release(cli): Release new CLI version with increased bounds for server (#5565)

Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>

.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

@@ -1,22 +1,22 @@
 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

.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@v2.0
             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

@@ -73,11 +73,12 @@ 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.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## 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/_scripts/notebook_hooks.py

@@ -3,6 +3,7 @@
 Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
 """
 
+import json
 import logging
 import os
 import posixpath
@@ -15,8 +16,8 @@ from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
 from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
 from _scripts.link_map import JS_LINK_MAP
+from _scripts.notebook_convert import convert_notebook
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -100,10 +101,6 @@ REDIRECT_MAP = {
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
     "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
-    # Time-travel
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
-    # breakpoints
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
     # misc
     "prebuilt.md": "agents/prebuilt.md",
     "reference/prebuilt.md": "reference/agents.md",
@@ -125,6 +122,11 @@ REDIRECT_MAP = {
     "how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
     "how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
     "agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
+    "concepts/breakpoints.md": "concepts/human_in_the_loop.md",
+    "how-tos/human_in_the_loop/breakpoints.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
+    "cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
 }
 
 
@@ -356,12 +358,16 @@ def _on_page_markdown_with_config(
 
 
 def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
+    finalized_markdown = (
+        _on_page_markdown_with_config(
+            markdown,
+            page,
+            add_api_references=True,
+            **kwargs,
+        )
     )
+    page.meta["original_markdown"] = finalized_markdown
+    return finalized_markdown
 
 
 # redirects
@@ -431,20 +437,51 @@ height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
     else:
         return html  # fallback if no <body> found
 
+def _inject_markdown_into_html(html: str, page: Page) -> str:
+    """Inject the original markdown content into the HTML page as JSON."""
+    original_markdown = page.meta.get("original_markdown", "")
+    if not original_markdown:
+        return html
+    markdown_data = {
+        "markdown": original_markdown,
+        "title": page.title or "Page Content",
+        "url": page.url or "",
+    }
 
-def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
+    # Properly escape the JSON for HTML
+    json_content = json.dumps(markdown_data, ensure_ascii=False)
+
+    json_content = (
+        json_content.replace("</", "\\u003c/")
+        .replace("<script", "\\u003cscript")
+        .replace("</script", "\\u003c/script")
+    )
+
+    script_content = (
+        f'<script id="page-markdown-content" '
+        f'type="application/json">{json_content}</script>'
+    )
+
+    # Insert before </head> if it exists, otherwise before </body>
+    if "</head>" not in html:
+        raise ValueError(
+            "HTML does not contain </head> tag. Cannot inject markdown content."
+        )
+    return html.replace("</head>", f"{script_content}</head>")
+
+def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
     """Inject Google Tag Manager noscript tag immediately after <body>.
 
     Args:
-        output: The HTML output of the page.
+        html: The HTML output of the page.
         page: The page instance.
         config: The MkDocs configuration object.
 
     Returns:
         modified HTML output with GTM code injected.
     """
-    return _inject_gtm(output)
-
+    html = _inject_markdown_into_html(html, page)
+    return _inject_gtm(html)
 
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):

docs/docs/agents/evals.md

@@ -15,7 +15,7 @@ To evaluate your agent's performance you can use `LangSmith` [evaluations](https
 def evaluator(*, outputs: dict, reference_outputs: dict):
     # compare agent outputs against reference outputs
     output_messages = outputs["messages"]
-    reference_messages = reference["messages"]
+    reference_messages = reference_outputs["messages"]
     score = compare_messages(output_messages, reference_messages)
     return {"key": "evaluator_score", "score": score}
 ```

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/agents/models.md

@@ -7,7 +7,7 @@ LangGraph provides built-in support for [LLMs (language models)](https://python.
 
 Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../snippets/chat_model_tabs.md" %}
 
 ### Instantiate a model directly
 

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -35,7 +35,6 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
 1. Configure your `langgraph-dataplane-values.yaml` file.
 
         config:
-          langgraphPlatformLicenseKey: "" # Your LangGraph Platform license key
           langsmithApiKey: "" # API Key of your Workspace
           langsmithWorkspaceId: "" # Workspace ID
           hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -2,7 +2,7 @@
 
 To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
 
-## LangGraph API invoke & resume
+## Dynamic interrupts
 
 === "Python"
 
@@ -305,6 +305,185 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
         }"
         ```
 
+## Static interrupts
+
+Static interrupts (also known as static breakpoints) are triggered either before or after a node executes. 
+
+!!! warning
+
+    Static interrupts are **not** recommended for human-in-the-loop workflows. They are best used for debugging and testing.
+
+You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time:
+
+```python
+# highlight-next-line
+graph = graph_builder.compile( # (1)!
+    # highlight-next-line
+    interrupt_before=["node_a"], # (2)!
+    # highlight-next-line
+    interrupt_after=["node_b", "node_c"], # (3)!
+)
+```
+
+1. The breakpoints are set during `compile` time.
+2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+Alternatively, you can set static interrupts at run time:
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    await client.runs.wait( # (1)!
+        thread_id,
+        assistant_id,
+        inputs=inputs,
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"] # (3)!
+    )
+    ```
+
+    1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+=== "JavaScript"
+
+    ```js
+    // highlight-next-line
+    await client.runs.wait( // (1)!
+        threadID,
+        assistantID,
+        {
+        input: input,
+        // highlight-next-line
+        interruptBefore: ["node_a"], // (2)!
+        // highlight-next-line
+        interruptAfter: ["node_b", "node_c"] // (3)!
+        }
+    )
+    ```
+
+    1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
+    3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+        \"assistant_id\": \"agent\",
+        \"interrupt_before\": [\"node_a\"],
+        \"interrupt_after\": [\"node_b\", \"node_c\"],
+        \"input\": <INPUT>
+    }"
+    ```
+
+The following example shows how to add static interrupts:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>)
+
+    # Using the graph deployed with the name "agent"
+    assistant_id = "agent"
+
+    # create a thread
+    thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph until the breakpoint
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=inputs   # (1)!
+    )
+
+    # Resume the graph
+    await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=None   # (2)!
+    )
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
+    // Using the graph deployed with the name "agent"
+    const assistantID = "agent";
+
+    // create a thread
+    const thread = await client.threads.create();
+    const threadID = thread["thread_id"];
+
+    // Run the graph until the breakpoint
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: input }   // (1)!
+    );
+
+    // Resume the graph
+    await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: null }   // (2)!
+    );
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "cURL"
+
+    Create a thread:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+    ```
+
+    Run the graph until the breakpoint:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <INPUT>
+    }"
+    ```
+
+    Resume the graph:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\"
+    }"
+    ```
+
+
 ## Learn more
 
 - [Human-in-the-loop conceptual guide](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features. 

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

@@ -1,185 +0,0 @@
-# Set breakpoints using Server API
-
-[Breakpoints](../../concepts/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](../../concepts/persistence.md), which saves the graph state after each step.
-
-With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses indefinitely until you resume, as the checkpointer preserves the state.
-
-!!! tip
-
-    For conceptual information on breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
-
-## Set static breakpoints
-
-Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
-
-=== "Compile time"
-
-    ```python
-    # highlight-next-line
-    graph = graph_builder.compile( # (1)!
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"], # (3)!
-    )
-    ```
-
-    1. The breakpoints are set during `compile` time.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-=== "Run time"
-
-    === "Python"
-
-        ```python
-        # highlight-next-line
-        await client.runs.wait( # (1)!
-            thread_id,
-            assistant_id,
-            inputs=inputs,
-            # highlight-next-line
-            interrupt_before=["node_a"], # (2)!
-            # highlight-next-line
-            interrupt_after=["node_b", "node_c"] # (3)!
-        )
-        ```
-
-        1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
-        2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-        3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-    === "JavaScript"
-
-        ```js
-        // highlight-next-line
-        await client.runs.wait( // (1)!
-          threadID,
-          assistantID,
-          {
-            input: input,
-            // highlight-next-line
-            interruptBefore: ["node_a"], // (2)!
-            // highlight-next-line
-            interruptAfter: ["node_b", "node_c"] // (3)!
-          }
-        )
-        ```
-
-        1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
-        2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
-        3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
-
-    === "cURL"
-
-        ```bash
-        curl --request POST \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-        --header 'Content-Type: application/json' \
-        --data "{
-          \"assistant_id\": \"agent\",
-          \"interrupt_before\": [\"node_a\"],
-          \"interrupt_after\": [\"node_b\", \"node_c\"],
-          \"input\": <INPUT>
-        }"
-        ```
-
-## Example
-
-This example shows how to add **static** breakpoints. See [Use breakpoints](../../how-tos/human_in_the_loop/breakpoints.md) for more options on adding breakpoints.
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-
-    # create a thread
-    thread = await client.threads.create()
-    thread_id = thread["thread_id"]
-
-    # Run the graph until the breakpoint
-    result = await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=inputs   # (1)!
-    )
-
-    # Resume the graph
-    await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=None   # (2)!
-    )
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-
-    // Using the graph deployed with the name "agent"
-    const assistantID = "agent";
-
-    // create a thread
-    const thread = await client.threads.create();
-    const threadID = thread["thread_id"];
-
-    // Run the graph until the breakpoint
-    const result = await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: input }   // (1)!
-    );
-
-    // Resume the graph
-    await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: null }   // (2)!
-    );
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "cURL"
-
-    Create a thread:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads \
-    --header 'Content-Type: application/json' \
-    --data '{}'
-    ```
-
-    Run the graph until the breakpoint:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <INPUT>
-    }"
-    ```
-
-    Resume the graph:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\"
-    }"
-    ```

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

@@ -29,7 +29,7 @@ Click the dropdown next to "Submit" and click the toggle to enable/disable strea
 To run your graph with breakpoints, click the "Interrupt" button. Select a node and whether to pause before and/or after that node has executed. Click "Continue" in the thread log to resume execution.
 
 
-For more information on breakpoints see [here](../../concepts/breakpoints.md).
+For more information on breakpoints see [here](../../concepts/human_in_the_loop.md).
 
 ### Submit run
 

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 +4,39 @@
 
 ---
 
+## 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.
+
+## v0.2.87 (2025-07-14)
+- Added more detailed logs for Redis worker signaling to improve debugging.
+
+## v0.2.86 (2025-07-11)
+- Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.
+
 ## v0.2.85 (2025-07-10)
 - Added support for the `on_disconnect` field to `runs/wait` and included disconnect logs for better debugging.
 

docs/docs/concepts/breakpoints.md

@@ -1,18 +0,0 @@
----
-search:
-  boost: 2
----
-
-# Breakpoints
-
-[Breakpoints](../how-tos/human_in_the_loop/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](./persistence.md), which saves the graph state after each step.
-
-With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
-
-<figure markdown="1">
-![image](img/breakpoints.png){: style="max-height:400px"}
-<figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
-
-!!! tip
-
-    For information on how to use breakpoints, see [Set breakpoints](../how-tos/human_in_the_loop/breakpoints.md) and [Set breakpoints using Server API](../cloud/how-tos/human_in_the_loop_breakpoint.md).

docs/docs/concepts/human_in_the_loop.md

@@ -23,9 +23,18 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'
 
 ## Key capabilities
 
-* **Persistent execution state**: LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
+* **Persistent execution state**: Interrupts use LangGraph's [persistence](../../concepts/persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
 
-* **Flexible integration points**: HIL logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
+    There are two ways to pause a graph:
+
+    - [Dynamic interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt): Use `interrupt` to pause a graph from inside a specific node, based on the current state of the graph.
+    - [Static interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#debug-with-interrupts): Use `interrupt_before` and `interrupt_after` to pause the graph at defined points, either before or after a node executes.
+
+    <figure markdown="1">
+    ![image](./img/breakpoints.png){: style="max-height:400px"}
+    <figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
+
+* **Flexible integration points**: Human-in-the-loop logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
 
 ## Patterns
 

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.
 
@@ -298,7 +298,7 @@ print(graph.invoke({"x": 5}, stream_mode='updates'))  # (2)!
 [{'expensive_node': {'result': 10}, '__metadata__': {'cached': True}}]
 ```
 
-1. First run takes the full second to run (due to mocked expensive computation).
+1. First run takes two seconds to run (due to mocked expensive computation).
 2. Second run utilizes cache and returns quickly.
 
 ## Edges

docs/docs/concepts/persistence.md

@@ -33,7 +33,7 @@ The state of a thread at a particular point in time is called a checkpoint. Chec
 - `metadata`: Metadata associated with this checkpoint.
 - `values`: Values of the state channels at this point in time.
 - `next` A tuple of the node names to execute next in the graph.
-- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.md#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.
+- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt) from within a node, tasks will contain additional data associated with interrupts.
 
 Checkpoints are persisted and can be used to restore the state of a thread at a later time.
 
@@ -525,7 +525,7 @@ When running on LangGraph Platform, encryption is automatically enabled whenever
 
 ### Human-in-the-loop
 
-First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [these how-to guides](../how-tos/human_in_the_loop/breakpoints.md) for concrete examples.
+First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [the how-to guides](../how-tos/human_in_the_loop/add-human-in-the-loop.md) for examples.
 
 ### Memory
 

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.
@@ -19,8 +24,7 @@ These capabilities are available in both LangGraph OSS and the LangGraph Platfor
 - [Context](../agents/context.md): Pass outside data to a LangGraph graph to provide context for the graph execution.
 - [Models](../agents/models.md): Integrate various LLMs into your LangGraph application.
 - [Tools](../concepts/tools.md): Interface directly with external systems.
-- [Human-in-the-loop](../concepts/human_in_the_loop.md): Enable human intervention at any point in a workflow.
-- [Breakpoints](../concepts/breakpoints.md): Pause the execution of a LangGraph graph at a specific point.
+- [Human-in-the-loop](../concepts/human_in_the_loop.md): Pause a graph and wait for human input at any point in a workflow.
 - [Time travel](../concepts/time-travel.md): Travel back in time to a specific point in the execution of a LangGraph graph.
 - [Subgraphs](../concepts/subgraphs.md): Build modular graphs.
 - [Multi-agent](../concepts/multi_agent.md): Break down a complex workflow into multiple agents.
@@ -31,11 +35,11 @@ These capabilities are available in both LangGraph OSS and the LangGraph Platfor
 
 These capabilities are only available in [LangGraph Platform](../concepts/langgraph_platform.md).
 
-- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a Langraph graph.
+- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a LangGraph graph.
 - [Assistants](../concepts/assistants.md): Build assistants that can be used to interact with a LangGraph graph.
 - [Double-texting](../concepts/double_texting.md): Handle double-texting (consecutive messages before a first response is returned) in a LangGraph graph.
 - [Webhooks](../cloud/concepts/webhooks.md): Send webhooks to a LangGraph graph.
 - [Cron jobs](../cloud/concepts/cron_jobs.md): Schedule jobs to run at a specific time.
 - [Server customization](../how-tos/http/custom_lifespan.md): Customize the server that runs a LangGraph graph.
 - [Data management](../cloud/concepts/data_storage_and_privacy.md): Manage data in a LangGraph graph.
-- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.
+- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.

docs/docs/how-tos/auth/custom_auth.md

@@ -31,12 +31,12 @@ To leverage custom authentication and access user-level metadata in your deploym
         api_key = headers.get("x-api-key")
         if not api_key or not is_valid_key(api_key):
             raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid API key")
-        
-        # Fetch user-specific tokens from your secret store  
+
+        # Fetch user-specific tokens from your secret store
         user_tokens = await fetch_user_tokens(api_key)
 
         return { # (2)!
-            "identity": api_key,  #  fetch user ID from LangSmith 
+            "identity": api_key,  #  fetch user ID from LangSmith
             "github_token" : user_tokens.github_token
             "jira_token" : user_tokens.jira_token
             # ... custom fields/secrets here
@@ -50,14 +50,14 @@ To leverage custom authentication and access user-level metadata in your deploym
 
     ```json hl_lines="7-9"
     {
-    "dependencies": ["."],
-    "graphs": {
+      "dependencies": ["."],
+      "graphs": {
         "agent": "./agent.py:graph"
-    },
-    "env": ".env",
-    "auth": {
+      },
+      "env": ".env",
+      "auth": {
         "path": "./auth.py:my_auth"
-    }
+      }
     }
     ```
 
@@ -80,7 +80,7 @@ To leverage custom authentication and access user-level metadata in your deploym
 
         ```python
         from langgraph.pregel.remote import RemoteGraph
-        
+
         my_token = "your-token" # In practice, you would generate a signed token with your auth provider
         remote_graph = RemoteGraph(
             "agent",
@@ -133,15 +133,44 @@ To allow an agent to perform authenticated actions on behalf of the user, access
 def my_node(state, config):
     user_config = config["configurable"].get("langgraph_auth_user")
     # token was resolved during the @auth.authenticate function
-    token = user_config.get("github_token","") 
+    token = user_config.get("github_token","")
     ...
 ```
 
 !!! note
     Fetch user credentials from a secure secret store. Storing secrets in graph state is not recommended.
 
+### Authorizing a Studio user
+
+By default, if you add custom authorization on your resources, this will also apply to interactions made from the Studio. If you want, you can handle logged-in Studio users differently by checking [is_studio_user()](../../reference/functions/sdk_auth.isStudioUser.html).
+
+!!! note
+    `is_studio_user` was added in version 0.1.73 of the langgraph-sdk. If you're on an older version, you can still check whether `isinstance(ctx.user, StudioUser)`.
+
+```python
+from langgraph_sdk.auth import is_studio_user, Auth
+auth = Auth()
+
+# ... Setup authenticate, etc.
+
+@auth.on
+async def add_owner(
+    ctx: Auth.types.AuthContext,
+    value: dict  # The payload being sent to this access method
+) -> dict:  # Returns a filter dict that restricts access to resources
+    if is_studio_user(ctx.user):
+        return {}
+
+    filters = {"owner": ctx.user.identity}
+    metadata = value.setdefault("metadata", {})
+    metadata.update(filters)
+    return filters
+```
+
+Only use this if you want to permit developer access to a graph deployed on the managed LangGraph Platform SaaS.
+
 ## Learn more
 
-* [Authentication & Access Control](../../concepts/auth.md)
-* [LangGraph Platform](../../concepts/langgraph_platform.md)
-* [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)
+- [Authentication & Access Control](../../concepts/auth.md)
+- [LangGraph Platform](../../concepts/langgraph_platform.md)
+- [Setting up custom authentication tutorial](../../tutorials/auth/getting_started.md)

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
@@ -1149,7 +1150,8 @@ Adding "C" to ['A']
 LangGraph supports map-reduce and other advanced branching patterns using the Send API. Here is an example of how to use it:
 
 ```python
-from langgraph.graph import StateGraph, START, END, Send
+from langgraph.graph import StateGraph, START, END
+from langgraph.types import Send
 from typing_extensions import TypedDict
 
 class OverallState(TypedDict):
@@ -1193,7 +1195,7 @@ from IPython.display import Image, display
 display(Image(graph.get_graph().draw_mermaid_png()))
 ```
 
-![Map-reduce graph with fanout](assets/graph_api_image_2.png)
+![Map-reduce graph with fanout](assets/graph_api_image_6.png)
 
 ```python
 # Call the graph: here we call it to generate a list of jokes
@@ -1445,7 +1447,7 @@ Recursion Error
     display(Image(graph.get_graph().draw_mermaid_png()))
     ```
 
-    ![Complex loop graph with branches](assets/graph_api_image_4.png)
+    ![Complex loop graph with branches](assets/graph_api_image_8.png)
 
     This graph looks complex, but can be conceptualized as loop of [supersteps](../concepts/low_level.md#graphs):
 
@@ -1507,7 +1509,7 @@ Because many LangChain objects implement the [Runnable Protocol](https://python.
 
 See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../snippets/chat_model_tabs.md" %}
 
 ```python
 from langchain.chat_models import init_chat_model

docs/docs/how-tos/human_in_the_loop/add-human-in-the-loop.md

@@ -11,11 +11,19 @@ hide:
 
 # Enable human intervention
 
-To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
+To review, edit, and approve tool calls in an agent or workflow, use interrupts to pause a graph and wait for human input. Interrupts use LangGraph's [persistence](../../concepts/persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume.
+
+!!! info
+
+    For more information about human-in-the-loop workflows, see the [Human-in-the-Loop](../../concepts/human_in_the_loop.md) conceptual guide.
 
 ## Pause using `interrupt`
 
-The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+[Dynamic interrupts](../../concepts/human_in_the_loop.md#key-capabilities) (also known as dynamic breakpoints) are triggered based on the current state of the graph. You can set dynamic interrupts by calling [`interrupt` function][langgraph.types.interrupt] in the appropriate place. The graph will pause, which allows for human intervention, and then resumes the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+
+!!! note
+
+    As of v1.0, `interrupt` is the recommended way to pause a graph. `NodeInterrupt` is deprecated and will be removed in v2.0.
 
 To use `interrupt` in your graph, you need to:
 
@@ -138,15 +146,10 @@ print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
 
 !!! warning
 
-      Interrupts are both powerful and ergonomic. However, while they may resemble Python's input() function in terms of developer experience, it's important to note that they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node.
-
+      Interrupts resemble Python's input() function in terms of developer experience, but they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node.
 
 ## Resume using the `Command` primitive
 
-!!! warning
-
-    Resuming from an `interrupt` is different from Python's `input()` function, where execution resumes from the exact point where the `input()` function was called.
-
 When the `interrupt` function is used within a graph, execution pauses at that point and awaits user input.
 
 To resume execution, use the [`Command`][langgraph.types.Command] primitive, which can be supplied via the `invoke`, `ainvoke`, `stream`, or `astream` methods. The graph resumes execution from the beginning of the node where `interrupt(...)` was initially called. This time, the `interrupt` function will return the value provided in `Command(resume=value)` rather than pausing again. All code from the beginning of the node to the `interrupt` will be re-executed.
@@ -712,6 +715,162 @@ def human_node(state: State):
     print(final_result)  # Should include the valid age
     ```
 
+## Debug with interrupts
+
+To debug and test a graph, use [static interrupts](../../concepts/human_in_the_loop.md#key-capabilities) (also known as static breakpoints) to step through the graph execution one node at a time or to pause the graph execution at specific nodes. Static interrupts are triggered at defined points either before or after a node executes. You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
+
+!!! warning
+
+    Static interrupts are **not** recommended for human-in-the-loop workflows. Use [dynamic interrupts](#pause-using-interrupt) instead.
+
+=== "Compile time"
+
+    ```python
+    # highlight-next-line
+    graph = graph_builder.compile( # (1)!
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"], # (3)!
+        checkpointer=checkpointer, # (4)!
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config) # (5)!
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config) # (6)!
+    ```
+
+    1. The breakpoints are set during `compile` time.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. A checkpointer is required to enable breakpoints.
+    5. The graph is run until the first breakpoint is hit.
+    6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "Run time"
+
+    ```python
+    # highlight-next-line
+    graph.invoke( # (1)!
+        inputs, 
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"] # (3)!
+        config={
+            "configurable": {"thread_id": "some_thread"}
+        }, 
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=config) # (4)!
+
+    # Resume the graph
+    graph.invoke(None, config=config) # (5)!
+    ```
+
+    1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. The graph is run until the first breakpoint is hit.
+    5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+    !!! note
+
+        You cannot set static breakpoints at runtime for **sub-graphs**.
+        If you have a sub-graph, you must set the breakpoints at compilation time.
+
+??? example "Setting static breakpoints"
+
+    ```python
+    from IPython.display import Image, display
+    from typing_extensions import TypedDict
+    
+    from langgraph.checkpoint.memory import InMemorySaver 
+    from langgraph.graph import StateGraph, START, END
+    
+    
+    class State(TypedDict):
+        input: str
+    
+    
+    def step_1(state):
+        print("---Step 1---")
+        pass
+    
+    
+    def step_2(state):
+        print("---Step 2---")
+        pass
+    
+    
+    def step_3(state):
+        print("---Step 3---")
+        pass
+    
+    
+    builder = StateGraph(State)
+    builder.add_node("step_1", step_1)
+    builder.add_node("step_2", step_2)
+    builder.add_node("step_3", step_3)
+    builder.add_edge(START, "step_1")
+    builder.add_edge("step_1", "step_2")
+    builder.add_edge("step_2", "step_3")
+    builder.add_edge("step_3", END)
+    
+    # Set up a checkpointer 
+    checkpointer = InMemorySaver() # (1)!
+    
+    graph = builder.compile(
+        checkpointer=checkpointer, # (2)!
+        interrupt_before=["step_3"] # (3)!
+    )
+    
+    # View
+    display(Image(graph.get_graph().draw_mermaid_png()))
+    
+    
+    # Input
+    initial_input = {"input": "hello world"}
+    
+    # Thread
+    thread = {"configurable": {"thread_id": "1"}}
+    
+    # Run the graph until the first interruption
+    for event in graph.stream(initial_input, thread, stream_mode="values"):
+        print(event)
+        
+    # This will run until the breakpoint
+    # You can get the state of the graph at this point
+    print(graph.get_state(config))
+    
+    # You can continue the graph execution by passing in `None` for the input
+    for event in graph.stream(None, thread, stream_mode="values"):
+        print(event)
+    ```
+
+### Use static interrupts in LangGraph Studio
+
+You can use [LangGraph Studio](../../concepts/langgraph_studio.md) to debug your graph. You can set static breakpoints in the UI and then run the graph. You can also use the UI to inspect the graph state at any point in the execution.
+
+![image](../../concepts/img/human_in_the_loop/static-interrupt.png){: style="max-height:400px"}
+
+LangGraph Studio is free with [locally deployed applications](../../tutorials/langgraph-platform/local-server.md) using `langgraph dev`. 
+
 ## Considerations
 
 When using human-in-the-loop, there are some considerations to keep in mind.
@@ -953,4 +1112,3 @@ To avoid issues, refrain from dynamically changing the node's structure between
     Name: N/A. Age: John
     {'human_node': {'age': 'John', 'name': 'N/A'}}
     ```
-

docs/docs/how-tos/human_in_the_loop/breakpoints.md

@@ -1,342 +0,0 @@
-# Set breakpoints
-
-There are two places where you can set breakpoints:
-
-1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
-2. **Inside** a node using the `NodeInterrupt` exception. We call these [**dynamic breakpoints**](#dynamic-breakpoints).
-
-To use breakpoints, you will need to:
-
-1. [**Specify a checkpointer**](../../concepts/persistence.md#checkpoints) to save the graph state after each step.
-2. **Set breakpoints** to specify where execution should pause.
-3. **Run the graph** with a [**thread ID**](../../concepts/persistence.md#threads) to pause execution at the breakpoint.
-4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` passing a `None` as the argument for the inputs.
-
-!!! tip
-
-    For a conceptual overview of breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
-
-## Static breakpoints
-
-Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
-
-Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
-node at a time or if you want to pause the graph execution at specific nodes.
-
-=== "Compile time"
-
-    ```python
-    # highlight-next-line
-    graph = graph_builder.compile( # (1)!
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"], # (3)!
-        checkpointer=checkpointer, # (4)!
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "some_thread"
-        }
-    }
-
-    # Run the graph until the breakpoint
-    graph.invoke(inputs, config=thread_config) # (5)!
-
-    # Resume the graph
-    graph.invoke(None, config=thread_config) # (6)!
-    ```
-
-    1. The breakpoints are set during `compile` time.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-    4. A checkpointer is required to enable breakpoints.
-    5. The graph is run until the first breakpoint is hit.
-    6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "Run time"
-
-    ```python
-    # highlight-next-line
-    graph.invoke( # (1)!
-        inputs, 
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"] # (3)!
-        config={
-            "configurable": {"thread_id": "some_thread"}
-        }, 
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "some_thread"
-        }
-    }
-
-    # Run the graph until the breakpoint
-    graph.invoke(inputs, config=config) # (4)!
-
-    # Resume the graph
-    graph.invoke(None, config=config) # (5)!
-    ```
-
-    1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-    4. The graph is run until the first breakpoint is hit.
-    5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-    !!! note
-
-        You cannot set static breakpoints at runtime for **sub-graphs**.
-        If you have a sub-graph, you must set the breakpoints at compilation time.
-
-??? example "Setting static breakpoints"
-
-    ```python
-    from IPython.display import Image, display
-    from typing_extensions import TypedDict
-    
-    from langgraph.checkpoint.memory import InMemorySaver 
-    from langgraph.graph import StateGraph, START, END
-    
-    
-    class State(TypedDict):
-        input: str
-    
-    
-    def step_1(state):
-        print("---Step 1---")
-        pass
-    
-    
-    def step_2(state):
-        print("---Step 2---")
-        pass
-    
-    
-    def step_3(state):
-        print("---Step 3---")
-        pass
-    
-    
-    builder = StateGraph(State)
-    builder.add_node("step_1", step_1)
-    builder.add_node("step_2", step_2)
-    builder.add_node("step_3", step_3)
-    builder.add_edge(START, "step_1")
-    builder.add_edge("step_1", "step_2")
-    builder.add_edge("step_2", "step_3")
-    builder.add_edge("step_3", END)
-    
-    # Set up a checkpointer 
-    checkpointer = InMemorySaver() # (1)!
-    
-    graph = builder.compile(
-        checkpointer=checkpointer, # (2)!
-        interrupt_before=["step_3"] # (3)!
-    )
-    
-    # View
-    display(Image(graph.get_graph().draw_mermaid_png()))
-    
-    
-    # Input
-    initial_input = {"input": "hello world"}
-    
-    # Thread
-    thread = {"configurable": {"thread_id": "1"}}
-    
-    # Run the graph until the first interruption
-    for event in graph.stream(initial_input, thread, stream_mode="values"):
-        print(event)
-        
-    # This will run until the breakpoint
-    # You can get the state of the graph at this point
-    print(graph.get_state(config))
-    
-    # You can continue the graph execution by passing in `None` for the input
-    for event in graph.stream(None, thread, stream_mode="values"):
-        print(event)
-    ```
-
-## Dynamic breakpoints
-
-Use dynamic breakpoints if you need to interrupt the graph from inside a given node based on a condition.
-
-```python
-from langgraph.errors import NodeInterrupt
-
-def step_2(state: State) -> State:
-    # highlight-next-line
-    if len(state["input"]) > 5:
-        # highlight-next-line
-        raise NodeInterrupt( # (1)!
-            f"Received input that is longer than 5 characters: {state['foo']}"
-        )
-    return state
-```
-
-1. raise NodeInterrupt exception based on a some condition. In this example, we create a dynamic breakpoint if the length of the attribute `input` is longer than 5 characters.
-
-<details class="example"><summary>Using dynamic breakpoints</summary>
-
-```python
-from typing_extensions import TypedDict
-from IPython.display import Image, display
-
-from langgraph.graph import StateGraph, START, END
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.errors import NodeInterrupt
-
-
-class State(TypedDict):
-    input: str
-
-
-def step_1(state: State) -> State:
-    print("---Step 1---")
-    return state
-
-
-def step_2(state: State) -> State:
-    # Let's optionally raise a NodeInterrupt
-    # if the length of the input is longer than 5 characters
-    if len(state["input"]) > 5:
-        raise NodeInterrupt(
-            f"Received input that is longer than 5 characters: {state['input']}"
-        )
-    print("---Step 2---")
-    return state
-
-
-def step_3(state: State) -> State:
-    print("---Step 3---")
-    return state
-
-
-builder = StateGraph(State)
-builder.add_node("step_1", step_1)
-builder.add_node("step_2", step_2)
-builder.add_node("step_3", step_3)
-builder.add_edge(START, "step_1")
-builder.add_edge("step_1", "step_2")
-builder.add_edge("step_2", "step_3")
-builder.add_edge("step_3", END)
-
-# Set up memory
-memory = MemorySaver()
-
-# Compile the graph with memory
-graph = builder.compile(checkpointer=memory)
-
-# View
-display(Image(graph.get_graph().draw_mermaid_png()))
-```
-
-First, let's run the graph with an input that <= 5 characters long. This should safely ignore the interrupt condition we defined and return the original input at the end of the graph execution.
-
-```python
-initial_input = {"input": "hello"}
-thread_config = {"configurable": {"thread_id": "1"}}
-
-for event in graph.stream(initial_input, thread_config, stream_mode="values"):
-    print(event)
-```
-
-If we inspect the graph at this point, we can see that there are no more tasks left to run and that the graph indeed finished execution.
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-Now, let's run the graph with an input that's longer than 5 characters. This should trigger the dynamic interrupt we defined via raising a `NodeInterrupt` error inside the `step_2` node.
-
-```python
-initial_input = {"input": "hello world"}
-thread_config = {"configurable": {"thread_id": "2"}}
-
-# Run the graph until the first interruption
-for event in graph.stream(initial_input, thread_config, stream_mode="values"):
-    print(event)
-```
-
-We can see that the graph now stopped while executing `step_2`. If we inspect the graph state at this point, we can see the information on what node is set to execute next (`step_2`), as well as what node raised the interrupt (also `step_2`), and additional information about the interrupt.
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-If we try to resume the graph from the breakpoint, we will simply interrupt again as our inputs & graph state haven't changed.
-
-```python
-# NOTE: to resume the graph from a dynamic interrupt we use the same syntax as with regular interrupts -- we pass None as the input
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
-```
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-</details>
-
-## Use with subgraphs
-
-To add breakpoints to subgraph either:
-
-* Define [static breakpoints](#static-breakpoints) by specifying them when **compiling** the subgraph.
-* Define [dynamic breakpoints](#dynamic-breakpoints).
-
-<details class="example"><summary>Add breakpoints to subgraphs</summary>
-
-```python
-from typing_extensions import TypedDict
-
-from langgraph.graph import START, StateGraph
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.types import interrupt
-
-
-class State(TypedDict):
-    foo: str
-
-
-def subgraph_node_1(state: State):
-    return {"foo": state["foo"]}
-
-
-subgraph_builder = StateGraph(State)
-subgraph_builder.add_node(subgraph_node_1)
-subgraph_builder.add_edge(START, "subgraph_node_1")
-
-subgraph = subgraph_builder.compile(interrupt_before=["subgraph_node_1"])
-
-builder = StateGraph(State)
-builder.add_node("node_1", subgraph)  # directly include subgraph as a node
-builder.add_edge(START, "node_1")
-
-checkpointer = InMemorySaver()
-graph = builder.compile(checkpointer=checkpointer)
-
-config = {"configurable": {"thread_id": "1"}}
-
-graph.invoke({"foo": ""}, config)
-
-# Fetch state including subgraph state.
-print(graph.get_state(config, subgraphs=True).tasks[0].state)
-
-# resume the subgraph
-graph.invoke(None, config)
-```
-
-</details> 

docs/docs/how-tos/human_in_the_loop/time-travel.md

@@ -4,7 +4,7 @@ To use [time-travel](../../concepts/time-travel.md) in LangGraph:
 
 1. [Run the graph](#1-run-the-graph) with initial inputs using [`invoke`][langgraph.graph.state.CompiledStateGraph.invoke] or [`stream`][langgraph.graph.state.CompiledStateGraph.stream] methods.
 2. [Identify a checkpoint in an existing thread](#2-identify-a-checkpoint): Use the [`get_state_history()`][langgraph.graph.state.CompiledStateGraph.get_state_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.  
-   Alternatively, set a [breakpoint](../../concepts/breakpoints.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
+   Alternatively, set an [interrupt](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that interrupt.
 3. [Update the graph state (optional)](#3-update-the-state-optional): Use the [`update_state`][langgraph.graph.state.CompiledStateGraph.update_state] method to modify the graph's state at the checkpoint and resume execution from alternative state.
 4. [Resume execution from the checkpoint](#4-resume-execution-from-the-checkpoint): Use the `invoke` or `stream` methods with an input of `None` and a configuration containing the appropriate `thread_id` and `checkpoint_id`.
 

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -125,7 +125,7 @@
     "memories = store.search((\"user_123\", \"memories\"), query=\"I like food?\", limit=5)\n",
     "\n",
     "for memory in memories:\n",
-    "    print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
+    "    print(f\"Memory: {memory.value['text']} (similarity: {memory.score})\")"
    ]
   },
   {

docs/docs/index.md

@@ -28,4 +28,4 @@ title: LangGraph
 }
 </style>
 
-{!../README.md!}
+{% include-markdown "../../README.md" %}

docs/docs/snippets/chat_model_tabs.md

@@ -0,0 +1,87 @@
+=== "OpenAI"
+
+    ```shell
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    llm = init_chat_model("openai:gpt-4.1")
+    ```
+
+    👉 Read the [OpenAI integration docs](https://python.langchain.com/docs/integrations/chat/openai/)
+
+=== "Anthropic"
+
+    ```shell
+    pip install -U "langchain[anthropic]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+    ```
+
+    👉 Read the [Anthropic integration docs](https://python.langchain.com/docs/integrations/chat/anthropic/)
+
+=== "Azure"
+
+    ```shell
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    llm = init_chat_model(
+        "azure_openai:gpt-4.1",
+        azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+    )
+    ```
+ 
+    👉 Read the [Azure integration docs](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/)
+
+=== "Google Gemini"
+
+    ```shell
+    pip install -U "langchain[google-genai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    llm = init_chat_model("google_genai:gemini-2.0-flash")
+    ```
+
+    👉 Read the [Google GenAI integration docs](https://python.langchain.com/docs/integrations/chat/google_generative_ai/)
+
+=== "AWS Bedrock"
+
+    ```shell
+    pip install -U "langchain[aws]"
+    ```
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    llm = init_chat_model(
+        "anthropic.claude-3-5-sonnet-20240620-v1:0",
+        model_provider="bedrock_converse",
+    )
+    ```
+
+    👉 Read the [AWS Bedrock integration docs](https://python.langchain.com/docs/integrations/chat/bedrock/)

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

@@ -63,7 +63,7 @@ Next, add a "`chatbot`" node. **Nodes** represent units of work and are typicall
 
 Let's first select a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

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

@@ -73,7 +73,7 @@ For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot
 
 Let's first select our LLM:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python
@@ -286,7 +286,7 @@ For ease of use, adjust your code to replace the following with LangGraph prebui
 - `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
 - `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 
 ```python hl_lines="25 30"

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

@@ -154,7 +154,7 @@ The snapshot above contains the current state values, corresponding config, and
   
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

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

@@ -14,7 +14,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
 
 Let's first select a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python
@@ -221,7 +221,7 @@ The input has been received and processed as a tool message. Review this call's
 
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 ```python
 from typing import Annotated

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

@@ -221,7 +221,7 @@ Manual state updates will [generate a trace](https://smith.langchain.com/public/
 
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

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

@@ -14,7 +14,7 @@ You can create these types of experiences using LangGraph's built-in **time trav
 
 Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

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

@@ -540,11 +540,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001B[1m System Message \u001B[0m================================\n",
+      "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
-      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m types:\n",
-      "\u001B[33;1m\u001B[1;3m{tool_descriptions}\u001B[0m\n",
-      "\u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m. join(): Collects and combines results from prior actions.\n",
+      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
+      "\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
       "\n",
       " - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
       " - join should always be the last action in the plan, and will be called in two scenarios:\n",
@@ -561,11 +561,11 @@
       " - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
       " - Never introduce new actions other than the ones provided.\n",
       "\n",
-      "=============================\u001B[1m Messages Placeholder \u001B[0m=============================\n",
+      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
       "\n",
-      "\u001B[33;1m\u001B[1;3m{messages}\u001B[0m\n",
+      "\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
       "\n",
-      "================================\u001B[1m System Message \u001B[0m================================\n",
+      "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
       "Remember, ONLY respond with the task list in the correct format! E.g.:\n",
       "idx. tool(arg_name=args)\n",
@@ -605,7 +605,7 @@
     "    llm: BaseChatModel, tools: Sequence[BaseTool], base_prompt: ChatPromptTemplate\n",
     "):\n",
     "    tool_descriptions = \"\\n\".join(\n",
-    "        f\"{i+1}. {tool.description}\\n\"\n",
+    "        f\"{i + 1}. {tool.description}\\n\"\n",
     "        for i, tool in enumerate(\n",
     "            tools\n",
     "        )  # +1 to offset the 0 starting index, we want it count normally from 1.\n",

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

@@ -378,7 +378,7 @@
     "\n",
     "async def execute_step(state: PlanExecute):\n",
     "    plan = state[\"plan\"]\n",
-    "    plan_str = \"\\n\".join(f\"{i+1}. {step}\" for i, step in enumerate(plan))\n",
+    "    plan_str = \"\\n\".join(f\"{i + 1}. {step}\" for i, step in enumerate(plan))\n",
     "    task = plan[0]\n",
     "    task_formatted = f\"\"\"For the following plan:\n",
     "{plan_str}\\n\\nYou are tasked with executing step {1}, {task}.\"\"\"\n",

docs/docs/tutorials/tnt-llm/tnt-llm.ipynb

@@ -302,7 +302,7 @@
     "def format_docs(docs: List[Doc]) -> str:\n",
     "    xml_table = \"<conversations>\\n\"\n",
     "    for doc in docs:\n",
-    "        xml_table += f'<conv_summ id={doc[\"id\"]}>{doc[\"summary\"]}</conv_summ>\\n'\n",
+    "        xml_table += f\"<conv_summ id={doc['id']}>{doc['summary']}</conv_summ>\\n\"\n",
     "    xml_table += \"</conversations>\"\n",
     "    return xml_table\n",
     "\n",
@@ -311,9 +311,9 @@
     "    xml = \"<cluster_table>\\n\"\n",
     "    for label in clusters:\n",
     "        xml += \"  <cluster>\\n\"\n",
-    "        xml += f'    <id>{label[\"id\"]}</id>\\n'\n",
-    "        xml += f'    <name>{label[\"name\"]}</name>\\n'\n",
-    "        xml += f'    <description>{label[\"description\"]}</description>\\n'\n",
+    "        xml += f\"    <id>{label['id']}</id>\\n\"\n",
+    "        xml += f\"    <name>{label['name']}</name>\\n\"\n",
+    "        xml += f\"    <description>{label['description']}</description>\\n\"\n",
     "        xml += \"  </cluster>\\n\"\n",
     "    xml += \"</cluster_table>\"\n",
     "    return xml\n",
@@ -600,13 +600,13 @@
     "    turns.append(\n",
     "        f\"\"\"\n",
     "<human idx={idx}>\n",
-    "{run.inputs['question']}\n",
+    "{run.inputs[\"question\"]}\n",
     "</human>\"\"\"\n",
     "    )\n",
     "    if run.outputs and run.outputs[\"output\"]:\n",
     "        turns.append(\n",
-    "            f\"\"\"<ai idx={idx+1}>\n",
-    "{run.outputs['output']}\n",
+    "            f\"\"\"<ai idx={idx + 1}>\n",
+    "{run.outputs[\"output\"]}\n",
     "</ai>\"\"\"\n",
     "        )\n",
     "    return {\n",

docs/mkdocs.yml

@@ -54,6 +54,7 @@ plugins:
       separator: '[\s\u200b\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;'
   - autorefs
   - tags
+  - include-markdown
   - mkdocstrings:
       custom_templates: templates
       handlers:
@@ -102,14 +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
@@ -141,10 +143,6 @@ nav:
           - Overview: concepts/human_in_the_loop.md
           - Add human intervention: how-tos/human_in_the_loop/add-human-in-the-loop.md
           - Use Server API: cloud/how-tos/add-human-in-the-loop.md
-      - Breakpoints:
-          - Overview: concepts/breakpoints.md
-          - Set breakpoints: how-tos/human_in_the_loop/breakpoints.md
-          - Use Server API: cloud/how-tos/human_in_the_loop_breakpoint.md
       - Time travel:
           - Overview: concepts/time-travel.md
           - Use time travel: how-tos/human_in_the_loop/time-travel.md
@@ -160,8 +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
@@ -357,8 +357,6 @@ markdown_extensions:
       combine_header_slug: true
   - pymdownx.tasklist:
       custom_checkbox: true
-  - markdown_include.include:
-      base_path: ./
   - github-callouts
 hooks:
   - _scripts/notebook_hooks.py

docs/overrides/copy-page.css

@@ -0,0 +1,16 @@
+/* Minimal CSS for copy page button */
+.copy-page-btn {
+    background: transparent;
+    border: 1px solid var(--md-default-fg-color--lightest);
+    padding: 6px 12px;
+    margin-right: 8px;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 14px;
+    color: var(--md-default-fg-color);
+    transition: all 0.2s ease;
+}
+
+.copy-page-btn:hover {
+    background: var(--md-default-fg-color--lightest);
+}

docs/overrides/copy-page.js

@@ -0,0 +1,38 @@
+// Simple copy page functionality - just copy the markdown content
+function copyPageAsMarkdown() {
+    const markdownScript = document.getElementById('page-markdown-content');
+    if (!markdownScript) {
+        alert('Markdown content not available for this page');
+        return;
+    }
+    
+    try {
+        const data = JSON.parse(markdownScript.textContent);
+        const content = `# ${data.title}\n\nSource: ${window.location.href}\n\n${data.markdown}`;
+        
+        navigator.clipboard.writeText(content).then(() => {
+            // Simple notification
+            const notification = document.createElement('div');
+            notification.textContent = 'Page content copied to clipboard';
+            notification.style.cssText = 'position:fixed;top:20px;right:20px;background:#4CAF50;color:white;padding:10px;border-radius:4px;z-index:9999;';
+            document.body.appendChild(notification);
+            setTimeout(() => notification.remove(), 3000);
+        }).catch(() => {
+            alert('Failed to copy content');
+        });
+    } catch (e) {
+        alert('Failed to parse page content');
+    }
+}
+
+// Add button to header - simpler approach
+document.addEventListener('DOMContentLoaded', function() {
+    const headerSource = document.querySelector('.md-header__source');
+    if (headerSource) {
+        const button = document.createElement('button');
+        button.textContent = 'Copy page';
+        button.onclick = copyPageAsMarkdown;
+        button.style.cssText = 'background:none;border:1px solid #ddd;padding:6px 12px;margin-right:8px;border-radius:4px;cursor:pointer;';
+        headerSource.parentNode.insertBefore(button, headerSource);
+    }
+});

docs/overrides/main.html

@@ -13,6 +13,130 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
 
 {% block extrahead %}
   <meta name="algolia-site-verification" content="165B7E7C89E49946" />
+  <script>
+    // Simple copy page functionality - uses original markdown source
+    function copyPageAsMarkdown() {
+        const markdownScript = document.getElementById('page-markdown-content');
+        if (!markdownScript) {
+            alert('Markdown content not available for this page');
+            return;
+        }
+        
+        try {
+            let rawContent = markdownScript.textContent;
+            
+            // Safe HTML entity decoding function
+            function decodeHtmlEntities(text) {
+                const parser = new DOMParser();
+                const doc = parser.parseFromString(text, 'text/html');
+                return doc.documentElement.textContent || '';
+            }
+            
+            // Always decode HTML entities since the browser might encode them
+            rawContent = decodeHtmlEntities(rawContent);
+            
+            
+            const data = JSON.parse(rawContent);
+            const content = `Source: ${window.location.href}\n\n${data.markdown}`;
+            
+            navigator.clipboard.writeText(content).then(() => {
+                // Simple notification
+                const notification = document.createElement('div');
+                notification.textContent = 'Page content copied to clipboard';
+                notification.style.cssText = 'position:fixed;top:20px;right:20px;background:#4CAF50;color:white;padding:10px 16px;border-radius:4px;z-index:9999;box-shadow:0 2px 10px rgba(0,0,0,0.2);';
+                document.body.appendChild(notification);
+                setTimeout(() => notification.remove(), 3000);
+            }).catch(() => {
+                alert('Failed to copy content');
+            });
+        } catch (e) {
+            console.error('Failed to parse page content:', e);
+            alert('Failed to parse page content: ' + e.message);
+        }
+    }
+
+    // Add dropdown button to header when page loads
+    document.addEventListener('DOMContentLoaded', function() {
+        const headerSource = document.querySelector('.md-header__source');
+        if (headerSource) {
+            // Create dropdown container
+            const dropdownContainer = document.createElement('div');
+            dropdownContainer.style.cssText = 'position:relative;display:inline-block;margin-left:8px;';
+            
+            // Create main button
+            const button = document.createElement('button');
+            button.innerHTML = 'Copy page <span style="margin-left:8px;font-size:12px;color:#9ca3af;">▾</span>';
+            button.style.cssText = 'background:transparent;border:1px solid #d1d5db;padding:6px 12px;border-radius:4px;cursor:pointer;font-size:14px;color:#374151;transition:all 0.2s ease;white-space:nowrap;display:flex;align-items:center;';
+            
+            // Create dropdown menu
+            const dropdown = document.createElement('div');
+            dropdown.className = 'copy-page-dropdown';
+            dropdown.style.cssText = 'position:absolute;top:100%;left:0;background:white;border:1px solid #e5e7eb;border-radius:6px;box-shadow:0 4px 12px rgba(0,0,0,0.15);z-index:1000;min-width:180px;display:none;padding:4px 0;';
+            
+            // Create dropdown options
+            const option1 = document.createElement('div');
+            option1.textContent = 'Copy as Markdown for LLMs';
+            option1.className = 'copy-page-option';
+            option1.style.cssText = 'padding:8px 16px;cursor:pointer;font-size:14px;color:#374151;margin:2px 0;';
+            option1.onmouseover = function() { 
+                this.style.background = document.documentElement.getAttribute('data-md-color-scheme') === 'slate' ? '#4a5568' : '#f8fafc'; 
+            };
+            option1.onmouseout = function() { this.style.background = 'transparent'; };
+            option1.onclick = function() {
+                // Check if we're on a reference page
+                if (window.location.pathname.includes('/reference/')) {
+                    alert('Copy Page not yet available in API reference pages.');
+                } else {
+                    copyPageAsMarkdown();
+                }
+                dropdown.style.display = 'none';
+            };
+            
+            const option2 = document.createElement('div');
+            option2.textContent = "View LangGraph's llms.txt";
+            option2.className = 'copy-page-option';
+            option2.style.cssText = 'padding:8px 16px;cursor:pointer;font-size:14px;color:#374151;margin:2px 0;';
+            option2.onmouseover = function() { 
+                this.style.background = document.documentElement.getAttribute('data-md-color-scheme') === 'slate' ? '#4a5568' : '#f8fafc'; 
+            };
+            option2.onmouseout = function() { this.style.background = 'transparent'; };
+            option2.onclick = function() {
+                window.open('/langgraph/llms-txt-overview/', '_blank');
+                dropdown.style.display = 'none';
+            };
+            
+            // Add options to dropdown
+            dropdown.appendChild(option1);
+            dropdown.appendChild(option2);
+            
+            // Button hover effects
+            button.onmouseover = function() { 
+                this.style.background = '#f3f4f6'; 
+                this.style.borderColor = '#9ca3af'; 
+            };
+            button.onmouseout = function() { 
+                this.style.background = 'transparent'; 
+                this.style.borderColor = '#d1d5db'; 
+            };
+            
+            // Toggle dropdown
+            button.onclick = function(e) {
+                e.stopPropagation();
+                dropdown.style.display = dropdown.style.display === 'none' ? 'block' : 'none';
+            };
+            
+            // Close dropdown when clicking outside
+            document.addEventListener('click', function() {
+                dropdown.style.display = 'none';
+            });
+            
+            // Assemble dropdown
+            dropdownContainer.appendChild(button);
+            dropdownContainer.appendChild(dropdown);
+            headerSource.parentNode.insertBefore(dropdownContainer, headerSource.nextSibling);
+        }
+    });
+  </script>
   <style>
     @import url("https://fonts.googleapis.com/css2?family=Public+Sans&display=swap");
     :root {
@@ -198,6 +322,17 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
       color: #000000;
     }
 
+    /* Copy page dropdown dark mode support */
+    [data-md-color-scheme="slate"] .copy-page-dropdown {
+      background: #1f2937 !important;
+      border-color: #374151 !important;
+      box-shadow: 0 4px 12px rgba(0,0,0,0.5) !important;
+    }
+
+    [data-md-color-scheme="slate"] .copy-page-option {
+      color: #e5e7eb !important;
+    }
+
   </style>
 {% endblock %}
 

docs/package.json

@@ -9,7 +9,8 @@
     "@langchain/core": "^0.3.38",
     "@langchain/openai": "^0.4.2",
     "msgpack-lite": "^0.1.26",
-    "nock": "^14.0.1"
+    "nock": "^14.0.1",
+    "he": "^1.2.0"
   },
   "devDependencies": {
     "@tsconfig/recommended": "^1.0.8",

docs/pyproject.toml

@@ -7,7 +7,7 @@ name = "langgraph-docs"
 version = "0.0.1"
 description = "LangGraph docs"
 authors = []
-requires-python = "~=3.10"
+requires-python = "~=3.11"
 readme = "README.md"
 license = "MIT"
 dependencies = [
@@ -48,6 +48,7 @@ docs = [
     "ruff",
     "jupyter",
     "langchain-cohere",
+    "mkdocs-include-markdown-plugin>=7.1.6",
 ]
 test = [
     "langchain",
@@ -111,4 +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/langgraph/store/postgres/base.py

@@ -756,6 +756,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
                 If provided, will create a connection pool and use it instead of a single connection.
                 This overrides the `pipeline` argument.
             index: The index configuration for the store.
+            ttl: The TTL configuration for the store.
 
         Returns:
             PostgresStore: A new PostgresStore instance.

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

@@ -28,11 +28,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.6.15"
+version = "2025.7.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
+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" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
+    { url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
 ]
 
 [[package]]
@@ -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" },
@@ -381,7 +381,7 @@ dev = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.4"
+version = "0.4.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -392,9 +392,9 @@ dependencies = [
     { name = "requests-toolbelt" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/20/c8/8d2e0fc438d2d3d8d4300f7684ea30a754344ed00d7ba9cc2705241d2a5f/langsmith-0.4.4.tar.gz", hash = "sha256:70c53bbff24a7872e88e6fa0af98270f4986a6e364f9e85db1cc5636defa4d66", size = 352105, upload-time = "2025-06-27T19:20:36.207Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/92/7885823f3d13222f57773921f0da19b37d628c64607491233dc853a0f6ea/langsmith-0.4.5.tar.gz", hash = "sha256:49444bd8ccd4e46402f1b9ff1d686fa8e3a31b175e7085e72175ab8ec6164a34", size = 352235, upload-time = "2025-07-10T22:08:04.505Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/1d/33/a3337eb70d795495a299a1640d7a75f17fb917155a64309b96106e7b9452/langsmith-0.4.4-py3-none-any.whl", hash = "sha256:014c68329bd085bd6c770a6405c61bb6881f82eb554ce8c4d1984b0035fd1716", size = 367687, upload-time = "2025-06-27T19:20:33.839Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/10/ad3107b666c3203b7938d10ea6b8746b9735c399cf737a51386d58e41d34/langsmith-0.4.5-py3-none-any.whl", hash = "sha256:4167717a2cccc4dff5809dbddc439628e836f6fd13d4fdb31ea013bc8d5cfaf5", size = 367795, upload-time = "2025-07-10T22:08:02.548Z" },
 ]
 
 [[package]]
@@ -977,27 +977,27 @@ wheels = [
 
 [[package]]
 name = "ruff"
-version = "0.12.2"
+version = "0.12.3"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/c3/2a/43955b530c49684d3c38fcda18c43caf91e99204c2a065552528e0552d4f/ruff-0.12.3.tar.gz", hash = "sha256:f1b5a4b6668fd7b7ea3697d8d98857390b40c1320a63a178eee6be0899ea2d77", size = 4459341, upload-time = "2025-07-11T13:21:16.086Z" }
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 [[package]]

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

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 [[package]]

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

@@ -595,6 +595,6 @@ class PersistentDict(defaultdict):
                 except EOFError:
                     return
                 except Exception:
-                    logging.error(f"Failed to load file: {fileobj.name}")
+                    logger.error(f"Failed to load file: {fileobj.name}")
                     raise
             raise ValueError("File not in a supported format")

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

@@ -34,11 +34,11 @@ wheels = [
 
 [[package]]
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-version = "2025.6.15"
+version = "2025.7.9"
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+    { url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
 ]
 
 [[package]]
@@ -323,7 +323,7 @@ wheels = [
 
 [[package]]
 name = "langgraph-checkpoint"
-version = "2.1.0"
+version = "2.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -371,7 +371,7 @@ dev = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.4"
+version = "0.4.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -382,9 +382,9 @@ dependencies = [
     { name = "requests-toolbelt" },
     { name = "zstandard" },
 ]
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+    { url = "https://files.pythonhosted.org/packages/c8/10/ad3107b666c3203b7938d10ea6b8746b9735c399cf737a51386d58e41d34/langsmith-0.4.5-py3-none-any.whl", hash = "sha256:4167717a2cccc4dff5809dbddc439628e836f6fd13d4fdb31ea013bc8d5cfaf5", size = 367795, upload-time = "2025-07-10T22:08:02.548Z" },
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@@ -773,7 +773,7 @@ wheels = [
 
 [[package]]
 name = "pandas"
-version = "2.3.0"
+version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
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 authors = []
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 [[package]]

libs/langgraph/README.md

@@ -73,11 +73,12 @@ 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.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## 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/func/__init__.py

@@ -180,6 +180,7 @@ def task(
         warnings.warn(
             "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
             category=LangGraphDeprecatedSinceV05,
+            stacklevel=2,
         )
         if retry_policy is None:
             retry_policy = retry  # type: ignore[assignment]
@@ -384,6 +385,7 @@ class entrypoint:
             warnings.warn(
                 "`retry` is deprecated and will be removed. Please use `retry_policy` instead.",
                 category=LangGraphDeprecatedSinceV05,
+                stacklevel=2,
             )
             if retry_policy is None:
                 retry_policy = retry  # type: ignore[assignment]

libs/langgraph/langgraph/graph/state.py

@@ -794,7 +794,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         interrupt_after: All | list[str] | None = None,
         debug: bool = False,
         name: str | None = None,
-    ) -> CompiledStateGraph[StateT, InputT]:
+    ) -> CompiledStateGraph[StateT, InputT, OutputT]:
         """Compiles the state graph into a `CompiledStateGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
@@ -996,7 +996,7 @@ class CompiledStateGraph(
                 writers=[ChannelWrite(write_entries)],
             )
         elif node is not None:
-            input_schema = node.input if node else self.builder._state_schema
+            input_schema = node.input if node else self.builder.state_schema
             input_channels = list(self.builder.schemas[input_schema])
             is_single_input = len(input_channels) == 1 and "__root__" in input_channels
             if input_schema in self.schema_to_mapper:

libs/langgraph/langgraph/pregel/protocol.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import Any, Generic
 
@@ -14,7 +14,7 @@ from langgraph.typing import InputT, OutputT, StateT
 
 
 # TODO: remove Runnable inheritance here!
-class PregelProtocol(Runnable[InputT, Any], Generic[StateT, InputT, OutputT], ABC):
+class PregelProtocol(Runnable[InputT, Any], Generic[StateT, InputT, OutputT]):
     @abstractmethod
     def with_config(
         self, config: RunnableConfig | None = None, **kwargs: Any

libs/langgraph/langgraph/pregel/runner.py

@@ -190,7 +190,7 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` schuduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
                 tasks = ()  # don't reschedule this task
@@ -326,7 +326,7 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` schuduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
                 tasks = ()  # don't reschedule this task

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.5.2"
+version = "0.5.3"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -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/uv.lock

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+version = "2.1.1"
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 [package.optional-dependencies]
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 [[package]]
@@ -3076,11 +3076,11 @@ wheels = [
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20250516"
+version = "2.9.0.20250708"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ef/88/d65ed807393285204ab6e2801e5d11fbbea811adcaa979a2ed3b67a5ef41/types_python_dateutil-2.9.0.20250516.tar.gz", hash = "sha256:13e80d6c9c47df23ad773d54b2826bd52dbbb41be87c3f339381c1700ad21ee5", size = 13943, upload-time = "2025-05-16T03:06:58.385Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c9/95/6bdde7607da2e1e99ec1c1672a759d42f26644bbacf939916e086db34870/types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab", size = 15834, upload-time = "2025-07-08T03:14:03.382Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/72/52/43e70a8e57fefb172c22a21000b03ebcc15e47e97f5cb8495b9c2832efb4/types_python_dateutil-2.9.0.20250708-py3-none-any.whl", hash = "sha256:4d6d0cc1cc4d24a2dc3816024e502564094497b713f7befda4d5bc7a8e3fd21f", size = 17724, upload-time = "2025-07-08T03:14:02.593Z" },
 ]
 
 [[package]]

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/interrupt.py

@@ -80,7 +80,7 @@ class HumanResponse(TypedDict):
             - "ignore": Skips/ignores the current step
             - "response": Provides text feedback or instructions
             - "edit": Modifies the current state/content
-        arg: The response payload:
+        args: The response payload:
             - None: For ignore/accept actions
             - str: For text responses
             - ActionRequest: For edit actions with updated content

libs/prebuilt/langgraph/prebuilt/tool_node.py

@@ -1,3 +1,36 @@
+"""Tool execution node for LangGraph workflows.
+
+This module provides prebuilt functionality for executing tools in LangGraph.
+
+Tools are functions that models can call to interact with external systems,
+APIs, databases, or perform computations.
+
+The module implements several key design patterns:
+- Parallel execution of multiple tool calls for efficiency
+- Robust error handling with customizable error messages
+- State injection for tools that need access to graph state
+- Store injection for tools that need persistent storage
+- Command-based state updates for advanced control flow
+
+Key Components:
+    ToolNode: Main class for executing tools in LangGraph workflows
+    InjectedState: Annotation for injecting graph state into tools
+    InjectedStore: Annotation for injecting persistent store into tools
+    tools_condition: Utility function for conditional routing based on tool calls
+
+Typical Usage:
+    ```python
+    from langchain_core.tools import tool
+    from langgraph.prebuilt import ToolNode
+
+    @tool
+    def my_tool(x: int) -> str:
+        return f"Result: {x}"
+
+    tool_node = ToolNode([my_tool])
+    ```
+"""
+
 import asyncio
 import inspect
 import json
@@ -49,6 +82,24 @@ TOOL_CALL_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
 
 
 def msg_content_output(output: Any) -> Union[str, list[dict]]:
+    """Convert tool output to valid message content format.
+
+    LangChain ToolMessages accept either string content or a list of content blocks.
+    This function ensures tool outputs are properly formatted for message consumption
+    by attempting to preserve structured data when possible, falling back to JSON
+    serialization or string conversion.
+
+    Args:
+        output: The raw output from a tool execution. Can be any type.
+
+    Returns:
+        Either a string representation of the output or a list of content blocks
+        if the output is already in the correct format for structured content.
+
+    Note:
+        This function prioritizes backward compatibility by defaulting to JSON
+        serialization rather than supporting all possible message content formats.
+    """
     if isinstance(output, str):
         return output
     elif isinstance(output, list) and all(
@@ -58,9 +109,10 @@ def msg_content_output(output: Any) -> Union[str, list[dict]]:
         ]
     ):
         return output
-    # Technically a list of strings is also valid message content but it's not currently
-    # well tested that all chat models support this. And for backwards compatibility
-    # we want to make sure we don't break any existing ToolNode usage.
+    # Technically a list of strings is also valid message content, but it's
+    # not currently well tested that all chat models support this.
+    # And for backwards compatibility we want to make sure we don't break
+    # any existing ToolNode usage.
     else:
         try:
             return json.dumps(output, ensure_ascii=False)
@@ -78,6 +130,30 @@ def _handle_tool_error(
         tuple[type[Exception], ...],
     ],
 ) -> str:
+    """Generate error message content based on exception handling configuration.
+
+    This function centralizes error message generation logic, supporting different
+    error handling strategies configured via the ToolNode's handle_tool_errors
+    parameter.
+
+    Args:
+        e: The exception that occurred during tool execution.
+        flag: Configuration for how to handle the error. Can be:
+            - bool: If True, use default error template
+            - str: Use this string as the error message
+            - Callable: Call this function with the exception to get error message
+            - tuple: Not used in this context (handled by caller)
+
+    Returns:
+        A string containing the error message to include in the ToolMessage.
+
+    Raises:
+        ValueError: If flag is not one of the supported types.
+
+    Note:
+        The tuple case is handled by the caller through exception type checking,
+        not by this function directly.
+    """
     if isinstance(flag, (bool, tuple)):
         content = TOOL_CALL_ERROR_TEMPLATE.format(error=repr(e))
     elif isinstance(flag, str):
@@ -93,6 +169,29 @@ def _handle_tool_error(
 
 
 def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception], ...]:
+    """Infer exception types handled by a custom error handler function.
+
+    This function analyzes the type annotations of a custom error handler to determine
+    which exception types it's designed to handle. This enables type-safe error handling
+    where only specific exceptions are caught and processed by the handler.
+
+    Args:
+        handler: A callable that takes an exception and returns an error message string.
+                The first parameter (after self/cls if present) should be type-annotated
+                with the exception type(s) to handle.
+
+    Returns:
+        A tuple of exception types that the handler can process. Returns (Exception,)
+        if no specific type information is available for backward compatibility.
+
+    Raises:
+        ValueError: If the handler's annotation contains non-Exception types or
+                   if Union types contain non-Exception types.
+
+    Note:
+        This function supports both single exception types and Union types for
+        handlers that need to handle multiple exception types differently.
+    """
     sig = inspect.signature(handler)
     params = list(sig.parameters.values())
     if params:
@@ -111,8 +210,9 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
                     return tuple(args)
                 else:
                     raise ValueError(
-                        "All types in the error handler error annotation must be Exception types. "
-                        "For example, `def custom_handler(e: Union[ValueError, TypeError])`. "
+                        "All types in the error handler error annotation must be "
+                        "Exception types. For example, "
+                        "`def custom_handler(e: Union[ValueError, TypeError])`. "
                         f"Got '{first_param.annotation}' instead."
                     )
 
@@ -121,13 +221,16 @@ def _infer_handled_types(handler: Callable[..., str]) -> tuple[type[Exception],
                 return (exception_type,)
             else:
                 raise ValueError(
-                    f"Arbitrary types are not supported in the error handler signature. "
-                    "Please annotate the error with either a specific Exception type or a union of Exception types. "
-                    "For example, `def custom_handler(e: ValueError)` or `def custom_handler(e: Union[ValueError, TypeError])`. "
+                    f"Arbitrary types are not supported in the error handler "
+                    f"signature. Please annotate the error with either a "
+                    f"specific Exception type or a union of Exception types. "
+                    "For example, `def custom_handler(e: ValueError)` or "
+                    "`def custom_handler(e: Union[ValueError, TypeError])`. "
                     f"Got '{exception_type}' instead."
                 )
 
-    # If no type information is available, return (Exception,) for backwards compatibility.
+    # If no type information is available, return (Exception,)
+    # for backwards compatibility.
     return (Exception,)
 
 
@@ -141,60 +244,72 @@ class ToolNode(RunnableCallable):
     Tool calls can also be passed directly as a list of `ToolCall` dicts.
 
     Args:
-        tools: A sequence of tools that can be invoked by the ToolNode.
-        name: The name of the ToolNode in the graph. Defaults to "tools".
-        tags: Optional tags to associate with the node. Defaults to None.
-        handle_tool_errors: How to handle tool errors raised by tools inside the node. Defaults to True.
-            Must be one of the following:
+        tools: A sequence of tools that can be invoked by this node. Tools can be
+            BaseTool instances or plain functions that will be converted to tools.
+        name: The name identifier for this node in the graph. Used for debugging
+            and visualization. Defaults to "tools".
+        tags: Optional metadata tags to associate with the node for filtering
+            and organization. Defaults to None.
+        handle_tool_errors: Configuration for error handling during tool execution.
+            Defaults to True. Supports multiple strategies:
 
-            - True: all errors will be caught and
-                a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
-            - str: all errors will be caught and
-                a ToolMessage with the string value of 'handle_tool_errors' will be returned.
-            - tuple[type[Exception], ...]: exceptions in the tuple will be caught and
-                a ToolMessage with a default error message (TOOL_CALL_ERROR_TEMPLATE) will be returned.
-            - Callable[..., str]: exceptions from the signature of the callable will be caught and
-                a ToolMessage with the string value of the result of the 'handle_tool_errors' callable will be returned.
-            - False: none of the errors raised by the tools will be caught
-        messages_key: The state key in the input that contains the list of messages.
-            The same key will be used for the output from the ToolNode.
-            Defaults to "messages".
+            - True: Catch all errors and return a ToolMessage with the default
+              error template containing the exception details.
+            - str: Catch all errors and return a ToolMessage with this custom
+              error message string.
+            - tuple[type[Exception], ...]: Only catch exceptions of the specified
+              types and return default error messages for them.
+            - Callable[..., str]: Catch exceptions matching the callable's signature
+              and return the string result of calling it with the exception.
+            - False: Disable error handling entirely, allowing exceptions to propagate.
 
-    The `ToolNode` is roughly analogous to:
+        messages_key: The key in the state dictionary that contains the message list.
+            This same key will be used for the output ToolMessages. Defaults to "messages".
 
-    ```python
-    tools_by_name = {tool.name: tool for tool in tools}
-    def tool_node(state: dict):
-        result = []
-        for tool_call in state["messages"][-1].tool_calls:
-            tool = tools_by_name[tool_call["name"]]
-            observation = tool.invoke(tool_call["args"])
-            result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
-        return {"messages": result}
-    ```
+    Example:
+        Basic usage with simple tools:
 
-    Tool calls can also be passed directly to a ToolNode. This can be useful when using
-    the Send API, e.g., in a conditional edge:
+        ```python
+        from langgraph.prebuilt import ToolNode
+        from langchain_core.tools import tool
 
-    ```python
-    def example_conditional_edge(state: dict) -> List[Send]:
-        tool_calls = state["messages"][-1].tool_calls
-        # If tools rely on state or store variables (whose values are not generated
-        # directly by a model), you can inject them into the tool calls.
-        tool_calls = [
-            tool_node.inject_tool_args(call, state, store)
-            for call in last_message.tool_calls
-        ]
-        return [Send("tools", [tool_call]) for tool_call in tool_calls]
-    ```
+        @tool
+        def calculator(a: int, b: int) -> int:
+            \"\"\"Add two numbers.\"\"\"
+            return a + b
 
-    Important:
-        - The input state can be one of the following:
-            - A dict with a messages key containing a list of messages.
-            - A list of messages.
-            - A list of tool calls.
-        - If operating on a message list, the last message must be an `AIMessage` with
-            `tool_calls` populated.
+        tool_node = ToolNode([calculator])
+        ```
+
+        Custom error handling:
+
+        ```python
+        def handle_math_errors(e: ZeroDivisionError) -> str:
+            return "Cannot divide by zero!"
+
+        tool_node = ToolNode([calculator], handle_tool_errors=handle_math_errors)
+        ```
+
+        Direct tool call execution:
+
+        ```python
+        tool_calls = [{"name": "calculator", "args": {"a": 5, "b": 3}, "id": "1", "type": "tool_call"}]
+        result = tool_node.invoke(tool_calls)
+        ```
+
+    Note:
+        The ToolNode expects input in one of three formats:
+        1. A dictionary with a messages key containing a list of messages
+        2. A list of messages directly
+        3. A list of tool call dictionaries
+
+        When using message formats, the last message must be an AIMessage with
+        tool_calls populated. The node automatically extracts and processes these
+        tool calls concurrently.
+
+        For advanced use cases involving state injection or store access, tools
+        can be annotated with InjectedState or InjectedStore to receive graph
+        context automatically.
     """
 
     name: str = "ToolNode"
@@ -210,6 +325,15 @@ class ToolNode(RunnableCallable):
         ] = True,
         messages_key: str = "messages",
     ) -> None:
+        """Initialize the ToolNode with the provided tools and configuration.
+
+        Args:
+            tools: Sequence of tools to make available for execution.
+            name: Node name for graph identification.
+            tags: Optional metadata tags.
+            handle_tool_errors: Error handling configuration.
+            messages_key: State key containing messages.
+        """
         super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
         self.tools_by_name: dict[str, BaseTool] = {}
         self.tool_to_state_args: dict[str, dict[str, Optional[str]]] = {}
@@ -541,20 +665,38 @@ class ToolNode(RunnableCallable):
         ],
         store: Optional[BaseStore],
     ) -> ToolCall:
-        """Injects the state and store into the tool call.
+        """Inject graph state and store into tool call arguments.
 
-        Tool arguments with types annotated as `InjectedState` and `InjectedStore` are
-        ignored in tool schemas for generation purposes. This method injects them into
-        tool calls for tool invocation.
+        This method enables tools to access graph context that should not be controlled
+        by the model. Tools can declare dependencies on graph state or persistent storage
+        using InjectedState and InjectedStore annotations. This method automatically
+        identifies these dependencies and injects the appropriate values.
+
+        The injection process preserves the original tool call structure while adding
+        the necessary context arguments. This allows tools to be both model-callable
+        and context-aware without exposing internal state management to the model.
 
         Args:
-            tool_call: The tool call to inject state and store into.
-            input: The input state
-                to inject.
-            store: The store to inject.
+            tool_call: The tool call dictionary to augment with injected arguments.
+                Must contain 'name', 'args', 'id', and 'type' fields.
+            input: The current graph state to inject into tools requiring state access.
+                Can be a message list, state dictionary, or BaseModel instance.
+            store: The persistent store instance to inject into tools requiring storage.
+                Will be None if no store is configured for the graph.
 
         Returns:
-            ToolCall: The tool call with injected state and store.
+            A new ToolCall dictionary with the same structure as the input but with
+            additional arguments injected based on the tool's annotation requirements.
+
+        Raises:
+            ValueError: If a tool requires store injection but no store is provided,
+                       or if state injection requirements cannot be satisfied.
+
+        Note:
+            This method is automatically called during tool execution but can also
+            be used manually when working with the Send API or custom routing logic.
+            The injection is performed on a copy of the tool call to avoid mutating
+            the original.
         """
         if tool_call["name"] not in self.tools_by_name:
             return tool_call
@@ -625,55 +767,66 @@ def tools_condition(
     state: Union[list[AnyMessage], dict[str, Any], BaseModel],
     messages_key: str = "messages",
 ) -> Literal["tools", "__end__"]:
-    """Use in the conditional_edge to route to the ToolNode if the last message
+    """Conditional routing function for tool-calling workflows.
 
-    has tool calls. Otherwise, route to the end.
+    This utility function implements the standard conditional logic for ReAct-style
+    agents: if the last AI message contains tool calls, route to the tool execution
+    node; otherwise, end the workflow. This pattern is fundamental to most tool-calling
+    agent architectures.
+
+    The function handles multiple state formats commonly used in LangGraph applications,
+    making it flexible for different graph designs while maintaining consistent behavior.
 
     Args:
-        state: The state to check for
-            tool calls. Must have a list of messages (MessageGraph) or have the
-            "messages" key (StateGraph).
+        state: The current graph state to examine for tool calls. Supported formats:
+            - List of messages (for MessageGraph)
+            - Dictionary containing a messages key (for StateGraph)
+            - BaseModel instance with a messages attribute
+        messages_key: The key or attribute name containing the message list in the state.
+            This allows customization for graphs using different state schemas.
+            Defaults to "messages".
 
     Returns:
-        The next node to route to.
+        Either "tools" if tool calls are present in the last AI message, or "__end__"
+        to terminate the workflow. These are the standard routing destinations for
+        tool-calling conditional edges.
 
+    Raises:
+        ValueError: If no messages can be found in the provided state format.
 
-    Examples:
-        Create a custom ReAct-style agent with tools.
+    Example:
+        Basic usage in a ReAct agent:
 
-        ```pycon
-        >>> from langchain_anthropic import ChatAnthropic
-        >>> from langchain_core.tools import tool
-        ...
-        >>> from langgraph.graph import StateGraph
-        >>> from langgraph.prebuilt import ToolNode, tools_condition
-        >>> from langgraph.graph.message import add_messages
-        ...
-        >>> from typing import Annotated
-        >>> from typing_extensions import TypedDict
-        ...
-        >>> @tool
-        >>> def divide(a: float, b: float) -> int:
-        ...     \"\"\"Return a / b.\"\"\"
-        ...     return a / b
-        ...
-        >>> llm = ChatAnthropic(model="claude-3-haiku-20240307")
-        >>> tools = [divide]
-        ...
-        >>> class State(TypedDict):
-        ...     messages: Annotated[list, add_messages]
-        >>>
-        >>> graph_builder = StateGraph(State)
-        >>> graph_builder.add_node("tools", ToolNode(tools))
-        >>> graph_builder.add_node("chatbot", lambda state: {"messages":llm.bind_tools(tools).invoke(state['messages'])})
-        >>> graph_builder.add_edge("tools", "chatbot")
-        >>> graph_builder.add_conditional_edges(
-        ...     "chatbot", tools_condition
-        ... )
-        >>> graph_builder.set_entry_point("chatbot")
-        >>> graph = graph_builder.compile()
-        >>> graph.invoke({"messages": {"role": "user", "content": "What's 329993 divided by 13662?"}})
+        ```python
+        from langgraph.graph import StateGraph
+        from langgraph.prebuilt import ToolNode, tools_condition
+        from typing_extensions import TypedDict
+
+        class State(TypedDict):
+            messages: list
+
+        graph = StateGraph(State)
+        graph.add_node("llm", call_model)
+        graph.add_node("tools", ToolNode([my_tool]))
+        graph.add_conditional_edges(
+            "llm",
+            tools_condition,  # Routes to "tools" or "__end__"
+            {"tools": "tools", "__end__": "__end__"}
+        )
         ```
+
+        Custom messages key:
+
+        ```python
+        def custom_condition(state):
+            return tools_condition(state, messages_key="chat_history")
+        ```
+
+    Note:
+        This function is designed to work seamlessly with ToolNode and standard
+        LangGraph patterns. It expects the last message to be an AIMessage when
+        tool calls are present, which is the standard output format for tool-calling
+        language models.
     """
     if isinstance(state, list):
         ai_message = state[-1]
@@ -689,16 +842,18 @@ def tools_condition(
 
 
 class InjectedState(InjectedToolArg):
-    """Annotation for a Tool arg that is meant to be populated with the graph state.
+    """Annotation for injecting graph state into tool arguments.
 
-    Any Tool argument annotated with InjectedState will be hidden from a tool-calling
-    model, so that the model doesn't attempt to generate the argument. If using
-    ToolNode, the appropriate graph state field will be automatically injected into
-    the model-generated tool args.
+    This annotation enables tools to access graph state without exposing state
+    management details to the language model. Tools annotated with InjectedState
+    receive state data automatically during execution while remaining invisible
+    to the model's tool-calling interface.
 
     Args:
-        field: The key from state to insert. If None, the entire state is expected to
-            be passed in.
+        field: Optional key to extract from the state dictionary. If None, the entire
+            state is injected. If specified, only that field's value is injected.
+            This allows tools to request specific state components rather than
+            processing the full state structure.
 
     Example:
         ```python
@@ -745,6 +900,15 @@ class InjectedState(InjectedToolArg):
             ToolMessage(content='bar2', name='foo_tool', tool_call_id='2')
         ]
         ```
+
+    Note:
+        - InjectedState arguments are automatically excluded from tool schemas
+          presented to language models
+        - ToolNode handles the injection process during execution
+        - Tools can mix regular arguments (controlled by the model) with injected
+          arguments (controlled by the system)
+        - State injection occurs after the model generates tool calls but before
+          tool execution
     """  # noqa: E501
 
     def __init__(self, field: Optional[str] = None) -> None:
@@ -752,61 +916,97 @@ class InjectedState(InjectedToolArg):
 
 
 class InjectedStore(InjectedToolArg):
-    """Annotation for a Tool arg that is meant to be populated with LangGraph store.
+    """Annotation for injecting persistent store into tool arguments.
 
-    Any Tool argument annotated with InjectedStore will be hidden from a tool-calling
-    model, so that the model doesn't attempt to generate the argument. If using
-    ToolNode, the appropriate store field will be automatically injected into
-    the model-generated tool args. Note: if a graph is compiled with a store object,
-    the store will be automatically propagated to the tools with InjectedStore args
-    when using ToolNode.
+    This annotation enables tools to access LangGraph's persistent storage system
+    without exposing storage details to the language model. Tools annotated with
+    InjectedStore receive the store instance automatically during execution while
+    remaining invisible to the model's tool-calling interface.
+
+    The store provides persistent, cross-session data storage that tools can use
+    for maintaining context, user preferences, or any other data that needs to
+    persist beyond individual workflow executions.
 
     !!! Warning
         `InjectedStore` annotation requires `langchain-core >= 0.3.8`
 
     Example:
         ```python
-        from typing import Any
         from typing_extensions import Annotated
-
-        from langchain_core.messages import AIMessage
         from langchain_core.tools import tool
-
         from langgraph.store.memory import InMemoryStore
         from langgraph.prebuilt import InjectedStore, ToolNode
 
-        store = InMemoryStore()
-        store.put(("values",), "foo", {"bar": 2})
+        @tool
+        def save_preference(
+            key: str,
+            value: str,
+            store: Annotated[Any, InjectedStore()]
+        ) -> str:
+            \"\"\"Save user preference to persistent storage.\"\"\"
+            store.put(("preferences",), key, value)
+            return f"Saved {key} = {value}"
 
         @tool
-        def store_tool(x: int, my_store: Annotated[Any, InjectedStore()]) -> str:
-            '''Do something with store.'''
-            stored_value = my_store.get(("values",), "foo").value["bar"]
-            return stored_value + x
-
-        node = ToolNode([store_tool])
-
-        tool_call = {"name": "store_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
-        state = {
-            "messages": [AIMessage("", tool_calls=[tool_call])],
-        }
-
-        node.invoke(state, store=store)
+        def get_preference(
+            key: str,
+            store: Annotated[Any, InjectedStore()]
+        ) -> str:
+            \"\"\"Retrieve user preference from persistent storage.\"\"\"
+            result = store.get(("preferences",), key)
+            return result.value if result else "Not found"
         ```
 
-        ```pycon
-        {
-            "messages": [
-                ToolMessage(content='3', name='store_tool', tool_call_id='1'),
-            ]
-        }
+        Usage with ToolNode and graph compilation:
+
+        ```python
+        from langgraph.graph import StateGraph
+        from langgraph.store.memory import InMemoryStore
+
+        store = InMemoryStore()
+        tool_node = ToolNode([save_preference, get_preference])
+
+        graph = StateGraph(State)
+        graph.add_node("tools", tool_node)
+        compiled_graph = graph.compile(store=store)  # Store is injected automatically
         ```
+
+        Cross-session persistence:
+
+        ```python
+        # First session
+        result1 = graph.invoke({"messages": [HumanMessage("Save my favorite color as blue")]})
+
+        # Later session - data persists
+        result2 = graph.invoke({"messages": [HumanMessage("What's my favorite color?")]})
+        ```
+
+    Note:
+        - InjectedStore arguments are automatically excluded from tool schemas
+          presented to language models
+        - The store instance is automatically injected by ToolNode during execution
+        - Tools can access namespaced storage using the store's get/put methods
+        - Store injection requires the graph to be compiled with a store instance
+        - Multiple tools can share the same store instance for data consistency
     """  # noqa: E501
 
 
 def _is_injection(
     type_arg: Any, injection_type: Union[Type[InjectedState], Type[InjectedStore]]
 ) -> bool:
+    """Check if a type argument represents an injection annotation.
+
+    This utility function determines whether a type annotation indicates that
+    an argument should be injected with state or store data. It handles both
+    direct annotations and nested annotations within Union or Annotated types.
+
+    Args:
+        type_arg: The type argument to check for injection annotations.
+        injection_type: The injection type to look for (InjectedState or InjectedStore).
+
+    Returns:
+        True if the type argument contains the specified injection annotation.
+    """
     if isinstance(type_arg, injection_type) or (
         isinstance(type_arg, type) and issubclass(type_arg, injection_type)
     ):
@@ -818,6 +1018,19 @@ def _is_injection(
 
 
 def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
+    """Extract state injection mappings from tool annotations.
+
+    This function analyzes a tool's input schema to identify arguments that should
+    be injected with graph state. It processes InjectedState annotations to build
+    a mapping of tool argument names to state field names.
+
+    Args:
+        tool: The tool to analyze for state injection requirements.
+
+    Returns:
+        A dictionary mapping tool argument names to state field names. If a field
+        name is None, the entire state should be injected for that argument.
+    """
     full_schema = tool.get_input_schema()
     tool_args_to_state_fields: dict = {}
 
@@ -844,6 +1057,22 @@ def _get_state_args(tool: BaseTool) -> dict[str, Optional[str]]:
 
 
 def _get_store_arg(tool: BaseTool) -> Optional[str]:
+    """Extract store injection argument from tool annotations.
+
+    This function analyzes a tool's input schema to identify the argument that
+    should be injected with the graph store. Only one store argument is supported
+    per tool.
+
+    Args:
+        tool: The tool to analyze for store injection requirements.
+
+    Returns:
+        The name of the argument that should receive the store injection, or None
+        if no store injection is required.
+
+    Raises:
+        ValueError: If a tool argument has multiple InjectedStore annotations.
+    """
     full_schema = tool.get_input_schema()
     for name, type_ in get_all_basemodel_annotations(full_schema).items():
         injections = [

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/uv.lock

@@ -40,11 +40,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.6.15"
+version = "2025.7.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
+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" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl", hash = "sha256:2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057", size = 157650, upload-time = "2025-06-15T02:45:49.977Z" },
+    { url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
 ]
 
 [[package]]
@@ -316,7 +316,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.5.2"
+version = "0.5.3"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -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 = "0.1.72"
+version = "0.1.73"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },
@@ -533,7 +533,7 @@ dev = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.4"
+version = "0.4.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -544,9 +544,9 @@ dependencies = [
     { name = "requests-toolbelt" },
     { name = "zstandard" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/20/c8/8d2e0fc438d2d3d8d4300f7684ea30a754344ed00d7ba9cc2705241d2a5f/langsmith-0.4.4.tar.gz", hash = "sha256:70c53bbff24a7872e88e6fa0af98270f4986a6e364f9e85db1cc5636defa4d66", size = 352105, upload-time = "2025-06-27T19:20:36.207Z" }
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 [[package]]

libs/sdk-py/langgraph_sdk/auth/__init__.py

@@ -335,8 +335,10 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
     @typing.overload
     def __call__(
         self,
-        fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
-        | _ActionHandler[dict[str, typing.Any]],
+        fn: (
+            _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
+            | _ActionHandler[dict[str, typing.Any]]
+        ),
     ) -> _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]: ...
 
     @typing.overload
@@ -352,9 +354,11 @@ class _ResourceOn(typing.Generic[VCreate, VRead, VUpdate, VDelete, VSearch]):
 
     def __call__(
         self,
-        fn: _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
-        | _ActionHandler[dict[str, typing.Any]]
-        | None = None,
+        fn: (
+            _ActionHandler[VCreate | VUpdate | VRead | VDelete | VSearch]
+            | _ActionHandler[dict[str, typing.Any]]
+            | None
+        ) = None,
         *,
         resources: str | Sequence[str] | None = None,
         actions: str | Sequence[str] | None = None,
@@ -476,9 +480,13 @@ class _StoreOn:
     def __call__(
         self,
         *,
-        actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-        | Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
-        | None = None,
+        actions: (
+            typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+            | Sequence[
+                typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+            ]
+            | None
+        ) = None,
     ) -> Callable[[AHO], AHO]: ...
 
     @typing.overload
@@ -488,9 +496,13 @@ class _StoreOn:
         self,
         fn: AHO | None = None,
         *,
-        actions: typing.Literal["put", "get", "search", "list_namespaces", "delete"]
-        | Sequence[typing.Literal["put", "get", "search", "list_namespaces", "delete"]]
-        | None = None,
+        actions: (
+            typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+            | Sequence[
+                typing.Literal["put", "get", "search", "list_namespaces", "delete"]
+            ]
+            | None
+        ) = None,
     ) -> AHO | Callable[[AHO], AHO]:
         """Register a handler for specific resources and actions.
 
@@ -708,4 +720,12 @@ def _validate_handler(fn: Callable[..., typing.Any]) -> None:
         )
 
 
+def is_studio_user(user: types.MinimalUser | types.User | types.UserDict) -> bool:
+    return (
+        isinstance(user, types.StudioUser)
+        or isinstance(user, dict)
+        and user.get("kind") == "StudioUser"
+    )
+
+
 __all__ = ["Auth", "types", "exceptions"]

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.1.72"
+version = "0.1.73"
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/sdk-py/uv.lock

@@ -19,11 +19,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.6.15"
+version = "2025.7.14"
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