x

2026-10-05 16:05:09 +02:00 · 2025-06-25 10:25:06 -04:00 · 2025-06-24 14:47:20 -04:00 · 2025-06-24 12:35:31 -04:00 · 2025-06-24 11:17:21 -04:00 · 2025-06-24 09:56:19 -04:00
186 changed files with 16436 additions and 12409 deletions
@@ -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:
@@ -1,4 +1,4 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
  - name: 🤔 Question or Problem
@@ -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
@@ -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.
@@ -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
@@ -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.

@@ -61,6 +61,7 @@ REDIRECT_MAP = {
    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
    "cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
+    "cloud/concepts/threads.md": "concepts/persistence.md#threads",
    # tool calling how-tos
    "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
@@ -86,6 +87,8 @@ REDIRECT_MAP = {
    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
+    "cloud/concepts/streaming.md": "concepts/streaming.md",
+    "agents/streaming.md": "how-tos/streaming.md",
    # prebuit redirects
    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
    "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
@@ -107,8 +110,10 @@ REDIRECT_MAP = {
    # deployment redirects
    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
+    "tutorials/deployment.md": "concepts/deployment_options.md",
    # assistant redirects
    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
+    "cloud/concepts/runs.md": "concepts/assistants.md#execution",
 }


@@ -89,4 +89,4 @@ LangGraph Studio Web is a specialized UI that you can connect to LangGraph API s

 ## Deployment

-Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.
+Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../concepts/deployment_options.md) for detailed instructions on all supported deployment models.
@@ -29,10 +29,10 @@ LangGraph includes several capabilities essential for building robust, productio

 - [**Memory integration**](./memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
 - [**Human-in-the-loop control**](./human-in-the-loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
- [**Streaming support**](./streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
+- [**Streaming support**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
 - [**Deployment tooling**](./deployment.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
    - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
-    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for production.
+    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.

 ## High-level building blocks

@@ -109,7 +109,7 @@ Streaming is available in both sync and async modes:

 !!! tip

-    For full details, see the [streaming guide](./streaming.md).
+    For full details, see the [streaming guide](../how-tos/streaming.md).

 ## Max iterations

@@ -1,223 +0,0 @@
---
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
---
-
-# Streaming
-
-Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
-
-1. [**Agent progress**](#agent-progress) — get updates after each node in the agent graph is executed.
-2. [**LLM tokens**](#llm-tokens) — stream tokens as they are generated by the language model.
-3. [**Custom updates**](#tool-updates) — emit custom data from tools during execution (e.g., "Fetched 10/100 records")
-
-You can stream [more than one type of data](#stream-multiple-modes) at a time. 
-
-
-<figure markdown="1">
-![image](./assets/fast_parrot.png){: style="max-height:300px"}
-<figcaption>
-Waiting is for pigeons.
-</figcaption>
-</figure>
-
-## Agent progress
-
-To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with [`stream_mode="updates"`](https://langchain-ai.github.io/langgraph/how-tos/streaming/#updates). This emits an event after every agent step.
-
-For example, if you have an agent that calls a tool once, you should see the following updates:
-
-* **LLM node**: AI message with tool call requests
-* **Tool node**: Tool message with execution result
-* **LLM node**: Final AI response
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## LLM tokens
-
-To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for token, metadata in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for token, metadata in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-## Tool updates
-
-To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
-
-=== "Sync"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-!!! Note
-    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
-
-## Stream multiple modes
-
-You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for stream_mode, chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for stream_mode, chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## Disable streaming
-
-In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
-
-See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
-
-## Additional resources
-
-* [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)
@@ -1,310 +1 @@
---
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
---
-
-# Tools
-
-[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
-
-You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
-
-## Define simple tools
-
-You can pass a vanilla function to `create_react_agent` to use as a tool:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-create_react_agent(
-    model="anthropic:claude-3-7-sonnet",
-    tools=[multiply]
-)
-```
-
-`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
-
-## Customize tools
-
-For more control over tool behavior, use the `@tool` decorator:
-
-```python
-# highlight-next-line
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool("multiply_tool", parse_docstring=True)
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-
-    Args:
-        a: First operand
-        b: Second operand
-    """
-    return a * b
-```
-
-You can also define a custom input schema using Pydantic:
-
-```python
-from pydantic import BaseModel, Field
-
-class MultiplyInputSchema(BaseModel):
-    """Multiply two numbers"""
-    a: int = Field(description="First operand")
-    b: int = Field(description="Second operand")
-
-# highlight-next-line
-@tool("multiply_tool", args_schema=MultiplyInputSchema)
-def multiply(a: int, b: int) -> int:
-    return a * b
-```
-
-For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
-
-## Hide arguments from the model
-
-Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
-
-You can put these arguments in the `state` or `config` of the agent, and access
-this information inside the tool:
-
-```python
-from langgraph.prebuilt import InjectedState
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langchain_core.runnables import RunnableConfig
-
-def my_tool(
-    # This will be populated by an LLM
-    tool_arg: str,
-    # access information that's dynamically updated inside the agent
-    # highlight-next-line
-    state: Annotated[AgentState, InjectedState],
-    # access static data that is passed at agent invocation
-    # highlight-next-line
-    config: RunnableConfig,
-) -> str:
-    """My tool."""
-    do_something_with_state(state["messages"])
-    do_something_with_config(config)
-    ...
-```
-
-## Disable parallel tool calling
-
-Some model providers support executing multiple tools in parallel, but
-allow users to disable this feature.
-
-For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
-
-```python
-from langchain.chat_models import init_chat_model
-
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
-tools = [add, multiply]
-agent = create_react_agent(
-    # disable parallel tool calls
-    # highlight-next-line
-    model=model.bind_tools(tools, parallel_tool_calls=False),
-    tools=tools
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
-)
-```
-
-## Return tool results directly
-
-Use `return_direct=True` to return tool results immediately and stop the agent loop:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[add]
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
-)
-```
-
-## Force tool use
-
-To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def greet(user_name: str) -> int:
-    """Greet user."""
-    return f"Hello {user_name}!"
-
-tools = [greet]
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
-    tools=tools
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
-)
-```
-
-!!! Warning "Avoid infinite loops"
-
-    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
-
-    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
-    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
-
-## Handle tool errors
-
-By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
-
-=== "Enable error handling (default)"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # Run with error handling (default)
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[multiply]
-    )
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-=== "Disable error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=False  # (1)!
-    )
-    agent_no_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_no_error_handling.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-=== "Custom error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=(
-            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
-        )
-    )
-    agent_custom_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_custom_error_handling.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
-
-## Working with memory
-
-LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
-
-* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
-* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
-
-## Prebuilt tools
-
-You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-agent = create_react_agent(
-    model="openai:gpt-4o-mini", 
-    tools=[{"type": "web_search_preview"}]
-)
-response = agent.invoke(
-    {"messages": ["What was a positive news story from today?"]}
-)
-```
-
-Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
-
-You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
-
-Some commonly used tool categories include:
-
- **Search**: Bing, SerpAPI, Tavily
- **Code interpreters**: Python REPL, Node.js REPL
- **Databases**: SQL, MongoDB, Redis
- **Web data**: Web scraping and browsing
- **APIs**: OpenWeatherMap, NewsAPI, and others
-
-These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
-
+delete me
@@ -1,5 +0,0 @@
-# Runs
-
-A run is an invocation of an [assistant](../../concepts/assistants.md). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./threads.md).
-
-The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
@@ -1,138 +0,0 @@
-# Streaming
-
-Streaming is critical for making LLM applications feel responsive to end users.  
-When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
-
-## Supported streaming modes
-
-LangGraph Platform supports the following streaming modes:
-
-| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
-|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
-| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
-| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
-| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
-| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
-| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
-| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
-
-✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
-
-## Stateless runs
-
-If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    async for chunk in client.runs.stream(
-        # highlight-next-line
-        None,  # (1)!
-        assistant_id,
-        input=inputs,
-        stream_mode="updates"
-    ):
-        print(chunk.data)
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // create a streaming run
-    // highlight-next-line
-    const streamResponse = client.runs.stream(
-      // highlight-next-line
-      null,  // (1)!
-      assistantID,
-      {
-        input,
-        streamMode: "updates"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk.data);
-    }
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "cURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <inputs>,
-      \"stream_mode\": \"updates\"
-    }"
-    ```
-
-## Join and stream
-
-LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    # highlight-next-line
-    async for chunk in client.runs.join_stream(
-        thread_id,
-        # highlight-next-line
-        run_id,  # (1)!
-    ):
-        print(chunk)
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // highlight-next-line
-    const streamResponse = client.runs.joinStream(
-      threadID,
-      // highlight-next-line
-      runId  // (1)!
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk);
-    }
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-=== "cURL"
-
-    ```bash
-    curl --request GET \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    ```
-
-!!! warning "Outputs not buffered"
-
-    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
-
-## API Reference
-
-For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
-
@@ -62,6 +62,15 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.

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

 Interrupting a revision will stop deployment of the revision.
@@ -20,7 +20,7 @@ my-app/
 |-- openai_agent.py     # code for your graph
 ```

-where the graph is defined in `openai_agent.py`.
+where the graph is defined in `openai_agent.py`. 

 ### No rebuild

@@ -28,11 +28,11 @@ In the standard LangGraph API configuration, the server uses the compiled graph

 ```python
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.graph import END, START, MessageGraph

 model = ChatOpenAI(temperature=0)

-graph_workflow = StateGraph(MessagesState)
+graph_workflow = MessageGraph()

 graph_workflow.add_node("agent", model)
 graph_workflow.add_edge("agent", END)
@@ -61,7 +61,7 @@ To make your graph rebuild on each new run with custom configuration, you need t
 from typing import Annotated
 from typing_extensions import TypedDict
 from langchain_openai import ChatOpenAI
-from langgraph.graph import END, START
+from langgraph.graph import END, START, MessageGraph
 from langgraph.graph.state import StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode
@@ -144,4 +144,4 @@ Finally, you need to specify the path to your graph-making function (`make_graph
 }
 ```

-See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
@@ -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:
@@ -33,7 +33,7 @@ For more information on breakpoints see [here](../../concepts/breakpoints.md).

 ### Submit run

-To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../concepts/runs.md) to the existing selected [thread](../concepts/threads.md). If no thread is currently selected, a new one will be created.
+To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../concepts/runs.md) to the existing selected [thread](../../concepts/persistence.md#threads). If no thread is currently selected, a new one will be created.

 To cancel the ongoing run, click the "Cancel" button.

@@ -1,8 +1,12 @@
-# Stream outputs
+# Streaming API

-## Streaming API
+[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to [stream outputs](../../concepts/streaming.md) from the LangGraph API server.

-[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) allows you to stream outputs from the LangGraph API server.
+!!! note
+
+    LangGraph SDK and LangGraph Server are a part of [LangGraph Platform](../../concepts/langgraph_platform.md).
+
+## Basic usage

 Basic usage example:

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

@@ -13,7 +13,7 @@ LangGraph Studio is accessed from the LangSmith UI, within the LangGraph Platfor

 For applications that are [deployed](../../quick_start.md) on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.

-This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../concepts/threads.md), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
+This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../../concepts/persistence.md#threads), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.

 ## Local development server

@@ -0,0 +1,57 @@
+# Run experiments over a dataset
+
+LangGraph Studio supports evaluations by allowing you to run your assistant over a pre-defined LangSmith dataset. This enables you to understand how your application performs over a variety of inputs, compare the results to reference outputs, and score the results using [evaluators](../../../agents/evals.md).
+
+This guide shows you how to run an experiment end-to-end from Studio.
+
+---
+
+## Prerequisites
+
+Before running an experiment, ensure you have the following:
+
+1.  **A LangSmith dataset**: Your dataset should contain the inputs you want to test and optionally, reference outputs for comparison.
+
+    - The schema for the inputs must match the required input schema for the assistant. For more information on schemas, see [here](../../../concepts/low_level.md#schema).
+    - For more on creating datasets, see [How to Manage Datasets](https://docs.smith.langchain.com/evaluation/how_to_guides/manage_datasets_in_application#set-up-your-dataset).
+
+2.  **(Optional) Evaluators**: You can attach evaluators (e.g., LLM-as-a-Judge, heuristics, or custom functions) to your dataset in LangSmith. These will run automatically after the graph has processed all inputs.
+
+    - To learn more, read about [Evaluation Concepts](https://docs.smith.langchain.com/evaluation/concepts#evaluators).
+
+3.  **A running application**: The experiment can be run against:
+    - An application deployed on [LangGraph Platform](../../quick_start.md).
+    - A locally running application started via the [langgraph-cli](../../../tutorials/langgraph-platform/local-server.md).
+
+---
+
+## Step-by-step guide
+
+### 1. Launch the experiment
+
+Click the **Run experiment** button in the top right corner of the Studio page.
+
+### 2. Select your dataset
+
+In the modal that appears, select the dataset (or a specific dataset split) to use for the experiment and click **Start**.
+
+### 3. Monitor the progress
+
+All of the inputs in the dataset will now be run against the active assistant. Monitor the experiment's progress via the badge in the top right corner.
+
+You can continue to work in Studio while the experiment runs in the background. Click the arrow icon button at any time to navigate to LangSmith and view the detailed experiment results.
+
+---
+
+## Troubleshooting
+
+### "Run experiment" button is disabled
+
+If the "Run experiment" button is disabled, check the following:
+
+- **Deployed application**: If your application is deployed on LangGraph Platform, you may need to create a new revision to enable this feature.
+- **Local development server**: If you are running your application locally, make sure you have upgraded to the latest version of the `langgraph-cli` (`pip install -U langgraph-cli`). Additionally, ensure you have tracing enabled by setting the `LANGSMITH_API_KEY` in your project's `.env` file.
+
+### Evaluator results are missing
+
+When you run an experiment, any attached evaluators are scheduled for execution in a queue. If you don't see results immediately, it likely means they are still pending.
@@ -1,10 +1,6 @@
 # Manage threads

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

 ## View threads

@@ -1,10 +1,6 @@
 # Use threads

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

 ## Create a thread

@@ -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,
@@ -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.
@@ -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`.
@@ -1,29 +1,31 @@
 # Assistants

-!!! info "Prerequisites"
+**Assistants** allow you to manage configurations (like prompts, LLM selection, tools) separately from your graph's core logic, enabling rapid changes that don't alter the graph architecture. It is a way to create multiple specialized versions of the same graph architecture, each optimized for different use cases through configuration variations rather than structural changes.

-    - [LangGraph Server](./langgraph_server.md)
-    - [Configuration](./low_level.md#configuration)
-
-When building agents, it is common to make rapid changes that _do not_ alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agent but does not require updating your graph's architecture. Assistants offer a straightforward way to manage these configurations separately from your graph's core logic.
-
-Imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.
+For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.

 ![assistant versions](img/assistants.png)

-## Configuring assistants
+The LangGraph Cloud API provides several endpoints for creating and managing assistants and their versions. See the [API reference](../cloud/reference/api/api_ref.html#tag/assistants) for more details.
+
+!!! info
+
+    Assistants are a [LangGraph Platform](langgraph_platform.md) concept. They are not available in the open source LangGraph library.
+
+## Configuration

 Assistants build on the LangGraph open source concept of [configuration](low_level.md#configuration).
-While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md).
-This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.
+While configuration is available in the open source LangGraph library, assistants are only present in [LangGraph Platform](langgraph_platform.md). This is due to the fact that assistants are tightly coupled to your deployed graph. Upon deployment, LangGraph Server will automatically create a default assistant for each graph using the graph's default configuration settings.

 In practice, an assistant is just an _instance_ of a graph with a specific configuration. Therefore, multiple assistants can reference the same graph but can contain different configurations (e.g. prompts, models, tools). The LangGraph Server API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.

-## Versioning assistants
+## Versioning

 Assistants support versioning to track changes over time.
 Once you've created an assistant, subsequent edits to that assistant will create new versions. See [this how-to](../cloud/how-tos/configuration_cloud.md#create-a-new-version-for-your-assistant) for more details on how to manage assistant versions.

-## Learn more
+## Execution

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

@@ -5,7 +5,16 @@ search:

 # Deployment Options

-There are 4 main options for deploying with the LangGraph Platform:
+## Free deployment
+
+There are two free options for deploying LangGraph applications via the LangGraph Server:
+
+1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+
+## Production deployment
+
+There are 4 main options for deploying with the [LangGraph Platform](langgraph_platform.md):

 1. [Cloud SaaS](#cloud-saas)

@@ -22,7 +31,7 @@ A quick comparison:
 |----------------------|----------------|----------------------------|-------------------------------|--------------------------|
 | **[Control plane UI/API](../concepts/langgraph_control_plane.md)** | Yes | Yes | Yes | No |
 | **CI/CD** | Managed internally by platform | Managed externally by you | Managed externally by you | Managed externally by you |
-| **Data/compute residency** | LangChain’s cloud | Your cloud | Your cloud | Your cloud |
+| **Data/compute residency** | LangChain's cloud | Your cloud | Your cloud | Your cloud |
 | **LangSmith compatibility** | Trace to LangSmith SaaS | Trace to LangSmith SaaS | Trace to Self-Hosted LangSmith | Optional tracing |
 | **[Server version compatibility](../concepts/langgraph_server.md#server-versions)** | Enterprise | Enterprise | Enterprise | Lite, Enterprise |
 | **[Pricing](https://www.langchain.com/pricing-langgraph-platform)** | Plus | Enterprise | Enterprise | Developer |
@@ -19,6 +19,7 @@ From the control plane UI, you can:
 - Update a deployment.
 - Update environment variables for a deployment.
 - View build and server logs of a deployment.
+- View deployment metrics like CPU and memory usage.
 - Delete a deployment.

 The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com/langgraph_cloud).
@@ -88,6 +89,15 @@ Infrastructure for deployments and revisions are provisioned and deployed asynch

 The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to achieve asynchronous deployments.

+### Monitoring
+
+After a deployment is ready, the control plane monitors the deployment and records various metrics, such as:
+
+- CPU and memory usage of the deployment.
+- Number of container restarts.
+
+These metrics are displayed as charts in the Control Plane UI.
+
 ### LangSmith Integration

 A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
@@ -17,7 +17,7 @@ Develop, deploy, scale, and manage agents with **LangGraph Platform** — the pu

 LangGraph Platform makes it easy to get your agent running in production —  whether it’s built with LangGraph or another framework — so you can focus on your app logic, not infrastructure. Deploy with one click to get a live endpoint, and use our robust APIs and built-in task queues to handle production scale. 

- **[Streaming Support](../cloud/concepts/streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides multiple streaming modes optimized for various application needs.
+- **[Streaming Support](../cloud/how-tos/streaming.md)**: As agents grow more sophisticated, they often benefit from streaming both token outputs and intermediate states back to the user. Without this, users are left waiting for potentially long operations with no feedback. LangGraph Server provides multiple streaming modes optimized for various application needs.

 - **[Background Runs](../cloud/how-tos/background_run.md)**: For agents that take longer to process (e.g., hours), maintaining an open connection can be impractical. The LangGraph Server supports launching agent runs in the background and provides both polling endpoints and webhooks to monitor run status effectively.
 
@@ -3,7 +3,7 @@
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).

 !!! info "Important"
-    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](plans.md) plan.

 ## Requirements

@@ -8,7 +8,7 @@ search:
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).

 !!! info "Important"
-    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](plans.md) plan.

 ## Requirements

@@ -7,7 +7,7 @@ search:

 **LangGraph Server** offers an API for creating and managing agent-based applications. It is built on the concept of [assistants](assistants.md), which are agents configured for specific tasks, and includes built-in [persistence](persistence.md#memory-store) and a **task queue**. This versatile API supports a wide range of agentic application use cases, from background processing to real-time interactions.

-Use LangGraph Server to create and manage [assistants](assistants.md), [threads](../cloud/concepts/threads.md), [runs](../cloud/concepts/runs.md), [cron jobs](../cloud/concepts/cron_jobs.md), [webhooks](../cloud/concepts/webhooks.md), and more.
+Use LangGraph Server to create and manage [assistants](assistants.md), [threads](./persistence.md#threads), [runs](../cloud/concepts/runs.md), [cron jobs](../cloud/concepts/cron_jobs.md), [webhooks](../cloud/concepts/webhooks.md), and more.

 !!! tip "API reference"
  
@@ -24,6 +24,7 @@ Key features of LangGraph Studio:
 - [Manage assistants](../cloud/how-tos/studio/manage_assistants.md)
 - [Manage threads](../cloud/how-tos/threads_studio.md)
 - [Iterate on prompts](../cloud/how-tos/iterate_graph_studio.md)
+- [Run experiments over a dataset](../cloud/how-tos/studio/run_evals.md)
 - Manage [long term memory](memory.md)
 - Debug agent state via [time travel](time-travel.md)

@@ -41,4 +42,4 @@ Chat mode is a simpler UI for iterating on and testing chat-specific agents. It

 ## Learn more

- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.
+- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.
@@ -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


@@ -15,15 +15,19 @@ LangGraph has a built-in persistence layer, implemented through checkpointers. W

 ## Threads

-A thread is a unique ID or [thread identifier](#threads) assigned to each checkpoint saved by a checkpointer. When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:
+A thread is a unique ID or thread identifier assigned to each checkpoint saved by a checkpointer. It contains the accumulated state of a sequence of [runs](../cloud/concepts/runs.md). When a run is executed, the [state](../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
+
+When invoking graph with a checkpointer, you **must** specify a `thread_id` as part of the `configurable` portion of the config:

 ```python
 {"configurable": {"thread_id": "1"}}
 ```

+A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run. The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../cloud/reference/api/api_ref.html#tag/threads) for more details.
+
 ## Checkpoints

-Checkpoint is a snapshot of the graph state saved at each super-step and is represented by `StateSnapshot` object with the following key properties:
+The state of a thread at a particular point in time is called a checkpoint. Checkpoint is a snapshot of the graph state saved at each super-step and is represented by `StateSnapshot` object with the following key properties:

 - `config`: Config associated with this checkpoint. 
 - `metadata`: Metadata associated with this checkpoint.
@@ -31,6 +35,8 @@ Checkpoint is a snapshot of the graph state saved at each super-step and is repr
 - `next` A tuple of the node names to execute next in the graph.
 - `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.ipynb#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.

+Checkpoints are persisted and can be used to restore the state of a thread at a later time.
+
 Let's see what checkpoints are saved when a simple graph is invoked as follows:

 ```python
@@ -470,9 +476,51 @@ 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
@@ -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)
@@ -18,6 +18,6 @@ There are three main categories of data you can stream:

 - [**Stream LLM tokens**](../how-tos/streaming.md#messages) — capture token streams from anywhere: inside nodes, subgraphs, or tools.
 - [**Emit progress notifications from tools**](../how-tos/streaming.md#stream-custom-data) — send custom updates or progress signals directly from tool functions.
- [**Stream from subgraphs**](../how-tos/streaming.md#subgraphs) — include outputs from both the parent graph and any nested subgraphs.
+- [**Stream from subgraphs**](../how-tos/streaming.md#stream-subgraph-outputs) — include outputs from both the parent graph and any nested subgraphs.
 - [**Use any LLM**](../how-tos/streaming.md#use-with-any-llm) — stream tokens from any LLM, even if it's not a LangChain model using the `custom` streaming mode.
 - [**Use multiple streaming modes**](../how-tos/streaming.md#stream-multiple-modes) — choose from `values` (full state), `updates` (state deltas), `messages` (LLM tokens + metadata), `custom` (arbitrary user data), or `debug` (detailed traces).
@@ -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()
@@ -1,62 +1,64 @@
 # Tools

-Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language.
-But what about cases where we want a model to also interact *directly* with systems, such as databases or an API?
-These systems often have a particular input schema; for example, APIs frequently have a required payload structure. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema.
+Many AI applications interact with users via natural language. However, some use cases require models to interface directly with external systems—such as APIs, databases, or file systems—using structured input. In these scenarios, **tool calling** enables models to generate requests that conform to a specified input schema.

-[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
-
-**Tools** can be passed to [chat models](https://python.langchain.com/docs/concepts/chat_models) that support [tool calling](https://python.langchain.com/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.
-
-You can [create custom tools](https://python.langchain.com/docs/how_to/custom_tools/) or use [prebuilt](#prebuilt-tools) tools.
+[Tools](https://python.langchain.com/docs/concepts/tools/) encapsulate a callable function and its input schema. These can be passed to compatible [chat models](https://python.langchain.com/docs/concepts/chat_models), allowing the model to decide whether to invoke a tool and with what arguments.

 ## Tool calling

 ![Diagram of a tool call by a model](./img/tool_call.png)

-A key principle of tool calling is that the model decides when to use a tool based on the input's relevance. The model doesn't always need to call a tool.
-For example, given an input that is *irrelevant to the tool*, the model would not call the tool:
+Tool calling is typically **conditional**. Based on the user input and available tools, the model may choose to issue a tool call request. This request is returned in an `AIMessage` object, which includes a `tool_calls` field that specifies the tool name and input arguments:

 ```python
-result = llm_with_tools.invoke("Hello world!")
+llm_with_tools.invoke("What is 2 multiplied by 3?")
+# -> AIMessage(tool_calls=[{'name': 'multiply', 'args': {'a': 2, 'b': 3}, ...}])
 ```

-The result would be an `AIMessage` containing the model's response in natural language (e.g., "Hello!").
-However, if we pass an input *relevant to the tool*, the model should choose to call it:
+If the input is unrelated to any tool, the model returns only a natural language message:

 ```python
-result = llm_with_tools.invoke("What is 2 multiplied by 3?")
+llm_with_tools.invoke("Hello world!")  # -> AIMessage(content="Hello!")
 ```

-As before, the output `result` will be an `AIMessage`. 
-But, if the tool was called, `result` will have a `tool_calls` attribute.
-This attribute includes everything needed to execute the tool, including the tool name and input arguments:
+Importantly, the model does not execute the tool—it only generates a request. A separate executor (such as a runtime or agent) is responsible for handling the tool call and returning the result.

-```
-result.tool_calls
-{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
-```
-
-For more details on usage, see the [how-to guide](../how-tos/tool-calling.ipynb).
-
-## Execute tools
-
-LangGraph offers pre-built components — [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] and [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] — that invoke the tools on behalf of the user.
-
-See this [how-to guide](../how-tos/tool-calling.ipynb#use-prebuilt-toolnode) on tool calling.
+See the [tool calling guide](../how-tos/tool-calling.md) for more details.

 ## Prebuilt tools

-LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+LangChain provides prebuilt tool integrations for common external systems including APIs, databases, file systems, and web data.

-You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
+Browse the [integrations directory](https://python.langchain.com/docs/integrations/tools/) for available tools.

-Some commonly used tool categories include:
+Common categories:

- **Search**: Bing, SerpAPI, Tavily
- **Code interpreters**: Python REPL, Node.js REPL
- **Databases**: SQL, MongoDB, Redis
- **Web data**: Web scraping and browsing
- **APIs**: OpenWeatherMap, NewsAPI, and others
+* **Search**: Bing, SerpAPI, Tavily
+* **Code execution**: Python REPL, Node.js REPL
+* **Databases**: SQL, MongoDB, Redis
+* **Web data**: Scraping and browsing
+* **APIs**: OpenWeatherMap, NewsAPI, etc.

-These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
+## Custom tools
+
+You can define custom tools using the `@tool` decorator or plain Python functions. For example:
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+```
+
+See the [tool calling guide](../how-tos/tool-calling.md) for more details.
+
+## Tool execution
+
+While the model determines *when* to call a tool, **execution** of the tool call must be handled by a runtime component.
+
+LangGraph provides prebuilt components for this:
+
+* [`ToolNode`][oolNode]: Executes tools based on AI tool calls.
+* [`create_react_agent`][create_react_agent]: Constructs a full agent that manages tool calling automatically.
@@ -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,
@@ -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",
@@ -1,11 +1,220 @@
 # Stream outputs

-## Streaming API
+You can [stream outputs](../concepts/streaming.md) from a LangGraph agent or workflow.
+
+## Supported stream modes
+
+Pass one or more of the following stream modes as a list to the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods:
+
+| Mode | Description |
+|------|-------------|
+| `values` | Streams the full value of the state after each step of the graph. |
+| `updates` | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. |
+| `custom` | Streams custom data from inside your graph nodes. |
+| `messages` | Streams 2-tuples (LLM token, metadata) from any graph nodes where an LLM is invoked. |
+| `debug` | Streams as much information as possible throughout the execution of the graph.   
+
+## Stream from an agent
+
+### Agent progress
+
+To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with `stream_mode="updates"`. This emits an event after every agent step.
+
+For example, if you have an agent that calls a tool once, you should see the following updates:
+
+* **LLM node**: AI message with tool call requests
+* **Tool node**: Tool message with execution result
+* **LLM node**: Final AI response
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+### LLM tokens
+
+To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for token, metadata in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for token, metadata in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+### Tool updates
+
+To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
+
+=== "Sync"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+!!! Note
+    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
+
+### Stream multiple modes
+
+You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for stream_mode, chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for stream_mode, chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+### Disable streaming
+
+In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](../agents/multi-agent.md) systems to control which agents stream their output.
+
+See the [Models](../agents/models.md#disable-streaming) guide to learn how to disable streaming.
+
+## Stream from a workflow 
+
+### Basic usage example

 LangGraph graphs expose the [`.stream()`][langgraph.pregel.Pregel.stream] (sync) and [`.astream()`][langgraph.pregel.Pregel.astream] (async) methods to yield streamed outputs as iterators.

-Basic usage example:
-
 === "Sync"

    ```python
@@ -61,18 +270,7 @@ Basic usage example:
      ```output
      {'refine_topic': {'topic': 'ice cream and cats'}}
      {'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}
-      ```
-
-
-### Supported stream modes
-
-| Mode                             | Description                                                                                                                                                                         |
-|----------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [`values`](#stream-graph-state)  | Streams the full value of the state after each step of the graph.                                                                                                                   |
-| [`updates`](#stream-graph-state) | Streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. |
-| [`custom`](#stream-custom-data)  | Streams custom data from inside your graph nodes.                                                                                                                                   |
-| [`messages`](#messages)          | Streams 2-tuples (LLM token, metadata) from any graph nodes where an LLM is invoked.                                                                                                        |
-| [`debug`](#debug)                | Streams as much information as possible throughout the execution of the graph.                                                                                                      |
+      ```                                                                                                   |

 ### Stream multiple modes

@@ -94,7 +292,7 @@ The streamed outputs will be tuples of `(mode, chunk)` where `mode` is the name
        print(chunk)
    ```

-## Stream graph state
+### Stream graph state

 Use the stream modes `updates` and `values` to stream the state of the graph as it executes.

@@ -157,7 +355,7 @@ graph = (
    ```


-## Subgraphs
+### Stream subgraph outputs

 To include outputs from [subgraphs](../concepts/subgraphs.md) in the streamed outputs, you can set `subgraphs=True` in the `.stream()` method of the parent graph. This will stream outputs from both the parent graph and any subgraphs.

@@ -233,7 +431,7 @@ for chunk in graph.stream(

      **Note** that we are receiving not just the node updates, but we also the namespaces which tell us what graph (or subgraph) we are streaming from.

-## Debugging {#debug}
+### Debugging {#debug}

 Use the `debug` streaming mode to stream as much information as possible throughout the execution of the graph. The streamed outputs include the name of the node as well as the full state.

@@ -247,7 +445,7 @@ for chunk in graph.stream(
 ```


-## LLM tokens {#messages}
+### LLM tokens {#messages}

 Use the `messages` streaming mode to stream Large Language Model (LLM) outputs **token by token** from any part of your graph, including nodes, tools, subgraphs, or tasks.

@@ -307,7 +505,7 @@ for message_chunk, metadata in graph.stream( # (2)!
 2. The "messages" stream mode returns an iterator of tuples `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.


-### Filter by LLM invocation
+#### Filter by LLM invocation

 You can associate `tags` with LLM invocations to filter the streamed tokens by LLM invocation.

@@ -391,7 +589,7 @@ async for msg, metadata in graph.astream(  # (3)!
      4. The `stream_mode` is set to "messages" to stream LLM tokens. The `metadata` contains information about the LLM invocation, including the tags.


-### Filter by node
+#### Filter by node

 To stream tokens only from specific nodes, use `stream_mode="messages"` and filter the outputs by the `langgraph_node` field in the streamed metadata:

@@ -464,7 +662,7 @@ for msg, metadata in graph.stream( # (1)!
      1. The "messages" stream mode returns a tuple of `(message_chunk, metadata)` where `message_chunk` is the token streamed by the LLM and `metadata` is a dictionary with information about the graph node where the LLM was called and other information.
      2. Filter the streamed tokens by the `langgraph_node` field in the metadata to only include the tokens from the `write_poem` node.

-## Stream custom data
+### Stream custom data

 To send **custom user-defined data** from inside a LangGraph node or tool, follow these steps:

@@ -541,7 +739,7 @@ To send **custom user-defined data** from inside a LangGraph node or tool, follo
      3. Emit another custom key-value pair.
      4. Set `stream_mode="custom"` to receive the custom data in the stream.

-## Use with any LLM
+### Use with any LLM

 You can use `stream_mode="custom"` to stream data from **any LLM API**  — even if that API does **not** implement the LangChain chat model interface.

@@ -701,7 +899,7 @@ for chunk in graph.stream(
      ```


-## Disable streaming for specific chat models
+### Disable streaming for specific chat models

 If your application mixes models that support streaming with those that do not, you may need to explicitly disable streaming for 
 models that do not support it.
@@ -733,7 +931,7 @@ Set `disable_streaming=True` when initializing the model.
      1. Set `disable_streaming=True` to disable streaming for the chat model.


-## Async with Python < 3.11 { #async }
+### Async with Python < 3.11 { #async }

 In Python versions < 3.11, [asyncio tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) do not support the `context` parameter.  
 This limits LangGraph ability to automatically propagate context, and affects LangGraph’s streaming mechanisms in two key ways:
@@ -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
@@ -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.
@@ -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",
@@ -1,22 +0,0 @@
---
-search:
-  boost: 2
---
-
-# Deployment 🚀
-
-There are two free options for deploying LangGraph applications via the LangGraph Server:
-
- [Local](./langgraph-platform/local-server.md): Deploy for local testing and development. 
- [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
-
-## Other deployment options
-
-Additionally, you can deploy to production with [LangGraph Platform](../concepts/langgraph_platform.md):
-
- [Cloud SaaS](../concepts/langgraph_cloud.md): Connect your GitHub repositories and deploy LangGraph Servers within LangChain's cloud. *We manage everything.*
- [Self-Hosted Data Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_data_plane.md): Create deployments from the [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *We manage the [control plane](../concepts/langgraph_control_plane.md). You manage the deployments.*
- [Self-Hosted Control Plane<sup>(Beta)</sup>](../concepts/langgraph_self_hosted_control_plane.md): Create deployments from a self-hosted [Control Plane UI](../concepts/langgraph_control_plane.md#control-plane-ui) and deploy LangGraph Servers to **your** cloud. *You manage everything.*
- [Standalone Container](../concepts/langgraph_standalone_container.md): Deploy LangGraph Server Docker images however you like.
-
-For more information, see [Deployment options](../concepts/deployment_options.md).
@@ -89,7 +89,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
   "id": "baf669a0-04ee-492d-80d8-8fcb658ed128",
   "metadata": {},
   "outputs": [],
@@ -313,8 +313,8 @@
    "\n",
    "    builder.add_edge(\"finalizer\", END)\n",
    "\n",
-    "    # These functions let the step be used in a\n",
-    "    # StateGraph with 'messages' as the key.\n",
+    "    # These functions let the step be used in a MessageGraph\n",
+    "    # or a StateGraph with 'messages' as the key.\n",
    "    def encode(x: Union[Sequence[AnyMessage], PromptValue]) -> dict:\n",
    "        \"\"\"Ensure the input is the correct format.\"\"\"\n",
    "        if isinstance(x, PromptValue):\n",
@@ -32,7 +32,7 @@ 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


@@ -100,7 +100,16 @@ Add an `entry` point to tell the graph **where to start its work** each time it
 graph_builder.add_edge(START, "chatbot")
 ```

-## 5. Compile the graph
+## 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
+graph_builder.add_edge("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.
@@ -109,7 +118,7 @@ on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
 graph = graph_builder.compile()
 ```

-## 6. Visualize the graph (optional)
+## 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.

@@ -126,7 +135,7 @@ except Exception:
 ![basic chatbot diagram](basic-chatbot.png)


-## 7. Run the chatbot
+## 8. Run the chatbot

 Now run the chatbot! 

@@ -171,7 +180,7 @@ 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


@@ -194,6 +203,7 @@ 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()
 ```

@@ -164,7 +164,7 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
 ```
 -->

-```python
+```python hl_lines="36 37"
 from typing import Annotated

 from langchain.chat_models import init_chat_model
@@ -206,4 +206,4 @@ graph = graph_builder.compile(checkpointer=memory)

 ## Next steps

-In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
+In the next tutorial, you will [add human-in-the-loop to the chatbot](./4-human-in-the-loop.md) to handle situations where it may need guidance or verification before proceeding.
@@ -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",
@@ -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",
@@ -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."
   ]
  },
  {
@@ -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",
@@ -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."
   ]
  },
@@ -89,162 +89,145 @@ plugins:
                - "!^_"

 nav:
-  - Guides:
+  - Get started:
    - index.md
-    - Get started:
-      - Quickstart: agents/agents.md
-      - LangGraph basics:
-        -  concepts/why-langgraph.md
-        -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
-        -  tutorials/get-started/2-add-tools.md
-        -  tutorials/get-started/3-add-memory.md
-        -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
-        -  tutorials/get-started/5-customize-state.md
-        -  tutorials/get-started/6-time-travel.md
-      - Deployment: tutorials/deployment.md
-    - Prebuilt agents:
-      - Overview: agents/overview.md
-      - agents/run_agents.md
-      - agents/streaming.md
-      - agents/models.md
-      - agents/tools.md
-      - agents/mcp.md
-      - agents/context.md
-      - agents/memory.md
-      - agents/human-in-the-loop.md
-      - agents/multi-agent.md
-      - agents/evals.md
-      - agents/deployment.md
-      - agents/ui.md
-    - LangGraph framework:
-      - Agent architectures:
-        - Overview: concepts/agentic_concepts.md
+    - Quickstarts:
+      - Agent: agents/agents.md
+      - Local server: tutorials/langgraph-platform/local-server.md
+      - Deployment: cloud/quick_start.md
+    - General concepts:
+      - Common patterns:
+        - Agent architectures: concepts/agentic_concepts.md
        - Workflows & agents: tutorials/workflows.md
-      - Graphs:
-        - Overview: concepts/low_level.md
-        - Runtime overview: concepts/pregel.md
-        - Use the Graph API: how-tos/graph-api.ipynb
-      - Streaming:
-          - Overview: concepts/streaming.md
-          - "Stream outputs": how-tos/streaming.md
-      - Persistence:
-        - Overview: concepts/persistence.md
-        - concepts/durable_execution.md
-        - how-tos/persistence.ipynb
-      - Memory:
-        - Overview: concepts/memory.md
-        - Manage memory: how-tos/memory.ipynb
-      - Human-in-the-loop:
-        - Overview: concepts/human_in_the_loop.md
-        - how-tos/human_in_the_loop/add-human-in-the-loop.md
-      - Breakpoints:
-        - Overview: concepts/breakpoints.md
-        - how-tos/human_in_the_loop/breakpoints.ipynb
-      - Time travel:
-        - Overview: concepts/time-travel.md
-        - how-tos/human_in_the_loop/time-travel.ipynb
-      - Tools:
-        - Overview: concepts/tools.md
-        - how-tos/tool-calling.ipynb
-      - Subgraphs:
-        - Overview: concepts/subgraphs.md
-        - how-tos/subgraph.ipynb
-      - Multi-agent:
-        - Overview: concepts/multi_agent.md
-        - how-tos/multi_agent.ipynb
-      - Functional API:
-        - Overview: concepts/functional_api.md
-        - how-tos/use-functional-api.md
-
-    - LangGraph Platform:
-      - Overview: concepts/langgraph_platform.md
-      - Get started:
-        - Quickstart: tutorials/langgraph-platform/local-server.md
-        - Deployment quickstart: cloud/quick_start.md
-      - Components:
-        - Overview: concepts/langgraph_components.md
-        - LangGraph Server:
-          - Overview: concepts/langgraph_server.md
-          - Application structure:
-            - Overview: concepts/application_structure.md
-            - cloud/deployment/setup.md
-            - cloud/deployment/setup_pyproject.md
-            - cloud/deployment/setup_javascript.md
-            - cloud/deployment/custom_docker.md
-        - LangGraph CLI: concepts/langgraph_cli.md
-        - LangGraph Studio:
-          - Overview: concepts/langgraph_studio.md
-          - Quickstart: cloud/how-tos/studio/quick_start.md
-          - cloud/how-tos/invoke_studio.md
-          - cloud/how-tos/studio/manage_assistants.md
-          - cloud/how-tos/threads_studio.md
-          - cloud/how-tos/iterate_graph_studio.md
-          - cloud/how-tos/clone_traces_studio.md
-          - cloud/how-tos/datasets_studio.md
-        - LangGraph SDK: concepts/sdk.md
-      - Data management:
-        - Add semantic search: cloud/deployment/semantic_search.md
-        - Add TTLs: how-tos/ttl/configure_ttl.md
+      - Agent development: agents/overview.md
+      - Workflow orchestration:
+        - Graphs: concepts/low_level.md
+        - Subgraphs: concepts/subgraphs.md
+        - Runtime: concepts/pregel.md
+        - Functional API: concepts/functional_api.md
+    - Core capabilities:
+      - Streaming: concepts/streaming.md
+      - Persistence: concepts/persistence.md
+      - Durable execution: concepts/durable_execution.md
+      - Memory: concepts/memory.md
+      - Tools: concepts/tools.md
+      - Human-in-the-loop: concepts/human_in_the_loop.md
+      - Breakpoints: concepts/breakpoints.md
+      - Time travel: concepts/time-travel.md
+      - Multi-agent: concepts/multi_agent.md
+    - Platform capabilities:
+      - LangGraph Platform:
+          - Overview: concepts/langgraph_platform.md
+          - Components:
+            - Overview: concepts/langgraph_components.md
+            - LangGraph Server:
+              - Overview: concepts/langgraph_server.md
+              - Data plane: concepts/langgraph_data_plane.md
+              - Control plane: concepts/langgraph_control_plane.md
+            - LangGraph CLI: concepts/langgraph_cli.md
+            - LangGraph Studio: concepts/langgraph_studio.md
+            - LangGraph SDK: concepts/sdk.md
+          - Plans & pricing: concepts/plans.md
+          - Application structure: concepts/application_structure.md
+          - Scalability & resilience: concepts/scalability_and_resilience.md
+      - Authentication & access control: concepts/auth.md
+      - Assistants: concepts/assistants.md
+      - Double-texting: concepts/double_texting.md
+      - Webhooks: cloud/concepts/webhooks.md
+      - Cron jobs: cloud/concepts/cron_jobs.md
+      - Deployment:
+        - Overview: concepts/deployment_options.md
+        - Deployment options:
+          - Cloud SaaS: concepts/langgraph_cloud.md
+          - Self-Hosted Data Plane: concepts/langgraph_self_hosted_data_plane.md
+          - Self-Hosted Control Plane: concepts/langgraph_self_hosted_control_plane.md
+          - Standalone Container: concepts/langgraph_standalone_container.md
+    
+  - Guides:
+    - LangGraph APIs:
+      - Use the Graph API: how-tos/graph-api.ipynb
+      - Use the Functional API: how-tos/use-functional-api.md
+    - Models:
+      - Configure model: agents/models.md
+    - Streaming:
+      - Stream outputs: how-tos/streaming.md
+      - Use Server API: cloud/how-tos/streaming.md
+    - Context:
+      - Use in agent: agents/context.md
+    - Memory:
+      - Basic implementation: agents/memory.md
+      - Persistence: how-tos/persistence.ipynb # MERGE
+      - Custom implementation: how-tos/memory.ipynb
+    - Human-in-the-loop:
+      - Add to agent: agents/human-in-the-loop.md
+      - Add to workflow: how-tos/human_in_the_loop/add-human-in-the-loop.md
+      - Use Server API: cloud/how-tos/add-human-in-the-loop.md
+    - Time travel:
+      - Use Server API: cloud/how-tos/human_in_the_loop_time_travel.md
+    - Breakpoints:
+      - Set breakpoints: how-tos/human_in_the_loop/breakpoints.ipynb
+      - Use Server API: cloud/how-tos/human_in_the_loop_breakpoint.md
+    - Tools:
+      - Call tools: how-tos/tool-calling.md
+    - Subgraphs:
+      - Use subgraphs: how-tos/subgraph.ipynb
+    - Multi-agent:
+      - Prebuilt implementation: agents/multi-agent.md
+      - Custom implementation: how-tos/multi_agent.ipynb
+    - MCP:
+      - Use MCP tools: agents/mcp.md
+      - Server deployment via MCP: concepts/server-mcp.md
+    - Deployment:
+      - Basic deployment: agents/deployment.md
+      - Set up your application:
+        - Use requirements.txt: cloud/deployment/setup.md
+        - Use pyproject.toml: cloud/deployment/setup_pyproject.md
+        - Use JavaScript: cloud/deployment/setup_javascript.md
+        - Use custom Docker: cloud/deployment/custom_docker.md
+      - Deploy to production:
+         - Cloud SaaS: cloud/deployment/cloud.md
+         - Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
+         - Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
+         - Standalone Container: cloud/deployment/standalone_container.md
+    - Evaluation:
+      - Basic implementation: agents/evals.md
+    - Platform capabilities:
+      - LangGraph Studio:
+        - Quickstart: cloud/how-tos/studio/quick_start.md
+        - cloud/how-tos/invoke_studio.md
+        - cloud/how-tos/studio/manage_assistants.md
+        - cloud/how-tos/threads_studio.md
+        - cloud/how-tos/iterate_graph_studio.md
+        - cloud/how-tos/studio/run_evals.md
+        - cloud/how-tos/clone_traces_studio.md
+        - cloud/how-tos/datasets_studio.md
      - Authentication & access control:
-        - Overview: concepts/auth.md
        - how-tos/auth/custom_auth.md
        - how-tos/auth/openapi_security.md
      - Assistants:
-        - Overview: concepts/assistants.md
        - cloud/how-tos/configuration_cloud.md
-      - Threads:
-        - Overview: cloud/concepts/threads.md
-        - cloud/how-tos/use_threads.md
-      - Runs:
-        - Overview: cloud/concepts/runs.md
-        - cloud/how-tos/background_run.md
-        - cloud/how-tos/same-thread.md
-        - cloud/how-tos/cron_jobs.md
-        - cloud/how-tos/stateless_runs.md
-        - cloud/how-tos/configurable_headers.md
-      - Streaming:
-        - Overview: cloud/concepts/streaming.md
-        - cloud/how-tos/streaming.md
-      - Human-in-the-loop: cloud/how-tos/add-human-in-the-loop.md
-      - Breakpoints: cloud/how-tos/human_in_the_loop_breakpoint.md
-      - Time travel: cloud/how-tos/human_in_the_loop_time_travel.md
-      - MCP: concepts/server-mcp.md
+        - Threads: cloud/how-tos/use_threads.md
+        - Runs:
+          - cloud/how-tos/background_run.md
+          - cloud/how-tos/same-thread.md
+          - cloud/how-tos/cron_jobs.md
+          - cloud/how-tos/stateless_runs.md
+          - cloud/how-tos/configurable_headers.md
      - Double-texting:
-        - Overview: concepts/double_texting.md
        - cloud/how-tos/interrupt_concurrent.md
        - cloud/how-tos/rollback_concurrent.md
        - cloud/how-tos/reject_concurrent.md
        - cloud/how-tos/enqueue_concurrent.md
-      - Webhooks:
-        - Overview: cloud/concepts/webhooks.md
-        - cloud/how-tos/webhooks.md
-      - Cron jobs:
-        - Overview: cloud/concepts/cron_jobs.md
-        - cloud/how-tos/cron_jobs.md
+      - Webhooks: cloud/how-tos/webhooks.md
+      - Cron jobs: cloud/how-tos/cron_jobs.md
      - Server customization:
        - how-tos/http/custom_lifespan.md
        - how-tos/http/custom_middleware.md
        - how-tos/http/custom_routes.md
-      - Deployment:
-        - Overview: concepts/deployment_options.md
-        - Data plane: concepts/langgraph_data_plane.md
-        - Control plane: concepts/langgraph_control_plane.md
-        - Deployment options:
-          - Cloud SaaS:
-            - Overview: concepts/langgraph_cloud.md
-            - Deploy Cloud SaaS: cloud/deployment/cloud.md
-          - Self-Hosted Data Plane:
-            - Overview: concepts/langgraph_self_hosted_data_plane.md
-            - Deploy Self-Hosted Data Plane: cloud/deployment/self_hosted_data_plane.md
-          - Self-Hosted Control Plane:
-            - Overview: concepts/langgraph_self_hosted_control_plane.md
-            - Deploy Self-Hosted Control Plane: cloud/deployment/self_hosted_control_plane.md
-          - Standalone Container:
-            - Overview: concepts/langgraph_standalone_container.md
-            - Deploy Standalone Container: cloud/deployment/standalone_container.md
-        - Scalability & resilience: concepts/scalability_and_resilience.md
-        - Plans & pricing: concepts/plans.md
-      
+      - Data management:
+        - Add semantic search: cloud/deployment/semantic_search.md
+        - Add TTLs: how-tos/ttl/configure_ttl.md
+
  - Reference:
    - reference/index.md
    - LangGraph:
@@ -273,9 +256,20 @@ nav:
      - Environment variables: cloud/reference/env_var.md

  - Examples:
+    - agents/run_agents.md
+    - LangGraph basics:
+      -  concepts/why-langgraph.md
+      -  Build a basic chatbot: tutorials/get-started/1-build-basic-chatbot.md
+      -  tutorials/get-started/2-add-tools.md
+      -  tutorials/get-started/3-add-memory.md
+      -  Add human-in-the-loop: tutorials/get-started/4-human-in-the-loop.md
+      -  tutorials/get-started/5-customize-state.md
+      -  tutorials/get-started/6-time-travel.md
+    - Template applications: concepts/template_applications.md  # TODO: make tutorial
    - Agentic RAG: tutorials/rag/langgraph_agentic_rag.ipynb
    - Agent Supervisor: tutorials/multi_agent/agent_supervisor.ipynb
    - SQL agent: tutorials/sql-agent.ipynb
+    - Prebuilt chat UI: agents/ui.md
    - Graph runs in LangSmith: how-tos/run-id-langsmith.ipynb
    - LangGraph Platform:
      - Authentication:
@@ -290,11 +284,12 @@ nav:
        - Integrate LangGraph into a React app: cloud/how-tos/use_stream_react.md
        - Implement generative UI with LangGraph: cloud/how-tos/generative_ui_react.md

-  - Resources:
-    - concepts/faq.md
-    - Template applications: concepts/template_applications.md  # TODO: make tutorial
-    - llms.txt: llms-txt-overview.md
+  - Additional resources:
    - agents/prebuilt.md # NOTE: prebuilt.md is auto-generated by `make build-prebuilt`
+    - LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph
+    - Case studies: adopters.md
+    - concepts/faq.md
+    - llms.txt: llms-txt-overview.md
    - Troubleshooting:
        - Errors:
          - troubleshooting/errors/index.md
@@ -305,9 +300,7 @@ nav:
          - troubleshooting/errors/INVALID_CHAT_HISTORY.md
          - troubleshooting/errors/INVALID_LICENSE.md
        - LangGraph Studio: troubleshooting/studio.md
-    - Learn:
-      - LangGraph Academy course: https://academy.langchain.com/courses/intro-to-langgraph
-      - Case studies: adopters.md
+    

 markdown_extensions:
  - abbr
@@ -364,16 +357,6 @@ markdown_extensions:
 hooks:
  - _scripts/notebook_hooks.py
 extra:
-  consent:
-    title: Cookie consent
-    actions:
-      - accept
-      - reject
-    description: >- 
-      We use cookies to recognize your repeated visits and preferences, as well
-      as to measure the effectiveness of our documentation and whether users
-      find what they're searching for. <strong>Clicking "Accept" makes our
-      documentation better. Thank you!</strong> ❤️
  social:
    - icon: fontawesome/brands/js
      link: https://langchain-ai.github.io/langgraphjs/
@@ -381,25 +364,6 @@ 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.
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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": {
@@ -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
@@ -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
@@ -21,6 +23,7 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.postgres import _internal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol

 Conn = _internal.Conn  # For backward compatibility
@@ -34,8 +37,8 @@ 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 +55,7 @@ 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 +102,11 @@ 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 +176,9 @@ 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 +247,7 @@ 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 +417,44 @@ 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.

-__all__ = ["PostgresSaver", "BasePostgresSaver", "Conn"]
+        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", "ShallowPostgresSaver", "Conn"]
@@ -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
@@ -21,6 +23,7 @@ from langgraph.checkpoint.base import (
 )
 from langgraph.checkpoint.postgres import _ainternal
 from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
 from langgraph.checkpoint.serde.base import SerializerProtocol

 Conn = _ainternal.Conn  # For backward compatibility
@@ -34,8 +37,8 @@ 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 +59,8 @@ 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 +107,11 @@ 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 +163,9 @@ 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 +214,7 @@ 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 +375,52 @@ 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 +458,7 @@ 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
@@ -568,4 +560,4 @@ class AsyncPostgresSaver(BasePostgresSaver):
        ).result()


-__all__ = ["AsyncPostgresSaver", "Conn"]
+__all__ = ["AsyncPostgresSaver", "AsyncShallowPostgresSaver", "Conn"]
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import random
 from collections.abc import Sequence
 from typing import Any, Optional, cast
@@ -166,7 +168,7 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
        checkpoint["channel_versions"][TASKS] = (
            max(checkpoint["channel_versions"].values())
            if checkpoint["channel_versions"]
-            else self.get_next_version(None)
+            else self.get_next_version(None, None)
        )

    def _load_blobs(
@@ -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, channel: 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.

@@ -0,0 +1,959 @@
+import asyncio
+import threading
+import warnings
+from collections.abc import AsyncIterator, Iterator, Sequence
+from contextlib import asynccontextmanager, contextmanager
+from typing import Any, Optional
+
+from langchain_core.runnables import RunnableConfig
+from psycopg import (
+    AsyncConnection,
+    AsyncCursor,
+    AsyncPipeline,
+    Capabilities,
+    Connection,
+    Cursor,
+    Pipeline,
+)
+from psycopg.rows import DictRow, dict_row
+from psycopg.types.json import Jsonb
+from psycopg_pool import AsyncConnectionPool, ConnectionPool
+
+from langgraph.checkpoint.base import (
+    WRITES_IDX_MAP,
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+    get_checkpoint_metadata,
+)
+from langgraph.checkpoint.postgres import _ainternal, _internal
+from langgraph.checkpoint.postgres.base import BasePostgresSaver
+from langgraph.checkpoint.serde.base import SerializerProtocol
+from langgraph.checkpoint.serde.types import TASKS
+
+"""
+To add a new migration, add a new string to the MIGRATIONS list.
+The position of the migration in the list is the version number.
+"""
+MIGRATIONS = [
+    """CREATE TABLE IF NOT EXISTS checkpoint_migrations (
+    v INTEGER PRIMARY KEY
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoints (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    type TEXT,
+    checkpoint JSONB NOT NULL,
+    metadata JSONB NOT NULL DEFAULT '{}',
+    PRIMARY KEY (thread_id, checkpoint_ns)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_blobs (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    channel TEXT NOT NULL,
+    type TEXT NOT NULL,
+    blob BYTEA,
+    PRIMARY KEY (thread_id, checkpoint_ns, channel)
+);""",
+    """CREATE TABLE IF NOT EXISTS checkpoint_writes (
+    thread_id TEXT NOT NULL,
+    checkpoint_ns TEXT NOT NULL DEFAULT '',
+    checkpoint_id TEXT NOT NULL,
+    task_id TEXT NOT NULL,
+    idx INTEGER NOT NULL,
+    channel TEXT NOT NULL,
+    type TEXT,
+    blob BYTEA NOT NULL,
+    PRIMARY KEY (thread_id, checkpoint_ns, checkpoint_id, task_id, idx)
+);""",
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoints_thread_id_idx ON checkpoints(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_blobs_thread_id_idx ON checkpoint_blobs(thread_id);
+    """,
+    """
+    CREATE INDEX CONCURRENTLY IF NOT EXISTS checkpoint_writes_thread_id_idx ON checkpoint_writes(thread_id);
+    """,
+    """
+    ALTER TABLE checkpoint_writes ADD COLUMN task_path TEXT NOT NULL DEFAULT '';
+    """,
+]
+
+SELECT_SQL = f"""
+select
+    thread_id,
+    checkpoint,
+    checkpoint_ns,
+    metadata,
+    (
+        select array_agg(array[bl.channel::bytea, bl.type::bytea, bl.blob])
+        from jsonb_each_text(checkpoint -> 'channel_versions')
+        inner join checkpoint_blobs bl
+            on bl.thread_id = checkpoints.thread_id
+            and bl.checkpoint_ns = checkpoints.checkpoint_ns
+            and bl.channel = jsonb_each_text.key
+    ) as channel_values,
+    (
+        select
+        array_agg(array[cw.task_id::text::bytea, cw.channel::bytea, cw.type::bytea, cw.blob] order by cw.task_id, cw.idx)
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.checkpoint_id = (checkpoint->>'id')
+    ) as pending_writes,
+    (
+        select array_agg(array[cw.type::bytea, cw.blob] order by cw.task_path, cw.task_id, cw.idx)
+        from checkpoint_writes cw
+        where cw.thread_id = checkpoints.thread_id
+            and cw.checkpoint_ns = checkpoints.checkpoint_ns
+            and cw.channel = '{TASKS}'
+    ) as pending_sends
+from checkpoints """
+
+UPSERT_CHECKPOINT_BLOBS_SQL = """
+    INSERT INTO checkpoint_blobs (thread_id, checkpoint_ns, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, channel) DO UPDATE SET
+        type = EXCLUDED.type,
+        blob = EXCLUDED.blob;
+"""
+
+UPSERT_CHECKPOINTS_SQL = """
+    INSERT INTO checkpoints (thread_id, checkpoint_ns, checkpoint, metadata)
+    VALUES (%s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns)
+    DO UPDATE SET
+        checkpoint = EXCLUDED.checkpoint,
+        metadata = EXCLUDED.metadata;
+"""
+
+UPSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO UPDATE SET
+        channel = EXCLUDED.channel,
+        type = EXCLUDED.type,
+        blob = EXCLUDED.blob;
+"""
+
+INSERT_CHECKPOINT_WRITES_SQL = """
+    INSERT INTO checkpoint_writes (thread_id, checkpoint_ns, checkpoint_id, task_id, task_path, idx, channel, type, blob)
+    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+    ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
+"""
+
+
+def _dump_blobs(
+    serde: SerializerProtocol,
+    thread_id: str,
+    checkpoint_ns: str,
+    values: dict[str, Any],
+    versions: ChannelVersions,
+) -> list[tuple[str, str, str, str, Optional[bytes]]]:
+    if not versions:
+        return []
+
+    return [
+        (
+            thread_id,
+            checkpoint_ns,
+            k,
+            *(serde.dumps_typed(values[k]) if k in values else ("empty", None)),
+        )
+        for k in versions
+    ]
+
+
+class ShallowPostgresSaver(BasePostgresSaver):
+    """A checkpoint saver that uses Postgres to store checkpoints.
+
+    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
+    It is meant to be a light-weight drop-in replacement for the PostgresSaver that
+    supports most of the LangGraph persistence functionality with the exception of time travel.
+    """
+
+    SELECT_SQL = SELECT_SQL
+    MIGRATIONS = MIGRATIONS
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
+
+    lock: threading.Lock
+
+    def __init__(
+        self,
+        conn: _internal.Conn,
+        pipe: Optional[Pipeline] = None,
+        serde: Optional[SerializerProtocol] = None,
+    ) -> None:
+        warnings.warn(
+            "ShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use PostgresSaver instead, and invoke the graph with `graph.invoke(..., checkpoint_during=False)`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(serde=serde)
+        if isinstance(conn, ConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single Connection, not ConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = threading.Lock()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @contextmanager
+    def from_conn_string(
+        cls, conn_string: str, *, pipeline: bool = False
+    ) -> Iterator["ShallowPostgresSaver"]:
+        """Create a new ShallowPostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use Pipeline
+
+        Returns:
+            ShallowPostgresSaver: A new ShallowPostgresSaver instance.
+        """
+        with Connection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                with conn.pipeline() as pipe:
+                    yield cls(conn, pipe)
+            else:
+                yield cls(conn)
+
+    def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        with self._cursor() as cur:
+            cur.execute(self.MIGRATIONS[0])
+            results = cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+            ):
+                cur.execute(migration)
+                cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+        if self.pipe:
+            self.pipe.sync()
+
+    def list(
+        self,
+        config: Optional[RunnableConfig],
+        *,
+        filter: Optional[dict[str, Any]] = None,
+        before: Optional[RunnableConfig] = None,
+        limit: Optional[int] = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where
+        if limit:
+            query += f" LIMIT {limit}"
+        with self._cursor() as cur:
+            cur.execute(self.SELECT_SQL + where, args, binary=True)
+            for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                yield CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": value["thread_id"],
+                            "checkpoint_ns": value["checkpoint_ns"],
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=self._load_writes(value["pending_writes"]),
+                )
+
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+
+        Examples:
+
+            Basic:
+            >>> config = {"configurable": {"thread_id": "1"}}
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+
+            With timestamp:
+
+            >>> config = {
+            ...    "configurable": {
+            ...        "thread_id": "1",
+            ...        "checkpoint_ns": "",
+            ...        "checkpoint_id": "1ef4f797-8335-6428-8001-8a1503f9b875",
+            ...    }
+            ... }
+            >>> checkpoint_tuple = memory.get_tuple(config)
+            >>> print(checkpoint_tuple)
+            CheckpointTuple(...)
+        """  # noqa
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        args = (thread_id, checkpoint_ns)
+        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
+
+        with self._cursor() as cur:
+            cur.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                return CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=self._load_writes(value["pending_writes"]),
+                )
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For ShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+
+        Examples:
+
+            >>> from langgraph.checkpoint.postgres import ShallowPostgresSaver
+            >>> DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
+            >>> with ShallowPostgresSaver.from_conn_string(DB_URI) as memory:
+            >>>     config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
+            >>>     checkpoint = {"ts": "2024-05-04T06:32:42.235444+00:00", "id": "1ef4f797-8335-6428-8001-8a1503f9b875", "channel_values": {"key": "value"}}
+            >>>     saved_config = memory.put(config, checkpoint, {"source": "input", "step": 1, "writes": {"key": "value"}}, {})
+            >>> print(saved_config)
+            {'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef4f797-8335-6428-8001-8a1503f9b875'}}
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        with self._cursor(pipeline=True) as cur:
+            cur.execute(
+                """DELETE FROM checkpoint_writes
+                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    configurable.get("checkpoint_id", ""),
+                ),
+            )
+            cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                _dump_blobs(
+                    self.serde,
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),  # type: ignore[misc]
+                    new_versions,
+                ),
+            )
+            cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    Jsonb(copy),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the Postgres database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        with self._cursor(pipeline=True) as cur:
+            cur.executemany(
+                query,
+                self._dump_writes(
+                    config["configurable"]["thread_id"],
+                    config["configurable"]["checkpoint_ns"],
+                    config["configurable"]["checkpoint_id"],
+                    task_id,
+                    task_path,
+                    writes,
+                ),
+            )
+
+    @contextmanager
+    def _cursor(self, *, pipeline: bool = False) -> Iterator[Cursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the ShallowPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        with _internal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                with self.lock, conn.cursor(binary=True, row_factory=dict_row) as cur:
+                    yield cur
+
+
+class AsyncShallowPostgresSaver(BasePostgresSaver):
+    """A checkpoint saver that uses Postgres to store checkpoints asynchronously.
+
+    This checkpointer ONLY stores the most recent checkpoint and does NOT retain any history.
+    It is meant to be a light-weight drop-in replacement for the AsyncPostgresSaver that
+    supports most of the LangGraph persistence functionality with the exception of time travel.
+    """
+
+    SELECT_SQL = SELECT_SQL
+    MIGRATIONS = MIGRATIONS
+    UPSERT_CHECKPOINT_BLOBS_SQL = UPSERT_CHECKPOINT_BLOBS_SQL
+    UPSERT_CHECKPOINTS_SQL = UPSERT_CHECKPOINTS_SQL
+    UPSERT_CHECKPOINT_WRITES_SQL = UPSERT_CHECKPOINT_WRITES_SQL
+    INSERT_CHECKPOINT_WRITES_SQL = INSERT_CHECKPOINT_WRITES_SQL
+    lock: asyncio.Lock
+
+    def __init__(
+        self,
+        conn: _ainternal.Conn,
+        pipe: Optional[AsyncPipeline] = None,
+        serde: Optional[SerializerProtocol] = None,
+    ) -> None:
+        warnings.warn(
+            "AsyncShallowPostgresSaver is deprecated as of version 2.0.20 and will be removed in 3.0.0. "
+            "Use AsyncPostgresSaver instead, and invoke the graph with `await graph.ainvoke(..., checkpoint_during=False)`.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(serde=serde)
+        if isinstance(conn, AsyncConnectionPool) and pipe is not None:
+            raise ValueError(
+                "Pipeline should be used only with a single AsyncConnection, not AsyncConnectionPool."
+            )
+
+        self.conn = conn
+        self.pipe = pipe
+        self.lock = asyncio.Lock()
+        self.loop = asyncio.get_running_loop()
+        self.supports_pipeline = Capabilities().has_pipeline()
+
+    @classmethod
+    @asynccontextmanager
+    async def from_conn_string(
+        cls,
+        conn_string: str,
+        *,
+        pipeline: bool = False,
+        serde: Optional[SerializerProtocol] = None,
+    ) -> AsyncIterator["AsyncShallowPostgresSaver"]:
+        """Create a new AsyncShallowPostgresSaver instance from a connection string.
+
+        Args:
+            conn_string: The Postgres connection info string.
+            pipeline: whether to use AsyncPipeline
+
+        Returns:
+            AsyncShallowPostgresSaver: A new AsyncShallowPostgresSaver instance.
+        """
+        async with await AsyncConnection.connect(
+            conn_string, autocommit=True, prepare_threshold=0, row_factory=dict_row
+        ) as conn:
+            if pipeline:
+                async with conn.pipeline() as pipe:
+                    yield cls(conn=conn, pipe=pipe, serde=serde)
+            else:
+                yield cls(conn=conn, serde=serde)
+
+    async def setup(self) -> None:
+        """Set up the checkpoint database asynchronously.
+
+        This method creates the necessary tables in the Postgres database if they don't
+        already exist and runs database migrations. It MUST be called directly by the user
+        the first time checkpointer is used.
+        """
+        async with self._cursor() as cur:
+            await cur.execute(self.MIGRATIONS[0])
+            results = await cur.execute(
+                "SELECT v FROM checkpoint_migrations ORDER BY v DESC LIMIT 1"
+            )
+            row = await results.fetchone()
+            if row is None:
+                version = -1
+            else:
+                version = row["v"]
+            for v, migration in zip(
+                range(version + 1, len(self.MIGRATIONS)),
+                self.MIGRATIONS[version + 1 :],
+            ):
+                await cur.execute(migration)
+                await cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
+        if self.pipe:
+            await self.pipe.sync()
+
+    async def alist(
+        self,
+        config: Optional[RunnableConfig],
+        *,
+        filter: Optional[dict[str, Any]] = None,
+        before: Optional[RunnableConfig] = None,
+        limit: Optional[int] = None,
+    ) -> AsyncIterator[CheckpointTuple]:
+        """List checkpoints from the database asynchronously.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        where, args = self._search_where(config, filter, before)
+        query = self.SELECT_SQL + where
+        if limit:
+            query += f" LIMIT {limit}"
+        async with self._cursor() as cur:
+            await cur.execute(self.SELECT_SQL + where, args, binary=True)
+            async for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                yield CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": value["thread_id"],
+                            "checkpoint_ns": value["checkpoint_ns"],
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=await asyncio.to_thread(
+                        self._load_writes, value["pending_writes"]
+                    ),
+                )
+
+    async def aget_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        """Get a checkpoint tuple from the database asynchronously.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        thread_id = config["configurable"]["thread_id"]
+        checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
+        args = (thread_id, checkpoint_ns)
+        where = "WHERE thread_id = %s AND checkpoint_ns = %s"
+
+        async with self._cursor() as cur:
+            await cur.execute(
+                self.SELECT_SQL + where,
+                args,
+                binary=True,
+            )
+
+            async for value in cur:
+                checkpoint: Checkpoint = {
+                    **value["checkpoint"],
+                    "channel_values": self._load_blobs(value["channel_values"]),
+                    "pending_sends": [
+                        self.serde.loads_typed((t.decode(), v))
+                        for t, v in value["pending_sends"]
+                    ]
+                    if value["pending_sends"]
+                    else [],
+                }
+                return CheckpointTuple(
+                    config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": checkpoint["id"],
+                        }
+                    },
+                    checkpoint=checkpoint,
+                    metadata=value["metadata"],
+                    pending_writes=await asyncio.to_thread(
+                        self._load_writes, value["pending_writes"]
+                    ),
+                )
+
+    async def aput(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database asynchronously.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        configurable = config["configurable"].copy()
+        thread_id = configurable.pop("thread_id")
+        checkpoint_ns = configurable.pop("checkpoint_ns")
+
+        copy = checkpoint.copy()
+        next_config = {
+            "configurable": {
+                "thread_id": thread_id,
+                "checkpoint_ns": checkpoint_ns,
+                "checkpoint_id": checkpoint["id"],
+            }
+        }
+
+        async with self._cursor(pipeline=True) as cur:
+            await cur.execute(
+                """DELETE FROM checkpoint_writes
+                WHERE thread_id = %s AND checkpoint_ns = %s AND checkpoint_id NOT IN (%s, %s)""",
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    checkpoint["id"],
+                    configurable.get("checkpoint_id", ""),
+                ),
+            )
+            await cur.executemany(
+                self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                _dump_blobs(
+                    self.serde,
+                    thread_id,
+                    checkpoint_ns,
+                    copy.pop("channel_values"),  # type: ignore[misc]
+                    new_versions,
+                ),
+            )
+            await cur.execute(
+                self.UPSERT_CHECKPOINTS_SQL,
+                (
+                    thread_id,
+                    checkpoint_ns,
+                    Jsonb(copy),
+                    Jsonb(get_checkpoint_metadata(config, metadata)),
+                ),
+            )
+        return next_config
+
+    async def aput_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint asynchronously.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+        """
+        query = (
+            self.UPSERT_CHECKPOINT_WRITES_SQL
+            if all(w[0] in WRITES_IDX_MAP for w in writes)
+            else self.INSERT_CHECKPOINT_WRITES_SQL
+        )
+        params = await asyncio.to_thread(
+            self._dump_writes,
+            config["configurable"]["thread_id"],
+            config["configurable"]["checkpoint_ns"],
+            config["configurable"]["checkpoint_id"],
+            task_id,
+            task_path,
+            writes,
+        )
+        async with self._cursor(pipeline=True) as cur:
+            await cur.executemany(query, params)
+
+    @asynccontextmanager
+    async def _cursor(
+        self, *, pipeline: bool = False
+    ) -> AsyncIterator[AsyncCursor[DictRow]]:
+        """Create a database cursor as a context manager.
+
+        Args:
+            pipeline: whether to use pipeline for the DB operations inside the context manager.
+                Will be applied regardless of whether the AsyncShallowPostgresSaver instance was initialized with a pipeline.
+                If pipeline mode is not supported, will fall back to using transaction context manager.
+        """
+        async with _ainternal.get_connection(self.conn) as conn:
+            if self.pipe:
+                # a connection in pipeline mode can be used concurrently
+                # in multiple threads/coroutines, but only one cursor can be
+                # used at a time
+                try:
+                    async with conn.cursor(binary=True, row_factory=dict_row) as cur:
+                        yield cur
+                finally:
+                    if pipeline:
+                        await self.pipe.sync()
+            elif pipeline:
+                # a connection not in pipeline mode can only be used by one
+                # thread/coroutine at a time, so we acquire a lock
+                if self.supports_pipeline:
+                    async with (
+                        self.lock,
+                        conn.pipeline(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+                else:
+                    # Use connection's transaction context manager when pipeline mode not supported
+                    async with (
+                        self.lock,
+                        conn.transaction(),
+                        conn.cursor(binary=True, row_factory=dict_row) as cur,
+                    ):
+                        yield cur
+            else:
+                async with (
+                    self.lock,
+                    conn.cursor(binary=True, row_factory=dict_row) as cur,
+                ):
+                    yield cur
+
+    def list(
+        self,
+        config: Optional[RunnableConfig],
+        *,
+        filter: Optional[dict[str, Any]] = None,
+        before: Optional[RunnableConfig] = None,
+        limit: Optional[int] = None,
+    ) -> Iterator[CheckpointTuple]:
+        """List checkpoints from the database.
+
+        This method retrieves a list of checkpoint tuples from the Postgres database based
+        on the provided config. For ShallowPostgresSaver, this method returns a list with
+        ONLY the most recent checkpoint.
+        """
+        aiter_ = self.alist(config, filter=filter, before=before, limit=limit)
+        while True:
+            try:
+                yield asyncio.run_coroutine_threadsafe(
+                    anext(aiter_),  # noqa: F821
+                    self.loop,
+                ).result()
+            except StopAsyncIteration:
+                break
+
+    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
+        """Get a checkpoint tuple from the database.
+
+        This method retrieves a checkpoint tuple from the Postgres database based on the
+        provided config (matching the thread ID in the config).
+
+        Args:
+            config: The config to use for retrieving the checkpoint.
+
+        Returns:
+            Optional[CheckpointTuple]: The retrieved checkpoint tuple, or None if no matching checkpoint was found.
+        """
+        try:
+            # check if we are in the main thread, only bg threads can block
+            # we don't check in other methods to avoid the overhead
+            if asyncio.get_running_loop() is self.loop:
+                raise asyncio.InvalidStateError(
+                    "Synchronous calls to AsyncShallowPostgresSaver are only allowed from a "
+                    "different thread. From the main thread, use the async interface."
+                    "For example, use `await checkpointer.aget_tuple(...)` or `await "
+                    "graph.ainvoke(...)`."
+                )
+        except RuntimeError:
+            pass
+        return asyncio.run_coroutine_threadsafe(
+            self.aget_tuple(config), self.loop
+        ).result()
+
+    def put(
+        self,
+        config: RunnableConfig,
+        checkpoint: Checkpoint,
+        metadata: CheckpointMetadata,
+        new_versions: ChannelVersions,
+    ) -> RunnableConfig:
+        """Save a checkpoint to the database.
+
+        This method saves a checkpoint to the Postgres database. The checkpoint is associated
+        with the provided config. For AsyncShallowPostgresSaver, this method saves ONLY the most recent
+        checkpoint and overwrites a previous checkpoint, if it exists.
+
+        Args:
+            config: The config to associate with the checkpoint.
+            checkpoint: The checkpoint to save.
+            metadata: Additional metadata to save with the checkpoint.
+            new_versions: New channel versions as of this write.
+
+        Returns:
+            RunnableConfig: Updated configuration after storing the checkpoint.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput(config, checkpoint, metadata, new_versions), self.loop
+        ).result()
+
+    def put_writes(
+        self,
+        config: RunnableConfig,
+        writes: Sequence[tuple[str, Any]],
+        task_id: str,
+        task_path: str = "",
+    ) -> None:
+        """Store intermediate writes linked to a checkpoint.
+
+        This method saves intermediate writes associated with a checkpoint to the database.
+
+        Args:
+            config: Configuration of the related checkpoint.
+            writes: List of writes to store, each as (channel, value) pair.
+            task_id: Identifier for the task creating the writes.
+            task_path: Path of the task creating the writes.
+        """
+        return asyncio.run_coroutine_threadsafe(
+            self.aput_writes(config, writes, task_id, task_path), self.loop
+        ).result()
@@ -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:
@@ -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
@@ -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
@@ -1,51 +0,0 @@
-from collections.abc import Mapping
-from datetime import datetime, timezone
-from typing import Any, Optional, Protocol
-
-from langgraph.checkpoint.base import Checkpoint, EmptyChannelError
-from langgraph.checkpoint.base.id import uuid6
-
-
-class ChannelProtocol(Protocol):
-    def checkpoint(self) -> Optional[Any]: ...
-
-
-def empty_checkpoint() -> Checkpoint:
-    return Checkpoint(
-        v=1,
-        id=str(uuid6(clock_seq=-2)),
-        ts=datetime.now(timezone.utc).isoformat(),
-        channel_values={},
-        channel_versions={},
-        versions_seen={},
-    )
-
-
-def create_checkpoint(
-    checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, ChannelProtocol]],
-    step: int,
-    *,
-    id: Optional[str] = None,
-) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
-    ts = datetime.now(timezone.utc).isoformat()
-    if channels is None:
-        values = checkpoint["channel_values"]
-    else:
-        values = {}
-        for k, v in channels.items():
-            if k not in checkpoint["channel_versions"]:
-                continue
-            try:
-                values[k] = v.checkpoint()
-            except EmptyChannelError:
-                pass
-    return Checkpoint(
-        v=1,
-        ts=ts,
-        id=id or str(uuid6(clock_seq=step)),
-        channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
-        versions_seen=checkpoint["versions_seen"],
-    )
@@ -14,10 +14,14 @@ from langgraph.checkpoint.base import (
    EXCLUDED_METADATA_KEYS,
    Checkpoint,
    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
+)
+from langgraph.checkpoint.postgres.aio import (
+    AsyncPostgresSaver,
+    AsyncShallowPostgresSaver,
 )
-from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 from langgraph.checkpoint.serde.types import TASKS
-from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 from tests.conftest import DEFAULT_POSTGRES_URI


@@ -108,11 +112,41 @@ async def _base_saver():
            await conn.execute(f"DROP DATABASE {database}")


+@asynccontextmanager
+async def _shallow_saver():
+    """Fixture for shallow connection mode testing."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    async with await AsyncConnection.connect(
+        DEFAULT_POSTGRES_URI, autocommit=True
+    ) as conn:
+        await conn.execute(f"CREATE DATABASE {database}")
+    try:
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = AsyncShallowPostgresSaver(conn)
+            await checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        async with await AsyncConnection.connect(
+            DEFAULT_POSTGRES_URI, autocommit=True
+        ) as conn:
+            await conn.execute(f"DROP DATABASE {database}")
+
+
@asynccontextmanager
 async def _saver(name: str):
    if name == "base":
        async with _base_saver() as saver:
            yield saver
+    elif name == "shallow":
+        async with _shallow_saver() as saver:
+            yield saver
    elif name == "pool":
        async with _pool_saver() as saver:
            yield saver
@@ -172,7 +206,7 @@ def test_data():
    }


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 async def test_combined_metadata(saver_name: str, test_data) -> None:
    async with _saver(saver_name) as saver:
        config = {
@@ -199,7 +233,7 @@ async def test_combined_metadata(saver_name: str, test_data) -> None:
        }


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 async def test_asearch(saver_name: str, test_data) -> None:
    async with _saver(saver_name) as saver:
        configs = test_data["configs"]
@@ -250,7 +284,7 @@ async def test_asearch(saver_name: str, test_data) -> None:
        } == {"", "inner"}


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 async def test_null_chars(saver_name: str, test_data) -> None:
    async with _saver(saver_name) as saver:
        config = await saver.aput(
@@ -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):
@@ -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."""
@@ -15,10 +15,11 @@ from langgraph.checkpoint.base import (
    EXCLUDED_METADATA_KEYS,
    Checkpoint,
    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
 )
-from langgraph.checkpoint.postgres import PostgresSaver
+from langgraph.checkpoint.postgres import PostgresSaver, ShallowPostgresSaver
 from langgraph.checkpoint.serde.types import TASKS
-from tests.checkpoint_utils import create_checkpoint, empty_checkpoint
 from tests.conftest import DEFAULT_POSTGRES_URI


@@ -97,11 +98,37 @@ def _base_saver():
            conn.execute(f"DROP DATABASE {database}")


+@contextmanager
+def _shallow_saver():
+    """Fixture for regular connection mode testing with a shallow checkpointer."""
+    database = f"test_{uuid4().hex[:16]}"
+    # create unique db
+    with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+        conn.execute(f"CREATE DATABASE {database}")
+    try:
+        with Connection.connect(
+            DEFAULT_POSTGRES_URI + database,
+            autocommit=True,
+            prepare_threshold=0,
+            row_factory=dict_row,
+        ) as conn:
+            checkpointer = ShallowPostgresSaver(conn)
+            checkpointer.setup()
+            yield checkpointer
+    finally:
+        # drop unique db
+        with Connection.connect(DEFAULT_POSTGRES_URI, autocommit=True) as conn:
+            conn.execute(f"DROP DATABASE {database}")
+
+
@contextmanager
 def _saver(name: str):
    if name == "base":
        with _base_saver() as saver:
            yield saver
+    elif name == "shallow":
+        with _shallow_saver() as saver:
+            yield saver
    elif name == "pool":
        with _pool_saver() as saver:
            yield saver
@@ -161,7 +188,7 @@ def test_data():
    }


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 def test_combined_metadata(saver_name: str, test_data) -> None:
    with _saver(saver_name) as saver:
        config = {
@@ -188,7 +215,7 @@ def test_combined_metadata(saver_name: str, test_data) -> None:
        }


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 def test_search(saver_name: str, test_data) -> None:
    with _saver(saver_name) as saver:
        configs = test_data["configs"]
@@ -237,7 +264,7 @@ def test_search(saver_name: str, test_data) -> None:
        } == {"", "inner"}


-@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
+@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe", "shallow"])
 def test_null_chars(saver_name: str, test_data) -> None:
    with _saver(saver_name) as saver:
        config = saver.put(
@@ -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, channel: 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.
@@ -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, channel: 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.
@@ -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.
@@ -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:
@@ -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):
@@ -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
@@ -1,51 +0,0 @@
-from collections.abc import Mapping
-from datetime import datetime, timezone
-from typing import Any, Optional, Protocol
-
-from langgraph.checkpoint.base import Checkpoint, EmptyChannelError
-from langgraph.checkpoint.base.id import uuid6
-
-
-class ChannelProtocol(Protocol):
-    def checkpoint(self) -> Optional[Any]: ...
-
-
-def empty_checkpoint() -> Checkpoint:
-    return Checkpoint(
-        v=1,
-        id=str(uuid6(clock_seq=-2)),
-        ts=datetime.now(timezone.utc).isoformat(),
-        channel_values={},
-        channel_versions={},
-        versions_seen={},
-    )
-
-
-def create_checkpoint(
-    checkpoint: Checkpoint,
-    channels: Optional[Mapping[str, ChannelProtocol]],
-    step: int,
-    *,
-    id: Optional[str] = None,
-) -> Checkpoint:
-    """Create a checkpoint for the given channels."""
-    ts = datetime.now(timezone.utc).isoformat()
-    if channels is None:
-        values = checkpoint["channel_values"]
-    else:
-        values = {}
-        for k, v in channels.items():
-            if k not in checkpoint["channel_versions"]:
-                continue
-            try:
-                values[k] = v.checkpoint()
-            except EmptyChannelError:
-                pass
-    return Checkpoint(
-        v=1,
-        ts=ts,
-        id=id or str(uuid6(clock_seq=step)),
-        channel_values=values,
-        channel_versions=checkpoint["channel_versions"],
-        versions_seen=checkpoint["versions_seen"],
-    )
@@ -6,9 +6,10 @@ from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.base import (
    Checkpoint,
    CheckpointMetadata,
+    create_checkpoint,
+    empty_checkpoint,
 )
 from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
-from tests.checkpoint_utils import create_checkpoint, empty_checkpoint


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


 class TestSqliteSaver:
@@ -1,11 +1,11 @@
-from collections.abc import AsyncIterator, Iterator, Sequence
+from __future__ import annotations
+
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from typing import (  # noqa: UP035
    Any,
    Generic,
-    List,
    Literal,
    NamedTuple,
-    Optional,
    TypedDict,
    TypeVar,
    Union,
@@ -13,6 +13,7 @@ from typing import (  # noqa: UP035

 from langchain_core.runnables import RunnableConfig

+from langgraph.checkpoint.base.id import uuid6
 from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from langgraph.checkpoint.serde.types import (
@@ -20,6 +21,7 @@ from langgraph.checkpoint.serde.types import (
    INTERRUPT,
    RESUME,
    SCHEDULED,
+    ChannelProtocol,
 )

 V = TypeVar("V", int, float, str)
@@ -89,6 +91,7 @@ def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
        channel_values=checkpoint["channel_values"].copy(),
        channel_versions=checkpoint["channel_versions"].copy(),
        versions_seen={k: v.copy() for k, v in checkpoint["versions_seen"].items()},
+        pending_sends=checkpoint.get("pending_sends", []).copy(),
    )


@@ -98,8 +101,8 @@ 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 +124,20 @@ 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]:
+    @property
+    def config_specs(self) -> list:
+        """Define the configuration options for the checkpoint saver.
+
+        Returns:
+            list: List of configuration field specs.
+        """
+        return []
+
+    def get(self, config: RunnableConfig) -> Checkpoint | None:
        """Fetch a checkpoint using the given configuration.

        Args:
@@ -137,7 +149,7 @@ 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 +165,11 @@ 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 +241,7 @@ 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 +253,7 @@ 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 +269,11 @@ 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 +346,7 @@ class BaseCheckpointSaver(Generic[V]):
        """
        raise NotImplementedError

-    def get_next_version(self, current: Optional[V]) -> V:
+    def get_next_version(self, current: V | None, channel: 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,
@@ -342,6 +354,7 @@ class BaseCheckpointSaver(Generic[V]):

        Args:
            current: The current version identifier (int, float, or str).
+            channel: Deprecated argument, kept for backwards compatibility.

        Returns:
            V: The next version identifier, which must be increasing.
@@ -361,7 +374,7 @@ 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")
@@ -404,3 +417,54 @@ EXCLUDED_METADATA_KEYS = {
    "checkpoint_ns",
    "checkpoint_map",
 }
+
+# --- below are deprecated utilities used by past versions of LangGraph ---
+
+LATEST_VERSION = 2
+
+
+def empty_checkpoint() -> Checkpoint:
+    from datetime import datetime, timezone
+
+    return Checkpoint(
+        v=LATEST_VERSION,
+        id=str(uuid6(clock_seq=-2)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values={},
+        channel_versions={},
+        versions_seen={},
+        pending_sends=[],
+    )
+
+
+def create_checkpoint(
+    checkpoint: Checkpoint,
+    channels: Mapping[str, ChannelProtocol] | None,
+    step: int,
+    *,
+    id: str | None = None,
+) -> Checkpoint:
+    """Create a checkpoint for the given channels."""
+    from datetime import datetime, timezone
+
+    ts = datetime.now(timezone.utc).isoformat()
+    if channels is None:
+        values = checkpoint["channel_values"]
+    else:
+        values = {}
+        for k, v in channels.items():
+            if k not in checkpoint["channel_versions"]:
+                continue
+            try:
+                values[k] = v.checkpoint()
+            except EmptyChannelError:
+                pass
+    return Checkpoint(
+        v=LATEST_VERSION,
+        ts=ts,
+        id=id or str(uuid6(clock_seq=step)),
+        channel_values=values,
+        channel_versions=checkpoint["channel_versions"],
+        versions_seen=checkpoint["versions_seen"],
+        pending_sends=checkpoint.get("pending_sends", []),
+    )
@@ -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
@@ -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, channel: 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:
@@ -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 = (
@@ -1,4 +1,13 @@
-from typing import Any, Protocol, TypeVar, runtime_checkable
+from collections.abc import Sequence
+from typing import (
+    Any,
+    Optional,
+    Protocol,
+    TypeVar,
+    runtime_checkable,
+)
+
+from typing_extensions import Self

 ERROR = "__error__"
 SCHEDULED = "__scheduled__"
@@ -11,6 +20,25 @@ Update = TypeVar("Update", contravariant=True)
 C = TypeVar("C")


+class ChannelProtocol(Protocol[Value, Update, C]):
+    # Mirrors langgraph.channels.base.BaseChannel
+    @property
+    def ValueType(self) -> Any: ...
+
+    @property
+    def UpdateType(self) -> Any: ...
+
+    def checkpoint(self) -> Optional[C]: ...
+
+    def from_checkpoint(self, checkpoint: Optional[C]) -> Self: ...
+
+    def update(self, values: Sequence[Update]) -> bool: ...
+
+    def get(self) -> Value: ...
+
+    def consume(self) -> bool: ...
+
+
@runtime_checkable
 class SendProtocol(Protocol):
    # Mirrors langgraph.constants.Send
@@ -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")
@@ -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:
@@ -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

@@ -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
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Eugene Yurtsev	0b8634b4c6	x	2025-06-25 10:25:06 -04:00
Eugene Yurtsev	bc3ef7f913	x	2025-06-24 14:47:20 -04:00
Eugene Yurtsev	0dda1b4b1e	x	2025-06-24 12:35:31 -04:00
Eugene Yurtsev	d22c2c4dac	x	2025-06-24 11:17:21 -04:00
Eugene Yurtsev	db03dccb2b	x	2025-06-24 09:56:19 -04:00
Eugene Yurtsev	c79c9ea733	x	2025-06-20 17:06:13 -04:00
Eugene Yurtsev	b115e1dcde	tools	2025-06-20 17:04:41 -04:00
Eugene Yurtsev	3b59213311	fix tools	2025-06-20 16:45:24 -04:00
Eugene Yurtsev	0c0e5a299d	Replace notebook with markdown file	2025-06-20 16:33:42 -04:00
Lauren Hirata Singh	69d4c37d25	Consolidate assistant conceptual guides	2025-06-18 19:05:34 -04:00
Lauren Hirata Singh	0e7554a1a1	Update navigation	2025-06-18 10:58:18 -04:00
Lauren Hirata Singh	596c60a65c	Move LGP to platform section	2025-06-18 10:58:12 -04:00
Lauren Hirata Singh	e45797ce19	Fix titles based on feedback	2025-06-18 10:49:55 -04:00
Lauren Hirata Singh	e746b54a57	Change titles	2025-06-17 16:30:35 -04:00
Lauren Hirata SinghandGitHub	c88e22ffa7	Merge branch 'main' into get-started	2025-06-17 16:16:36 -04:00
Lauren Hirata Singh	dfdeb6a6f1	Edit stream modes	2025-06-17 15:41:57 -04:00
lc-arjun GitHub Marco Perini	1309243b29	docs: studio evals (#5129 ) * docs: studio evals * docs: added studio evals images (#5076) * docs: added studio evals images * Update docs/docs/cloud/how-tos/studio/run_evals.md Co-authored-by: lc-arjun <arjun@langchain.dev> * Update docs/docs/cloud/how-tos/studio/run_evals.md Co-authored-by: lc-arjun <arjun@langchain.dev> * Update docs/docs/cloud/how-tos/studio/run_evals.md Co-authored-by: lc-arjun <arjun@langchain.dev> * docs: updated studio evals * Update docs/docs/cloud/how-tos/studio/run_evals.md Co-authored-by: lc-arjun <arjun@langchain.dev> * docs: removed images --------- Co-authored-by: lc-arjun <arjun@langchain.dev> * final changes * i think its this --------- Co-authored-by: Marco Perini <perinim.98@gmail.com>	2025-06-17 12:32:04 -07:00
Lauren Hirata Singh	ba08acb71c	Fix broken links	2025-06-17 15:21:50 -04:00
Lauren Hirata Singh	dd64636ca8	Remove agents/streaming	2025-06-17 15:14:48 -04:00
Lauren Hirata Singh	664475887d	Consolidate streaming	2025-06-17 15:07:07 -04:00
Lauren Hirata Singh	3d3a2bfacd	edits	2025-06-16 21:07:12 -04:00
Nuno Campos	771c6150a4	langgraph 0.5.0rc1	2025-06-16 17:52:13 -07:00
Nuno Campos	edfb65fd3a	langgraph-prebuilt 0.5.0rc0	2025-06-16 17:47:21 -07:00
Lauren Hirata SinghandGitHub	0f92470e49	docs: Remove cookie consent (#5123 )	2025-06-16 18:41:55 -04:00
Nuno Campos	dfcaf97c73	langgraph 0.5.0rc0	2025-06-16 15:17:56 -07:00
Nuno Campos	63a0028372	langgraph-checkpoint 2.1.0	2025-06-16 14:58:50 -07:00
Nuno CamposandGitHub	1134017d07	Preparation for 0.5 release: langgraph-checkpoint (#5124 ) Prepare langgraph-checkpoint for 0.5 - Given we have no upper bound on langgraph-checkpoint dep need to undo all changes in langgraph-checkpoint that might break previous versions of langgraph	2025-06-16 21:57:11 +00:00
Lauren Hirata Singh	bbe90e04ca	Remove cookie consent popup	2025-06-16 16:35:27 -04:00
Lauren Hirata Singh	33feba4877	Remove cookie consent	2025-06-16 16:33:32 -04:00
Lauren Hirata Singh	905fcb3d02	Fix broken links	2025-06-16 16:25:31 -04:00
Lauren Hirata Singh	543d7d85af	Organize existing content differently	2025-06-16 16:17:52 -04:00
Nuno CamposandGitHub	4fec8e9dec	Preparation for 0.5 release (#5121 )	2025-06-16 13:14:25 -07:00
Nuno Campos	c137169325	Preparation for 0.5 release - Update deprecation warnings to mention 0.5, no 1.0 - Add back type hint support for Runnable arg to add_node	2025-06-16 13:07:52 -07:00
Nuno CamposandGitHub	1e2672e63d	Restore shallow checkpointer (#5105 )	2025-06-16 11:23:27 -07:00
Nuno CamposandGitHub	06803ab683	Add migration for pending_sends (#5106 )	2025-06-16 11:23:17 -07:00
hari-dhanushkodiandGitHub	3488ee47e0	chore: add docs for lgp deployment monitoring (#5104 )	2025-06-16 10:21:42 -07:00
Nuno CamposandGitHub	289bdd0cea	Introduce "tasks" and "checkpoints" stream modes (#5117 )	2025-06-16 10:14:18 -07:00
Nuno Campos	417103066b	Lint	2025-06-16 09:29:03 -07:00
Nuno Campos	25a59447c1	Introduce "tasks" and "checkpoints" stream modes - These are split out of "debug" stream mode, which is now an alias for ["tasks", "checkpoints"]	2025-06-16 08:47:45 -07:00
Nuno Campos	21906d2b7b	Add migration for pending_sends - Checkpoints saved on older versions of langgraph will be compatible with langgraph 0.5 and 1.0	2025-06-13 17:42:14 -07:00
Nuno Campos	0cad7019cb	Restore shallow checkpointer - This should definitely be removed soon, but let's give people more time to update	2025-06-13 17:37:40 -07:00
Nuno CamposandGitHub	7e735672bf	Restore compatibility with custom checkpointer classes created in prior versions (#5103 )	2025-06-13 16:36:36 -07:00
Nuno Campos	5498893780	Restore compatibility with custom checkpointer classes created in prior versions - Ensure existing custom checkpointer classes are compatible with new langgraph-checkpoint release	2025-06-13 16:29:55 -07:00
Nuno CamposandGitHub	e80f47aa01	Revert removals of APIs that were slated for removal in 1.0 (#5101 )	2025-06-13 16:09:20 -07:00
Nuno Campos	a0b2f742a3	Revert "Remove UntrackedValue channel" This reverts commit `05f3904d09`.	2025-06-13 15:36:47 -07:00
William FHandGitHub	b7973d65db	fix: Update lockfile (#5102 )	2025-06-13 14:53:40 -07:00
Nuno Campos	3fa3a586b5	Revert "Remove `MessageGraph` (#4875 )" This reverts commit `a5e6223569`.	2025-06-13 14:21:05 -07:00
William FHandGitHub	053b606b46	cli: 0.3.3 (#5100 )	2025-06-13 13:15:26 -07:00
William FHandGitHub	4548a0ebe8	feat: Customizable Pip Installer (#5098 ) Let you set "pip_installer": "pip" (or uv) to handle corner cases in install compatibilities	2025-06-13 10:35:28 -07:00
Sydney RunkleandGitHub	0171e9a323	fix(langgraph): remove deprecated `output` usage in favor of `output_schema` (#5095 ) use output_schema	2025-06-13 12:34:39 -04:00
Sydney RunkleandGitHub	c439cb0872	refactor(langgraph): Remove `PregelNode`'s inheritance from `Runnable` (#5093 ) remove Runnable inheritance for PregelNode	2025-06-13 10:17:42 -04:00
Nuno CamposandGitHub	2a4d7e8889	Remove support for node reading a single managed value (#5083 )	2025-06-12 15:19:55 -07:00
Nuno Campos	7f3578e0f1	Remove support for node reading a single managed value - This has never been used and is not useful or intended functionality	2025-06-12 15:11:19 -07:00
Lauren Hirata SinghandGitHub	e2f96b5ae5	revert incident banner (#5082 )	2025-06-12 17:24:36 -04:00
Lauren Hirata Singh	0d5f7e55bf	revert incident banner	2025-06-12 17:10:22 -04:00
Lauren Hirata SinghandGitHub	9209f11187	incident banner (#5081 )	2025-06-12 16:04:20 -04:00
Lauren Hirata SinghandGitHub	bb1c5b8cdf	Update docs/overrides/main.html	2025-06-12 15:57:14 -04:00
Nuno CamposandGitHub	d6bb008ff4	PregelLoop: Simplify tick() method (#5080 ) * PregelLoop: Simplify tick() method - Split out superstep finish into separate after_tick() method - Handle input in __enter__ - Remove unnecessary recursive shortcut - Remove input sentinel objects * Lint	2025-06-12 19:53:55 +00:00
Lauren Hirata Singh	6130e08fa6	incident banner	2025-06-12 15:52:36 -04:00
Sydney RunkleandGitHub	3ad061f0d7	serialize/deserialize pandas with pickle fallback (#5057 )	2025-06-12 15:14:00 -04:00
Nuno CamposandGitHub	116b5d1cac	Remove code paths no longer needed (#5079 )	2025-06-12 11:47:10 -07:00
Nuno Campos	0aff02e180	Remove code paths no longer needed - These were only used by the kafka scheduler	2025-06-12 11:25:20 -07:00
Nuno CamposandGitHub	074af5c122	Avoid saving checkpoints for subgraphs when checkpoint_during=False (#5051 )	2025-06-11 11:11:02 -07:00
langchain-infraandGitHub	29ffaa0e0b	docs: fix config section (#5066 )	2025-06-11 13:23:33 -04:00
Sydney RunkleandGitHub	45cd4e1928	oss: auto apply labels to contributor issues (#5067 ) auto apply labels	2025-06-11 17:19:49 +00:00
langchain-infraandGitHub	480271f753	docs: add mount prefix environment variable (#5060 )	2025-06-11 11:20:19 -04:00
infra	66fdf60e47	docs: add mount prefix environment variable	2025-06-11 11:18:16 -04:00
infra	0894daf3fc	docs: add mount prefix environment variable	2025-06-11 11:17:45 -04:00
Lauren Hirata SinghandGitHub	850c55d630	Revert "fix assistants overview link" (#5059 )	2025-06-11 11:02:17 -04:00
Lauren Hirata SinghandGitHub	c0d65ff409	Revert "fix assistants overview link (#5058 )" This reverts commit `be7b60a722`.	2025-06-11 10:58:52 -04:00
Lauren Hirata SinghandGitHub	be7b60a722	fix assistants overview link (#5058 )	2025-06-11 10:58:07 -04:00
Eugene YurtsevandGitHub	d467ec6556	Remove gitmcp badge (#5055 ) * Remove gitmcp badge * xt * x	2025-06-11 10:55:05 -04:00
Chitikela Veera Sai Charan Teja GitHub Eugene Yurtsev	b8683ab67a	docs: Update subgraphs.md (#5052 ) * Update subgraphs.md The state while defining the Subgraph is updated. Also an edge connecting START to the call_model node in the subgraph was created. * Update docs/docs/concepts/subgraphs.md * Update docs/docs/concepts/subgraphs.md --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>	2025-06-11 13:49:51 +00:00
William Fu-Hinthorn	6a9ca8d67e	Update existing	2025-06-10 17:59:41 -07:00
William Fu-Hinthorn	3b98044f2f	Add tests	2025-06-10 17:29:27 -07:00
Nuno Campos	a4a8934bd3	Avoid saving checkpoints for subgraphs when checkpoint_during=False - We can avoid saving checkpoints for successful subgraphs which do not request multi-turn memory	2025-06-10 17:25:05 -07:00
Nuno CamposandGitHub	470b9a4b97	Clean up PregelNode attributes (#5049 )	2025-06-10 17:24:03 -07:00
Nuno Campos	516175780d	Clean up things for Matt!	2025-06-10 16:14:15 -07:00
William FHandGitHub	571780f74c	fix: header merging (#4926 )	2025-06-10 14:44:34 -07:00
Emmanuel FerdmanandGitHub	d719438307	fix: throw exception on multiple injections (#5033 ) Throw exception on for multiple injections Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>	2025-06-10 16:54:01 -04:00
Simon FrankandGitHub	85c809a651	docs: fixed a wrong import in persistence docs (#5045 )	2025-06-10 20:53:50 +00:00
Nuno CamposandGitHub	0441fd156f	Add docs for checkpoint encryption (#5047 ) docs: list CipherProtocol in API	2025-06-10 16:52:45 -04:00
Nuno CamposandGitHub	37b5d3886c	Add library overview to AGENTS.md (#5044 )	2025-06-10 10:08:55 -07:00
Nuno Campos	b95267a3cc	Refine dependency map	2025-06-10 10:06:08 -07:00
Nuno CamposandGitHub	2e33c520a5	Support numpy array serialization in JsonPlusSerializer (#5035 ) * Handle numpy Fortran arrays * Lint * Lint * Lint	2025-06-10 01:17:28 +00:00
Nuno CamposandGitHub	67b1dc602e	Update ormsgpack (#5034 ) * Update ormsgpack - Now supports bytearray/memoryview passthrough * Lint	2025-06-10 00:30:58 +00:00
Naohiro YoshidaandGitHub	1519b90414	Centralized CheckpointTuple creation into a shared function for checkpoint_postgres (#4970 )	2025-06-09 18:40:17 +00:00
YkohandGitHub	0035ab9825	docs: Replace unsupported models with structured output-supported models (#3982 )	2025-06-09 14:17:05 -04:00
Eyal Schuller GitHub William FH	c42cd57a32	chore: Update variable naming in postgres store (#4096 ) Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>	2025-06-09 17:54:06 +00:00
ms-tungtt GitHub William FH	acc56e094a	docs: add query params for Store semantic search (#4828 ) Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>	2025-06-09 17:47:50 +00:00
Yassin NouhandGitHub	6b30d4fd8f	docs: enhance PostgresSaver connection requirements explanation (#4953 ) docs: enhance PostgresSaver connection requirements explanation - Add detailed explanation of why autocommit=True and row_factory=dict_row are required - Include example of incorrect usage and resulting errors - Addresses issue #4937 about incomplete setup documentation	2025-06-09 17:12:44 +00:00
Jong Hyun Park GitHub Sydney Runkle	fcc37cd06b	docs: update tutorial/rag/langgraph_adaptive_rag.ipynb (#2006 ) - add some explanations of ipynb code in markdown cell. Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>	2025-06-09 12:54:29 -04:00
William FHandGitHub	c17ee1bf5a	feat: [CLI] Add support for building deps with uv (#4995 )	2025-06-09 08:57:29 -07:00
William FHandGitHub	88c603b00b	fix: (sdk-js) Expand ToolMessage Type (#5015 )	2025-06-09 08:22:35 -07:00
Sydney RunkleandGitHub	c12f7cb2b9	github: support blank issues (help with v1 planning) (#4999 ) blank issues	2025-06-09 13:52:14 +00:00
🤖Esteban Dalel RandGitHub	6d7d689578	docs: highlight changed lines in 3-add-memory.md (#4930 )	2025-06-08 14:08:04 +00:00
LostInCode404andGitHub	f1b7eca7fc	docs: Update `1-build-basic-chatbot.md` to add a section about `END` node (#4886 ) Update `1-build-basic-chatbot.md` to add a section about `END` node	2025-06-08 13:51:17 +00:00
Michael LiandGitHub	93766a6df1	docs: fix assistants url at manage_assistants.md (#4993 ) * docs: fix agent supervisor doc codes * docs: fix assistants url at manage_assistants.md	2025-06-08 13:49:45 +00:00
Dionysis GlytsosandGitHub	a9d4e0da29	docs: fix typos (#4992 ) Fix typos	2025-06-08 13:46:41 +00:00
Sydney RunkleandGitHub	9105e60a34	graph: improve generics on `StateGraph` etc + move typing utils to private file (#4982 )	2025-06-06 19:51:05 -04:00
Sydney RunkleandGitHub	b735452153	deprecate `input` and `output` in favor of `input_schema` and `output_schema` (#4983 )	2025-06-06 19:44:56 -04:00
Sydney Runkle	5920d8aa92	using StateT as default for InputT	2025-06-06 12:58:19 -04:00
Michael Li GitHub vbarda	533f5b3d6f	docs: fix task description example in the agent supervisor tutorial (#4938 ) * docs: fix agent supervisor doc codes --------- Co-authored-by: vbarda <vadym@langchain.dev>	2025-06-06 13:41:41 +00:00
Asamu DavidandGitHub	be5889a7df	docs: add docs for image_distro cli option (#4974 )	2025-06-05 23:11:47 +01:00
David Asamu	0bf268feca	add docs for image_distro cli option	2025-06-05 17:23:05 +01:00
Sydney RunkleandGitHub	5e7566f4a3	lint: use `pep 604` union syntax and `pep 585` generic syntax (#4963 ) * new union syntax * fix test * second round of conversions by injecting future annotations * format + add top level makefile	2025-06-04 21:50:16 -04:00
Sydney RunkleandGitHub	494c8ef0d2	docs: remove references to `StateGraph(dict)` (#4964 ) remove StateGraph(dict)	2025-06-04 21:29:19 -04:00
lc-arjunandGitHub	45e60ff9e1	fix: camel case to snake case conversion (#4966 )	2025-06-04 17:31:12 -07:00