_constructor_args(
         self,
-        constructor: Union[Callable, type[Any]],
+        constructor: Callable | type[Any],
         *,
-        method: Union[None, str, Sequence[Union[None, str]]] = None,
-        args: Optional[Sequence[Any]] = None,
-        kwargs: Optional[dict[str, Any]] = None,
+        method: None | str | Sequence[None | str] = None,
+        args: Sequence[Any] | None = None,
+        kwargs: dict[str, Any] | None = None,
     ) -> dict[str, Any]:
         out = {
             "lc": 2,
@@ -71,7 +74,7 @@ class JsonPlusSerializer(SerializerProtocol):
             out["kwargs"] = kwargs
         return out
 
-    def _default(self, obj: Any) -> Union[str, dict[str, Any]]:
+    def _default(self, obj: Any) -> str | dict[str, Any]:
         if isinstance(obj, Serializable):
             return cast(dict[str, Any], obj.to_json())
         elif hasattr(obj, "model_dump") and callable(obj.model_dump):
@@ -249,9 +252,10 @@ EXT_CONSTRUCTOR_KW_ARGS = 2
 EXT_METHOD_SINGLE_ARG = 3
 EXT_PYDANTIC_V1 = 4
 EXT_PYDANTIC_V2 = 5
+EXT_NUMPY_ARRAY = 6
 
 
-def _msgpack_default(obj: Any) -> Union[str, ormsgpack.Ext]:
+def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
     if hasattr(obj, "model_dump") and callable(obj.model_dump):  # pydantic v2
         return ormsgpack.Ext(
             EXT_PYDANTIC_V2,
@@ -318,13 +322,6 @@ def _msgpack_default(obj: Any) -> Union[str, ormsgpack.Ext]:
                 (obj.__class__.__module__, obj.__class__.__name__, obj.hex),
             ),
         )
-    elif isinstance(obj, bytearray):
-        return ormsgpack.Ext(
-            EXT_CONSTRUCTOR_SINGLE_ARG,
-            _msgpack_enc(
-                (obj.__class__.__module__, obj.__class__.__name__, bytes(obj)),
-            ),
-        )
     elif isinstance(obj, decimal.Decimal):
         return ormsgpack.Ext(
             EXT_CONSTRUCTOR_SINGLE_ARG,
@@ -463,6 +460,22 @@ def _msgpack_default(obj: Any) -> Union[str, ormsgpack.Ext]:
                 ),
             ),
         )
+    elif (np_mod := sys.modules.get("numpy")) is not None and isinstance(
+        obj, np_mod.ndarray
+    ):
+        order = "F" if obj.flags.f_contiguous and not obj.flags.c_contiguous else "C"
+        if obj.flags.c_contiguous:
+            mv = memoryview(obj)
+            try:
+                meta = (obj.dtype.str, obj.shape, order, mv)
+                return ormsgpack.Ext(EXT_NUMPY_ARRAY, _msgpack_enc(meta))
+            finally:
+                mv.release()
+        else:
+            buf = obj.tobytes(order="A")
+            meta = (obj.dtype.str, obj.shape, order, buf)
+            return ormsgpack.Ext(EXT_NUMPY_ARRAY, _msgpack_enc(meta))
+
     elif isinstance(obj, BaseException):
         return repr(obj)
     else:
@@ -544,6 +557,17 @@ def _msgpack_ext_hook(code: int, data: bytes) -> Any:
                 return tup[2]
             except NameError:
                 return
+    elif code == EXT_NUMPY_ARRAY:
+        try:
+            import numpy as _np
+
+            dtype_str, shape, order, buf = ormsgpack.unpackb(
+                data, ext_hook=_msgpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
+            )
+            arr = _np.frombuffer(buf, dtype=_np.dtype(dtype_str))
+            return arr.reshape(shape, order=order)
+        except Exception:
+            return
 
 
 def _msgpack_ext_hook_to_json(code: int, data: bytes) -> Any:
@@ -624,6 +648,19 @@ def _msgpack_ext_hook_to_json(code: int, data: bytes) -> Any:
             return tup[2]
         except Exception:
             return
+    elif code == EXT_NUMPY_ARRAY:
+        try:
+            import numpy as _np
+
+            dtype_str, shape, order, buf = ormsgpack.unpackb(
+                data,
+                ext_hook=_msgpack_ext_hook_to_json,
+                option=ormsgpack.OPT_NON_STR_KEYS,
+            )
+            arr = _np.frombuffer(buf, dtype=_np.dtype(dtype_str))
+            return arr.reshape(shape, order=order).tolist()
+        except Exception:
+            return
 
 
 _option = (

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

@@ -9,6 +9,8 @@ Core types:
     - Op: Get/Put/Search/List operations
 """
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
 from datetime import datetime
@@ -16,7 +18,6 @@ from typing import (
     Any,
     Literal,
     NamedTuple,
-    Optional,
     TypedDict,
     Union,
     cast,
@@ -127,7 +128,7 @@ class SearchItem(Item):
         value: dict[str, Any],
         created_at: datetime,
         updated_at: datetime,
-        score: Optional[float] = None,
+        score: float | None = None,
     ) -> None:
         """Initialize a result item.
 
@@ -242,7 +243,7 @@ class SearchOp(NamedTuple):
         ```
     """
 
-    filter: Optional[dict[str, Any]] = None
+    filter: dict[str, Any] | None = None
     """Key-value pairs for filtering results based on exact matches or comparison operators.
 
     The filter supports both exact matches and operator-based comparisons.
@@ -284,7 +285,7 @@ class SearchOp(NamedTuple):
     offset: int = 0
     """Number of matching items to skip for pagination."""
 
-    query: Optional[str] = None
+    query: str | None = None
     """Natural language search query for semantic search capabilities.
 
     ???+ example "Examples"
@@ -379,7 +380,7 @@ class ListNamespacesOp(NamedTuple):
 
     """
 
-    match_conditions: Optional[tuple[MatchCondition, ...]] = None
+    match_conditions: tuple[MatchCondition, ...] | None = None
     """Optional conditions for filtering namespaces.
 
     ???+ example "Examples"
@@ -397,7 +398,7 @@ class ListNamespacesOp(NamedTuple):
         ```
     """
 
-    max_depth: Optional[int] = None
+    max_depth: int | None = None
     """Maximum depth of namespace hierarchy to return.
 
     Note:
@@ -452,7 +453,7 @@ class PutOp(NamedTuple):
         the full path would effectively be "documents/user123/report1"
     """
 
-    value: Optional[dict[str, Any]]
+    value: dict[str, Any] | None
     """The data to store, or None to mark the item for deletion.
 
     The value must be a dictionary with string keys and JSON-serializable values.
@@ -466,7 +467,7 @@ class PutOp(NamedTuple):
         }
     """
 
-    index: Optional[Union[Literal[False], list[str]]] = None  # type: ignore[assignment]
+    index: Literal[False] | list[str] | None = None  # type: ignore[assignment]
     """Controls how the item's fields are indexed for search operations.
 
     Indexing configuration determines how the item can be found through search:
@@ -501,7 +502,7 @@ class PutOp(NamedTuple):
         ]
         ```
     """
-    ttl: Optional[float] = None
+    ttl: float | None = None
     """Controls the TTL (time-to-live) for the item in minutes.
 
     If provided, and if the store you are using supports this feature, the item
@@ -530,14 +531,14 @@ class TTLConfig(TypedDict, total=False):
     This can be overridden per-operation by explicitly setting refresh_ttl.
     Defaults to True if not configured.
     """
-    default_ttl: Optional[float]
+    default_ttl: float | None
     """Default TTL (time-to-live) in minutes for new items.
     
     If provided, new items will expire after this many minutes after their last access.
     The expiration timer refreshes on both read and write operations.
     Defaults to None (no expiration).
     """
-    sweep_interval_minutes: Optional[int]
+    sweep_interval_minutes: int | None
     """Interval in minutes between TTL sweep operations.
     
     If provided, the store will periodically delete expired items based on TTL.
@@ -565,7 +566,7 @@ class IndexConfig(TypedDict, total=False):
         - cohere:embed-multilingual-light-v3.0: 384
     """
 
-    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str]
+    embed: Embeddings | EmbeddingsFunc | AEmbeddingsFunc | str
     """Optional function to generate embeddings from text.
     
     Can be specified in three ways:
@@ -633,7 +634,7 @@ class IndexConfig(TypedDict, total=False):
         ```
     """
 
-    fields: Optional[list[str]]
+    fields: list[str] | None
     """Fields to extract text from for embedding generation.
     
     Controls which parts of stored items are embedded for semantic search. Follows JSON path syntax:
@@ -690,7 +691,7 @@ class BaseStore(ABC):
     """
 
     supports_ttl: bool = False
-    ttl_config: Optional[TTLConfig] = None
+    ttl_config: TTLConfig | None = None
 
     __slots__ = ("__weakref__",)
 
@@ -723,8 +724,8 @@ class BaseStore(ABC):
         namespace: tuple[str, ...],
         key: str,
         *,
-        refresh_ttl: Optional[bool] = None,
-    ) -> Optional[Item]:
+        refresh_ttl: bool | None = None,
+    ) -> Item | None:
         """Retrieve a single item.
 
         Args:
@@ -746,11 +747,11 @@ class BaseStore(ABC):
         namespace_prefix: tuple[str, ...],
         /,
         *,
-        query: Optional[str] = None,
-        filter: Optional[dict[str, Any]] = None,
+        query: str | None = None,
+        filter: dict[str, Any] | None = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: Optional[bool] = None,
+        refresh_ttl: bool | None = None,
     ) -> list[SearchItem]:
         """Search for items within a namespace prefix.
 
@@ -817,9 +818,9 @@ class BaseStore(ABC):
         namespace: tuple[str, ...],
         key: str,
         value: dict[str, Any],
-        index: Optional[Union[Literal[False], list[str]]] = None,
+        index: Literal[False] | list[str] | None = None,
         *,
-        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
+        ttl: float | None | NotProvided = NOT_PROVIDED,
     ) -> None:
         """Store or update an item in the store.
 
@@ -901,9 +902,9 @@ class BaseStore(ABC):
     def list_namespaces(
         self,
         *,
-        prefix: Optional[NamespacePath] = None,
-        suffix: Optional[NamespacePath] = None,
-        max_depth: Optional[int] = None,
+        prefix: NamespacePath | None = None,
+        suffix: NamespacePath | None = None,
+        max_depth: int | None = None,
         limit: int = 100,
         offset: int = 0,
     ) -> list[tuple[str, ...]]:
@@ -956,8 +957,8 @@ class BaseStore(ABC):
         namespace: tuple[str, ...],
         key: str,
         *,
-        refresh_ttl: Optional[bool] = None,
-    ) -> Optional[Item]:
+        refresh_ttl: bool | None = None,
+    ) -> Item | None:
         """Asynchronously retrieve a single item.
 
         Args:
@@ -984,11 +985,11 @@ class BaseStore(ABC):
         namespace_prefix: tuple[str, ...],
         /,
         *,
-        query: Optional[str] = None,
-        filter: Optional[dict[str, Any]] = None,
+        query: str | None = None,
+        filter: dict[str, Any] | None = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: Optional[bool] = None,
+        refresh_ttl: bool | None = None,
     ) -> list[SearchItem]:
         """Asynchronously search for items within a namespace prefix.
 
@@ -1058,9 +1059,9 @@ class BaseStore(ABC):
         namespace: tuple[str, ...],
         key: str,
         value: dict[str, Any],
-        index: Optional[Union[Literal[False], list[str]]] = None,
+        index: Literal[False] | list[str] | None = None,
         *,
-        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
+        ttl: float | None | NotProvided = NOT_PROVIDED,
     ) -> None:
         """Asynchronously store or update an item in the store.
 
@@ -1150,9 +1151,9 @@ class BaseStore(ABC):
     async def alist_namespaces(
         self,
         *,
-        prefix: Optional[NamespacePath] = None,
-        suffix: Optional[NamespacePath] = None,
-        max_depth: Optional[int] = None,
+        prefix: NamespacePath | None = None,
+        suffix: NamespacePath | None = None,
+        max_depth: int | None = None,
         limit: int = 100,
         offset: int = 0,
     ) -> list[tuple[str, ...]]:
@@ -1226,7 +1227,7 @@ def _validate_namespace(namespace: tuple[str, ...]) -> None:
 
 
 def _ensure_refresh(
-    ttl_config: Optional[TTLConfig], refresh_ttl: Optional[bool] = None
+    ttl_config: TTLConfig | None, refresh_ttl: bool | None = None
 ) -> bool:
     if refresh_ttl is not None:
         return refresh_ttl
@@ -1236,9 +1237,9 @@ def _ensure_refresh(
 
 
 def _ensure_ttl(
-    ttl_config: Optional[TTLConfig],
-    ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
-) -> Optional[float]:
+    ttl_config: TTLConfig | None,
+    ttl: float | None | NotProvided = NOT_PROVIDED,
+) -> float | None:
     if ttl is NOT_PROVIDED:
         if ttl_config:
             return ttl_config.get("default_ttl")

libs/checkpoint/langgraph/store/base/batch.py

@@ -1,10 +1,12 @@
 """Utilities for batching operations in a background task."""
 
+from __future__ import annotations
+
 import asyncio
 import functools
 import weakref
 from collections.abc import Iterable
-from typing import Any, Callable, Literal, Optional, TypeVar, Union
+from typing import Any, Callable, Literal, TypeVar
 
 from langgraph.store.base import (
     NOT_PROVIDED,
@@ -30,7 +32,7 @@ F = TypeVar("F", bound=Callable)
 
 def _check_loop(func: F) -> F:
     @functools.wraps(func)
-    def wrapper(store: "AsyncBatchedBaseStore", *args: Any, **kwargs: Any) -> Any:
+    def wrapper(store: AsyncBatchedBaseStore, *args: Any, **kwargs: Any) -> Any:
         method_name: str = func.__name__
         try:
             current_loop = asyncio.get_running_loop()
@@ -75,8 +77,8 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
         *,
-        refresh_ttl: Optional[bool] = None,
-    ) -> Optional[Item]:
+        refresh_ttl: bool | None = None,
+    ) -> Item | None:
         assert not self._task.done()
         fut = self._loop.create_future()
         self._aqueue.put_nowait(
@@ -96,11 +98,11 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace_prefix: tuple[str, ...],
         /,
         *,
-        query: Optional[str] = None,
-        filter: Optional[dict[str, Any]] = None,
+        query: str | None = None,
+        filter: dict[str, Any] | None = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: Optional[bool] = None,
+        refresh_ttl: bool | None = None,
     ) -> list[SearchItem]:
         assert not self._task.done()
         fut = self._loop.create_future()
@@ -124,9 +126,9 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
         value: dict[str, Any],
-        index: Optional[Union[Literal[False], list[str]]] = None,
+        index: Literal[False] | list[str] | None = None,
         *,
-        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
+        ttl: float | None | NotProvided = NOT_PROVIDED,
     ) -> None:
         assert not self._task.done()
         _validate_namespace(namespace)
@@ -154,9 +156,9 @@ class AsyncBatchedBaseStore(BaseStore):
     async def alist_namespaces(
         self,
         *,
-        prefix: Optional[NamespacePath] = None,
-        suffix: Optional[NamespacePath] = None,
-        max_depth: Optional[int] = None,
+        prefix: NamespacePath | None = None,
+        suffix: NamespacePath | None = None,
+        max_depth: int | None = None,
         limit: int = 100,
         offset: int = 0,
     ) -> list[tuple[str, ...]]:
@@ -187,8 +189,8 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
         *,
-        refresh_ttl: Optional[bool] = None,
-    ) -> Optional[Item]:
+        refresh_ttl: bool | None = None,
+    ) -> Item | None:
         return asyncio.run_coroutine_threadsafe(
             self.aget(namespace, key=key, refresh_ttl=refresh_ttl), self._loop
         ).result()
@@ -199,11 +201,11 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace_prefix: tuple[str, ...],
         /,
         *,
-        query: Optional[str] = None,
-        filter: Optional[dict[str, Any]] = None,
+        query: str | None = None,
+        filter: dict[str, Any] | None = None,
         limit: int = 10,
         offset: int = 0,
-        refresh_ttl: Optional[bool] = None,
+        refresh_ttl: bool | None = None,
     ) -> list[SearchItem]:
         return asyncio.run_coroutine_threadsafe(
             self.asearch(
@@ -223,9 +225,9 @@ class AsyncBatchedBaseStore(BaseStore):
         namespace: tuple[str, ...],
         key: str,
         value: dict[str, Any],
-        index: Optional[Union[Literal[False], list[str]]] = None,
+        index: Literal[False] | list[str] | None = None,
         *,
-        ttl: Union[Optional[float], "NotProvided"] = NOT_PROVIDED,
+        ttl: float | None | NotProvided = NOT_PROVIDED,
     ) -> None:
         _validate_namespace(namespace)
         asyncio.run_coroutine_threadsafe(
@@ -253,9 +255,9 @@ class AsyncBatchedBaseStore(BaseStore):
     def list_namespaces(
         self,
         *,
-        prefix: Optional[NamespacePath] = None,
-        suffix: Optional[NamespacePath] = None,
-        max_depth: Optional[int] = None,
+        prefix: NamespacePath | None = None,
+        suffix: NamespacePath | None = None,
+        max_depth: int | None = None,
         limit: int = 100,
         offset: int = 0,
     ) -> list[tuple[str, ...]]:
@@ -271,7 +273,7 @@ class AsyncBatchedBaseStore(BaseStore):
         ).result()
 
 
-def _dedupe_ops(values: list[Op]) -> tuple[Optional[list[int]], list[Op]]:
+def _dedupe_ops(values: list[Op]) -> tuple[list[int] | None, list[Op]]:
     """Dedupe operations while preserving order for results.
 
     Args:

libs/checkpoint/langgraph/store/base/embed.py

@@ -6,11 +6,13 @@ with LangChain-compatible tools while maintaining support for both synchronous a
 asynchronous operations.
 """
 
+from __future__ import annotations
+
 import asyncio
 import functools
 import json
 from collections.abc import Awaitable, Sequence
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable
 
 from langchain_core.embeddings import Embeddings
 
@@ -30,7 +32,7 @@ Similar to EmbeddingsFunc, but returns an awaitable that resolves to the embeddi
 
 
 def ensure_embeddings(
-    embed: Union[Embeddings, EmbeddingsFunc, AEmbeddingsFunc, str, None],
+    embed: Embeddings | EmbeddingsFunc | AEmbeddingsFunc | str | None,
 ) -> Embeddings:
     """Ensure that an embedding function conforms to LangChain's Embeddings interface.
 
@@ -141,7 +143,7 @@ class EmbeddingsLambda(Embeddings):
 
     def __init__(
         self,
-        func: Union[EmbeddingsFunc, AEmbeddingsFunc],
+        func: EmbeddingsFunc | AEmbeddingsFunc,
     ) -> None:
         if func is None:
             raise ValueError("func must be provided")
@@ -221,7 +223,7 @@ class EmbeddingsLambda(Embeddings):
         return (await afunc([text]))[0]
 
 
-def get_text_at_path(obj: Any, path: Union[str, list[str]]) -> list[str]:
+def get_text_at_path(obj: Any, path: str | list[str]) -> list[str]:
     """Extract text from an object using a path expression or pre-tokenized path.
 
     Args:
@@ -279,7 +281,7 @@ def get_text_at_path(obj: Any, path: Union[str, list[str]]) -> list[str]:
             for field in fields:
                 nested_tokens = tokenize_path(field)
                 if nested_tokens:
-                    current_obj: Optional[dict] = obj
+                    current_obj: dict | None = obj
                     for nested_token in nested_tokens:
                         if (
                             isinstance(current_obj, dict)
@@ -404,7 +406,7 @@ def _is_async_callable(
 
 
 @functools.lru_cache
-def _get_init_embeddings() -> Optional[Callable[[str], Embeddings]]:
+def _get_init_embeddings() -> Callable[[str], Embeddings] | None:
     try:
         from langchain.embeddings import init_embeddings  # type: ignore
 

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

@@ -99,6 +99,8 @@ Tip:
     ```
 """
 
+from __future__ import annotations
+
 import asyncio
 import concurrent.futures as cf
 import functools
@@ -107,7 +109,7 @@ from collections import defaultdict
 from collections.abc import Iterable
 from datetime import datetime, timezone
 from importlib import util
-from typing import Any, Optional
+from typing import Any
 
 from langchain_core.embeddings import Embeddings
 
@@ -178,7 +180,7 @@ class InMemoryStore(BaseStore):
         "embeddings",
     )
 
-    def __init__(self, *, index: Optional[IndexConfig] = None) -> None:
+    def __init__(self, *, index: IndexConfig | None = None) -> None:
         # Both _data and _vectors are wrapped in the In-memory API
         # Do not change their names
         self._data: dict[tuple[str, ...], dict[str, Item]] = defaultdict(dict)
@@ -189,7 +191,7 @@ class InMemoryStore(BaseStore):
         self.index_config = index
         if self.index_config:
             self.index_config = self.index_config.copy()
-            self.embeddings: Optional[Embeddings] = ensure_embeddings(
+            self.embeddings: Embeddings | None = ensure_embeddings(
                 self.index_config.get("embed"),
             )
             self.index_config["__tokenized_fields"] = [
@@ -325,7 +327,7 @@ class InMemoryStore(BaseStore):
                 )
                 # max pooling
                 seen: set[tuple[tuple[str, ...], str]] = set()
-                kept: list[tuple[Optional[float], Item]] = []
+                kept: list[tuple[float | None, Item]] = []
                 for score, item in sorted_results:
                     key = (item.namespace, item.key)
                     if key in seen:
@@ -494,7 +496,7 @@ def _cosine_similarity(X: list[float], Y: list[list[float]]) -> list[float]:
     if not Y:
         return []
     if _check_numpy():
-        import numpy as np  # type: ignore[import-not-found]
+        import numpy as np
 
         X_arr = np.array(X) if not isinstance(X, np.ndarray) else X
         Y_arr = np.array(Y) if not isinstance(Y, np.ndarray) else Y

libs/checkpoint/pyproject.toml

@@ -13,7 +13,7 @@ license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
     "langchain-core>=0.2.38",
-    "ormsgpack>=1.8.0",
+    "ormsgpack>=1.10.0",
 ]
 
 [project.urls]
@@ -29,6 +29,8 @@ dev = [
   "pytest-watcher",
   "mypy",
   "dataclasses-json",
+  "numpy",
+  "pandas",
 ]
 
 [tool.hatch.build.targets.wheel]
@@ -46,7 +48,7 @@ lint.select = [
   "B",  # flake8-bugbear
   "I",  # isort
 ]
-lint.ignore = ["E501", "B008", "UP007", "UP006"]
+lint.ignore = ["E501", "B008"]
 
 [tool.pytest-watcher]
 now = true

libs/checkpoint/tests/checkpoint_utils.py

@@ -1,13 +1,15 @@
+from __future__ import annotations
+
 from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Any, Optional, Protocol
+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) -> Optional[Any]: ...
+    def checkpoint(self) -> Any | None: ...
 
 
 def empty_checkpoint() -> Checkpoint:
@@ -23,10 +25,10 @@ def empty_checkpoint() -> Checkpoint:
 
 def create_checkpoint(
     checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, ChannelProtocol]],
+    channels: Mapping[str, ChannelProtocol] | None,
     step: int,
     *,
-    id: Optional[str] = None,
+    id: str | None = None,
 ) -> Checkpoint:
     """Create a checkpoint for the given channels."""
     ts = datetime.now(timezone.utc).isoformat()

libs/checkpoint/tests/test_jsonplus.py

@@ -11,6 +11,9 @@ from ipaddress import IPv4Address
 from zoneinfo import ZoneInfo
 
 import dataclasses_json
+import numpy as np
+import pandas as pd
+import pytest
 from pydantic import BaseModel, SecretStr
 from pydantic.v1 import BaseModel as BaseModelV1
 from pydantic.v1 import SecretStr as SecretStrV1
@@ -295,19 +298,174 @@ def test_serde_jsonplus_bytearray() -> None:
     assert serde.loads_typed(dumped) == some_bytearray
 
 
-def test_loads_cannot_find() -> None:
+@pytest.mark.parametrize(
+    "arr",
+    [
+        np.arange(9, dtype=np.int32).reshape(3, 3),
+        np.asfortranarray(np.arange(9, dtype=np.float64).reshape(3, 3)),
+        np.arange(12, dtype=np.int16)[::2].reshape(3, 2),
+    ],
+)
+def test_serde_jsonplus_numpy_array(arr: np.ndarray) -> None:
     serde = JsonPlusSerializer()
 
-    dumped = (
-        "json",
-        b'{"lc": 2, "type": "constructor", "id": ["tests", "test_jsonplus", "MyPydanticccc"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}',
-    )
+    dumped = serde.dumps_typed(arr)
+    assert dumped[0] == "msgpack"
+    result = serde.loads_typed(dumped)
+    assert isinstance(result, np.ndarray)
+    assert result.dtype == arr.dtype
+    assert np.array_equal(result, arr)
 
-    assert serde.loads_typed(dumped) is None, "Should return None if cannot find class"
 
-    dumped = (
-        "json",
-        b'{"lc": 2, "type": "constructor", "id": ["tests", "test_jsonpluss", "MyPydantic"], "method": null, "args": [], "kwargs": {"foo": "foo", "bar": 1}}',
-    )
+@pytest.mark.parametrize(
+    "arr",
+    [
+        np.arange(6, dtype=np.float32).reshape(2, 3),
+        np.asfortranarray(np.arange(4, dtype=np.complex128).reshape(2, 2)),
+    ],
+)
+def test_serde_jsonplus_numpy_array_json_hook(arr: np.ndarray) -> None:
+    serde = JsonPlusSerializer(__unpack_ext_hook__=_msgpack_ext_hook_to_json)
+    dumped = serde.dumps_typed(arr)
+    assert dumped[0] == "msgpack"
+    result = serde.loads_typed(dumped)
+    assert isinstance(result, list)
+    assert result == arr.tolist()
 
-    assert serde.loads_typed(dumped) is None, "Should return None if cannot find module"
+
+@pytest.mark.parametrize(
+    "df",
+    [
+        pd.DataFrame(),
+        pd.DataFrame({"int_col": [1, 2, 3]}),
+        pd.DataFrame({"float_col": [1.1, 2.2, 3.3]}),
+        pd.DataFrame({"str_col": ["a", "b", "c"]}),
+        pd.DataFrame({"bool_col": [True, False, True]}),
+        pd.DataFrame(
+            {
+                "datetime_col": [
+                    datetime(2024, 1, 1),
+                    datetime(2024, 1, 2),
+                    datetime(2024, 1, 3),
+                ]
+            }
+        ),
+        pd.DataFrame(
+            {
+                "int_col": [1, 2, 3],
+                "float_col": [1.1, 2.2, 3.3],
+                "str_col": ["a", "b", "c"],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "int_col": [1, 2, None],
+                "float_col": [1.1, None, 3.3],
+                "str_col": ["a", None, "c"],
+            }
+        ),
+        pd.DataFrame({"cat_col": pd.Categorical(["a", "b", "a", "c"])}),
+        pd.DataFrame(
+            {
+                "int8": pd.array([1, 2, 3], dtype="int8"),
+                "int16": pd.array([10, 20, 30], dtype="int16"),
+                "int32": pd.array([100, 200, 300], dtype="int32"),
+                "int64": pd.array([1000, 2000, 3000], dtype="int64"),
+                "float32": pd.array([1.1, 2.2, 3.3], dtype="float32"),
+                "float64": pd.array([10.1, 20.2, 30.3], dtype="float64"),
+            }
+        ),
+        pd.DataFrame({"value": [1, 2, 3]}, index=["x", "y", "z"]),
+        pd.DataFrame(
+            [[1, 2, 3, 4]],
+            columns=pd.MultiIndex.from_tuples(
+                [("A", "X"), ("A", "Y"), ("B", "X"), ("B", "Y")]
+            ),
+        ),
+        pd.DataFrame(
+            {"value": [1, 2, 3]}, index=pd.date_range("2024-01-01", periods=3, freq="D")
+        ),
+        pd.DataFrame(
+            {
+                "col1": range(1000),
+                "col2": [f"str_{i}" for i in range(1000)],
+                "col3": np.random.rand(1000),
+            }
+        ),
+        pd.DataFrame(
+            {"tz_datetime": pd.date_range("2024-01-01", periods=3, freq="D", tz="UTC")}
+        ),
+        pd.DataFrame({"timedelta": pd.to_timedelta([1, 2, 3], unit="D")}),
+        pd.DataFrame({"period": pd.period_range("2024-01", periods=3, freq="M")}),
+        pd.DataFrame({"interval": pd.interval_range(start=0, end=3, periods=3)}),
+        pd.DataFrame({"unicode": ["Hello 🌍", "Python 🐍", "Data 📊"]}),
+        pd.DataFrame({"mixed": [1, "string", [1, 2, 3], {"key": "value"}]}),
+        pd.DataFrame({"a": [1], "b": ["test"], "c": [3.14]}),
+        pd.DataFrame({"single": [42]}),
+        pd.DataFrame(
+            {
+                "small": [sys.float_info.min, 0, sys.float_info.max],
+                "large_int": [-(2**63), 0, 2**63 - 1],
+            }
+        ),
+        pd.DataFrame({"special_strings": ["", "null", "None", "NaN", "inf", "-inf"]}),
+        pd.DataFrame({"bytes_col": [b"hello", b"world", b"\x00\x01\x02"]}),
+    ],
+)
+def test_serde_jsonplus_pandas_dataframe(df: pd.DataFrame) -> None:
+    serde = JsonPlusSerializer(pickle_fallback=True)
+
+    dumped = serde.dumps_typed(df)
+    assert dumped[0] == "pickle"
+    result = serde.loads_typed(dumped)
+    assert result.equals(df)
+
+
+@pytest.mark.parametrize(
+    "series",
+    [
+        pd.Series([]),
+        pd.Series([1, 2, 3]),
+        pd.Series([1.1, 2.2, 3.3]),
+        pd.Series(["a", "b", "c"]),
+        pd.Series([True, False, True]),
+        pd.Series([datetime(2024, 1, 1), datetime(2024, 1, 2), datetime(2024, 1, 3)]),
+        pd.Series([1, 2, None]),
+        pd.Series([1.1, None, 3.3]),
+        pd.Series(["a", None, "c"]),
+        pd.Series(pd.Categorical(["a", "b", "a", "c"])),
+        pd.Series([1, 2, 3], dtype="int8"),
+        pd.Series([10, 20, 30], dtype="int16"),
+        pd.Series([100, 200, 300], dtype="int32"),
+        pd.Series([1000, 2000, 3000], dtype="int64"),
+        pd.Series([1.1, 2.2, 3.3], dtype="float32"),
+        pd.Series([10.1, 20.2, 30.3], dtype="float64"),
+        pd.Series([1, 2, 3], index=["x", "y", "z"]),
+        pd.Series([1, 2, 3], index=pd.date_range("2024-01-01", periods=3, freq="D")),
+        pd.Series(range(1000)),
+        pd.Series(pd.date_range("2024-01-01", periods=3, freq="D", tz="UTC")),
+        pd.Series(pd.to_timedelta([1, 2, 3], unit="D")),
+        pd.Series(pd.period_range("2024-01", periods=3, freq="M")),
+        pd.Series(pd.interval_range(start=0, end=3, periods=3)),
+        pd.Series(["Hello 🌍", "Python 🐍", "Data 📊"]),
+        pd.Series([1, "string", [1, 2, 3], {"key": "value"}]),
+        pd.Series([42], name="single"),
+        pd.Series([sys.float_info.min, 0, sys.float_info.max]),
+        pd.Series([-(2**63), 0, 2**63 - 1]),
+        pd.Series(["", "null", "None", "NaN", "inf", "-inf"]),
+        pd.Series([b"hello", b"world", b"\x00\x01\x02"]),
+        pd.Series([1, 2, 3], name="named_series"),
+        pd.Series(
+            [10, 20],
+            index=pd.MultiIndex.from_tuples([("a", 1), ("b", 2)], names=["x", "y"]),
+        ),
+    ],
+)
+def test_serde_jsonplus_pandas_series(series: pd.Series) -> None:
+    serde = JsonPlusSerializer(pickle_fallback=True)
+    dumped = serde.dumps_typed(series)
+
+    assert dumped[0] == "pickle"
+    result = serde.loads_typed(dumped)
+
+    assert result.equals(series)

libs/checkpoint/uv.lock

@@ -3,7 +3,10 @@ revision = 1
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12.4'",
-    "python_full_version < '3.12.4'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version < '3.10'",
 ]
 
 [[package]]
@@ -218,7 +221,7 @@ name = "exceptiongroup"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.12.4'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749 }
 wheels = [
@@ -333,6 +336,10 @@ dev = [
     { name = "codespell" },
     { name = "dataclasses-json" },
     { name = "mypy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "pandas" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
@@ -343,7 +350,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "langchain-core", specifier = ">=0.2.38" },
-    { name = "ormsgpack", specifier = ">=1.8.0" },
+    { name = "ormsgpack", specifier = ">=1.10.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -351,6 +358,8 @@ dev = [
     { name = "codespell" },
     { name = "dataclasses-json" },
     { name = "mypy" },
+    { name = "numpy" },
+    { name = "pandas" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
@@ -441,6 +450,189 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963 },
 ]
 
+[[package]]
+name = "numpy"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a9/75/10dd1f8116a8b796cb2c737b674e02d02e80454bda953fa7e65d8c12b016/numpy-2.0.2.tar.gz", hash = "sha256:883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78", size = 18902015 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/21/91/3495b3237510f79f5d81f2508f9f13fea78ebfdf07538fc7444badda173d/numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece", size = 21165245 },
+    { url = "https://files.pythonhosted.org/packages/05/33/26178c7d437a87082d11019292dce6d3fe6f0e9026b7b2309cbf3e489b1d/numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04", size = 13738540 },
+    { url = "https://files.pythonhosted.org/packages/ec/31/cc46e13bf07644efc7a4bf68df2df5fb2a1a88d0cd0da9ddc84dc0033e51/numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66", size = 5300623 },
+    { url = "https://files.pythonhosted.org/packages/6e/16/7bfcebf27bb4f9d7ec67332ffebee4d1bf085c84246552d52dbb548600e7/numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b", size = 6901774 },
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225 },
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.2"
@@ -879,6 +1149,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/95/3a/2e8704d19f376c799748ff9cb041225c1d59f3e7711bc5596c8cfdc24925/ruff-0.11.10-py3-none-win_arm64.whl", hash = "sha256:ef69637b35fb8b210743926778d0e45e1bffa850a7c61e428c6b971549b5f5d1", size = 10765278 },
 ]
 
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -970,6 +1249,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/08/aa4fdfb71f7de5176385bd9e90852eaf6b5d622735020ad600f2bab54385/typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f", size = 14125 },
 ]
 
+[[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839 },
+]
+
 [[package]]
 name = "urllib3"
 version = "2.4.0"

libs/cli/examples/graphs/storm.py

@@ -22,10 +22,10 @@ from langgraph.graph import END, StateGraph
 from pydantic import BaseModel, Field
 from typing_extensions import TypedDict
 
-fast_llm = ChatOpenAI(model="gpt-3.5-turbo")
+fast_llm = ChatOpenAI(model="gpt-4o-mini")
 # Uncomment for a Fireworks model
 # fast_llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", max_tokens=32_000)
-long_context_llm = ChatOpenAI(model="gpt-4-turbo-preview")
+long_context_llm = ChatOpenAI(model="gpt-4o")
 
 
 direct_gen_outline_prompt = ChatPromptTemplate.from_messages(
@@ -144,7 +144,7 @@ gen_perspectives_prompt = ChatPromptTemplate.from_messages(
 )
 
 gen_perspectives_chain = gen_perspectives_prompt | ChatOpenAI(
-    model="gpt-3.5-turbo"
+    model="gpt-4o-mini"
 ).with_structured_output(Perspectives)
 
 
@@ -270,7 +270,7 @@ gen_queries_prompt = ChatPromptTemplate.from_messages(
     ]
 )
 gen_queries_chain = gen_queries_prompt | ChatOpenAI(
-    model="gpt-3.5-turbo"
+    model="gpt-4o-mini"
 ).with_structured_output(Queries, include_raw=True)
 
 

libs/cli/langgraph_cli/config.py

@@ -1,6 +1,7 @@
 import json
 import os
 import pathlib
+import re
 import textwrap
 from collections import Counter
 from typing import Any, Literal, NamedTuple, Optional, TypedDict, Union
@@ -382,6 +383,14 @@ class Config(TypedDict, total=False):
     Only relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.
     """
 
+    pip_installer: Optional[str]
+    """Optional. Python package installer to use ('auto', 'pip', 'uv').
+    
+    - 'auto' (default): Use uv for supported base images, otherwise pip
+    - 'pip': Force use of pip regardless of base image support
+    - 'uv': Force use of uv (will fail if base image doesn't support it)
+    """
+
     dockerfile_lines: list[str]
     """Optional. Additional Docker instructions that will be appended to your base Dockerfile.
     
@@ -461,7 +470,7 @@ class Config(TypedDict, total=False):
 PIP_CLEANUP_LINES = """# -- Ensure user deps didn't inadvertently overwrite langgraph-api
 RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && \
     touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir --no-deps -e /api
+RUN PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir --no-deps -e /api
 # -- End of ensuring user deps didn't inadvertently overwrite langgraph-api --
 # -- Removing pip from the final image ~<:===~~~ --
 RUN pip uninstall -y pip setuptools wheel && \
@@ -470,6 +479,7 @@ RUN pip uninstall -y pip setuptools wheel && \
 # pip removal for wolfi
 RUN rm -rf /usr/lib/python*/site-packages/pip* /usr/lib/python*/site-packages/setuptools* /usr/lib/python*/site-packages/wheel* && \
     find /usr/bin -name "pip*" -delete || true
+{uv_removal}
 # -- End of pip removal --"""
 
 
@@ -534,6 +544,7 @@ def validate_config(config: Config) -> Config:
         "node_version": node_version,
         "python_version": python_version,
         "pip_config_file": config.get("pip_config_file"),
+        "pip_installer": config.get("pip_installer", "auto"),
         "_INTERNAL_docker_tag": config.get("_INTERNAL_docker_tag"),
         "base_image": config.get("base_image"),
         "image_distro": image_distro,
@@ -598,6 +609,13 @@ def validate_config(config: Config) -> Config:
                 "Must be either 'debian' or 'wolfi'."
             )
 
+    if pip_installer := config.get("pip_installer"):
+        if pip_installer not in ["auto", "pip", "uv"]:
+            raise click.UsageError(
+                f"Invalid pip_installer: '{pip_installer}'. "
+                "Must be 'auto', 'pip', or 'uv'."
+            )
+
     # Validate auth config
     if auth_conf := config.get("auth"):
         if "path" in auth_conf:
@@ -1089,16 +1107,47 @@ def _get_node_pm_install_cmd(config_path: pathlib.Path, config: Config) -> str:
     return install_cmd
 
 
+semver_pattern = re.compile(r":(\d+(?:\.\d+)?(?:\.\d+)?)(?:-|$)")
+
+
+def _image_supports_uv(base_image: str) -> bool:
+    if base_image == "langchain/langgraph-trial":
+        return False
+    match = semver_pattern.search(base_image)
+    if not match:
+        # Default image (langchain/langgraph-api) supports it.
+        return True
+
+    version_str = match.group(1)
+    version = tuple(map(int, version_str.split(".")))
+    min_uv = (0, 2, 47)
+    return version >= min_uv
+
+
 def python_config_to_docker(
     config_path: pathlib.Path,
     config: Config,
     base_image: str,
 ) -> tuple[str, dict[str, str]]:
     """Generate a Dockerfile from the configuration."""
+    pip_installer = config.get("pip_installer", "auto")
+
+    if pip_installer == "uv":
+        install_cmd = "uv pip install --system"
+        uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx"
+    elif pip_installer == "pip":
+        install_cmd = "pip install"
+        uv_removal = ""
+    else:
+        if _image_supports_uv(base_image):
+            install_cmd = "uv pip install --system"
+            uv_removal = "RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx"
+        else:
+            install_cmd = "pip install"
+            uv_removal = ""
+
     # configure pip
-    pip_install = (
-        "PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt"
-    )
+    pip_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt"
     if config.get("pip_config_file"):
         pip_install = f"PIP_CONFIG_FILE=/pipconfig.txt {pip_install}"
     pip_config_file_str = (
@@ -1151,7 +1200,10 @@ RUN set -ex && \\
                 'name = "{fullpath.name}"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_{fullpath.name}/pyproject.toml; \\
     done
 # -- End of non-package dependency {fullpath.name} --"""
@@ -1240,7 +1292,8 @@ ADD {relpath} /deps/{name}
         "",
         js_inst_str,
         "",
-        PIP_CLEANUP_LINES,  # Add pip cleanup after all installations are complete
+        # Add pip cleanup after all installations are complete
+        PIP_CLEANUP_LINES.format(install_cmd=install_cmd, uv_removal=uv_removal),
         "",
         f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
     ]

libs/cli/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-cli"
-version = "0.2.12"
+version = "0.3.2"
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/cli/schemas/schema.json

@@ -134,6 +134,17 @@
               ],
               "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
             },
+            "pip_installer": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
+            },
             "store": {
               "anyOf": [
                 {
@@ -287,6 +298,17 @@
               ],
               "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
             },
+            "pip_installer": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
+            },
             "store": {
               "anyOf": [
                 {

libs/cli/schemas/schema.v0.json

@@ -134,6 +134,17 @@
               ],
               "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
             },
+            "pip_installer": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
+            },
             "store": {
               "anyOf": [
                 {
@@ -287,6 +298,17 @@
               ],
               "description": "Optional. Linux distribution for the base image.\n\nMust be either 'debian' or 'wolfi'. If omitted, defaults to 'debian'.\n"
             },
+            "pip_installer": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
+            },
             "store": {
               "anyOf": [
                 {

libs/cli/tests/unit_tests/cli/test_cli.py

@@ -14,6 +14,10 @@ from langgraph_cli.config import PIP_CLEANUP_LINES, Config, validate_config
 from langgraph_cli.docker import DEFAULT_POSTGRES_URI, DockerCapabilities, Version
 from langgraph_cli.util import clean_empty_lines
 
+FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format(
+    install_cmd="uv pip install --system",
+    uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx",
+)
 DEFAULT_DOCKER_CAPABILITIES = DockerCapabilities(
     version_docker=Version(26, 1, 1),
     version_compose=Version(2, 27, 0),
@@ -144,10 +148,10 @@ services:
                 COPY --from=cli_1 . /deps/cli_1
                 # -- End of local package ../../.. --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/cli
         
         develop:

libs/cli/tests/unit_tests/test_config.py

@@ -1,3 +1,4 @@
+import copy
 import json
 import os
 import pathlib
@@ -17,6 +18,11 @@ from langgraph_cli.config import (
 )
 from langgraph_cli.util import clean_empty_lines
 
+FORMATTED_CLEANUP_LINES = PIP_CLEANUP_LINES.format(
+    install_cmd="uv pip install --system",
+    uv_removal="RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx",
+)
+
 PATH_TO_CONFIG = pathlib.Path(__file__).parent / "test_config.json"
 
 
@@ -35,6 +41,7 @@ def test_validate_config():
         "python_version": "3.11",
         "node_version": None,
         "pip_config_file": None,
+        "pip_installer": "auto",
         "image_distro": "debian",
         "dockerfile_lines": [],
         "env": {},
@@ -56,6 +63,7 @@ def test_validate_config():
         "python_version": "3.12",
         "node_version": None,
         "pip_config_file": "pipconfig.txt",
+        "pip_installer": "auto",
         "image_distro": "debian",
         "dockerfile_lines": ["ARG meow"],
         "dependencies": [".", "langchain"],
@@ -211,6 +219,74 @@ def test_validate_config_image_distro():
     assert config["image_distro"] == "debian"
 
 
+def test_validate_config_pip_installer():
+    """Test validation of pip_installer field."""
+    # Valid pip_installer values should work
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "pip_installer": "auto",
+        }
+    )
+    assert config["pip_installer"] == "auto"
+
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "pip_installer": "pip",
+        }
+    )
+    assert config["pip_installer"] == "pip"
+
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+            "pip_installer": "uv",
+        }
+    )
+    assert config["pip_installer"] == "uv"
+
+    # Missing pip_installer should default to "auto"
+    config = validate_config(
+        {
+            "python_version": "3.11",
+            "dependencies": ["."],
+            "graphs": {"agent": "./agent.py:graph"},
+        }
+    )
+    assert config["pip_installer"] == "auto"
+
+    # Invalid pip_installer values should raise error
+    with pytest.raises(click.UsageError) as exc_info:
+        validate_config(
+            {
+                "python_version": "3.11",
+                "dependencies": ["."],
+                "graphs": {"agent": "./agent.py:graph"},
+                "pip_installer": "conda",
+            }
+        )
+    assert "Invalid pip_installer: 'conda'" in str(exc_info.value)
+    assert "Must be 'auto', 'pip', or 'uv'" in str(exc_info.value)
+
+    with pytest.raises(click.UsageError) as exc_info:
+        validate_config(
+            {
+                "python_version": "3.11",
+                "dependencies": ["."],
+                "graphs": {"agent": "./agent.py:graph"},
+                "pip_installer": "invalid",
+            }
+        )
+    assert "Invalid pip_installer: 'invalid'" in str(exc_info.value)
+
+
 def test_validate_config_file():
     with tempfile.TemporaryDirectory() as tmpdir:
         tmpdir_path = pathlib.Path(tmpdir)
@@ -345,7 +421,7 @@ def test_config_to_docker_simple():
 FROM langchain/langgraph-api:3.11
 # -- Installing local requirements --
 COPY --from=__outer_requirements.txt requirements.txt /deps/__outer_graphs_reqs_a/graphs_reqs_a/requirements.txt
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -r /deps/__outer_graphs_reqs_a/graphs_reqs_a/requirements.txt
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -r /deps/__outer_graphs_reqs_a/graphs_reqs_a/requirements.txt
 # -- End of local requirements install --
 # -- Adding local package ../../examples --
 COPY --from=examples . /deps/examples
@@ -357,7 +433,10 @@ RUN set -ex && \\
                 'name = "unit_tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency unit_tests --
@@ -368,16 +447,19 @@ RUN set -ex && \\
                 'name = "graphs_reqs_a"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_graphs_reqs_a/pyproject.toml; \\
     done
 # -- End of non-package dependency graphs_reqs_a --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGGRAPH_HTTP='{{"app": "/deps/examples/my_app.py:app"}}'
 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{PIP_CLEANUP_LINES}
+{FORMATTED_CLEANUP_LINES}
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
     assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
@@ -407,7 +489,10 @@ RUN set -ex && \\
                 'name = "unit_tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency unit_tests --
@@ -418,16 +503,19 @@ RUN set -ex && \\
                 'name = "tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency tests --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}'
 """
-        + PIP_CLEANUP_LINES
+        + FORMATTED_CLEANUP_LINES
         + """
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
@@ -462,16 +550,19 @@ RUN set -ex && \\
                 'name = "unit_tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency unit_tests --
 # -- Installing all local dependencies --
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}'
 """
-        + PIP_CLEANUP_LINES
+        + FORMATTED_CLEANUP_LINES
         + """
 WORKDIR /deps/__outer_unit_tests/unit_tests\
 """
@@ -521,15 +612,18 @@ RUN set -ex && \\
                 'name = "graphs"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_graphs/pyproject.toml; \\
     done
 # -- End of non-package dependency graphs --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_graphs/src/agent.py:graph"}}'
-{PIP_CLEANUP_LINES}\
+{FORMATTED_CLEANUP_LINES}\
 """
     assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
     assert additional_contexts == {}
@@ -562,11 +656,11 @@ dependencies = ["langchain"]"""
 ADD . /deps/unit_tests
 # -- End of local package . --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{"agent": "/deps/unit_tests/graphs/agent.py:graph"}'
 """
-        + PIP_CLEANUP_LINES
+        + FORMATTED_CLEANUP_LINES
         + "\n"
         + "WORKDIR /deps/unit_tests"
         ""
@@ -594,7 +688,7 @@ def test_config_to_docker_end_to_end():
 ARG meow
 ARG foo
 ADD pipconfig.txt /pipconfig.txt
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain langchain_openai
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt langchain langchain_openai
 # -- Adding non-package dependency graphs --
 ADD ./graphs/ /deps/__outer_graphs/src
 RUN set -ex && \\
@@ -602,15 +696,18 @@ RUN set -ex && \\
                 'name = "graphs"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_graphs/pyproject.toml; \\
     done
 # -- End of non-package dependency graphs --
 # -- Installing all local dependencies --
-RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_graphs/src/agent.py:graph"}}'
-{PIP_CLEANUP_LINES}"""
+{FORMATTED_CLEANUP_LINES}"""
     assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
     assert additional_contexts == {}
 
@@ -705,12 +802,15 @@ RUN set -ex && \\
                 'name = "unit_tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency unit_tests --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGGRAPH_UI='{{"agent": "./graphs/agent.ui.jsx"}}'
 ENV LANGGRAPH_UI_CONFIG='{{"shared": ["nuqs"]}}'
@@ -719,7 +819,7 @@ ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:g
 ENV NODE_VERSION=20
 RUN cd /deps/__outer_unit_tests/unit_tests && npm i && tsx /api/langgraph_api/js/build.mts
 # -- End of JS dependencies install --
-{PIP_CLEANUP_LINES}
+{FORMATTED_CLEANUP_LINES}
 WORKDIR /deps/__outer_unit_tests/unit_tests"""
 
     assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
@@ -748,29 +848,87 @@ RUN set -ex && \\
                 'name = "unit_tests"' \\
                 'version = "0.1"' \\
                 '[tool.setuptools.package-data]' \\
-                '"*" = ["**/*"]'; do \\
+                '"*" = ["**/*"]' \\
+                '[build-system]' \\
+                'requires = ["setuptools>=61"]' \\
+                'build-backend = "setuptools.build_meta"'; do \\
         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
     done
 # -- End of non-package dependency unit_tests --
 # -- Installing all local dependencies --
-RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
 # -- End of local dependencies install --
 ENV LANGSERVE_GRAPHS='{{"python": "/deps/__outer_unit_tests/unit_tests/multiplatform/python.py:graph", "js": "/deps/__outer_unit_tests/unit_tests/multiplatform/js.mts:graph"}}'
 # -- Installing JS dependencies --
 ENV NODE_VERSION=22
 RUN cd /deps/__outer_unit_tests/unit_tests && npm i && tsx /api/langgraph_api/js/build.mts
 # -- End of JS dependencies install --
-{PIP_CLEANUP_LINES}
+{FORMATTED_CLEANUP_LINES}
 WORKDIR /deps/__outer_unit_tests/unit_tests"""
 
     assert clean_empty_lines(actual_docker_stdin) == expected_docker_stdin
     assert additional_contexts == {}
 
 
+def test_config_to_docker_pip_installer():
+    """Test that pip_installer setting affects the generated Dockerfile."""
+    graphs = {"agent": "./graphs/agent.py:graph"}
+    base_config = {
+        "python_version": "3.11",
+        "dependencies": ["."],
+        "graphs": graphs,
+    }
+
+    # Test default (auto) behavior with UV-supporting image
+    config_auto = validate_config(
+        {**copy.deepcopy(base_config), "pip_installer": "auto"}
+    )
+    docker_auto, _ = config_to_docker(
+        PATH_TO_CONFIG, config_auto, "langchain/langgraph-api:0.2.47"
+    )
+    assert "uv pip install --system" in docker_auto
+    assert "rm /usr/bin/uv /usr/bin/uvx" in docker_auto
+
+    # Test explicit pip setting
+    config_pip = validate_config({**copy.deepcopy(base_config), "pip_installer": "pip"})
+    docker_pip, _ = config_to_docker(
+        PATH_TO_CONFIG, config_pip, "langchain/langgraph-api:0.2.47"
+    )
+    assert "uv pip install --system" not in docker_pip
+    assert "pip install" in docker_pip
+    assert "rm /usr/bin/uv" not in docker_pip
+
+    # Test explicit uv setting
+    config_uv = validate_config({**copy.deepcopy(base_config), "pip_installer": "uv"})
+    docker_uv, _ = config_to_docker(
+        PATH_TO_CONFIG, config_uv, "langchain/langgraph-api:0.2.47"
+    )
+    assert "uv pip install --system" in docker_uv
+    assert "rm /usr/bin/uv /usr/bin/uvx" in docker_uv
+
+    # Test auto behavior with older image (should use pip)
+    config_auto_old = validate_config(
+        {**copy.deepcopy(base_config), "pip_installer": "auto"}
+    )
+    docker_auto_old, _ = config_to_docker(
+        PATH_TO_CONFIG, config_auto_old, "langchain/langgraph-api:0.2.46"
+    )
+    assert "uv pip install --system" not in docker_auto_old
+    assert "pip install" in docker_auto_old
+    assert "rm /usr/bin/uv" not in docker_auto_old
+
+    # Test that missing pip_installer defaults to auto behavior
+    config_default = validate_config(copy.deepcopy(base_config))
+    docker_default, _ = config_to_docker(
+        PATH_TO_CONFIG, config_default, "langchain/langgraph-api:0.2.47"
+    )
+    assert "uv pip install --system" in docker_default
+
+
 # config_to_compose
 def test_config_to_compose_simple_config():
     graphs = {"agent": "./agent.py:graph"}
-    # Create a properly indented version of PIP_CLEANUP_LINES for compose files
+    # Create a properly indented version of FORMATTED_CLEANUP_LINES for compose files
     expected_compose_stdin = f"""
         pull_policy: build
         build:
@@ -784,15 +942,18 @@ def test_config_to_compose_simple_config():
                                 'name = "unit_tests"' \\
                                 'version = "0.1"' \\
                                 '[tool.setuptools.package-data]' \\
-                                '"*" = ["**/*"]'; do \\
+                                '"*" = ["**/*"]' \\
+                                '[build-system]' \\
+                                'requires = ["setuptools>=61"]' \\
+                                'build-backend = "setuptools.build_meta"'; do \\
                         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/__outer_unit_tests/unit_tests
         """
     actual_compose_stdin = config_to_compose(
@@ -822,15 +983,18 @@ def test_config_to_compose_env_vars():
                                 'name = "unit_tests"' \\
                                 'version = "0.1"' \\
                                 '[tool.setuptools.package-data]' \\
-                                '"*" = ["**/*"]'; do \\
+                                '"*" = ["**/*"]' \\
+                                '[build-system]' \\
+                                'requires = ["setuptools>=61"]' \\
+                                'build-backend = "setuptools.build_meta"'; do \\
                         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/__outer_unit_tests/unit_tests
         """
     openai_api_key = "key"
@@ -864,15 +1028,18 @@ def test_config_to_compose_env_file():
                                 'name = "unit_tests"' \\
                                 'version = "0.1"' \\
                                 '[tool.setuptools.package-data]' \\
-                                '"*" = ["**/*"]'; do \\
+                                '"*" = ["**/*"]' \\
+                                '[build-system]' \\
+                                'requires = ["setuptools>=61"]' \\
+                                'build-backend = "setuptools.build_meta"'; do \\
                         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/__outer_unit_tests/unit_tests
         """
     actual_compose_stdin = config_to_compose(
@@ -899,15 +1066,18 @@ def test_config_to_compose_watch():
                                 'name = "unit_tests"' \\
                                 'version = "0.1"' \\
                                 '[tool.setuptools.package-data]' \\
-                                '"*" = ["**/*"]'; do \\
+                                '"*" = ["**/*"]' \\
+                                '[build-system]' \\
+                                'requires = ["setuptools>=61"]' \\
+                                'build-backend = "setuptools.build_meta"'; do \\
                         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/__outer_unit_tests/unit_tests
         
         develop:
@@ -943,15 +1113,18 @@ def test_config_to_compose_end_to_end():
                                 'name = "unit_tests"' \\
                                 'version = "0.1"' \\
                                 '[tool.setuptools.package-data]' \\
-                                '"*" = ["**/*"]'; do \\
+                                '"*" = ["**/*"]' \\
+                                '[build-system]' \\
+                                'requires = ["setuptools>=61"]' \\
+                                'build-backend = "setuptools.build_meta"'; do \\
                         echo "$line" >> /deps/__outer_unit_tests/pyproject.toml; \\
                     done
                 # -- End of non-package dependency unit_tests --
                 # -- Installing all local dependencies --
-                RUN PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+                RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e /deps/*
                 # -- End of local dependencies install --
                 ENV LANGSERVE_GRAPHS='{{"agent": "/deps/__outer_unit_tests/unit_tests/agent.py:graph"}}'
-{textwrap.indent(textwrap.dedent(PIP_CLEANUP_LINES), "                ")}
+{textwrap.indent(textwrap.dedent(FORMATTED_CLEANUP_LINES), "                ")}
                 WORKDIR /deps/__outer_unit_tests/unit_tests
         
         develop:

libs/cli/uv.lock

@@ -501,7 +501,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-cli"
-version = "0.2.12"
+version = "0.3.2"
 source = { editable = "." }
 dependencies = [
     { name = "click" },

libs/langgraph/README.md

@@ -12,7 +12,6 @@
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
-[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)
 
 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 

libs/langgraph/bench/fanout_to_subgraph.py

@@ -37,7 +37,7 @@ def fanout_to_subgraph() -> StateGraph:
         return END if state["jokes"][0].endswith(" a" * 10) else "bump"
 
     # subgraph
-    subgraph = StateGraph(JokeState, input=JokeInput, output=JokeOutput)
+    subgraph = StateGraph(JokeState, input_schema=JokeInput, output_schema=JokeOutput)
     subgraph.add_node("edit", edit)
     subgraph.add_node("generate", generate)
     subgraph.add_node("bump", bump)
@@ -87,7 +87,7 @@ def fanout_to_subgraph_sync() -> StateGraph:
         return END if state["jokes"][0].endswith(" a" * 10) else "bump"
 
     # subgraph
-    subgraph = StateGraph(JokeState, input=JokeInput, output=JokeOutput)
+    subgraph = StateGraph(JokeState, input_schema=JokeInput, output_schema=JokeOutput)
     subgraph.add_node("edit", edit)
     subgraph.add_node("generate", generate)
     subgraph.add_node("bump", bump)

libs/langgraph/langgraph/_typing.py

@@ -0,0 +1,54 @@
+"""Private typing utilities for LangGraph."""
+
+from __future__ import annotations
+
+from dataclasses import Field
+from typing import Any, ClassVar, Protocol, Union
+
+from pydantic import BaseModel
+from typing_extensions import TypeAlias, TypedDict
+
+
+class TypedDictLikeV1(Protocol):
+    """Protocol to represent types that behave like TypedDicts
+
+    Version 1: using `ClassVar` for keys."""
+
+    __required_keys__: ClassVar[frozenset[str]]
+    __optional_keys__: ClassVar[frozenset[str]]
+
+
+class TypedDictLikeV2(Protocol):
+    """Protocol to represent types that behave like TypedDicts
+
+    Version 2: not using `ClassVar` for keys."""
+
+    __required_keys__: frozenset[str]
+    __optional_keys__: frozenset[str]
+
+
+class DataclassLike(Protocol):
+    """Protocol to represent types that behave like dataclasses.
+
+    Inspired by the private _DataclassT from dataclasses that uses a similar protocol as a bound."""
+
+    __dataclass_fields__: ClassVar[dict[str, Field[Any]]]
+
+
+StateLike: TypeAlias = Union[TypedDictLikeV1, TypedDictLikeV2, DataclassLike, BaseModel]
+"""Type alias for state-like types.
+
+It can either be a `TypedDict`, `dataclass`, or Pydantic `BaseModel`.
+Note: we cannot use either `TypedDict` or `dataclass` directly due to limitations in type checking.
+"""
+
+
+class Unset:
+    """A sentinel value to represent an unset type."""
+
+
+UNSET: Unset = Unset()
+
+
+class DeprecatedKwargs(TypedDict):
+    """TypedDict to use for extra keyword arguments, enabling type checking warnings for deprecated arguments."""

libs/langgraph/langgraph/channels/topic.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from collections.abc import Iterator, Sequence
 from typing import Any, Generic, Union
 
@@ -8,7 +10,7 @@ from langgraph.constants import MISSING
 from langgraph.errors import EmptyChannelError
 
 
-def flatten(values: Sequence[Union[Value, list[Value]]]) -> Iterator[Value]:
+def flatten(values: Sequence[Value | list[Value]]) -> Iterator[Value]:
     for value in values:
         if isinstance(value, list):
             yield from value
@@ -70,7 +72,7 @@ class Topic(
                 empty.values = checkpoint
         return empty
 
-    def update(self, values: Sequence[Union[Value, list[Value]]]) -> bool:
+    def update(self, values: Sequence[Value | list[Value]]) -> bool:
         updated = False
         if not self.accumulate:
             updated = bool(self.values)

libs/langgraph/langgraph/constants.py

@@ -39,8 +39,6 @@ ERROR = sys.intern("__error__")
 # for errors raised by nodes
 NO_WRITES = sys.intern("__no_writes__")
 # marker to signal node didn't write anything
-SCHEDULED = sys.intern("__scheduled__")
-# marker to signal node was scheduled (in distributed mode)
 TASKS = sys.intern("__pregel_tasks")
 # for Send objects returned by nodes/edges, corresponds to PUSH below
 RETURN = sys.intern("__return__")
@@ -71,13 +69,6 @@ CONFIG_KEY_RESUMING = sys.intern("__pregel_resuming")
 # holds a boolean indicating if subgraphs should resume from a previous checkpoint
 CONFIG_KEY_TASK_ID = sys.intern("__pregel_task_id")
 # holds the task ID for the current task
-CONFIG_KEY_DEDUPE_TASKS = sys.intern("__pregel_dedupe_tasks")
-# holds a boolean indicating if tasks should be deduplicated (for distributed mode)
-CONFIG_KEY_ENSURE_LATEST = sys.intern("__pregel_ensure_latest")
-# holds a boolean indicating whether to assert the requested checkpoint is the latest
-# (for distributed mode)
-CONFIG_KEY_DELEGATE = sys.intern("__pregel_delegate")
-# holds a boolean indicating whether to delegate subgraphs (for distributed mode)
 CONFIG_KEY_THREAD_ID = sys.intern("thread_id")
 # holds the thread ID for the current invocation
 CONFIG_KEY_CHECKPOINT_MAP = sys.intern("checkpoint_map")
@@ -121,7 +112,6 @@ RESERVED = {
     RESUME,
     ERROR,
     NO_WRITES,
-    SCHEDULED,
     # reserved config.configurable keys
     CONFIG_KEY_SEND,
     CONFIG_KEY_READ,
@@ -132,9 +122,6 @@ RESERVED = {
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_RESUMING,
     CONFIG_KEY_TASK_ID,
-    CONFIG_KEY_DEDUPE_TASKS,
-    CONFIG_KEY_ENSURE_LATEST,
-    CONFIG_KEY_DELEGATE,
     CONFIG_KEY_CHECKPOINT_MAP,
     CONFIG_KEY_CHECKPOINT_ID,
     CONFIG_KEY_CHECKPOINT_NS,

libs/langgraph/langgraph/errors.py

@@ -78,13 +78,6 @@ class NodeInterrupt(GraphInterrupt):
         super().__init__([Interrupt(value=value)])
 
 
-class GraphDelegate(GraphBubbleUp):
-    """Raised when a graph is delegated (for distributed mode)."""
-
-    def __init__(self, *args: dict[str, Any]) -> None:
-        super().__init__(*args)
-
-
 class ParentCommand(GraphBubbleUp):
     args: tuple[Command]
 
@@ -102,9 +95,3 @@ class TaskNotFound(Exception):
     """Raised when the executor is unable to find a task (for distributed mode)."""
 
     pass
-
-
-class CheckpointNotLatest(Exception):
-    """Raised when the checkpoint is not the latest version (for distributed mode)."""
-
-    pass

libs/langgraph/langgraph/func/__init__.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import asyncio
 import concurrent.futures
 import functools
@@ -9,9 +11,7 @@ from typing import (
     Any,
     Callable,
     Generic,
-    Optional,
     TypeVar,
-    Union,
     get_args,
     get_origin,
     overload,
@@ -19,6 +19,7 @@ from typing import (
 
 from typing_extensions import Unpack
 
+from langgraph._typing import UNSET, DeprecatedKwargs
 from langgraph.cache.base import BaseCache
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
@@ -37,7 +38,6 @@ 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.typing import DeprecatedKwargs
 from langgraph.warnings import LangGraphDeprecatedSinceV10
 
 
@@ -47,8 +47,8 @@ class TaskFunction(Generic[P, T]):
         func: Callable[P, T],
         *,
         retry_policy: Sequence[RetryPolicy],
-        cache_policy: Optional[CachePolicy[Callable[P, Union[str, bytes]]]] = None,
-        name: Optional[str] = None,
+        cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
+        name: str | None = None,
     ) -> None:
         if name is not None:
             if hasattr(func, "__func__"):
@@ -91,36 +91,33 @@ class TaskFunction(Generic[P, T]):
 @overload
 def task(
     *,
-    name: Optional[str] = None,
-    retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-    cache_policy: Optional[CachePolicy[Callable[P, Union[str, bytes]]]] = None,
+    name: str | None = None,
+    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+    cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
     **kwargs: Unpack[DeprecatedKwargs],
 ) -> Callable[
-    [Union[Callable[P, Awaitable[T]], Callable[P, T]]],
+    [Callable[P, Awaitable[T]] | Callable[P, T]],
     TaskFunction[P, T],
 ]: ...
 
 
 @overload
 def task(
-    __func_or_none__: Union[Callable[P, Awaitable[T]], Callable[P, T]],
+    __func_or_none__: Callable[P, Awaitable[T]] | Callable[P, T],
 ) -> TaskFunction[P, T]: ...
 
 
 def task(
-    __func_or_none__: Optional[Union[Callable[P, Awaitable[T]], Callable[P, T]]] = None,
+    __func_or_none__: Callable[P, Awaitable[T]] | Callable[P, T] | None = None,
     *,
-    name: Optional[str] = None,
-    retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-    cache_policy: Optional[CachePolicy[Callable[P, Union[str, bytes]]]] = None,
+    name: str | None = None,
+    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+    cache_policy: CachePolicy[Callable[P, str | bytes]] | None = None,
     **kwargs: Unpack[DeprecatedKwargs],
-) -> Union[
-    Callable[
-        [Union[Callable[P, Awaitable[T]], Callable[P, T]]],
-        TaskFunction[P, T],
-    ],
-    TaskFunction[P, T],
-]:
+) -> (
+    Callable[[Callable[P, Awaitable[T]] | Callable[P, T]], TaskFunction[P, T]]
+    | TaskFunction[P, T]
+):
     """Define a LangGraph task using the `task` decorator.
 
     !!! important "Requires python 3.11 or higher for async functions"
@@ -179,7 +176,7 @@ def task(
         await add_one.ainvoke([1, 2, 3])  # Returns [2, 3, 4]
         ```
     """
-    if (retry := kwargs.get("retry")) is not None:
+    if (retry := kwargs.get("retry", UNSET)) is not UNSET:
         warnings.warn(
             "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
             category=LangGraphDeprecatedSinceV10,
@@ -196,10 +193,8 @@ def task(
     )
 
     def decorator(
-        func: Union[Callable[P, Awaitable[T]], Callable[P, T]],
-    ) -> Union[
-        Callable[P, concurrent.futures.Future[T]], Callable[P, asyncio.Future[T]]
-    ]:
+        func: Callable[P, Awaitable[T]] | Callable[P, T],
+    ) -> Callable[P, concurrent.futures.Future[T]] | Callable[P, asyncio.Future[T]]:
         return TaskFunction(
             func, retry_policy=retry_policies, cache_policy=cache_policy, name=name
         )
@@ -376,16 +371,16 @@ class entrypoint:
 
     def __init__(
         self,
-        checkpointer: Optional[BaseCheckpointSaver] = None,
-        store: Optional[BaseStore] = None,
-        cache: Optional[BaseCache] = None,
-        config_schema: Optional[type[Any]] = None,
-        cache_policy: Optional[CachePolicy] = None,
-        retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
+        checkpointer: BaseCheckpointSaver | None = None,
+        store: BaseStore | None = None,
+        cache: BaseCache | None = None,
+        config_schema: type[Any] | None = None,
+        cache_policy: CachePolicy | None = None,
+        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> None:
         """Initialize the entrypoint decorator."""
-        if (retry := kwargs.get("retry")) is not None:
+        if (retry := kwargs.get("retry", UNSET)) is not UNSET:
             warnings.warn(
                 "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
                 category=LangGraphDeprecatedSinceV10,
@@ -504,7 +499,7 @@ class entrypoint:
                 func.__name__: PregelNode(
                     bound=bound,
                     triggers=[START],
-                    channels=[START],
+                    channels=START,
                     writers=[
                         ChannelWrite(
                             [

libs/langgraph/langgraph/graph/branch.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from collections.abc import Awaitable, Hashable, Sequence
 from inspect import (
     isfunction,
@@ -11,7 +13,6 @@ from typing import (
     Callable,
     Literal,
     NamedTuple,
-    Optional,
     Union,
     cast,
     get_args,
@@ -40,21 +41,18 @@ Writer = Callable[
 
 
 def _get_branch_path_input_schema(
-    path: Union[
-        Callable[..., Union[Hashable, list[Hashable]]],
-        Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
-        Runnable[Any, Union[Hashable, list[Hashable]]],
-    ],
-) -> Optional[type[Any]]:
+    path: Callable[..., Hashable | list[Hashable]]
+    | Callable[..., Awaitable[Hashable | list[Hashable]]]
+    | Runnable[Any, Hashable | list[Hashable]],
+) -> type[Any] | None:
     input = None
     # detect input schema annotation in the branch callable
     try:
-        callable_: Optional[
-            Union[
-                Callable[..., Union[Hashable, list[Hashable]]],
-                Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
-            ]
-        ] = None
+        callable_: (
+            Callable[..., Hashable | list[Hashable]]
+            | Callable[..., Awaitable[Hashable | list[Hashable]]]
+            | None
+        ) = None
         if isinstance(path, (RunnableCallable, RunnableLambda)):
             if isfunction(path.func) or ismethod(path.func):
                 callable_ = path.func
@@ -85,19 +83,19 @@ def _get_branch_path_input_schema(
 
 
 class Branch(NamedTuple):
-    path: Runnable[Any, Union[Hashable, list[Hashable]]]
-    ends: Optional[dict[Hashable, str]]
-    input_schema: Optional[type[Any]] = None
+    path: Runnable[Any, Hashable | list[Hashable]]
+    ends: dict[Hashable, str] | None
+    input_schema: type[Any] | None = None
 
     @classmethod
     def from_path(
         cls,
-        path: Runnable[Any, Union[Hashable, list[Hashable]]],
-        path_map: Optional[Union[dict[Hashable, str], list[str]]],
+        path: Runnable[Any, Hashable | list[Hashable]],
+        path_map: dict[Hashable, str] | list[str] | None,
         infer_schema: bool = False,
-    ) -> "Branch":
+    ) -> Branch:
         # coerce path_map to a dictionary
-        path_map_: Optional[dict[Hashable, str]] = None
+        path_map_: dict[Hashable, str] | None = None
         try:
             if isinstance(path_map, dict):
                 path_map_ = path_map.copy()
@@ -105,7 +103,7 @@ class Branch(NamedTuple):
                 path_map_ = {name: name for name in path_map}
             else:
                 # find func
-                func: Optional[Callable] = None
+                func: Callable | None = None
                 if isinstance(path, (RunnableCallable, RunnableLambda)):
                     func = path.func or path.afunc
                 if func is not None:
@@ -126,7 +124,7 @@ class Branch(NamedTuple):
     def run(
         self,
         writer: Writer,
-        reader: Optional[Callable[[RunnableConfig], Any]] = None,
+        reader: Callable[[RunnableConfig], Any] | None = None,
     ) -> RunnableCallable:
         return ChannelWrite.register_writer(
             RunnableCallable(
@@ -153,7 +151,7 @@ class Branch(NamedTuple):
         input: Any,
         config: RunnableConfig,
         *,
-        reader: Optional[Callable[[RunnableConfig], Any]],
+        reader: Callable[[RunnableConfig], Any] | None,
         writer: Writer,
     ) -> Runnable:
         if reader:
@@ -176,7 +174,7 @@ class Branch(NamedTuple):
         input: Any,
         config: RunnableConfig,
         *,
-        reader: Optional[Callable[[RunnableConfig], Any]],
+        reader: Callable[[RunnableConfig], Any] | None,
         writer: Writer,
     ) -> Runnable:
         if reader:
@@ -200,11 +198,11 @@ class Branch(NamedTuple):
         input: Any,
         result: Any,
         config: RunnableConfig,
-    ) -> Union[Runnable, Any]:
+    ) -> Runnable | Any:
         if not isinstance(result, (list, tuple)):
             result = [result]
         if self.ends:
-            destinations: Sequence[Union[Send, str]] = [
+            destinations: Sequence[Send | str] = [
                 r if isinstance(r, Send) else self.ends[r] for r in result
             ]
         else:

libs/langgraph/langgraph/graph/message.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import uuid
 import warnings
 from collections.abc import Sequence
@@ -7,7 +9,6 @@ from typing import (
     Any,
     Callable,
     Literal,
-    Optional,
     Union,
     cast,
 )
@@ -32,8 +33,8 @@ REMOVE_ALL_MESSAGES = "__remove_all__"
 
 def _add_messages_wrapper(func: Callable) -> Callable[[Messages, Messages], Messages]:
     def _add_messages(
-        left: Optional[Messages] = None, right: Optional[Messages] = None, **kwargs: Any
-    ) -> Union[Messages, Callable[[Messages, Messages], Messages]]:
+        left: Messages | None = None, right: Messages | None = None, **kwargs: Any
+    ) -> Messages | Callable[[Messages, Messages], Messages]:
         if left is not None and right is not None:
             return func(left, right, **kwargs)
         elif left is not None or right is not None:
@@ -54,7 +55,7 @@ def add_messages(
     left: Messages,
     right: Messages,
     *,
-    format: Optional[Literal["langchain-openai"]] = None,
+    format: Literal["langchain-openai"] | None = None,
 ) -> Messages:
     """Merges two lists of messages, updating existing messages by ID.
 
@@ -246,9 +247,9 @@ def _format_messages(messages: Sequence[BaseMessage]) -> list[BaseMessage]:
 
 
 def push_message(
-    message: Union[MessageLikeRepresentation, BaseMessageChunk],
+    message: MessageLikeRepresentation | BaseMessageChunk,
     *,
-    state_key: Optional[str] = "messages",
+    state_key: str | None = "messages",
 ) -> AnyMessage:
     """Write a message manually to the `messages` / `messages-tuple` stream mode.
 

libs/langgraph/langgraph/graph/state.py

@@ -15,7 +15,6 @@ from typing import (
     Generic,
     Literal,
     NamedTuple,
-    Optional,
     Protocol,
     Union,
     cast,
@@ -29,6 +28,7 @@ from langchain_core.runnables import Runnable, RunnableConfig
 from pydantic import BaseModel
 from typing_extensions import Self, TypeAlias, Unpack
 
+from langgraph._typing import UNSET, DeprecatedKwargs
 from langgraph.cache.base import BaseCache
 from langgraph.channels.base import BaseChannel
 from langgraph.channels.binop import BinaryOperatorAggregate
@@ -78,7 +78,7 @@ from langgraph.types import (
     Send,
     StreamWriter,
 )
-from langgraph.typing import DeprecatedKwargs, InputT, StateT, StateT_contra, Unset
+from langgraph.typing import InputT, OutputT, StateT, StateT_contra
 from langgraph.utils.fields import (
     get_cached_annotated_keys,
     get_field_default,
@@ -91,7 +91,7 @@ from langgraph.warnings import LangGraphDeprecatedSinceV10
 logger = logging.getLogger(__name__)
 
 
-def _warn_invalid_state_schema(schema: Union[type[Any], Any]) -> None:
+def _warn_invalid_state_schema(schema: type[Any] | Any) -> None:
     if isinstance(schema, type):
         return
     if typing.get_args(schema):
@@ -174,15 +174,17 @@ class StateNodeSpec(NamedTuple):
     # TODO: rename this callable, also move away from NamedTuple so that we can use
     # a generic StateNode, so maybe a dataclass
     runnable: StateNode
-    metadata: Optional[dict[str, Any]]
+    metadata: dict[str, Any] | None
+    # TODO: rename to input_schema, though we really just want to modify this structure to
+    # be a dataclass
     input: type[Any]
-    retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]]
-    cache_policy: Optional[CachePolicy]
-    ends: Optional[Union[tuple[str, ...], dict[str, str]]] = EMPTY_SEQ
+    retry_policy: RetryPolicy | Sequence[RetryPolicy] | None
+    cache_policy: CachePolicy | None
+    ends: tuple[str, ...] | dict[str, str] | None = EMPTY_SEQ
     defer: bool = False
 
 
-class StateGraph(Generic[StateT, InputT]):
+class StateGraph(Generic[StateT, InputT, OutputT]):
     """A graph whose nodes communicate by reading and writing to a shared state.
     The signature of each node is State -> Partial<State>.
 
@@ -239,35 +241,58 @@ class StateGraph(Generic[StateT, InputT]):
     branches: defaultdict[str, dict[str, Branch]]
     channels: dict[str, BaseChannel]
     managed: dict[str, ManagedValueSpec]
-    schemas: dict[type[Any], dict[str, Union[BaseChannel, ManagedValueSpec]]]
+    schemas: dict[type[Any], dict[str, BaseChannel | ManagedValueSpec]]
+    waiting_edges: set[tuple[tuple[str, ...], str]]
+
+    compiled: bool
+    state_schema: type[StateT]
+    input_schema: type[InputT]
+    output_schema: type[OutputT]
 
     def __init__(
         self,
         state_schema: type[StateT],
         config_schema: type[Any] | None = None,
         *,
-        input: type[InputT] | None = None,
-        output: type[Any] | None = None,
+        input_schema: type[InputT] | None = None,
+        output_schema: type[OutputT] | None = None,
+        **kwargs: Unpack[DeprecatedKwargs],
     ) -> None:
-        input = input or state_schema
-        output = output or state_schema
+        if (input_ := kwargs.get("input", UNSET)) is not UNSET:
+            warnings.warn(
+                "`input` is deprecated and will be removed. Please use `input_schema` instead.",
+                category=LangGraphDeprecatedSinceV10,
+                stacklevel=2,
+            )
+            if input_schema is None:
+                input_schema = cast(Union[type[InputT], None], input_)
+
+        if (output := kwargs.get("output", UNSET)) is not UNSET:
+            warnings.warn(
+                "`output` is deprecated and will be removed. Please use `output_schema` instead.",
+                category=LangGraphDeprecatedSinceV10,
+                stacklevel=2,
+            )
+            if output_schema is None:
+                output_schema = cast(Union[type[OutputT], None], output)
 
         self.nodes = {}
-        self.edges = set[tuple[str, str]]()
+        self.edges = set()
         self.branches = defaultdict(dict)
-        self.support_multiple_edges = False
-        self.compiled = False
         self.schemas = {}
         self.channels = {}
         self.managed = {}
-        self.schema = state_schema
-        self.input = input
-        self.output = output
-        self._add_schema(state_schema)
-        self._add_schema(input, allow_managed=False)
-        self._add_schema(output, allow_managed=False)
+        self.compiled = False
+        self.waiting_edges = set()
+
+        self.state_schema = state_schema
+        self.input_schema = cast(type[InputT], input_schema or state_schema)
+        self.output_schema = cast(type[OutputT], output_schema or state_schema)
         self.config_schema = config_schema
-        self.waiting_edges: set[tuple[tuple[str, ...], str]] = set()
+
+        self._add_schema(self.state_schema)
+        self._add_schema(self.input_schema, allow_managed=False)
+        self._add_schema(self.output_schema, allow_managed=False)
 
     @property
     def _all_edges(self) -> set[tuple[str, str]]:
@@ -313,11 +338,11 @@ class StateGraph(Generic[StateT, InputT]):
         node: StateNode[StateT],
         *,
         defer: bool = False,
-        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[type[Any]] = None,
-        retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-        cache_policy: Optional[CachePolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
+        metadata: dict[str, Any] | None = None,
+        input_schema: type[Any] | None = None,
+        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        cache_policy: CachePolicy | None = None,
+        destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the state graph.
@@ -332,11 +357,11 @@ class StateGraph(Generic[StateT, InputT]):
         action: StateNode[StateT],
         *,
         defer: bool = False,
-        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[type[Any]] = None,
-        retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-        cache_policy: Optional[CachePolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
+        metadata: dict[str, Any] | None = None,
+        input_schema: type[Any] | None = None,
+        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        cache_policy: CachePolicy | None = None,
+        destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the state graph."""
@@ -344,15 +369,15 @@ class StateGraph(Generic[StateT, InputT]):
 
     def add_node(
         self,
-        node: Union[str, StateNode[StateT]],
-        action: Optional[StateNode[StateT]] = None,
+        node: str | StateNode[StateT],
+        action: StateNode[StateT] | None = None,
         *,
         defer: bool = False,
-        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[type[Any]] = None,
-        retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-        cache_policy: Optional[CachePolicy] = None,
-        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
+        metadata: dict[str, Any] | None = None,
+        input_schema: type[Any] | None = None,
+        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        cache_policy: CachePolicy | None = None,
+        destinations: dict[str, str] | tuple[str, ...] | None = None,
         **kwargs: Unpack[DeprecatedKwargs],
     ) -> Self:
         """Add a new node to the state graph.
@@ -364,7 +389,7 @@ class StateGraph(Generic[StateT, InputT]):
                 Will be used as the node function or runnable if `node` is a string (node name).
             defer: Whether to defer the execution of the node until the run is about to end.
             metadata: The metadata associated with the node. (default: None)
-            input: The input schema for the node. (default: the graph's input schema)
+            input_schema: The input schema for the node. (default: the graph's state schema)
             retry_policy: The retry policy for the node. (default: None)
                 If a sequence is provided, the first matching policy will be applied.
             cache_policy: The cache policy for the node. (default: None)
@@ -376,12 +401,18 @@ class StateGraph(Generic[StateT, InputT]):
 
         Example:
             ```python
+            from typing_extensions import TypedDict
+
+            from langchain_core.runnables import RunnableConfig
             from langgraph.graph import START, StateGraph
 
-            def my_node(state, config):
+            class State(TypedDict):
+                x: int
+
+            def my_node(state: State, config: RunnableConfig) -> State:
                 return {"x": state["x"] + 1}
 
-            builder = StateGraph(dict)
+            builder = StateGraph(State)
             builder.add_node(my_node)  # node name will be 'my_node'
             builder.add_edge(START, "my_node")
             graph = builder.compile()
@@ -391,7 +422,7 @@ class StateGraph(Generic[StateT, InputT]):
 
         Example: Customize the name:
             ```python
-            builder = StateGraph(dict)
+            builder = StateGraph(State)
             builder.add_node("my_fair_node", my_node)
             builder.add_edge(START, "my_fair_node")
             graph = builder.compile()
@@ -402,7 +433,7 @@ class StateGraph(Generic[StateT, InputT]):
         Returns:
             Self: The instance of the state graph, allowing for method chaining.
         """
-        if (retry := kwargs.get("retry")) is not None:
+        if (retry := kwargs.get("retry", UNSET)) is not UNSET:
             warnings.warn(
                 "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
                 category=LangGraphDeprecatedSinceV10,
@@ -410,6 +441,14 @@ class StateGraph(Generic[StateT, InputT]):
             if retry_policy is None:
                 retry_policy = retry  # type: ignore[assignment]
 
+        if (input_ := kwargs.get("input", UNSET)) is not UNSET:
+            warnings.warn(
+                "`input` is deprecated and will be removed. Please use `input_schema` instead.",
+                category=LangGraphDeprecatedSinceV10,
+            )
+            if input_schema is None:
+                input_schema = cast(Union[type[InputT], None], input_)
+
         if not isinstance(node, str):
             action = node
             if isinstance(action, Runnable):
@@ -445,7 +484,7 @@ class StateGraph(Generic[StateT, InputT]):
                     f"'{character}' is a reserved character and is not allowed in the node names."
                 )
 
-        ends: Union[tuple[str, ...], dict[str, str]] = EMPTY_SEQ
+        ends: tuple[str, ...] | dict[str, str] = EMPTY_SEQ
         try:
             if (
                 isfunction(action)
@@ -455,7 +494,7 @@ class StateGraph(Generic[StateT, InputT]):
                 hints := get_type_hints(getattr(action, "__call__"))
                 or get_type_hints(action)
             ):
-                if input is None:
+                if input_schema is None:
                     first_parameter_name = next(
                         iter(
                             inspect.signature(
@@ -465,7 +504,7 @@ class StateGraph(Generic[StateT, InputT]):
                     )
                     if input_hint := hints.get(first_parameter_name):
                         if isinstance(input_hint, type) and get_type_hints(input_hint):
-                            input = input_hint
+                            input_schema = input_hint
                 if rtn := hints.get("return"):
                     # Handle Union types
                     rtn_origin = get_origin(rtn)
@@ -493,12 +532,12 @@ class StateGraph(Generic[StateT, InputT]):
         if destinations is not None:
             ends = destinations
 
-        if input is not None:
-            self._add_schema(input)
+        if input_schema is not None:
+            self._add_schema(input_schema)
         self.nodes[node] = StateNodeSpec(
             coerce_to_runnable(action, name=node, trace=False),  # type: ignore
             metadata,
-            input=input or self.schema,
+            input=input_schema or self.state_schema,
             retry_policy=retry_policy,
             cache_policy=cache_policy,
             ends=ends,
@@ -506,7 +545,7 @@ class StateGraph(Generic[StateT, InputT]):
         )
         return self
 
-    def add_edge(self, start_key: Union[str, list[str]], end_key: str) -> Self:
+    def add_edge(self, start_key: str | list[str], end_key: str) -> Self:
         """Add a directed edge from the start node (or list of start nodes) to the end node.
 
         When a single start node is provided, the graph will wait for that node to complete
@@ -563,12 +602,10 @@ class StateGraph(Generic[StateT, InputT]):
     def add_conditional_edges(
         self,
         source: str,
-        path: Union[
-            Callable[..., Union[Hashable, list[Hashable]]],
-            Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
-            Runnable[Any, Union[Hashable, list[Hashable]]],
-        ],
-        path_map: Optional[Union[dict[Hashable, str], list[str]]] = None,
+        path: Callable[..., Hashable | list[Hashable]]
+        | Callable[..., Awaitable[Hashable | list[Hashable]]]
+        | Runnable[Any, Hashable | list[Hashable]],
+        path_map: dict[Hashable, str] | list[str] | None = None,
     ) -> Self:
         """Add a conditional edge from the starting node to any number of destination nodes.
 
@@ -610,7 +647,7 @@ class StateGraph(Generic[StateT, InputT]):
 
     def add_sequence(
         self,
-        nodes: Sequence[Union[StateNode[StateT], tuple[str, StateNode[StateT]]]],
+        nodes: Sequence[StateNode[StateT] | tuple[str, StateNode[StateT]]],
     ) -> Self:
         """Add a sequence of nodes that will be executed in the provided order.
 
@@ -629,7 +666,7 @@ class StateGraph(Generic[StateT, InputT]):
         if len(nodes) < 1:
             raise ValueError("Sequence requires at least one node.")
 
-        previous_name: Optional[str] = None
+        previous_name: str | None = None
         for node in nodes:
             if isinstance(node, tuple) and len(node) == 2:
                 name, node = node
@@ -665,12 +702,10 @@ class StateGraph(Generic[StateT, InputT]):
 
     def set_conditional_entry_point(
         self,
-        path: Union[
-            Callable[..., Union[Hashable, list[Hashable]]],
-            Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
-            Runnable[Any, Union[Hashable, list[Hashable]]],
-        ],
-        path_map: Optional[Union[dict[Hashable, str], list[str]]] = None,
+        path: Callable[..., Hashable | list[Hashable]]
+        | Callable[..., Awaitable[Hashable | list[Hashable]]]
+        | Runnable[Any, Hashable | list[Hashable]],
+        path_map: dict[Hashable, str] | list[str] | None = None,
     ) -> Self:
         """Sets a conditional entry point in the graph.
 
@@ -699,7 +734,7 @@ class StateGraph(Generic[StateT, InputT]):
         """
         return self.add_edge(key, END)
 
-    def validate(self, interrupt: Optional[Sequence[str]] = None) -> Self:
+    def validate(self, interrupt: Sequence[str] | None = None) -> Self:
         # assemble sources
         all_sources = {src for src, _ in self._all_edges}
         for start, branches in self.branches.items():
@@ -748,43 +783,17 @@ class StateGraph(Generic[StateT, InputT]):
         self.compiled = True
         return self
 
-    @overload
-    def compile(
-        self: StateGraph[StateT, Unset],
-        checkpointer: Checkpointer = None,
-        *,
-        cache: Optional[BaseCache] = None,
-        store: Optional[BaseStore] = None,
-        interrupt_before: Optional[Union[All, list[str]]] = None,
-        interrupt_after: Optional[Union[All, list[str]]] = None,
-        debug: bool = False,
-        name: Optional[str] = None,
-    ) -> CompiledStateGraph[StateT, StateT]: ...
-
-    @overload
-    def compile(
-        self: StateGraph[StateT, InputT],
-        checkpointer: Checkpointer = None,
-        *,
-        cache: Optional[BaseCache] = None,
-        store: Optional[BaseStore] = None,
-        interrupt_before: Optional[Union[All, list[str]]] = None,
-        interrupt_after: Optional[Union[All, list[str]]] = None,
-        debug: bool = False,
-        name: Optional[str] = None,
-    ) -> CompiledStateGraph[StateT, InputT]: ...
-
     def compile(
         self,
         checkpointer: Checkpointer = None,
         *,
-        cache: Optional[BaseCache] = None,
-        store: Optional[BaseStore] = None,
-        interrupt_before: Optional[Union[All, list[str]]] = None,
-        interrupt_after: Optional[Union[All, list[str]]] = None,
+        cache: BaseCache | None = None,
+        store: BaseStore | None = None,
+        interrupt_before: All | list[str] | None = None,
+        interrupt_after: All | list[str] | None = None,
         debug: bool = False,
-        name: Optional[str] = None,
-    ) -> Union[CompiledStateGraph[StateT, StateT], CompiledStateGraph[StateT, InputT]]:
+        name: str | None = None,
+    ) -> CompiledStateGraph[StateT, InputT]:
         """Compiles the state graph into a `CompiledStateGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
@@ -820,11 +829,11 @@ class StateGraph(Generic[StateT, InputT]):
         # prepare output channels
         output_channels = (
             "__root__"
-            if len(self.schemas[self.output]) == 1
-            and "__root__" in self.schemas[self.output]
+            if len(self.schemas[self.output_schema]) == 1
+            and "__root__" in self.schemas[self.output_schema]
             else [
                 key
-                for key, val in self.schemas[self.output].items()
+                for key, val in self.schemas[self.output_schema].items()
                 if not is_managed_value(val)
             ]
         )
@@ -836,23 +845,15 @@ class StateGraph(Generic[StateT, InputT]):
             ]
         )
 
-        ResolvedInputT: Union[type[InputT], type[StateT]] = self.input or self.schema
-        compiled = CompiledStateGraph[StateT, ResolvedInputT](  # type: ignore[valid-type]
+        compiled = CompiledStateGraph[StateT, InputT, OutputT](
             builder=self,
             schema_to_mapper={},
             config_type=self.config_schema,
-            input_model=(
-                self.input
-                if len(self.channels) > 1
-                and isclass(self.input)
-                and issubclass(self.input, BaseModel)
-                else None
-            ),
             nodes={},
             channels={
                 **self.channels,
                 **self.managed,
-                START: EphemeralValue(self.input),
+                START: EphemeralValue(self.input_schema),
             },
             input_channels=START,
             stream_mode="updates",
@@ -885,46 +886,46 @@ class StateGraph(Generic[StateT, InputT]):
         return compiled.validate()
 
 
-class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
-    builder: StateGraph[StateT, InputT]
-    schema_to_mapper: dict[type[Any], Optional[Callable[[Any], Any]]]
+class CompiledStateGraph(
+    Pregel[StateT, InputT, OutputT], Generic[StateT, InputT, OutputT]
+):
+    builder: StateGraph[StateT, InputT, OutputT]
+    schema_to_mapper: dict[type[Any], Callable[[Any], Any] | None]
 
     def __init__(
         self,
         *,
-        builder: StateGraph[StateT, InputT],
-        schema_to_mapper: dict[type[Any], Optional[Callable[[Any], Any]]],
+        builder: StateGraph[StateT, InputT, OutputT],
+        schema_to_mapper: dict[type[Any], Callable[[Any], Any] | None],
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
         self.builder = builder
         self.schema_to_mapper = schema_to_mapper
 
-    def get_input_schema(
-        self, config: Optional[RunnableConfig] = None
-    ) -> type[BaseModel]:
+    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
         return _get_schema(
-            typ=self.builder.input,
+            typ=self.builder.input_schema,
             schemas=self.builder.schemas,
             channels=self.builder.channels,
             name=self.get_name("Input"),
         )
 
     def get_output_schema(
-        self, config: Optional[RunnableConfig] = None
+        self, config: RunnableConfig | None = None
     ) -> type[BaseModel]:
         return _get_schema(
-            typ=self.builder.output,
+            typ=self.builder.output_schema,
             schemas=self.builder.schemas,
             channels=self.builder.channels,
             name=self.get_name("Output"),
         )
 
-    def attach_node(self, key: str, node: Optional[StateNodeSpec]) -> None:
+    def attach_node(self, key: str, node: StateNodeSpec | None) -> None:
         if key == START:
             output_keys = [
                 k
-                for k, v in self.builder.schemas[self.builder.input].items()
+                for k, v in self.builder.schemas[self.builder.input_schema].items()
                 if not is_managed_value(v)
             ]
         else:
@@ -933,8 +934,8 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
             ]
 
         def _get_updates(
-            input: Union[None, dict, Any],
-        ) -> Optional[Sequence[tuple[str, Any]]]:
+            input: None | dict | Any,
+        ) -> Sequence[tuple[str, Any]] | None:
             if input is None:
                 return None
             elif isinstance(input, dict):
@@ -971,7 +972,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
                 raise InvalidUpdateError(msg)
 
         # state updaters
-        write_entries: tuple[Union[ChannelWriteEntry, ChannelWriteTupleEntry], ...] = (
+        write_entries: tuple[ChannelWriteEntry | ChannelWriteTupleEntry, ...] = (
             ChannelWriteTupleEntry(
                 mapper=_get_root if output_keys == ["__root__"] else _get_updates
             ),
@@ -988,20 +989,17 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
             self.nodes[key] = PregelNode(
                 tags=[TAG_HIDDEN],
                 triggers=[START],
-                channels=[START],
+                channels=START,
                 writers=[ChannelWrite(write_entries)],
             )
         elif node is not None:
-            input_schema = node.input if node else self.builder.schema
-            input_values = {k: k for k in self.builder.schemas[input_schema]}
-            is_single_input = len(input_values) == 1 and "__root__" in input_values
+            input_schema = node.input if node else self.builder._state_schema
+            input_channels = list(self.builder.schemas[input_schema])
+            is_single_input = len(input_channels) == 1 and "__root__" in input_channels
             if input_schema in self.schema_to_mapper:
                 mapper = self.schema_to_mapper[input_schema]
             else:
-                mapper = _pick_mapper(
-                    list(input_values),
-                    input_schema,
-                )
+                mapper = _pick_mapper(input_channels, input_schema)
                 self.schema_to_mapper[input_schema] = mapper
 
             branch_channel = CHANNEL_BRANCH_TO.format(key)
@@ -1013,7 +1011,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
             self.nodes[key] = PregelNode(
                 triggers=[branch_channel],
                 # read state keys and managed values
-                channels=(list(input_values) if is_single_input else input_values),
+                channels=("__root__" if is_single_input else input_channels),
                 # coerce state dict to schema class (eg. pydantic model)
                 mapper=mapper,
                 # publish to state keys
@@ -1026,7 +1024,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
         else:
             raise RuntimeError
 
-    def attach_edge(self, starts: Union[str, Sequence[str]], end: str) -> None:
+    def attach_edge(self, starts: str | Sequence[str], end: str) -> None:
         if isinstance(starts, str):
             # subscribe to start channel
             if end != END:
@@ -1056,8 +1054,8 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
         self, start: str, name: str, branch: Branch, *, with_reader: bool = True
     ) -> None:
         def get_writes(
-            packets: Sequence[Union[str, Send]], static: bool = False
-        ) -> Sequence[Union[ChannelWriteEntry, Send]]:
+            packets: Sequence[str | Send], static: bool = False
+        ) -> Sequence[ChannelWriteEntry | Send]:
             writes = [
                 (
                     ChannelWriteEntry(
@@ -1078,7 +1076,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
             schema = branch.input_schema or (
                 self.builder.nodes[start].input
                 if start in self.builder.nodes
-                else self.builder.schema
+                else self.builder.state_schema
             )
             channels = list(self.builder.schemas[schema])
             # get mapper
@@ -1088,7 +1086,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
                 mapper = _pick_mapper(channels, schema)
                 self.schema_to_mapper[schema] = mapper
             # create reader
-            reader: Optional[Callable[[RunnableConfig], Any]] = partial(
+            reader: Callable[[RunnableConfig], Any] | None = partial(
                 ChannelRead.do_read,
                 select=channels[0] if channels == ["__root__"] else channels,
                 fresh=True,
@@ -1208,7 +1206,7 @@ class CompiledStateGraph(Pregel[InputT], Generic[StateT, InputT]):
 
 def _pick_mapper(
     state_keys: Sequence[str], schema: type[Any]
-) -> Optional[Callable[[Any], Any]]:
+) -> Callable[[Any], Any] | None:
     if state_keys == ["__root__"]:
         return None
     if isclass(schema) and issubclass(schema, dict):
@@ -1249,8 +1247,8 @@ def _control_branch(value: Any) -> Sequence[tuple[str, Any]]:
 
 
 def _control_static(
-    ends: Union[tuple[str, ...], dict[str, str]],
-) -> Sequence[tuple[str, Any, Optional[str]]]:
+    ends: tuple[str, ...] | dict[str, str],
+) -> Sequence[tuple[str, Any, str | None]]:
     if isinstance(ends, dict):
         return [
             (k if k == END else CHANNEL_BRANCH_TO.format(k), None, label)
@@ -1262,7 +1260,7 @@ def _control_static(
         ]
 
 
-def _get_root(input: Any) -> Optional[Sequence[tuple[str, Any]]]:
+def _get_root(input: Any) -> Sequence[tuple[str, Any]] | None:
     if isinstance(input, Command):
         if input.graph == Command.PARENT:
             return ()
@@ -1317,12 +1315,12 @@ def _get_channel(
 @overload
 def _get_channel(
     name: str, annotation: Any, *, allow_managed: Literal[True] = True
-) -> Union[BaseChannel, ManagedValueSpec]: ...
+) -> BaseChannel | ManagedValueSpec: ...
 
 
 def _get_channel(
     name: str, annotation: Any, *, allow_managed: bool = True
-) -> Union[BaseChannel, ManagedValueSpec]:
+) -> BaseChannel | ManagedValueSpec:
     if manager := _is_field_managed_value(name, annotation):
         if allow_managed:
             return manager
@@ -1340,7 +1338,7 @@ def _get_channel(
     return fallback
 
 
-def _is_field_channel(typ: type[Any]) -> Optional[BaseChannel]:
+def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
     if hasattr(typ, "__metadata__"):
         meta = typ.__metadata__
         if len(meta) >= 1 and isinstance(meta[-1], BaseChannel):
@@ -1350,7 +1348,7 @@ def _is_field_channel(typ: type[Any]) -> Optional[BaseChannel]:
     return None
 
 
-def _is_field_binop(typ: type[Any]) -> Optional[BinaryOperatorAggregate]:
+def _is_field_binop(typ: type[Any]) -> BinaryOperatorAggregate | None:
     if hasattr(typ, "__metadata__"):
         meta = typ.__metadata__
         if len(meta) >= 1 and callable(meta[-1]):
@@ -1371,7 +1369,7 @@ def _is_field_binop(typ: type[Any]) -> Optional[BinaryOperatorAggregate]:
     return None
 
 
-def _is_field_managed_value(name: str, typ: type[Any]) -> Optional[ManagedValueSpec]:
+def _is_field_managed_value(name: str, typ: type[Any]) -> ManagedValueSpec | None:
     if hasattr(typ, "__metadata__"):
         meta = typ.__metadata__
         if len(meta) >= 1: