feat(sdk-py): add interrupts to ThreadState (#5603)

Fixes https://github.com/langchain-ai/langgraph/issues/5572

End is a special terminal node, so we don't need a branch to channel
like we do for other values passed to `Command.goto`

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,29 +1,29 @@
 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.
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
 labels: [pending,bug]
 body:
   - type: markdown
     attributes:
-      value: >
+      value: |
         Thank you for taking the time to file a bug report. 
         
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
+        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
         
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
         if there's another way to solve your problem:
         
-        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
-        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [GitHub search](https://github.com/langchain-ai/langgraph),
+        * [LangChain Forum](https://forum.langchain.com/),
+        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        * [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
+        * [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        * [GitHub search](https://github.com/langchain-ai/langgraph),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
       description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
       options:
-        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
+        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
           required: true
         - label: I added a clear and detailed title that summarizes the issue.
           required: true
@@ -38,7 +38,7 @@ body:
     attributes:
       label: Example Code
       description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -78,7 +78,7 @@ body:
     attributes:
       label: System Info
       description: |
-        python -m langchain_core.sys_info
+        Run on your machine: `python -m langchain_core.sys_info`
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +1,6 @@
+blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: Feature Request
-    url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
-    about: Suggest a feature or an idea
   - name: LangChain Forum
     url:  https://forum.langchain.com/
-    about: General community discussions and support
+    about: General community discussions, support, and feature requests

.github/ISSUE_TEMPLATE/privileged.yml

@@ -1,25 +1,29 @@
 name: 🔒 Privileged
-description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
+description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
 body:
   - type: markdown
     attributes:
       value: |
-        Thanks for your interest in LangChain! 🚀
-        
-        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
-        
-        You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
-        or are a regular contributor to LangChain with previous merged merged pull requests.
+        Thanks for your interest in LangGraph! 🚀
+
+        If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
+
+        You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository 
+        or are a regular contributor to LangGraph with previous merged merged pull requests.
   - type: checkboxes
     id: privileged
     attributes:
       label: Privileged issue
       description: Confirm that you are allowed to create an issue here.
       options:
-        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
+        - label: I am a LangGraph maintainer, or was asked directly by a LangGraph maintainer to create an issue here.
           required: true
   - type: textarea
     id: content
     attributes:
       label: Issue Content
       description: Add the content of the issue here.
+  - type: markdown
+    attributes:
+      value: |
+        Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,31 @@
+Thank you for contributing to LangGraph! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
+
+- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
+  - Examples:
+    - feat(core): add multi-tenant support
+    - fix(cli): resolve flag parsing error
+    - docs(openai): update API usage examples
+  - Allowed `{TYPE}` values:
+    - feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
+  - Allowed `{SCOPE}` values (optional):
+    - langgraph, docs, cli, checkpoint, checkpoint-postgres, checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
+  - Once you've written the title, please delete this checklist item; do not include it in the PR.
+
+- [ ] **PR message**: ***Delete this entire checklist*** and replace with
+  - **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable.
+  - **Issue:** the issue # it fixes, if applicable
+  - **Dependencies:** any dependencies required for this change
+  - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
+
+- [ ] **Add tests and docs**: If you're adding a new integration, you must include:
+  1. A test for the integration, preferably unit tests that do not rely on network access,
+  2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
+
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
+
+Additional guidelines:
+
+- Make sure optional dependencies are imported within a function.
+- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
+- Most PRs should not touch more than one package.
+- Changes should be backwards compatible.

.github/workflows/codespell-ignore-words.txt

@@ -0,0 +1,11 @@
+LangChain
+LangGraph
+LangSmith
+thead
+stdio
+nd
+jupyter
+lets
+lite
+uis
+deque

.github/workflows/codespell.yml

@@ -34,10 +34,16 @@
             id: extract_ignore_words
     
           - name: Codespell
-            uses: codespell-project/actions-codespell@v2
+            uses: codespell-project/actions-codespell@v2.1
             with:
-              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
               ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
           # We do this to avoid spellchecking cell outputs
           - name: Codespell Notebooks
-            run: make codespell
+            run: make codespell
+          
+          - name: Codespell LangGraph Library
+            run: |
+              # Change to root directory to check the main LangGraph library
+              cd ..
+              codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map,*.pyc,__pycache__/*" --ignore-words-list="${{ steps.extract_ignore_words.outputs.ignore_words_list }}" libs/langgraph/langgraph/

CONTRIBUTING.md

@@ -9,7 +9,7 @@ Here are some things to keep in mind for all types of contributions:
 - Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
 - Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
 - Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
+  - If you would like comments or feedback, please tag a maintainer.
 - Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
 - Look for duplicate PRs or issues that have already been opened before opening a new one.
 - Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
@@ -20,7 +20,7 @@ For bug fixes, please open up an issue before proposing a fix to ensure the prop
 
 ### New features
 
-For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
+For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
 
 ## Contribute Documentation
 
@@ -111,7 +111,6 @@ in a more abstract way than how-to guides or tutorials, and should be geared tow
 gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
 impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
 
-
 To quote the Diataxis website:
 
 > The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
@@ -187,9 +186,9 @@ Be concise, including in code samples.
 
 ## Setup
 
-LangChain documentation consists of two components:
+LangGraph documentation consists of two components:
 
-1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
+1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
 this comprehensive resource serves as the primary user-facing documentation.
 It covers a wide array of topics, including tutorials, use cases, integrations,
 and more, offering extensive guidance on building with LangGraph.
@@ -250,17 +249,17 @@ make serve-docs
 
 #### Linting
 
-The documentation is linted from the **monorepo root**. To lint it, run the following from there:
+To spell check the docs, run the following from the `docs` directory:
 
 ```bash
-make spellcheck
+codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
 ```
 
 ### ️In-code Documentation
 
 The in-code documentation is autogenerated from docstrings.
 
-For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
+For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
 
 We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
 
@@ -291,4 +290,4 @@ def my_function(arg1: int, arg2: str) -> float:
         This is a description of the return value.
     """
     return 3.14
-```
+```

README.md

@@ -73,7 +73,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
@@ -81,4 +81,4 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Acknowledgements
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

docs/docs/agents/mcp.md

@@ -55,14 +55,16 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
 
 === "In a workflow"
 
-    ```python
+    ```python title="Workflow using MCP tools with ToolNode"
     from langchain_mcp_adapters.client import MultiServerMCPClient
-    from langgraph.graph import StateGraph, MessagesState, START
-    from langgraph.prebuilt import ToolNode, tools_condition
-
     from langchain.chat_models import init_chat_model
-    model = init_chat_model("openai:gpt-4.1")
+    from langgraph.graph import StateGraph, MessagesState, START, END
+    from langgraph.prebuilt import ToolNode
 
+    # Initialize the model
+    model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+
+    # Set up MCP client
     client = MultiServerMCPClient(
         {
             "math": {
@@ -80,22 +82,47 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
     )
     tools = await client.get_tools()
 
-    def call_model(state: MessagesState):
-        response = model.bind_tools(tools).invoke(state["messages"])
-        return {"messages": response}
+    # Bind tools to model
+    model_with_tools = model.bind_tools(tools)
 
+    # Create ToolNode
+    tool_node = ToolNode(tools)
+
+    def should_continue(state: MessagesState):
+        messages = state["messages"]
+        last_message = messages[-1]
+        if last_message.tool_calls:
+            return "tools"
+        return END
+
+    # Define call_model function
+    async def call_model(state: MessagesState):
+        messages = state["messages"]
+        response = await model_with_tools.ainvoke(messages)
+        return {"messages": [response]}
+
+    # Build the graph
     builder = StateGraph(MessagesState)
-    builder.add_node(call_model)
-    builder.add_node(ToolNode(tools))
+    builder.add_node("call_model", call_model)
+    builder.add_node("tools", tool_node)
+
     builder.add_edge(START, "call_model")
     builder.add_conditional_edges(
         "call_model",
-        tools_condition,
+        should_continue,
     )
     builder.add_edge("tools", "call_model")
+
+    # Compile the graph
     graph = builder.compile()
-    math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
-    weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
+
+    # Test the graph
+    math_response = await graph.ainvoke(
+        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+    )
+    weather_response = await graph.ainvoke(
+        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+    )
     ```
 
 
@@ -148,4 +175,4 @@ if __name__ == "__main__":
 
 - [MCP documentation](https://modelcontextprotocol.io/introduction)
 - [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
-- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
+- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)

docs/docs/cloud/deployment/egress.md

@@ -0,0 +1,119 @@
+# Egress for Subscription Metrics and Operational Metadata
+
+> **Important: Self Hosted Only** 
+> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
+> This does not apply to SaaS or Hybrid deployments.
+
+Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
+
+In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
+
+> **Warning**  
+> **This will require egress to `https://beacon.langchain.com` from your network.**
+> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
+
+Generally, data that we send to Beacon can be categorized as follows:
+
+- **Subscription Metrics**
+  - Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
+    - Nodes Executed
+    - Runs Executed
+    - License Key Verification
+- **Operational Metadata**
+  - This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
+
+## Example Payloads
+
+In an effort to maximize transparency, we provide sample payloads here:
+
+### License Verification (If using an Enterprise License)
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/beacon/verify`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>"
+}
+```
+
+**Response:**
+
+```json
+{
+  "token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
+}
+```
+
+### Api Key Verification (If using a LangSmith API Key)
+
+**Endpoint:**
+`POST api.smith.langchain.com/auth`
+
+**Request:**
+
+```json
+"Headers": {
+  X-Api-Key: <YOUR_API_KEY>
+}
+```
+
+**Response:**
+
+```json
+{
+  "org_config": {
+    "org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    ... // Additional organization details
+  }
+}
+```
+
+### Usage Reporting
+
+**Endpoint:**
+
+`POST beacon.langchain.com/v1/metadata/submit`
+
+**Request:**
+
+```json
+{
+  "license": "<YOUR_LICENSE_KEY>",
+  "from_timestamp": "2025-01-06T09:00:00Z",
+  "to_timestamp": "2025-01-06T10:00:00Z",
+  "tags": {
+    "langgraph.python.version": "0.1.0",
+    "langgraph_api.version": "0.2.0",
+    "langgraph.platform.revision": "abc123",
+    "langgraph.platform.variant": "standard",
+    "langgraph.platform.host": "host-1",
+    "langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
+    "langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
+    "langgraph.platform.plan": "enterprise",
+    "user_app.uses_indexing": "true",
+    "user_app.uses_custom_app": "false",
+    "user_app.uses_custom_auth": "true",
+    "user_app.uses_thread_ttl": "true",
+    "user_app.uses_store_ttl": "false"
+  },
+  "measures": {
+    "langgraph.platform.runs": 150,
+    "langgraph.platform.nodes": 450
+  },
+  "logs": []
+}
+```
+
+**Response:**
+
+```json
+"204 No Content"
+```
+
+## Our Commitment
+
+LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -23,6 +23,8 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 
         kubectl get storageclass
 
+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
+
 ## Setup
 
 1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.

docs/docs/cloud/deployment/standalone_container.md

@@ -24,6 +24,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
     1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
     1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
     1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
+1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
 
 ## Kubernetes (Helm)
 

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

@@ -30,17 +30,33 @@ export default {
 
 Next, define your UI components in your `langgraph.json` configuration:
 
-```json
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/agent/index.ts:graph"
-  },
-  "ui": {
-    "agent": "./src/agent/ui.tsx"
-  }
-}
-```
+=== "Python agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent.py:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```
+
+=== "JS agent"
+
+    ```json title="langgraph.json"
+    {
+      "node_version": "20",
+      "graphs": {
+        "agent": "./src/agent/index.ts:graph"
+      },
+      "ui": {
+        "agent": "./src/agent/ui.tsx"
+      }
+    }
+    ```
 
 The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
 

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

@@ -140,6 +140,22 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
 
 Your server should extract and validate this token before processing requests.
 
+## Disable webhooks
+
+As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
+
+```json
+{
+  "http": {
+    "disable_webhooks": true
+  }
+}
+```
+
+This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
+
+For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
+
 ## Test webhooks
 
 You can test your webhook using online services like:

docs/docs/cloud/reference/cli.md

@@ -409,8 +409,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                       | Default                   | Description                                                                                                             |
     | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
-| `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
+    | `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
+    | `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
     | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
     | `--watch`                    |                           | Restart on file changes                                                                                                 |
     | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
@@ -438,8 +438,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                                                                 | Default                   | Description                                                                                                             |
     | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
-| <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
-| <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
+    | <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
+    | <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
     | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
     | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
     | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |

docs/docs/cloud/reference/env_var.md

@@ -28,6 +28,9 @@ Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_
 
 If `DD_API_KEY` is specified, the application process is wrapped in the [`ddtrace-run` command](https://ddtrace.readthedocs.io/en/stable/installation_quickstart.html). Other `DD_*` environment variables (e.g. `DD_SITE`, `DD_ENV`, `DD_SERVICE`, `DD_TRACE_ENABLED`) are typically needed to properly configure the tracing instrumentation. See [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) for more details.
 
+!!! note 
+    Enabling `DD_API_KEY` (and thus `ddtrace-run`) can override or interfere with other auto-instrumentation solutions (such as OpenTelemetry) that you may have instrumented into your application code.
+
 ## `LANGCHAIN_TRACING_SAMPLING_RATE`
 
 Sampling rate for traces sent to LangSmith. Valid values: Any float between `0` and `1`.

docs/docs/cloud/reference/langgraph_server_changelog.md

@@ -4,6 +4,33 @@
 
 ---
 
+## v0.2.96 (2025-07-17)
+- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
+
+## v0.2.95 (2025-07-17)
+- Avoided setting the future if it is already done to prevent redundant operations.
+- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
+
+## v0.2.94 (2025-07-16)
+- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
+- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
+
+## v0.2.93 (2025-07-16)
+- Removed the GIN index for run metadata to improve performance.
+
+## v0.2.92 (2025-07-16)
+- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
+
+## v0.2.91 (2025-07-16)
+- Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.
+
+## v0.2.90 (2025-07-16)
+- Improve checkpoint writes via node-local background queueing.
+
+
+## v0.2.89 (2025-07-15)
+- Decoupled checkpoint writing from thread/run state by removing foreign keys and updated logger to prevent timeout-related failures.
+
 ## v0.2.88 (2025-07-14)
 - Removed the foreign key constraint for `thread` in the `run` table to simplify database schema.
 

docs/docs/concepts/deployment_options.md

@@ -10,7 +10,7 @@ search:
 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.
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 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
 

docs/docs/concepts/low_level.md

@@ -45,7 +45,7 @@ The first thing you do when you define a graph is define the `State` of the grap
 
 ### Schema
 
-The main documented way to specify the schema of a graph is by using `TypedDict`. However, we also support [using a Pydantic BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation.
+The main documented way to specify the schema of a graph is by using a [`TypedDict`](https://docs.python.org/3/library/typing.html#typing.TypedDict). If you want to provide default values in your state, use a [`dataclass`](https://docs.python.org/3/library/dataclasses.html). We also support using a Pydantic [BaseModel](../how-tos/graph-api.md#use-pydantic-models-for-graph-state) as your graph state if you want recursive data validation (though note that pydantic is less performant than a `TypedDict` or `dataclass`).
 
 By default, the graph will have the same input and output schemas. If you want to change this, you can also specify explicit input and output schemas directly. This is useful when you have a lot of keys, and some are explicitly for input and others for output. See the [guide here](../how-tos/graph-api.md#define-input-and-output-schemas) for how to use.
 

docs/docs/concepts/tracing.md

@@ -0,0 +1,17 @@
+# Tracing
+
+Traces are a series of steps that your application takes to go from input to output. Each of these individual steps is represented by a run. You can use [LangSmith](https://smith.langchain.com/) to visualize these execution steps. To use it, [enable tracing for your application](../how-tos/enable-tracing.md). This enables you to do the following:
+
+- [Debug a locally running application](../cloud/how-tos/clone_traces_studio.md).
+- [Evaluate the application performance](../agents/evals.md).
+- [Monitor the application](https://docs.smith.langchain.com/observability/how_to_guides/dashboards).
+
+To get started, sign up for a free account at [LangSmith](https://smith.langchain.com/).
+
+## Learn more
+
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
+- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
+- [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph)
+- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)
+

docs/docs/guides/index.md

@@ -2,6 +2,11 @@
 
 The pages in this section provide a conceptual overview and how-tos for the following topics:
 
+## Agent development
+
+- [Overview](../agents/overview.md): Use prebuilt components to build an agent.
+- [Run an agent](../agents/run_agents.md): Run an agent by providing input, interpreting output, enabling streaming, and controlling execution limits.
+
 ## LangGraph APIs
 
 - [Graph API](../concepts/low_level.md): Use the Graph API to define workflows using a graph paradigm.

docs/docs/how-tos/autogen-integration.md

@@ -290,7 +290,7 @@ my-autogen-agent/
 
     ```
     langgraph>=0.1.0
-    pyautogen>=0.2.0
+    ag2>=0.2.0
     langchain-core>=0.1.0
     langchain-openai>=0.0.5
     ```

docs/docs/how-tos/enable-tracing.md

@@ -0,0 +1,16 @@
+# Enable tracing for your application
+
+To enable [tracing](../concepts/tracing.md) for your application, set the following environment variables:
+
+```python
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=<your-api-key>
+```
+
+For more information, see [Trace with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/trace_with_langgraph).
+
+## Learn more
+
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md)
+- [LangSmith Observability quickstart](https://docs.smith.langchain.com/observability)
+- [Tracing conceptual guide](https://docs.smith.langchain.com/observability/concepts#traces)

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

@@ -328,14 +328,15 @@ Output of graph invocation: {'a': 'set by node_3'}
 
 A [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs.md#langgraph.graph.StateGraph) accepts a `state_schema` argument on initialization that specifies the "shape" of the state that the nodes in the graph can access and update.
 
-In our examples, we typically use a python-native `TypedDict` for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).
+In our examples, we typically use a python-native `TypedDict` or [`dataclass`](https://docs.python.org/3/library/dataclasses.html) for `state_schema`, but `state_schema` can be any [type](https://docs.python.org/3/library/stdtypes.html#type-objects).
 
-Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/). can be used for `state_schema` to add run time validation on **inputs**.
+Here, we'll see how a [Pydantic BaseModel](https://docs.pydantic.dev/latest/api/base_model/) can be used for `state_schema` to add run-time validation on **inputs**.
 
 !!! note "Known Limitations"
     - Currently, the output of the graph will **NOT** be an instance of a pydantic model.
     - Run-time validation only occurs on inputs into nodes, not on the outputs.
     - The validation error trace from pydantic does not show which node the error arises in.
+    - Pydantic's recursive validation can be slow. For performance-sensitive applications, you may want to consider using a `dataclass` instead.
 
 ```python
 from langgraph.graph import StateGraph, START, END
@@ -1151,12 +1152,13 @@ LangGraph supports map-reduce and other advanced branching patterns using the Se
 ```python
 from langgraph.graph import StateGraph, START, END
 from langgraph.types import Send
-from typing_extensions import TypedDict
+from typing_extensions import TypedDict, Annotated
+import operator
 
 class OverallState(TypedDict):
     topic: str
     subjects: list[str]
-    jokes: list[str]
+    jokes: Annotated[list[str], operator.add]
     best_selected_joke: str
 
 def generate_topics(state: OverallState):
@@ -1565,9 +1567,9 @@ class State(TypedDict):
 
 def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
     print("Called A")
-    value = random.choice(["a", "b"])
+    value = random.choice(["b", "c"])
     # this is a replacement for a conditional edge function
-    if value == "a":
+    if value == "b":
         goto = "node_b"
     else:
         goto = "node_c"

docs/docs/tutorials/tech-support-bot.md

@@ -1,465 +0,0 @@
-# Tech support bot with custom workflows
-
-In this tutorial, you'll build a sophisticated tech support bot using LangGraph that demonstrates how to create custom workflows with conditional routing, loops, and human escalation. This bot will guide users through a structured support process, automatically routing them based on their responses and issue type.
-
-!!! note "About escalation in this tutorial"
-
-    This tutorial demonstrates **workflow-based escalation** where the bot completes its workflow and indicates that human support is needed. This is different from LangGraph's **human-in-the-loop** functionality (using `interrupt`) which pauses execution and waits for human input. For human-in-the-loop examples, see the [human-in-the-loop tutorial](../get-started/4-human-in-the-loop.md).
-
-## What you'll learn
-
-By the end of this tutorial, you'll understand how to:
-
-- Create **conditional routing** that adapts based on user responses
-- Implement **loops** for iterative troubleshooting
-- Handle **human escalation** at multiple decision points
-- Use **state management** to track complex multi-step conversations
-- Build a complete customer service workflow
-
-## Prerequisites
-
-Before you start this tutorial, ensure you have access to a LLM that supports tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys), [Anthropic](https://console.anthropic.com/settings/keys), or [Google Gemini](https://ai.google.dev/gemini-api/docs/api-key).
-
-## The workflow
-
-Our tech support bot follows a 4-step decision tree:
-
-1. **Warranty Check** - Is the device under warranty?
-2. **Issue Classification** - Hardware or software issue?
-3. **Basic Troubleshooting** - Have they tried restarting/updating?
-4. **Solution Testing** - Did the suggested solution work?
-
-```mermaid
-flowchart TD
-    Start([Start]) --> Step1{Is device under warranty?}
-    
-    Step1 -->|Yes| Step2{What type of issue?}
-    Step1 -->|No| RepairChoice{Troubleshoot or speak to human?}
-    
-    RepairChoice -->|Human| Escalate1[🧑 Escalate to Human]
-    RepairChoice -->|Troubleshoot| Step2
-    
-    Step2 -->|Hardware| Escalate2[🧑 Escalate to Human]
-    Step2 -->|Software| Step3{Tried restarting/updating?}
-    
-    Step3 -->|No| Suggest[Suggest restart/update]
-    Step3 -->|Yes| Step4{Try solution - Did it work?}
-    
-    Suggest --> Step3
-    
-    Step4 -->|Yes| Success[✅ Issue Resolved]
-    Step4 -->|No| Escalate3[🧑 Escalate to Human]
-    
-    classDef stepNode fill:#e1f5fe,stroke:#0277bd,stroke-width:2px
-    classDef escalateNode fill:#ffebee,stroke:#d32f2f,stroke-width:2px
-    classDef successNode fill:#e8f5e8,stroke:#388e3c,stroke-width:2px
-    classDef loopNode fill:#fff3e0,stroke:#f57c00,stroke-width:2px
-    
-    class Step1,Step2,Step3,Step4,RepairChoice stepNode
-    class Escalate1,Escalate2,Escalate3 escalateNode
-    class Success successNode
-    class Suggest loopNode
-```
-
-## 1. Install packages
-
-Install the required packages:
-
-```bash
-pip install -U langgraph langsmith langchain-anthropic
-```
-
-!!! tip
-
-    Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
-
-## 2. Define the state
-
-First, define the state structure that will track the conversation and workflow progress:
-
-```python
-from dataclasses import dataclass
-from typing import List, Optional, Literal
-
-from langchain_core.messages import BaseMessage
-
-# Define all possible workflow steps
-WorkflowStep = Literal[
-    "check_warranty",
-    "ask_repair_or_continue", 
-    "ask_issue_type",
-    "check_troubleshooting",
-    "suggest_troubleshooting",
-    "offer_solution",
-    "success",
-    "escalate"
-]
-
-@dataclass
-class State:
-    messages: List[BaseMessage]
-    is_last_step: bool = False
-    workflow_step: WorkflowStep = "check_warranty"
-
-    # State tracking for our 4-step workflow
-    warranty_status: Optional[Literal["in", "out"]] = None
-    wants_human_help: Optional[bool] = None  # for out-of-warranty users
-    issue_type: Optional[Literal["hardware", "software"]] = None
-    tried_basic_steps: Optional[bool] = None
-    solution_successful: Optional[bool] = None
-```
-
-!!! tip "Concept"
-
-    The `State` class tracks both the conversation messages and the workflow progress. Each field represents a decision point in our support process, allowing the bot to remember where the user is in the troubleshooting flow.
-
-## 3. Create tools for state management
-
-Create tools that the LLM can use to update the workflow state based on user responses:
-
-```python
-from typing import Literal, Annotated
-
-from langchain_core.messages import ToolMessage
-from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.types import Command
-
-@tool
-def set_warranty_status(
-        value: Literal["in", "out"],
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Set whether device is under warranty"""
-    # Determine next step based on warranty status
-    next_step: WorkflowStep = "ask_repair_or_continue" if value == "out" else "ask_issue_type"
-    
-    return Command(update={
-        "warranty_status": value,
-        "workflow_step": next_step,
-        "messages": [ToolMessage(content=f"Warranty status set to '{value}'",
-                                 tool_call_id=tool_call_id)]
-    })
-
-@tool
-def set_wants_human_help(
-        value: Literal[True, False],
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Set whether user wants human help for out-of-warranty device"""
-    # If they want human help, escalate; otherwise continue to issue classification
-    next_step: WorkflowStep = "escalate" if value else "ask_issue_type"
-    
-    return Command(update={
-        "wants_human_help": value,
-        "workflow_step": next_step,
-        "messages": [
-            ToolMessage(content=f"Wants human help: {value}", tool_call_id=tool_call_id)]
-    })
-
-@tool
-def set_issue_type(
-        value: Literal["hardware", "software"],
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Classify the issue as hardware or software related"""
-    # Hardware issues escalate immediately; software issues go to troubleshooting
-    next_step: WorkflowStep = "escalate" if value == "hardware" else "check_troubleshooting"
-    
-    return Command(update={
-        "issue_type": value,
-        "workflow_step": next_step,
-        "messages": [
-            ToolMessage(content=f"Issue type set to '{value}'", tool_call_id=tool_call_id)]
-    })
-
-@tool
-def set_tried_basic_steps(
-        value: Literal[True, False],
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Record whether user has tried basic troubleshooting"""
-    # If they haven't tried basic steps, suggest them; otherwise offer solution
-    next_step: WorkflowStep = "suggest_troubleshooting" if not value else "offer_solution"
-    
-    return Command(update={
-        "tried_basic_steps": value,
-        "workflow_step": next_step,
-        "messages": [
-            ToolMessage(content=f"Tried basic steps: {value}", tool_call_id=tool_call_id)]
-    })
-
-@tool
-def confirm_troubleshooting_done(
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Confirm user has completed suggested troubleshooting steps"""
-    next_step: WorkflowStep = "check_troubleshooting"
-    
-    return Command(update={
-        "tried_basic_steps": True,
-        "workflow_step": next_step,
-        "messages": [
-            ToolMessage(content="Troubleshooting steps completed", tool_call_id=tool_call_id)]
-    })
-
-@tool
-def set_solution_successful(
-        value: Literal[True, False],
-        tool_call_id: Annotated[str, InjectedToolCallId]
-) -> Command:
-    """Record whether the suggested solution worked"""
-    # If solution worked, success; otherwise escalate
-    next_step: WorkflowStep = "success" if value else "escalate"
-    
-    return Command(update={
-        "solution_successful": value,
-        "workflow_step": next_step,
-        "messages": [
-            ToolMessage(content=f"Solution successful: {value}", tool_call_id=tool_call_id)]
-    })
-
-ALL_TOOLS = [
-    set_warranty_status,
-    set_wants_human_help,
-    set_issue_type,
-    set_tried_basic_steps,
-    confirm_troubleshooting_done,
-    set_solution_successful,
-]
-```
-
-These tools now handle both state updates and workflow transitions. Each tool determines the next step in the workflow based on the user's response, eliminating the need for complex routing logic.
-
-## 4. Set up the chat model
-
-{% include-markdown "../../snippets/chat_model_tabs.md" %}
-
-## 5. Create step-specific prompts
-
-Each workflow step needs a specific prompt to guide the LLM's behavior:
-
-```python
-from typing import Dict, List
-
-TOOL_MAP: Dict[WorkflowStep, List] = {
-    "check_warranty": [set_warranty_status],
-    "ask_repair_or_continue": [set_wants_human_help],
-    "ask_issue_type": [set_issue_type],
-    "check_troubleshooting": [set_tried_basic_steps],
-    "suggest_troubleshooting": [confirm_troubleshooting_done],
-    "offer_solution": [set_solution_successful],
-}
-
-def get_prompt_for_step(step: WorkflowStep) -> str:
-    """Get the appropriate prompt for each workflow step"""
-    prompts: Dict[WorkflowStep, str] = {
-        "check_warranty": """
-            Ask the user whether their device is under warranty. 
-            Use the set_warranty_status tool to record their response as 'in' or 'out'.
-            """,
-
-        "ask_repair_or_continue": """
-            The device is out of warranty. Ask if they'd like to:
-            1. Continue troubleshooting themselves, or 
-            2. Speak to a human about repair options
-            Use the set_wants_human_help tool to record their choice.
-            """,
-
-        "ask_issue_type": """
-            Ask what issue they are experiencing with their device. 
-            Based on their response, classify it as 'hardware' (physical problems, broken parts) 
-            or 'software' (app crashes, performance issues, etc.).
-            Use the set_issue_type tool to record the classification.
-            """,
-
-        "check_troubleshooting": """
-            Ask if they have already tried basic troubleshooting steps like:
-            - Restarting the device
-            - Updating the software/app
-            Use the set_tried_basic_steps tool to record their response.
-            """,
-
-        "suggest_troubleshooting": """
-            Suggest they try restarting their device and updating the software/app.
-            Ask them to try these steps and confirm once they're done.
-            Use the confirm_troubleshooting_done tool once they confirm they've tried.
-            """,
-
-        "offer_solution": """
-            Suggest they reset the app settings or clear the app cache.
-            Ask them to try this solution and confirm if it resolved the issue.
-            Use the set_solution_successful tool to record whether it worked.
-            """,
-    }
-    return prompts.get(step, "Continue helping the user with their issue.")
-```
-
-## 6. Create the model node
-
-The model node handles LLM interactions with the appropriate tools for each step:
-
-```python
-from typing import Dict, Literal
-
-from langchain.chat_models import init_chat_model
-from langchain_core.messages import AIMessage
-
-# Initialize the chat model
-llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
-
-async def call_model(state: State) -> Dict:
-    """Call the LLM with appropriate tools for the current step"""
-    # Handle terminal states
-    if state.workflow_step == "success":
-        return {
-            "messages": [AIMessage(
-                content="Great! I'm glad we could resolve your issue. Is there anything else I can help you with today?")],
-            "is_last_step": True
-        }
-    elif state.workflow_step == "escalate":
-        return {
-            "messages": [AIMessage(
-                content="I'm going to connect you with one of our human support specialists who can better assist you with this issue. Please hold on while I transfer you.")],
-            "is_last_step": True
-        }
-    
-    # For regular workflow steps, get the appropriate prompt and tools
-    prompt = get_prompt_for_step(state.workflow_step)
-    tools = TOOL_MAP.get(state.workflow_step, [])
-    model = llm.bind_tools(tools)
-
-    response = await model.ainvoke(
-        [{"role": "system", "content": prompt}, *state.messages]
-    )
-
-    return {"messages": [response]}
-
-def should_continue(state: State) -> Literal["tools", "call_model", "__end__"]:
-    """Determine whether to call tools or continue with the model"""
-    # If we've reached a terminal state, stop
-    if state.is_last_step:
-        return "__end__"
-    
-    # If the last message has tool calls, execute them
-    last_msg = state.messages[-1]
-    if isinstance(last_msg, AIMessage) and last_msg.tool_calls:
-        return "tools"
-    
-    # Otherwise, continue with the model
-    return "call_model"
-```
-
-!!! tip "Concept"
-
-    This simplified approach moves all the routing logic into the tools themselves. Each tool determines the next workflow step, eliminating the need for complex conditional routing functions. The `should_continue` function simply decides whether to execute tools or continue with the model.
-
-## 8. Build and compile the graph
-
-Now assemble all the components into a complete workflow:
-
-```python
-from langgraph.graph import StateGraph, START, END
-from langgraph.prebuilt import ToolNode
-
-builder = StateGraph(State)
-
-# Add nodes
-builder.add_node("call_model", call_model)
-builder.add_node("tools", ToolNode(ALL_TOOLS))
-
-# Set entry point
-builder.add_edge(START, "call_model")
-
-# Add conditional edges
-builder.add_conditional_edges(
-    "call_model",
-    should_continue,
-    ["tools", "call_model", END]
-)
-
-# Tools flow back to model
-builder.add_edge("tools", "call_model")
-
-# Compile the graph
-graph = builder.compile()
-```
-
-## 9. Test the workflow
-
-Run the tech support bot to see how it handles different scenarios:
-
-```python
-import asyncio
-from langchain_core.messages import HumanMessage, AIMessage
-
-async def run_example():
-    """Run an example conversation"""
-    print("\n🔁 Running Tech Support Workflow...\n")
-
-    initial_state = State(
-        messages=[
-            HumanMessage(content="Hi, my app is crashing a lot and I can't use it.")],
-        workflow_step="check_warranty"
-    )
-
-    final_state = await graph.ainvoke(initial_state)
-
-    print("\n✅ Conversation Complete!")
-    print(f"Final workflow step: {final_state.workflow_step}")
-    print(f"Warranty status: {final_state.warranty_status}")
-    print(f"Issue type: {final_state.issue_type}")
-    print(f"Tried basic steps: {final_state.tried_basic_steps}")
-    print(f"Solution successful: {final_state.solution_successful}")
-
-    print("\n💬 Final messages:")
-    for msg in final_state.messages[-3:]:  # Show last 3 messages
-        if isinstance(msg, HumanMessage):
-            print(f"User: {msg.content}")
-        elif isinstance(msg, AIMessage):
-            print(f"Bot: {msg.content}")
-
-if __name__ == "__main__":
-    asyncio.run(run_example())
-```
-
-!!! tip
-
-    You can exit the conversation at any time by typing `quit`, `exit`, or `q`.
-
-## Key concepts demonstrated
-
-This tech support bot showcases several important LangGraph concepts:
-
-### 1. **Conditional routing**
-The `route_workflow` function implements complex decision logic based on user responses:
-- Warranty status determines the initial path
-- Issue type (hardware vs software) triggers different responses  
-- Solution success determines the final outcome
-
-### 2. **Looping behavior**
-Step 3 creates a loop where users who haven't tried basic troubleshooting are guided through it:
-```
-check_troubleshooting → suggest_troubleshooting → check_troubleshooting
-```
-
-### 3. **Human escalation**
-Multiple escalation points ensure complex issues reach human agents:
-- Out-of-warranty users can choose human help
-- Hardware issues automatically escalate
-- Failed solutions trigger escalation
-
-### 4. **State management**
-The workflow tracks user progress through structured state variables, enabling complex multi-turn conversations.
-
-## Testing different scenarios
-
-Try these conversation paths to see how the bot handles various situations:
-
-1. **In-warranty software issue** → Full troubleshooting flow
-2. **Out-of-warranty hardware issue** → Immediate escalation
-3. **Software issue with successful solution** → Success completion
-4. **Software issue with failed solution** → Escalation
-
-## Next steps
-
-This implementation demonstrates how LangGraph can handle real-world customer service scenarios with sophisticated routing, looping, and escalation logic. You can extend this pattern to build more complex workflows for various business processes.

docs/mkdocs.yml

@@ -103,14 +103,15 @@ nav:
           -  5. Customize state: tutorials/get-started/5-customize-state.md
           -  6. Time travel: tutorials/get-started/6-time-travel.md
       - Run a local server: tutorials/langgraph-platform/local-server.md
-    - Agent development:
+    - General concepts:
       - Workflows & agents: tutorials/workflows.md
-      - Prebuilt components: agents/overview.md
-      - Run an agent: agents/run_agents.md
       - Agent architectures: concepts/agentic_concepts.md
 
   - Guides:
     - guides/index.md
+    - Agent development:
+      - Overview: agents/overview.md
+      - Run an agent: agents/run_agents.md
     - LangGraph APIs:
       - Graph API:
           - Overview: concepts/low_level.md
@@ -157,8 +158,10 @@ nav:
           - Overview: concepts/mcp.md
           - Use MCP: agents/mcp.md
           - Server API: concepts/server-mcp.md
-      - Evaluation:
-          - Basic implementation: agents/evals.md
+      - Tracing:
+          - Overview: concepts/tracing.md
+          - Enable tracing: how-tos/enable-tracing.md
+          - Evaluate performance: agents/evals.md
     - Platform-only capabilities:
       - LangGraph Platform:
           - Overview: concepts/langgraph_platform.md
@@ -270,7 +273,6 @@ nav:
     - examples/index.md
     - Template applications: concepts/template_applications.md  # TODO: make tutorial
     - Agentic RAG: tutorials/rag/langgraph_agentic_rag.md
-    - tutorials/tech-support-bot.md
     - Agent Supervisor: tutorials/multi_agent/agent_supervisor.md
     - SQL agent: tutorials/sql/sql-agent.md
     - Prebuilt chat UI: agents/ui.md

docs/pyproject.toml

@@ -112,4 +112,6 @@ extend-include = ["*.ipynb"]
 [tool.codespell]
 # https://mypy.readthedocs.io/en/stable/config_file.html
 # comma-separated list
-ignore-words-list = "infor"
+ignore-words-list = "infor,thead,stdio,nd,jupyter,lets,lite,uis,deque"
+# Exclude generated files and directories
+skip = "*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map"

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

@@ -289,6 +289,7 @@ class PostgresSaver(BasePostgresSaver):
         )
 
         copy = checkpoint.copy()
+        copy["channel_values"] = copy["channel_values"].copy()
         next_config = {
             "configurable": {
                 "thread_id": thread_id,
@@ -297,16 +298,28 @@ class PostgresSaver(BasePostgresSaver):
             }
         }
 
+        # inline primitive values in checkpoint table
+        # others are stored in blobs table
+        blob_values = {}
+        for k, v in checkpoint["channel_values"].items():
+            if v is None or isinstance(v, (str, int, float, bool)):
+                pass
+            else:
+                blob_values[k] = copy["channel_values"].pop(k)
+
         with self._cursor(pipeline=True) as cur:
-            cur.executemany(
-                self.UPSERT_CHECKPOINT_BLOBS_SQL,
-                self._dump_blobs(
-                    thread_id,
-                    checkpoint_ns,
-                    copy.pop("channel_values"),  # type: ignore[misc]
-                    new_versions,
-                ),
-            )
+            if blob_versions := {
+                k: v for k, v in new_versions.items() if k in blob_values
+            }:
+                cur.executemany(
+                    self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                    self._dump_blobs(
+                        thread_id,
+                        checkpoint_ns,
+                        blob_values,
+                        blob_versions,
+                    ),
+                )
             cur.execute(
                 self.UPSERT_CHECKPOINTS_SQL,
                 (
@@ -439,7 +452,10 @@ class PostgresSaver(BasePostgresSaver):
             },
             {
                 **value["checkpoint"],
-                "channel_values": self._load_blobs(value["channel_values"]),
+                "channel_values": {
+                    **value["checkpoint"].get("channel_values"),
+                    **self._load_blobs(value["channel_values"]),
+                },
             },
             value["metadata"],
             (

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

@@ -245,6 +245,7 @@ class AsyncPostgresSaver(BasePostgresSaver):
         )
 
         copy = checkpoint.copy()
+        copy["channel_values"] = copy["channel_values"].copy()
         next_config = {
             "configurable": {
                 "thread_id": thread_id,
@@ -253,17 +254,29 @@ class AsyncPostgresSaver(BasePostgresSaver):
             }
         }
 
+        # inline primitive values in checkpoint table
+        # others are stored in blobs table
+        blob_values = {}
+        for k, v in checkpoint["channel_values"].items():
+            if v is None or isinstance(v, (str, int, float, bool)):
+                pass
+            else:
+                blob_values[k] = copy["channel_values"].pop(k)
+
         async with self._cursor(pipeline=True) as cur:
-            await cur.executemany(
-                self.UPSERT_CHECKPOINT_BLOBS_SQL,
-                await asyncio.to_thread(
-                    self._dump_blobs,
-                    thread_id,
-                    checkpoint_ns,
-                    copy.pop("channel_values"),  # type: ignore[misc]
-                    new_versions,
-                ),
-            )
+            if blob_versions := {
+                k: v for k, v in new_versions.items() if k in blob_values
+            }:
+                await cur.executemany(
+                    self.UPSERT_CHECKPOINT_BLOBS_SQL,
+                    await asyncio.to_thread(
+                        self._dump_blobs,
+                        thread_id,
+                        checkpoint_ns,
+                        blob_values,
+                        blob_versions,
+                    ),
+                )
             await cur.execute(
                 self.UPSERT_CHECKPOINTS_SQL,
                 (
@@ -397,7 +410,10 @@ class AsyncPostgresSaver(BasePostgresSaver):
             },
             {
                 **value["checkpoint"],
-                "channel_values": self._load_blobs(value["channel_values"]),
+                "channel_values": {
+                    **value["checkpoint"].get("channel_values"),
+                    **self._load_blobs(value["channel_values"]),
+                },
             },
             value["metadata"],
             (

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.22"
+version = "2.0.23"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.9"

libs/checkpoint-postgres/uv.lock

@@ -304,7 +304,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -334,7 +334,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.22"
+version = "2.0.23"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },

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

@@ -29,7 +29,7 @@ _AIO_ERROR_MSG = (
     "from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver\n"
     "Note: AsyncSqliteSaver requires the aiosqlite package to use.\n"
     "Install with:\n`pip install aiosqlite`\n"
-    "See https://langchain-ai.github.io/langgraph/reference/checkpoints/asyncsqlitesaver"
+    "See https://langchain-ai.github.io/langgraph/reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver"
     "for more information."
 )
 

libs/checkpoint-sqlite/uv.lock

@@ -316,7 +316,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },

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

@@ -343,10 +343,14 @@ async def _run(
 
                     # set the results of each operation
                     for fut, result in zip(futs, results):
-                        fut.set_result(result)
+                        # guard against future being done (e.g. cancelled)
+                        if not fut.done():
+                            fut.set_result(result)
                 except Exception as e:
                     for fut in futs:
-                        fut.set_exception(e)
+                        # guard against future being done (e.g. cancelled)
+                        if not fut.done():
+                            fut.set_exception(e)
             finally:
                 # remove strong ref to store
                 del s

libs/checkpoint/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 requires-python = ">=3.9"

libs/checkpoint/tests/test_store.py

@@ -155,6 +155,43 @@ async def test_async_batch_store(mocker: MockerFixture) -> None:
     ]
 
 
+async def test_async_batch_store_handles_cancellation() -> None:
+    class MockStore(AsyncBatchedBaseStore):
+        def batch(self, ops: Iterable[Op]) -> list[Result]:
+            raise NotImplementedError
+
+        async def abatch(self, ops: Iterable[Op]) -> list[Result]:
+            assert all(isinstance(op, GetOp) for op in ops)
+            return [
+                Item(
+                    value={},
+                    key=getattr(op, "key", ""),
+                    namespace=getattr(op, "namespace", ()),
+                    created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+                    updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+                )
+                for op in ops
+            ]
+
+    store = MockStore()
+
+    # Simulate cancellation
+    task = asyncio.create_task(store.aget(namespace=("a",), key="b"))
+    await asyncio.sleep(0)
+    task.cancel()
+    await asyncio.sleep(0)
+
+    # Cancelling individual queries against the store should not break the store
+    result = await store.aget(namespace=("c",), key="d")
+    assert result == Item(
+        value={},
+        key="d",
+        namespace=("c",),
+        created_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+        updated_at=datetime(2024, 9, 24, 17, 29, 10, 128397),
+    )
+
+
 def test_list_namespaces_basic() -> None:
     store = InMemoryStore()
 

libs/checkpoint/uv.lock

@@ -323,7 +323,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },

libs/cli/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-cli"
-version = "0.3.4"
+version = "0.3.5"
 description = "CLI for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"
@@ -19,7 +19,7 @@ dependencies = [
 [project.optional-dependencies]
 inmem = [
     "langgraph-api>=0.2.67,<0.3.0 ; python_version >= '3.11'",
-    "langgraph-runtime-inmem>=0.3.0,<0.4.0 ; python_version >= '3.11'",
+    "langgraph-runtime-inmem>=0.6.0 ; python_version >= '3.11'",
     "python-dotenv>=0.8.0",
 ]
 

libs/cli/uv.lock

@@ -30,25 +30,34 @@ wheels = [
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 ]
 
+[[package]]
+name = "backports-asyncio-runner"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" },
+]
+
 [[package]]
 name = "blockbuster"
-version = "1.5.24"
+version = "1.5.25"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "forbiddenfruit", marker = "python_full_version >= '3.11' and implementation_name == 'cpython'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/35/c8/1e456a043179f2aef10bcaafea79f6d06c0ac45cc994767a54f680509f3b/blockbuster-1.5.24.tar.gz", hash = "sha256:97645775761a5d425666ec0bc99629b65c7eccdc2f770d2439850682567af4ec", size = 51245, upload-time = "2025-03-18T10:12:06.398Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7f/bc/57c49465decaeeedd58ce2d970b4cdfd93a74ba9993abff2dc498a31c283/blockbuster-1.5.25.tar.gz", hash = "sha256:b72f1d2aefdeecd2a820ddf1e1c8593bf00b96e9fdc4cd2199ebafd06f7cb8f0", size = 36058, upload-time = "2025-07-14T16:00:20.766Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a7/c8/57a4c80e5abec29fa9406307a5277527f21210bfc6c2c61c3d8ded36c09b/blockbuster-1.5.24-py3-none-any.whl", hash = "sha256:e703497b55bc72af09d60d1cd746c2f3ba7ce0c446fa256be6ccda5e7d403520", size = 13214, upload-time = "2025-03-18T10:12:04.802Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/01/dccc277c014f171f61a6047bb22c684e16c7f2db6bb5c8cce1feaf41ec55/blockbuster-1.5.25-py3-none-any.whl", hash = "sha256:cb06229762273e0f5f3accdaed3d2c5a3b61b055e38843de202311ede21bb0f5", size = 13196, upload-time = "2025-07-14T16:00:19.396Z" },
 ]
 
 [[package]]
 name = "certifi"
-version = "2025.7.9"
+version = "2025.7.14"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/de/8a/c729b6b60c66a38f590c4e774decc4b2ec7b0576be8f1aa984a53ffa812a/certifi-2025.7.9.tar.gz", hash = "sha256:c1d2ec05395148ee10cf672ffc28cd37ea0ab0d99f9cc74c43e588cbd111b079", size = 160386, upload-time = "2025-07-09T02:13:58.874Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b3/76/52c535bcebe74590f296d6c77c86dabf761c41980e1347a2422e4aa2ae41/certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995", size = 163981, upload-time = "2025-07-14T03:29:28.449Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
 ]
 
 [[package]]
@@ -444,7 +453,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.68"
+version = "0.3.69"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch", marker = "python_full_version >= '3.11'" },
@@ -455,14 +464,14 @@ dependencies = [
     { name = "tenacity", marker = "python_full_version >= '3.11'" },
     { name = "typing-extensions", marker = "python_full_version >= '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/23/20/f5b18a17bfbe3416177e702ab2fd230b7d168abb17be31fb48f43f0bb772/langchain_core-0.3.68.tar.gz", hash = "sha256:312e1932ac9aa2eaf111b70fdc171776fa571d1a86c1f873dcac88a094b19c6f", size = 563041, upload-time = "2025-07-03T17:02:28.704Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/26/c4770d3933237cde2918d502e3b0a8b6ce100b296840b632658f3e59b341/langchain_core-0.3.69.tar.gz", hash = "sha256:c132961117cc7f0227a4c58dd3e209674a6dd5b7e74abc61a0df93b0d736e283", size = 563824, upload-time = "2025-07-15T21:19:56.626Z" }
 wheels = [
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 ]
 
 [[package]]

libs/langgraph/README.md

@@ -73,7 +73,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
@@ -81,4 +81,4 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Acknowledgements
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

libs/langgraph/langgraph/graph/state.py

@@ -1236,17 +1236,18 @@ def _control_branch(value: Any) -> Sequence[tuple[str, Any]]:
     for command in commands:
         if command.graph == Command.PARENT:
             raise ParentCommand(command)
-        if isinstance(command.goto, Send):
-            rtn.append((TASKS, command.goto))
-        elif isinstance(command.goto, str):
-            rtn.append((CHANNEL_BRANCH_TO.format(command.goto), None))
-        else:
-            rtn.extend(
-                (TASKS, go)
-                if isinstance(go, Send)
-                else (CHANNEL_BRANCH_TO.format(go), None)
-                for go in command.goto
-            )
+
+        goto_targets = (
+            [command.goto] if isinstance(command.goto, (Send, str)) else command.goto
+        )
+
+        for go in goto_targets:
+            if isinstance(go, Send):
+                rtn.append((TASKS, go))
+            elif isinstance(go, str) and go != END:
+                # END is a special case, it's not actually a node in a practical sense
+                # but rather a special terminal node that we don't need to branch to
+                rtn.append((CHANNEL_BRANCH_TO.format(go), None))
     return rtn
 
 

libs/langgraph/pyproject.toml

@@ -106,3 +106,7 @@ packages = ["langgraph"]
 
 [tool.pytest.ini_options]
 addopts = "--full-trace --strict-markers --strict-config --durations=5 --snapshot-warn-unused"
+
+[tool.codespell]
+# Ignore words specific to the LangGraph library code
+ignore-words-list = "infor,thead,stdio,nd,jupyter,lets,lite,uis,deque,langgraph,langchain,pydantic,typing,async,await,coroutine,iterable,iterables,serializable,deserializable,checkpointer,checkpointing,stateful,statefulness,prebuilt,prebuilt,supervisor,supervisory,swarm,swarming,multiactor,multiactors,subgraph,subgraphs,workflow,workflows,streaming,streamable,streamed,streamer,streamers,streaming,streamable,streamed,streamer,streamers"

libs/langgraph/tests/test_large_cases.py

@@ -6955,3 +6955,30 @@ def test_weather_subgraph(
             },
         ),
     ]
+
+
+def test_subgraph_to_end_does_not_warn() -> None:
+    """Regression test for https://github.com/langchain-ai/langgraph/issues/5572."""
+
+    class State(TypedDict):
+        x: str
+
+    def update_x(state: State):
+        return Command(goto=END, update={"x": state["x"] + "!"})
+
+    # Subgraph
+    subgraph_builder = StateGraph(State)
+    subgraph_builder.add_node("update_x", update_x)
+    subgraph_builder.add_edge(START, "update_x")
+    subgraph_builder.add_edge("update_x", END)
+    subgraph = subgraph_builder.compile()
+
+    # Parent graph
+    builder = StateGraph(State)
+    builder.add_node("subgraph_node", subgraph)
+    builder.add_edge(START, "subgraph_node")
+    builder.add_edge("subgraph_node", END)
+    graph = builder.compile()
+
+    response = graph.invoke({"x": "hello"})
+    print(response)

libs/langgraph/uv.lock

@@ -1301,7 +1301,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -1331,7 +1331,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.22"
+version = "2.0.23"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -1463,7 +1463,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.74"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/prebuilt/Makefile

@@ -1,4 +1,4 @@
-.PHONY: all format lint test test_watch integration_tests spell_check spell_fix benchmark profile
+.PHONY: all format lint test test-fast test_watch integration_tests spell_check spell_fix benchmark profile
 
 # Default target executed when no arguments are given to make.
 all: help
@@ -15,14 +15,17 @@ stop-postgres:
 
 TEST ?= .
 
+test-fast:
+	LANGGRAPH_TEST_FAST=1 uv run pytest $(TEST)
+
 test:
-	make start-postgres && uv run pytest $(TEST); \
+	make start-postgres && LANGGRAPH_TEST_FAST=0 uv run pytest $(TEST); \
 	EXIT_CODE=$$?; \
 	make stop-postgres; \
 	exit $$EXIT_CODE
 
 test_watch:
-	make start-postgres && uv run ptw $(TEST); \
+	make start-postgres && LANGGRAPH_TEST_FAST=0 uv run ptw $(TEST); \
 	EXIT_CODE=$$?; \
 	make stop-postgres; \
 	exit $$EXIT_CODE
@@ -49,7 +52,7 @@ lint lint_diff lint_package lint_tests:
 
 format format_diff:
 	uv run ruff format $(PYTHON_FILES)
-	uv run ruff check --select I --fix $(PYTHON_FILES)
+	uv run ruff check --fix $(PYTHON_FILES)
 
 spell_check:
 	uv run codespell --toml pyproject.toml
@@ -74,5 +77,6 @@ help:
 	@echo '-- TESTS --'
 	@echo 'coverage                     - run unit tests and generate coverage report'
 	@echo 'test                         - run unit tests'
+	@echo 'test-fast                    - run unit tests with in-memory checkpointer only'
 	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
 	@echo 'test_watch                   - run unit tests in watch mode'

libs/prebuilt/langgraph/prebuilt/_internal.py

@@ -0,0 +1,26 @@
+from typing import Any, Literal, TypedDict
+
+from langchain_core.messages import ToolCall
+
+
+class ToolCallWithContext(TypedDict):
+    """ToolCall with additional context for graph state.
+
+    This is an internal data-structure meant to help the ToolNode accept
+    tools calls with additional context (e.g. state) when dispatched using the
+    `Send` API.
+
+    The Send API is used in create_react_agent to be able to distribute the tool
+    calls in parallel and support human-in-the-loop workflows where graph execution
+    may be paused for an indefinite time.
+    """
+
+    tool_call: ToolCall
+    __type: Literal["tool_call_with_context"]
+    """Type to parameterize the payload.
+    
+    Using "__" as a prefix to be defensive against potential name collisions with
+    regular user state.
+    """
+    state: Any
+    """The state is provided as additional context."""

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -39,6 +39,7 @@ from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.graph.state import CompiledStateGraph
 from langgraph.managed import IsLastStep, RemainingSteps
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
 from langgraph.types import Checkpointer, Send
@@ -650,11 +651,17 @@ def create_react_agent(
             elif version == "v2":
                 if post_model_hook is not None:
                     return "post_model_hook"
-                tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
-                    for call in last_message.tool_calls
+                return [
+                    Send(
+                        "tools",
+                        ToolCallWithContext(
+                            __type="tool_call_with_context",
+                            tool_call=tool_call,
+                            state=state,
+                        ),
+                    )
+                    for tool_call in last_message.tool_calls
                 ]
-                return [Send("tools", [tool_call]) for tool_call in tool_calls]
 
     # Define a new graph
     workflow = StateGraph(state_schema or AgentState, config_schema=config_schema)
@@ -733,11 +740,17 @@ def create_react_agent(
             ]
 
             if pending_tool_calls:
-                pending_tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
-                    for call in pending_tool_calls
+                return [
+                    Send(
+                        "tools",
+                        ToolCallWithContext(
+                            __type="tool_call_with_context",
+                            tool_call=tool_call,
+                            state=state,
+                        ),
+                    )
+                    for tool_call in pending_tool_calls
                 ]
-                return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
             elif isinstance(messages[-1], ToolMessage):
                 return entrypoint
             elif response_format is not None:

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -71,6 +71,7 @@ from pydantic import BaseModel
 from typing_extensions import Annotated, get_args, get_origin
 
 from langgraph.errors import GraphBubbleUp
+from langgraph.prebuilt._internal import ToolCallWithContext
 from langgraph.store.base import BaseStore
 from langgraph.types import Command, Send
 from langgraph.utils.runnable import RunnableCallable
@@ -358,7 +359,8 @@ class ToolNode(RunnableCallable):
         *,
         store: Optional[BaseStore],
     ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store)
+        tool_calls, input_type = self._parse_input(input)
+        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
         config_list = get_config_list(config, len(tool_calls))
         input_types = [input_type] * len(tool_calls)
         with get_executor_for_config(config) as executor:
@@ -379,7 +381,8 @@ class ToolNode(RunnableCallable):
         *,
         store: Optional[BaseStore],
     ) -> Any:
-        tool_calls, input_type = self._parse_input(input, store)
+        tool_calls, input_type = self._parse_input(input)
+        tool_calls = [self.inject_tool_args(call, input, store) for call in tool_calls]
         outputs = await asyncio.gather(
             *(self._arun_one(call, input_type, config) for call in tool_calls)
         )
@@ -436,18 +439,19 @@ class ToolNode(RunnableCallable):
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
     ) -> ToolMessage:
+        """Run a single tool call synchronously."""
         if invalid_tool_message := self._validate_tool_call(call):
             return invalid_tool_message
-
         try:
-            input = {**call, **{"type": "tool_call"}}
-            response = self.tools_by_name[call["name"]].invoke(input, config)
+            call_args = {**call, **{"type": "tool_call"}}
+            response = self.tools_by_name[call["name"]].invoke(call_args, config)
 
         # GraphInterrupt is a special exception that will always be raised.
         # It can be triggered in the following scenarios:
         # (1) a NodeInterrupt is raised inside a tool
         # (2) a NodeInterrupt is raised inside a graph node for a graph called as a tool
-        # (3) a GraphInterrupt is raised when a subgraph is interrupted inside a graph called as a tool
+        # (3) a GraphInterrupt is raised when a subgraph is interrupted inside a graph
+        #     called as a tool
         # (2 and 3 can happen in a "supervisor w/ tools" multi-agent architecture)
         except GraphBubbleUp as e:
             raise e
@@ -491,6 +495,7 @@ class ToolNode(RunnableCallable):
         input_type: Literal["list", "dict", "tool_calls"],
         config: RunnableConfig,
     ) -> ToolMessage:
+        """Run a single tool call asynchronously."""
         if invalid_tool_message := self._validate_tool_call(call):
             return invalid_tool_message
 
@@ -548,16 +553,25 @@ class ToolNode(RunnableCallable):
             dict[str, Any],
             BaseModel,
         ],
-        store: Optional[BaseStore],
     ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
+        input_type: Literal["list", "dict", "tool_calls"]
         if isinstance(input, list):
             if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
                 input_type = "tool_calls"
-                tool_calls = input
+                tool_calls = cast(list[ToolCall], input)
                 return tool_calls, input_type
             else:
                 input_type = "list"
                 messages = input
+        elif (
+            isinstance(input, dict) and input.get("__type") == "tool_call_with_context"
+        ):
+            # mypy will not be able to type narrow correctly since the signature
+            # for input contains dict[str, Any]. We'd need to type dict[str, Any]
+            # before we can apply correct typing.
+            input = cast(ToolCallWithContext, input)  # type: ignore[assignment]
+            input_type = "tool_calls"
+            return [input["tool_call"]], input_type
         elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
             input_type = "dict"
         elif messages := getattr(input, self.messages_key, []):
@@ -573,10 +587,7 @@ class ToolNode(RunnableCallable):
         except StopIteration:
             raise ValueError("No AIMessage found in input")
 
-        tool_calls = [
-            self.inject_tool_args(call, input, store)
-            for call in latest_ai_message.tool_calls
-        ]
+        tool_calls = [call for call in latest_ai_message.tool_calls]
         return tool_calls, input_type
 
     def _validate_tool_call(self, call: ToolCall) -> Optional[ToolMessage]:
@@ -618,15 +629,20 @@ class ToolNode(RunnableCallable):
                     required_fields_str = ", ".join(f for f in required_fields if f)
                     err_msg += f" State should contain fields {required_fields_str}."
                 raise ValueError(err_msg)
-        if isinstance(input, dict):
+
+        if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
+            state = input["state"]
+        else:
+            state = input
+
+        if isinstance(state, dict):
             tool_state_args = {
-                tool_arg: input[state_field] if state_field else input
+                tool_arg: state[state_field] if state_field else state
                 for tool_arg, state_field in state_args.items()
             }
-
         else:
             tool_state_args = {
-                tool_arg: getattr(input, state_field) if state_field else input
+                tool_arg: getattr(state, state_field) if state_field else state
                 for tool_arg, state_field in state_args.items()
             }
 

libs/prebuilt/tests/any_str.py

@@ -1,27 +1,5 @@
 import re
-from typing import Any, Sequence, Union
-
-from typing_extensions import Self
-
-
-class FloatBetween(float):
-    def __new__(cls, min_value: float, max_value: float) -> Self:
-        return super().__new__(cls, min_value)
-
-    def __init__(self, min_value: float, max_value: float) -> None:
-        super().__init__()
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __eq__(self, other: object) -> bool:
-        return (
-            isinstance(other, float)
-            and other >= self.min_value
-            and other <= self.max_value
-        )
-
-    def __hash__(self) -> int:
-        return hash((float(self), self.min_value, self.max_value))
+from typing import Union
 
 
 class AnyStr(str):
@@ -38,49 +16,3 @@ class AnyStr(str):
 
     def __hash__(self) -> int:
         return hash((str(self), self.prefix))
-
-
-class AnyDict(dict):
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, dict) or len(self) != len(other):
-            return False
-        for k, v in self.items():
-            if kk := next((kk for kk in other if kk == k), None):
-                if v == other[kk]:
-                    continue
-                else:
-                    return False
-        else:
-            return True
-
-
-class AnyVersion:
-    def __init__(self) -> None:
-        super().__init__()
-
-    def __eq__(self, other: object) -> bool:
-        return isinstance(other, (str, int, float))
-
-    def __hash__(self) -> int:
-        return hash(str(self))
-
-
-class UnsortedSequence:
-    def __init__(self, *values: Any) -> None:
-        self.seq = values
-
-    def __eq__(self, value: object) -> bool:
-        return (
-            isinstance(value, Sequence)
-            and len(self.seq) == len(value)
-            and all(a in value for a in self.seq)
-        )
-
-    def __hash__(self) -> int:
-        return hash(frozenset(self.seq))
-
-    def __repr__(self) -> str:
-        return repr(self.seq)

libs/prebuilt/tests/conftest.py

@@ -1,3 +1,4 @@
+import os
 from collections.abc import AsyncIterator, Iterator
 from uuid import UUID
 
@@ -29,6 +30,55 @@ from tests.conftest_store import (
 
 pytest.register_assert_rewrite("tests.memory_assert")
 
+# Global variables for checkpointer and store configurations
+FAST_MODE = os.getenv("LANGGRAPH_TEST_FAST", "true").lower() in ("true", "1", "yes")
+
+SYNC_CHECKPOINTER_PARAMS = (
+    ["memory"]
+    if FAST_MODE
+    else [
+        "memory",
+        "sqlite",
+        "postgres",
+        "postgres_pipe",
+        "postgres_pool",
+    ]
+)
+
+ASYNC_CHECKPOINTER_PARAMS = (
+    ["memory"]
+    if FAST_MODE
+    else [
+        "memory",
+        "sqlite_aio",
+        "postgres_aio",
+        "postgres_aio_pipe",
+        "postgres_aio_pool",
+    ]
+)
+
+SYNC_STORE_PARAMS = (
+    ["in_memory"]
+    if FAST_MODE
+    else [
+        "in_memory",
+        "postgres",
+        "postgres_pipe",
+        "postgres_pool",
+    ]
+)
+
+ASYNC_STORE_PARAMS = (
+    ["in_memory"]
+    if FAST_MODE
+    else [
+        "in_memory",
+        "postgres_aio",
+        "postgres_aio_pipe",
+        "postgres_aio_pool",
+    ]
+)
+
 
 @pytest.fixture
 def anyio_backend():
@@ -48,7 +98,7 @@ def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
 
 @pytest.fixture(
     scope="function",
-    params=["in_memory", "postgres", "postgres_pipe", "postgres_pool"],
+    params=SYNC_STORE_PARAMS,
 )
 def sync_store(request: pytest.FixtureRequest) -> Iterator[BaseStore]:
     store_name = request.param
@@ -72,7 +122,7 @@ def sync_store(request: pytest.FixtureRequest) -> Iterator[BaseStore]:
 
 @pytest.fixture(
     scope="function",
-    params=["in_memory", "postgres_aio", "postgres_aio_pipe", "postgres_aio_pool"],
+    params=ASYNC_STORE_PARAMS,
 )
 async def async_store(request: pytest.FixtureRequest) -> AsyncIterator[BaseStore]:
     store_name = request.param
@@ -96,13 +146,7 @@ async def async_store(request: pytest.FixtureRequest) -> AsyncIterator[BaseStore
 
 @pytest.fixture(
     scope="function",
-    params=[
-        "memory",
-        "sqlite",
-        "postgres",
-        "postgres_pipe",
-        "postgres_pool",
-    ],
+    params=SYNC_CHECKPOINTER_PARAMS,
 )
 def sync_checkpointer(
     request: pytest.FixtureRequest,
@@ -129,13 +173,7 @@ def sync_checkpointer(
 
 @pytest.fixture(
     scope="function",
-    params=[
-        "memory",
-        "sqlite_aio",
-        "postgres_aio",
-        "postgres_aio_pipe",
-        "postgres_aio_pool",
-    ],
+    params=ASYNC_CHECKPOINTER_PARAMS,
 )
 async def async_checkpointer(
     request: pytest.FixtureRequest,

libs/prebuilt/tests/memory_assert.py

@@ -1,31 +1,19 @@
-import asyncio
 import os
 import tempfile
 from collections import defaultdict
 from functools import partial
-from typing import Any, Optional
-
-from langchain_core.runnables import RunnableConfig
+from typing import Optional
 
 from langgraph.checkpoint.base import (
     ChannelVersions,
     Checkpoint,
     CheckpointMetadata,
-    CheckpointTuple,
     SerializerProtocol,
 )
 from langgraph.checkpoint.memory import InMemorySaver, PersistentDict
 from langgraph.pregel.checkpoint import copy_checkpoint
 
 
-class NoopSerializer(SerializerProtocol):
-    def loads_typed(self, data: tuple[str, bytes]) -> Any:
-        return data[1]
-
-    def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
-        return "type", obj
-
-
 class MemorySaverAssertImmutable(InMemorySaver):
     storage_for_copies: defaultdict[str, dict[str, dict[str, Checkpoint]]]
 
@@ -69,66 +57,3 @@ class MemorySaverAssertImmutable(InMemorySaver):
         )
         # call super to write checkpoint
         return super().put(config, checkpoint, metadata, new_versions)
-
-
-class MemorySaverAssertCheckpointMetadata(InMemorySaver):
-    """This custom checkpointer is for verifying that a run's configurable
-    fields are merged with the previous checkpoint config for each step in
-    the run. This is the desired behavior. Because the checkpointer's (a)put()
-    method is called for each step, the implementation of this checkpointer
-    should produce a side effect that can be asserted.
-    """
-
-    def put(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> None:
-        """The implementation of put() merges config["configurable"] (a run's
-        configurable fields) with the metadata field. The state of the
-        checkpoint metadata can be asserted to confirm that the run's
-        configurable fields were merged with the previous checkpoint config.
-        """
-        configurable = config["configurable"].copy()
-
-        # remove checkpoint_id to make testing simpler
-        checkpoint_id = configurable.pop("checkpoint_id", None)
-        thread_id = config["configurable"]["thread_id"]
-        checkpoint_ns = config["configurable"]["checkpoint_ns"]
-        self.storage[thread_id][checkpoint_ns].update(
-            {
-                checkpoint["id"]: (
-                    self.serde.dumps_typed(checkpoint),
-                    # merge configurable fields and metadata
-                    self.serde.dumps_typed({**configurable, **metadata}),
-                    checkpoint_id,
-                )
-            }
-        )
-        return {
-            "configurable": {
-                "thread_id": config["configurable"]["thread_id"],
-                "checkpoint_id": checkpoint["id"],
-            }
-        }
-
-    async def aput(
-        self,
-        config: RunnableConfig,
-        checkpoint: Checkpoint,
-        metadata: CheckpointMetadata,
-        new_versions: ChannelVersions,
-    ) -> RunnableConfig:
-        return await asyncio.get_running_loop().run_in_executor(
-            None, self.put, config, checkpoint, metadata, new_versions
-        )
-
-
-class MemorySaverNoPending(InMemorySaver):
-    def get_tuple(self, config: RunnableConfig) -> Optional[CheckpointTuple]:
-        result = super().get_tuple(config)
-        if result:
-            return CheckpointTuple(result.config, result.checkpoint, result.metadata)
-        return result

libs/prebuilt/tests/messages.py

@@ -9,33 +9,11 @@ subclassed strings.
 
 from typing import Any
 
-from langchain_core.documents import Document
-from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage, ToolMessage
+from langchain_core.messages import HumanMessage, ToolMessage
 
 from tests.any_str import AnyStr
 
 
-def _AnyIdDocument(**kwargs: Any) -> Document:
-    """Create a document with an id field."""
-    message = Document(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
-def _AnyIdAIMessage(**kwargs: Any) -> AIMessage:
-    """Create ai message with an any id field."""
-    message = AIMessage(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
-def _AnyIdAIMessageChunk(**kwargs: Any) -> AIMessageChunk:
-    """Create ai message with an any id field."""
-    message = AIMessageChunk(**kwargs)
-    message.id = AnyStr()
-    return message
-
-
 def _AnyIdHumanMessage(**kwargs: Any) -> HumanMessage:
     """Create a human message with an any id field."""
     message = HumanMessage(**kwargs)

libs/prebuilt/tests/test_react_agent.py

@@ -5,6 +5,7 @@ from functools import partial
 from typing import (
     Annotated,
     List,
+    Literal,
     Optional,
     Type,
     TypeVar,
@@ -509,7 +510,7 @@ class CustomStatePydantic(AgentStatePydantic):
 @pytest.mark.parametrize("state_schema", [CustomState, CustomStatePydantic])
 def test_react_agent_update_state(
     sync_checkpointer: BaseCheckpointSaver,
-    version: str,
+    version: Literal["v1", "v2"],
     state_schema: StateSchemaType,
 ) -> None:
     @dec_tool
@@ -557,7 +558,7 @@ def test_react_agent_update_state(
         version=version,
     )
     config = {"configurable": {"thread_id": "1"}}
-    # run until interrpupted
+    # Run until interrupted
     agent.invoke({"messages": [("user", "what's my name")]}, config)
     # supply the value for the interrupt
     response = agent.invoke(Command(resume="Archibald"), config)
@@ -781,8 +782,9 @@ class AgentStateExtraKeyPydantic(AgentStatePydantic):
     "state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
 )
 def test_create_react_agent_inject_vars(
-    version: str, state_schema: StateSchemaType
+    version: Literal["v1", "v2"], state_schema: StateSchemaType
 ) -> None:
+    """Test that the agent can inject state and store into tool functions."""
     store = InMemoryStore()
     namespace = ("test",)
     store.put(namespace, "test_key", {"bar": 3})
@@ -817,15 +819,14 @@ def test_create_react_agent_inject_vars(
     model = FakeToolCallingModel(tool_calls=[[tool_call], []])
     agent = create_react_agent(
         model,
-        [tool1],
+        ToolNode([tool1], handle_tool_errors=False),
         state_schema=state_schema,
         store=store,
         version=version,
     )
-    input_message = HumanMessage("hi")
-    result = agent.invoke({"messages": [input_message], "foo": 2})
+    result = agent.invoke({"messages": [{"role": "user", "content": "hi"}], "foo": 2})
     assert result["messages"] == [
-        input_message,
+        _AnyIdHumanMessage(content="hi"),
         AIMessage(content="hi", tool_calls=[tool_call], id="0"),
         _AnyIdToolMessage(content="6", name="tool1", tool_call_id="some 0"),
         AIMessage("hi-hi-6", id="1"),
@@ -1580,13 +1581,14 @@ def test_create_react_agent_inject_vars_with_post_model_hook(
         "type": "tool_call",
     }
 
-    def post_model_hook(state: dict) -> None:
-        return
+    def post_model_hook(state: dict) -> dict:
+        """Post model hook is injecting a new foo key."""
+        return {"foo": 2}
 
     model = FakeToolCallingModel(tool_calls=[[tool_call], []])
     agent = create_react_agent(
         model,
-        [tool1],
+        ToolNode([tool1], handle_tool_errors=False),
         state_schema=state_schema,
         store=store,
         post_model_hook=post_model_hook,

libs/prebuilt/uv.lock

@@ -367,7 +367,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "../checkpoint" }
 dependencies = [
     { name = "langchain-core" },
@@ -397,7 +397,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.22"
+version = "2.0.23"
 source = { editable = "../checkpoint-postgres" }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -507,7 +507,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.74"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/sdk-py/langgraph_sdk/schema.py

@@ -251,7 +251,7 @@ class Thread(TypedDict):
     values: Json
     """The current state of the thread."""
     interrupts: dict[str, list[Interrupt]]
-    """Interrupts which were thrown in this thread"""
+    """Mapping of task ids to interrupts that were raised in that task."""
 
 
 class ThreadTask(TypedDict):
@@ -284,6 +284,8 @@ class ThreadState(TypedDict):
     """The ID of the parent checkpoint. If missing, this is the root checkpoint."""
     tasks: Sequence[ThreadTask]
     """Tasks to execute in this step. If already attempted, may contain an error."""
+    interrupts: list[Interrupt]
+    """Interrupts which were thrown in this thread."""
 
 
 class ThreadUpdateStateResponse(TypedDict):

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.74"
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/sdk-py/uv.lock

@@ -119,7 +119,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.73"
+version = "0.1.74"
 source = { editable = "." }
 dependencies = [
     { name = "httpx" },