Reapply "Remove MessageGraph (#4875 )"

This reverts commit 3fa3a586b5.
2026-08-18 13:45:44 +02:00 · 2025-06-29 11:20:08 -04:00
379 changed files with 32196 additions and 27666 deletions
@@ -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 LangChain Forum at forum.langchain.com.
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
 labels: [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 the [LangChain Forum](https://forum.langchain.com/).
+        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).
        
        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:
        
-        * [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),
+        [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),
  - 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 the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
          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. Replace this code with your own!
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
      placeholder: |
        from langgraph.graph import StateGraph

@@ -78,7 +78,7 @@ body:
    attributes:
      label: System Info
      description: |
-        Run on your machine: `python -m langchain_core.sys_info`
+        python -m langchain_core.sys_info
      placeholder: |
        python -m langchain_core.sys_info
    validations:
@@ -1,6 +1,15 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
-  - name: LangChain Forum
-    url:  https://forum.langchain.com/
-    about: General community discussions, support, and feature requests
+  - name: 🤔 Question or Problem
+    about: Ask a question or ask about a problem in GitHub Discussions.
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/q-a
+  - name: Feature Request
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/ideas
+    about: Suggest a feature or an idea
+  - name: Show and tell
+    about: Show what you built with LangChain
+    url: https://github.com/langchain-ai/langgraph/discussions/categories/show-and-tell
+  - name: Slack
+    url:  https://www.langchain.com/join-community
+    about: General community discussions
@@ -1,29 +1,25 @@
 name: 🔒 Privileged
-description: You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
+description: You are a LangChain 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 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.
+        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.
  - type: checkboxes
    id: privileged
    attributes:
      label: Privileged issue
      description: Confirm that you are allowed to create an issue here.
      options:
-        - label: I am a LangGraph maintainer, or was asked directly by a LangGraph maintainer to create an issue here.
+        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
          required: true
  - type: textarea
    id: content
    attributes:
      label: Issue Content
      description: Add the content of the issue here.
-  - type: markdown
-    attributes:
-      value: |
-        Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.
@@ -1,31 +0,0 @@
-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.
@@ -1,18 +1,11 @@
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# and
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
 version: 2
 updates:
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "weekly"
-
-  - package-ecosystem: "pip"
-    directories:
-      - "libs/checkpoint"
-      - "libs/checkpoint-postgres"
-      - "libs/checkpoint-sqlite"
-      - "libs/cli"
-      - "libs/langgraph"
-      - "libs/prebuilt"
-      - "libs/sdk-py"
-    schedule:
-      interval: "weekly"
@@ -3,9 +3,6 @@ name: CLI integration test
 on:
  workflow_call:

-permissions:
-  contents: read
-
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -8,9 +8,6 @@ on:
        type: string
        description: "From which folder this pipeline executes"

-permissions:
-  contents: read
-
 env:
  # This env var allows us to get inline annotations when ruff has complaints.
  RUFF_OUTPUT_FORMAT: github
@@ -8,9 +8,6 @@ on:
        type: string
        description: "From which folder this pipeline executes"

-permissions:
-  contents: read
-
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -3,9 +3,6 @@ name: test
 on:
  workflow_call:

-permissions:
-  contents: read
-
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -11,9 +11,6 @@ on:
 env:
  PYTHON_VERSION: "3.10"

-permissions:
-  contents: read
-
 jobs:
  build:
    if: github.ref == 'refs/heads/main'
@@ -7,9 +7,6 @@ on:
    paths:
      - "libs/**"

-permissions:
-  contents: read
-
 jobs:
  benchmark:
    runs-on: ubuntu-latest
@@ -5,9 +5,6 @@ on:
    paths:
      - "libs/**"

-permissions:
-  contents: read
-
 jobs:
  benchmark:
    runs-on: ubuntu-latest
@@ -3,12 +3,9 @@ name: CI

 on:
  push:
-    branches: [main, v1]
+    branches: [main]
  pull_request:

-permissions:
-  contents: read
-
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
 #
@@ -24,7 +21,7 @@ jobs:
    runs-on: ubuntu-latest
    outputs:
      python: ${{ steps.filter.outputs.python }}
-      deps: ${{ steps.filter.outputs.deps }}
+      sdk-js: ${{ steps.filter.outputs.sdk-js }}
    steps:
      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
@@ -39,9 +36,8 @@ jobs:
              - 'libs/checkpoint-sqlite/**'
              - 'libs/checkpoint-postgres/**'
              - 'libs/prebuilt/**'
-            deps:
-              - '**/pyproject.toml'
-              - '**/uv.lock'
+            sdk-js:
+              - 'libs/sdk-js/**'

  lint:
    needs: changes
@@ -59,7 +55,7 @@ jobs:

            "libs/prebuilt",
          ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
    uses: ./.github/workflows/_lint.yml
    with:
      working-directory: ${{ matrix.working-directory }}
@@ -78,7 +74,7 @@ jobs:
            "libs/checkpoint-postgres",
            "libs/prebuilt",
          ]
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
    uses: ./.github/workflows/_test.yml
    with:
      working-directory: ${{ matrix.working-directory }}
@@ -87,7 +83,7 @@ jobs:
  # NOTE: we're testing langgraph separately because it requires a different matrix
  test-langgraph:
    needs: changes
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
    name: "cd libs/langgraph"
    uses: ./.github/workflows/_test_langgraph.yml
    secrets: inherit
@@ -144,21 +140,73 @@ jobs:

  integration-test:
    needs: changes
-    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
+    if: needs.changes.outputs.python == 'true'
    name: CLI integration test
    uses: ./.github/workflows/_integration_test.yml
    secrets: inherit

+  lint-js:
+    needs: changes
+    if: needs.changes.outputs.sdk-js == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+      - name: Install dependencies
+        run: yarn install
+      - name: Run lint
+        run: yarn lint
+      - name: Build
+        run: yarn build
+
+  test-js:
+    needs: changes
+    if: needs.changes.outputs.sdk-js == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+      - name: Install dependencies
+        run: yarn install
+      - name: Run tests
+        run: yarn test
+
  ci_success:
    name: "CI Success"
    needs:
      [
        lint,
+        lint-js,
        test,
        test-langgraph,
        check-sdk-methods,
        check-schema,
        integration-test,
+        test-js,
      ]
    if: |
      always()
@@ -1,11 +0,0 @@
-LangChain
-LangGraph
-LangSmith
-thead
-stdio
-nd
-jupyter
-lets
-lite
-uis
-deque
@@ -34,16 +34,10 @@
            id: extract_ignore_words
    
          - name: Codespell
-            uses: codespell-project/actions-codespell@v2.1
+            uses: codespell-project/actions-codespell@v2
            with:
-              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map'
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
              ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
          # We do this to avoid spellchecking cell outputs
          - name: Codespell Notebooks
-            run: make codespell
-          
-          - name: Codespell LangGraph Library
-            run: |
-              # Change to root directory to check the main LangGraph library
-              cd ..
-              codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map,*.pyc,__pycache__/*" --ignore-words-list="${{ steps.extract_ignore_words.outputs.ignore_words_list }}" libs/langgraph/langgraph/
+            run: make codespell
@@ -35,7 +35,16 @@ jobs:
        with:
          filter: "docs/docs/**"

+  # TODO: Uncomment this to run on PRs
+  # run-changed-notebooks:
+  #   needs: get-changed-files
+  #   uses: ./.github/workflows/run_notebooks.yml
+  #   secrets: inherit
+  #   with:
+  #     changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
+
  deploy:
+    # needs: run-changed-notebooks
    runs-on: ubuntu-latest
    timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
    env:
@@ -11,9 +11,6 @@ on:
    - cron: "0 5 * * *"
  workflow_dispatch:

-permissions:
-  contents: read
-
 jobs:
  markdown-link-check:
    runs-on: ubuntu-latest
@@ -1,45 +0,0 @@
-name: PR Title Lint
-
-permissions:
-  pull-requests: read
-
-on:
-  pull_request:
-    types: [opened, edited, synchronize]
-
-jobs:
-  lint-pr-title:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Validate PR Title
-        uses: amannn/action-semantic-pull-request@v5
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          types: |
-            feat
-            fix
-            docs
-            style
-            refactor
-            perf
-            test
-            build
-            ci
-            chore
-            revert
-            release
-          scopes: |
-            checkpoint
-            checkpoint-postgres
-            checkpoint-sqlite
-            cli
-            langgraph
-            prebuilt
-            scheduler-kafka
-            sdk-py
-            docs
-            ci
-          requireScope: false
-          ignoreLabels: |
-            ignore-lint-pr-title
@@ -8,9 +8,6 @@ on:
        type: string
        default: "libs/langgraph"

-permissions:
-  contents: read
-
 env:
  PYTHON_VERSION: "3.11"

@@ -137,9 +134,7 @@ jobs:
    needs:
      - build
      - release-notes
-    permissions:
-      contents: read
-      id-token: write
+    permissions: write-all
    uses: ./.github/workflows/_test_release.yml
    with:
      working-directory: ${{ inputs.working-directory }}
@@ -0,0 +1,38 @@
+name: JS Release
+
+on:
+  workflow_dispatch:
+
+jobs:
+  publish:
+    # Disallow publishing from branches that aren't `main`.
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        working-directory:
+          - "libs/sdk-js"
+
+    defaults:
+      run:
+        working-directory: ${{ matrix.working-directory }}
+
+    steps:
+      - uses: actions/checkout@v4
+      # JS Build
+      - name: Use Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+          cache: "yarn"
+          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
+
+      - name: Install dependencies
+        run: yarn install
+      - name: Build
+        run: yarn build
+      - name: Publish package to NPM
+        run: |
+          echo "//registry.npmjs.org/:_authToken=${{ secrets.NPM_TOKEN }}" > .npmrc
+          npm publish
@@ -11,9 +11,6 @@ on:
  schedule:
    - cron: "0 13 * * *"

-permissions:
-  contents: read
-
 defaults:
  run:
    working-directory: docs
@@ -1,45 +0,0 @@
-name: UV Lock Upgrade
-
-on:
-  schedule:
-    # run at midnight every Sunday
-    - cron: '0 0 * * 0'
-  # allow manual triggering
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  upgrade-dependencies:
-    runs-on: ubuntu-latest
-    
-    steps:
-      - uses: actions/checkout@v4
-      
-      - name: Set up uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          # use minimum supported Python version
-          python-version: "3.9"
-          enable-cache: true
-          cache-suffix: "uv-lock-upgrade"
-          
-      - name: Run uv lock --upgrade in all Python packages
-        run: make lock-upgrade
-          
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
-          title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
-          body: |
-            This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
-            
-            This is an automated PR created by the UV Lock Upgrade workflow.
-          branch: deps/uv-lock-upgrade
-          delete-branch: true
-          labels: |
-            dependencies
@@ -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 tag a maintainer.
+  - If you would like comments or feedback, please open an issue or discussion and 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://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
+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.

 ## Contribute Documentation

@@ -60,7 +60,7 @@ In LangGraph, these are often higher level guides that show off end-to-end use c
 Some examples include:

 - [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
+- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)

 Here are some high-level tips on writing a good tutorial:

@@ -111,6 +111,7 @@ 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.
@@ -186,9 +187,9 @@ Be concise, including in code samples.

 ## Setup

-LangGraph documentation consists of two components:
+LangChain documentation consists of two components:

-1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
+1. Main Documentation: Hosted at [https://langchain-ai.github.io](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.
@@ -249,17 +250,17 @@ make serve-docs

 #### Linting

-To spell check the docs, run the following from the `docs` directory:
+The documentation is linted from the **monorepo root**. To lint it, run the following from there:

 ```bash
-codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
+make spellcheck
 ```

 ### ️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 LangGraph 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 LangChain 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.

@@ -290,4 +291,4 @@ def my_function(arg1: int, arg2: str) -> float:
        This is a description of the return value.
    """
    return 3.14
-```
+```
@@ -47,16 +47,6 @@ lock:
 		fi; \
 	done

-# Lock all projects and upgrade dependencies
-.PHONY: lock-upgrade
-lock-upgrade:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lock-upgrade in $$dir"; \
-			(cd $$dir && uv lock --upgrade); \
-		fi; \
-	done
-
 # Test all projects
 .PHONY: test
 test:
@@ -63,7 +63,7 @@ LangGraph provides low-level supporting infrastructure for *any* long-running, s
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:

 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 - [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.

 > [!NOTE]
@@ -73,12 +73,11 @@ 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/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.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
 - [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.
@@ -1,3 +1,4 @@
 site/
+docs/cloud/reference/sdk/js_ts_sdk_ref.md

 .vercel
@@ -1,4 +1,10 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell llms-text build-prebuilt tests
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
+
+build-typedoc:
+	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md

 build-prebuilt:
 	# Use to create an update to date prebuilt page.
@@ -15,7 +21,7 @@ build-prebuilt:
 	fi
 	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python

-build-docs: build-prebuilt
+build-docs: build-typedoc build-prebuilt
 	uv run python -m mkdocs build --clean -f mkdocs.yml --strict

 llms-text:
@@ -39,7 +45,7 @@ vercel-build-docs: install-vercel-deps
 serve-clean-docs: clean-docs
 	uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph

-serve-docs:
+serve-docs: build-typedoc
 	uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty

 clean-docs:
@@ -14,7 +14,10 @@ from mkdocs.structure.pages import Page
 from pydantic import BaseModel, Field
 from yaml import SafeLoader

-from _scripts.notebook_hooks import _on_page_markdown_with_config
+from _scripts.notebook_hooks import (
+    _on_page_markdown_with_config,
+    _apply_conditional_rendering,
+)

 HERE = os.path.dirname(os.path.abspath(__file__))
 # Get source directory (parent of HERE / docs)
@@ -3,7 +3,6 @@
 Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
 """

-import json
 import logging
 import os
 import posixpath
@@ -16,8 +15,8 @@ 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.link_map import JS_LINK_MAP
 from _scripts.notebook_convert import convert_notebook
+from _scripts.link_map import JS_LINK_MAP

 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -35,20 +34,20 @@ REDIRECT_MAP = {
    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
    # graph-api
-    "how-tos/state-reducers.ipynb": "how-tos/graph-api.md#define-and-update-state",
-    "how-tos/sequence.ipynb": "how-tos/graph-api.md#create-a-sequence-of-steps",
-    "how-tos/branching.ipynb": "how-tos/graph-api.md#create-branches",
-    "how-tos/recursion-limit.ipynb": "how-tos/graph-api.md#create-and-control-loops",
-    "how-tos/visualization.ipynb": "how-tos/graph-api.md#visualize-your-graph",
-    "how-tos/input_output_schema.ipynb": "how-tos/graph-api.md#define-input-and-output-schemas",
-    "how-tos/pass_private_state.ipynb": "how-tos/graph-api.md#pass-private-state-between-nodes",
-    "how-tos/state-model.ipynb": "how-tos/graph-api.md#use-pydantic-models-for-graph-state",
-    "how-tos/map-reduce.ipynb": "how-tos/graph-api.md#map-reduce-and-the-send-api",
-    "how-tos/command.ipynb": "how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command",
-    "how-tos/configuration.ipynb": "how-tos/graph-api.md#add-runtime-configuration",
-    "how-tos/node-retries.ipynb": "how-tos/graph-api.md#add-retry-policies",
-    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api.md#impose-a-recursion-limit",
-    "how-tos/async.ipynb": "how-tos/graph-api.md#async",
+    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
+    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
+    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
+    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
+    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
+    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
+    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
+    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
+    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
+    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
+    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
+    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
+    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    "how-tos/async.ipynb": "how-tos/graph-api/#async",
    # memory how-tos
    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md",
    "how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages",
@@ -56,8 +55,8 @@ REDIRECT_MAP = {
    "how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
    "agents/memory.ipynb": "how-tos/memory/add-memory.md",
    # subgraph how-tos
-    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.md#different-state-schemas",
-    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.md#add-persistence",
+    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
+    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
    # persistence how-tos
    "how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production",
    "how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production",
@@ -73,11 +72,10 @@ REDIRECT_MAP = {
    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
    "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
    "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
-    "agents/tools.md": "how-tos/tool-calling.md",
    # multi-agent how-tos
-    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs",
-    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system",
-    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.md#multi-turn-conversation",
+    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
+    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
+    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
    # cloud redirects
    "cloud/index.md": "index.md",
    "cloud/how-tos/index.md": "concepts/langgraph_platform",
@@ -101,6 +99,10 @@ 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",
@@ -122,11 +124,6 @@ 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",
 }


@@ -310,12 +307,6 @@ def _highlight_code_blocks(markdown: str) -> str:
    return markdown


-TARGET_LANGUAGE = os.environ.get("TARGET_LANGUAGE", "python")
-
-if TARGET_LANGUAGE not in {"python", "js"}:
-    raise ValueError(f"TARGET_LANGUAGE must be 'python' or 'js', got {TARGET_LANGUAGE}")
-
-
 def _on_page_markdown_with_config(
    markdown: str,
    page: Page,
@@ -338,15 +329,16 @@ def _on_page_markdown_with_config(
    markdown = _highlight_code_blocks(markdown)

    # Apply conditional rendering for code blocks
-    markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
-    if TARGET_LANGUAGE == "js":
+    target_language = kwargs.get("target_language", "python")
+    markdown = _apply_conditional_rendering(markdown, target_language)
+    if target_language == "js":
        markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
-    elif TARGET_LANGUAGE == "python":
+    elif target_language == "python":
        # Via a dedicated plugin
        pass
    else:
        raise ValueError(
-            f"Unsupported target language: {TARGET_LANGUAGE}. "
+            f"Unsupported target language: {target_language}. "
            "Supported languages are 'python' and 'js'."
        )

@@ -363,16 +355,12 @@ def _on_page_markdown_with_config(


 def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    finalized_markdown = (
-        _on_page_markdown_with_config(
-            markdown,
-            page,
-            add_api_references=True,
-            **kwargs,
-        )
+    return _on_page_markdown_with_config(
+        markdown,
+        page,
+        add_api_references=True,
+        **kwargs,
    )
-    page.meta["original_markdown"] = finalized_markdown
-    return finalized_markdown


 # redirects
@@ -442,51 +430,20 @@ 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 "",
-    }

-    # 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:
+def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
    """Inject Google Tag Manager noscript tag immediately after <body>.

    Args:
-        html: The HTML output of the page.
+        output: The HTML output of the page.
        page: The page instance.
        config: The MkDocs configuration object.

    Returns:
        modified HTML output with GTM code injected.
    """
-    html = _inject_markdown_into_html(html, page)
-    return _inject_gtm(html)
+    return _inject_gtm(output)
+

 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):
@@ -1,11 +0,0 @@
-# Additional resources
-
-This section contains additional resources for LangGraph.
-
- [Community agents](../agents/prebuilt.md): A collection of prebuilt libraries that you can use in your LangGraph applications.
- [LangGraph Academy](https://academy.langchain.com/courses/intro-to-langgraph): A collection of courses that teach you how to use LangGraph.
- [Case studies](../adopters.md): A collection of case studies that show how LangGraph is used in production.
- [FAQ](../concepts/faq.md): A collection of frequently asked questions about LangGraph.
- [llms.txt](../llms-txt-overview.md): A list of documentation files in the `llms.txt` format that allow LLMs and agents to access our documentation.
- [LangChain Forum](https://forum.langchain.com/): A place to ask questions and get help from other LangGraph users.
- [Troubleshooting](../troubleshooting/errors/index.md): A collection of troubleshooting guides for common issues.
@@ -8,41 +8,24 @@ This list of companies using LangGraph and their success stories is compiled fro
 | [AirTop](https://www.airtop.ai/) | Software & Technology (GenAI Native) | Browser automation for AI agents | [Case study, 2024](https://blog.langchain.dev/customers-airtop/) |
 | [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) |
 | [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
-| [BlackRock](https://www.blackrock.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/oyqeCHFM5U4?feature=shared) |
 | [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
-| [Cisco CX](https://www.cisco.com/site/us/en/services/modern-data-center/index.html?CCID=cc005911&DTID=eivtotr001480&OID=srwsas032775) | Software & Technology | Customer support | [Interrupt Talk, 2025](https://youtu.be/gPhyPRtIMn0?feature=shared) |
-| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Video story, 2025](https://www.youtube.com/watch?v=htcb-vGR_x0); [Case study, 2025](https://blog.langchain.com/cisco-outshift/); [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
-| [Cisco TAC](https://www.cisco.com/c/en/us/support/index.html) | Software & Technology | Customer support | [Video story, 2025](https://youtu.be/EAj0HBDGqaE?feature=shared) |
-| [City of Hope](https://www.cityofhope.org/) | Non-profit | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/9ABwtK2gIZU?feature=shared) |
+| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
 | [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) |
-| [Definely](https://www.definely.com/) | Legal | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.com/customers-definely/) |
-| [Docent Pro](https://docentpro.com/) | Travel | GenAI embedded product experiences | [Case study, 2025](https://blog.langchain.com/customers-docentpro/) |
 | [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
-| [Exa](https://exa.ai/) | Software & Technology (GenAI Native) | Search | [Case study, 2025](https://blog.langchain.com/exa/) |
 | [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
-| [Harmonic](https://harmonic.ai/) | Software & Technology | Search | [Case study, 2025](https://blog.langchain.com/customers-harmonic/) |
 | [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) |
 | [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
-| [J.P. Morgan](https://www.jpmorganchase.com/) | Financial Services | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/yMalr0jiOAc?feature=shared) |
 | [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) |
 | [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) |
-| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Interrupt talk, 2025](https://youtu.be/NmblVxyBhi8?feature=shared); [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
+| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
 | [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
-| [Modern Treasury](https://www.moderntreasury.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/AwAiffXqaCU?feature=shared) |
-| [Monday](https://monday.com/) | Software & Technology | GenAI embedded product experiences | [Interrupt talk, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
-| [Morningstar](https://www.morningstar.com/) | Financial Services | Research & summarization | [Video story, 2025](https://youtu.be/6LidoFXCJPs?feature=shared) |
 | [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) |
-| [Pigment](https://www.pigment.com/) | Fintech | GenAI embedded product experiences | [Video story, 2025](https://youtu.be/5JVSO2KYOmE?feature=shared) |
-| [Prosper](https://www.prosper.com/) | Fintech | Customer support | [Video story, 2025](https://youtu.be/9RFNOYtkwsc?feature=shared) |
 | [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) |
-| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Video story, 2025](https://youtu.be/gD1LIjCkuA8?feature=shared); [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
+| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
 | [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) |
 | [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) |
-| [Abu Dhabi Government](https://www.tamm.abudhabi/) | Government | Search | [Case study, 2025](https://blog.langchain.com/customers-abu-dhabi-government/) |
 | [Tradestack](https://www.tradestack.uk/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-tradestack/) |
-| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Interrupt talk, 2025](https://youtu.be/Bugs0dVcNI8?feature=shared); [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
-| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Interrupt talk, 2025](https://youtu.be/pKk-LfhujwI?feature=shared); [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
-| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Video story, 2025](https://www.youtube.com/watch?v=vrjJ6NuyTWA); [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
+| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
+| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
+| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
 | [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) |
-| [WebToon](https://www.webtoons.com/en/) | Media & Entertainment | Data extraction | [Case study, 2025](https://blog.langchain.com/customers-webtoon/) |
-| [11x](https://www.11x.ai/) | Software & Technology (GenAI Native) | Research & outreach | [Interrupt talk, 2025](https://youtu.be/fegwPmaAPQk?feature=shared) |
@@ -52,7 +52,7 @@ agent.invoke(
 )
 ```

-1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](../how-tos/tool-calling.md) page.
+1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
 2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
 3. Provide a list of tools for the model to use.
 4. Provide a system prompt (instructions) to the language model used by the agent.
@@ -8,75 +8,60 @@ Context includes *any* data outside the message list that can shape behavior. Th
 - Internal state updated during a multi-step reasoning process.
 - Persistent memory or facts from previous interactions.

-LangGraph provides **three** primary ways to manage context:
+LangGraph provides **three** primary ways to supply context:

 | Type                                                                         | Description                                   | Mutable? | Lifetime                |
 |------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
-| [**Runtime Context**](#runtime-context)                                      | data passed at the start of a run             | ❌        | per run                 |
+| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
 | [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
 | [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |

-### Runtime Context
+## Provide runtime context

-Runtime context is for immutable data like user metadata, tools, db connections, etc. Use this when you have values that don't change mid-run.
+### Config (static context)

-!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces `config['configurable']`"
+Config is for immutable data like user metadata or API keys. Use
+when you have values that don't change mid-run.

-    The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
-
-!!! note 
-
-    Runtime context refers to local context: data and dependencies your code needs to run. It does not refer to:
-
-    * The LLM context, which is the data passed into the LLM's prompt.
-    * The "context window", which is the maximum number of tokens that can be passed to the LLM.
-
-    You likely want to use the local context to optimize the LLM's context window. For example, you
-    could use a user id to fetch a user's name and information from a database to populate the context window with relevant memories.
-
-Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose:
+Specify configuration using a key called **"configurable"** which is reserved
+for this purpose:

 ```python
-@dataclass
-class ContextSchema:
-    user_name: str
-
 graph.invoke( # (1)!
    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
    # highlight-next-line
-    context={"user_name": "John Smith"} # (3)!
+    config={"configurable": {"user_id": "user_123"}} # (3)!
 )
 ```

 1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
 2. This example uses messages as an input, which is common, but your application may use different input structures.
-3. This is where you pass the runtime data. The `context` parameter allows you to provide additional dependencies that the agent can use during its execution.
+3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.

 === "Agent prompt"

    ```python
    from langchain_core.messages import AnyMessage
-    from langgraph.runtime import get_runtime
+    from langchain_core.runnables import RunnableConfig
    from langgraph.prebuilt.chat_agent_executor import AgentState
    from langgraph.prebuilt import create_react_agent

    # highlight-next-line
-    def prompt(state: AgentState) -> list[AnyMessage]:
-        runtime = get_runtime(ContextSchema)
-        system_msg = f"You are a helpful assistant. Address the user as {runtime.context.user_name}."
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
        return [{"role": "system", "content": system_msg}] + state["messages"]

    agent = create_react_agent(
        model="anthropic:claude-3-7-sonnet-latest",
        tools=[get_weather],
-        prompt=prompt,
-        context_schema=ContextSchema
+        prompt=prompt
    )

    agent.invoke(
        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
-        context={"user_name": "John Smith"}
+        config={"configurable": {"user_name": "John Smith"}}
    )
    ```

@@ -85,11 +70,11 @@ graph.invoke( # (1)!
 === "Workflow node"

    ```python
-    from langgraph.runtime import Runtime
+    from langchain_core.runnables import RunnableConfig

    # highlight-next-line
-    def node(state: State, config: Runtime[ContextSchema]):
-        user_name = runtime.context.user_name
+    def node(state: State, config: RunnableConfig):
+        user_name = config["configurable"].get("user_name")
        ...
    ```

@@ -98,16 +83,14 @@ graph.invoke( # (1)!
 === "In a tool"

    ```python
-    from langgraph.runtime import get_runtime
+    from langchain_core.runnables import RunnableConfig

    @tool
    # highlight-next-line
-    def get_user_email() -> str:
+    def get_user_info(config: RunnableConfig) -> str:
        """Retrieve user information based on user ID."""
-        # simulate fetching user info from a database
-        runtime = get_runtime(ContextSchema)
-        email = get_user_email_from_db(runtime.context.user_name)
-        return email
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
    ```

    See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
@@ -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_outputs["messages"]
+    reference_messages = reference["messages"]
    score = compare_messages(output_messages, reference_messages)
    return {"key": "evaluator_score", "score": score}
 ```
@@ -9,12 +9,21 @@ hide:

 # Use MCP

-The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
+[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
+
+![MCP](./assets/mcp.png)
+
+Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
+
+```bash
+pip install langchain-mcp-adapters
+```

 ## Use MCP tools

 The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.

+
 === "In an agent"

    ```python title="Agent using tools defined on MCP servers"
@@ -55,16 +64,14 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across

 === "In a workflow"

-    ```python title="Workflow using MCP tools with ToolNode"
+    ```python
    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
-    from langgraph.graph import StateGraph, MessagesState, START, END
-    from langgraph.prebuilt import ToolNode
+    model = init_chat_model("openai:gpt-4.1")

-    # Initialize the model
-    model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
-
-    # Set up MCP client
    client = MultiServerMCPClient(
        {
            "math": {
@@ -82,47 +89,22 @@ The `langchain-mcp-adapters` package enables agents to use tools defined across
    )
    tools = await client.get_tools()

-    # Bind tools to model
-    model_with_tools = model.bind_tools(tools)
+    def call_model(state: MessagesState):
+        response = model.bind_tools(tools).invoke(state["messages"])
+        return {"messages": response}

-    # 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", call_model)
-    builder.add_node("tools", tool_node)
-
+    builder.add_node(call_model)
+    builder.add_node(ToolNode(tools))
    builder.add_edge(START, "call_model")
    builder.add_conditional_edges(
        "call_model",
-        should_continue,
+        tools_condition,
    )
    builder.add_edge("tools", "call_model")
-
-    # Compile the graph
    graph = builder.compile()
-
-    # 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?"}]}
-    )
+    math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
+    weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
    ```


@@ -175,4 +157,4 @@ 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)
@@ -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:

-{% include-markdown "../../snippets/chat_model_tabs.md" %}
+{!snippets/chat_model_tabs.md!}

 ### Instantiate a model directly

@@ -30,7 +30,7 @@ LangGraph includes several capabilities essential for building robust, productio
 - [**Memory integration**](../how-tos/memory/add-memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
 - [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
 - [**Streaming support**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
- [**Deployment tooling**](../tutorials/langgraph-platform/local-server.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
+- [**Deployment tooling**](./deployment.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
    - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.

@@ -60,7 +60,7 @@ Use the following tool to visualize the graph generated by
 and to view an outline of the corresponding code.
 It allows you to explore the infrastructure of the agent as defined by the presence of:

-* [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
 * [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
 * `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
 * [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
@@ -0,0 +1,310 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Tools
+
+[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
+
+You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
+
+## Define simple tools
+
+You can pass a vanilla function to `create_react_agent` to use as a tool:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+create_react_agent(
+    model="anthropic:claude-3-7-sonnet",
+    tools=[multiply]
+)
+```
+
+`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
+
+## Customize tools
+
+For more control over tool behavior, use the `@tool` decorator:
+
+```python
+# highlight-next-line
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool("multiply_tool", parse_docstring=True)
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+
+    Args:
+        a: First operand
+        b: Second operand
+    """
+    return a * b
+```
+
+You can also define a custom input schema using Pydantic:
+
+```python
+from pydantic import BaseModel, Field
+
+class MultiplyInputSchema(BaseModel):
+    """Multiply two numbers"""
+    a: int = Field(description="First operand")
+    b: int = Field(description="Second operand")
+
+# highlight-next-line
+@tool("multiply_tool", args_schema=MultiplyInputSchema)
+def multiply(a: int, b: int) -> int:
+    return a * b
+```
+
+For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
+
+## Hide arguments from the model
+
+Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
+
+You can put these arguments in the `state` or `config` of the agent, and access
+this information inside the tool:
+
+```python
+from langgraph.prebuilt import InjectedState
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langchain_core.runnables import RunnableConfig
+
+def my_tool(
+    # This will be populated by an LLM
+    tool_arg: str,
+    # access information that's dynamically updated inside the agent
+    # highlight-next-line
+    state: Annotated[AgentState, InjectedState],
+    # access static data that is passed at agent invocation
+    # highlight-next-line
+    config: RunnableConfig,
+) -> str:
+    """My tool."""
+    do_something_with_state(state["messages"])
+    do_something_with_config(config)
+    ...
+```
+
+## Disable parallel tool calling
+
+Some model providers support executing multiple tools in parallel, but
+allow users to disable this feature.
+
+For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
+
+```python
+from langchain.chat_models import init_chat_model
+
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
+tools = [add, multiply]
+agent = create_react_agent(
+    # disable parallel tool calls
+    # highlight-next-line
+    model=model.bind_tools(tools, parallel_tool_calls=False),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
+)
+```
+
+## Return tool results directly
+
+Use `return_direct=True` to return tool results immediately and stop the agent loop:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[add]
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
+)
+```
+
+## Force tool use
+
+To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def greet(user_name: str) -> int:
+    """Greet user."""
+    return f"Hello {user_name}!"
+
+tools = [greet]
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
+)
+```
+
+!!! Warning "Avoid infinite loops"
+
+    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
+
+    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
+    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
+
+## Handle tool errors
+
+By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
+
+=== "Enable error handling (default)"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # Run with error handling (default)
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[multiply]
+    )
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+=== "Disable error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=False  # (1)!
+    )
+    agent_no_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_no_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+=== "Custom error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=(
+            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
+        )
+    )
+    agent_custom_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_custom_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
+
+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](../how-tos/memory/add-memory.md) guide for more information on:
+
+* how to [read](../how-tos/memory/add-memory.md#read-short-term) from and [write](../how-tos/memory/add-memory.md#write-short-term) to **short-term** memory
+* how to [read](../how-tos/memory/add-memory.md#read-long-term) from and [write](../how-tos/memory/add-memory.md#write-long-term) to **long-term** memory
+
+## Prebuilt tools
+
+You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+
+You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
+
+Some commonly used tool categories include:
+
+- **Search**: Bing, SerpAPI, Tavily
+- **Code interpreters**: Python REPL, Node.js REPL
+- **Databases**: SQL, MongoDB, Redis
+- **Web data**: Web scraping and browsing
+- **APIs**: OpenWeatherMap, NewsAPI, and others
+
+These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
+
@@ -13,7 +13,7 @@ You can use a prebuilt chat UI for interacting with any LangGraph agent through

 ## Run agent in UI

-First, set up LangGraph API server [locally](../tutorials/langgraph-platform/local-server.md) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
+First, set up LangGraph API server [locally](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).

 Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):

@@ -25,7 +25,7 @@ Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the re

 ## Add human-in-the-loop

-Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](../tutorials/langgraph-platform/local-server.md) guide) with this [agent implementation](../how-tos/human_in_the_loop/add-human-in-the-loop.md#add-interrupts-to-any-tool):
+Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](./deployment.md) guide) with this [agent implementation](../how-tos/human_in_the_loop/add-human-in-the-loop.md#add-interrupts-to-any-tool):

 <video controls src="../assets/interrupt-chat-ui.mp4" type="video/mp4"></video>

@@ -1,54 +0,0 @@
-# Data Storage and Privacy
-
-This document describes how data is processed in the LangGraph CLI and the LangGraph Server for both the in-memory server (`langgraph dev`) and the local Docker server (`langgraph up`). It also describes what data is tracked when interacting with the hosted LangGraph Studio frontend.
-
-## CLI
-
-LangGraph **CLI** is the command-line interface for building and running LangGraph applications; see the [CLI guide](../../concepts/langgraph_cli.md) to learn more.
-
-By default, calls to most CLI commands log a single analytics event upon invocation. This helps us better prioritize improvements to the CLI experience. Each telemetry event contains the calling process's OS, OS version, Python version, the CLI version, the command name (`dev`, `up`, `run`, etc.), and booleans representing whether a flag was passed to the command. You can see the full analytics logic [here](https://github.com/langchain-ai/langgraph/blob/main/libs/cli/langgraph_cli/analytics.py). 
-
-You can disable all CLI telemetry by setting `LANGGRAPH_CLI_NO_ANALYTICS=1`.
-
-## LangGraph Server (in-memory & docker)
-
-The [LangGraph Server](../../concepts/langgraph_server.md) provides a durable execution runtime that relies on persisting checkpoints of your application state, long-term memories, thread metadata, assistants, and similar resources to the local file system or a database. Unless you have deliberately customized the storage location, this information is either written to local disk (for `langgraph dev`) or a PostgreSQL database (for `langgraph up` and in all deployments).
-
-### LangSmith Tracing
-
-When running the LangGraph server (either in-memory or in Docker), LangSmith tracing may be enabled to facilitate faster debugging and offer observability of graph state and LLM prompts in production. You can always disable tracing by setting `LANGSMITH_TRACING=false` in your server's runtime environment.
-
-### In-memory development server (`langgraph dev`)
-
-`langgraph dev` runs an [in-memory development server](../../tutorials/langgraph-platform/local-server.md) as a single Python process, designed for quick development and testing. It saves all checkpointing and memory data to disk within a `.langgraph_api` directory in the current working directory. Apart from the telemetry data described in the [CLI](#cli) section, no data leaves the machine unless you have enabled tracing or your graph code explicitly contacts an external service.
-
-### Standalone Container (`langgraph up`)
-
-`langgraph up` builds your local package into a Docker image and runs the server as a [standalone container](../../concepts/deployment_options.md#standalone-container) consisting of three containers: the API server, a PostgreSQL container, and a Redis container. All persistent data (checkpoints, assistants, etc.) are stored in the PostgreSQL database. Redis is used as a pubsub connection for real-time streaming of events. You can encrypt all checkpoints before saving to the database by setting a valid `LANGGRAPH_AES_KEY` environment variable. You can also specify [TTLs](../../how-tos/ttl/configure_ttl.md) for checkpoints and cross-thread memories in `langgraph.json` to control how long data is stored. All persisted threads, memories, and other data can be deleted via the relevant API endpoints.
-
-Additional API calls are made to confirm that the server has a valid license and to track the number of executed runs and tasks. Periodically, the API server validates the provided license key (or API key).
-
-If you've disabled [tracing](#langsmith-tracing), no user data is persisted externally unless your graph code explicitly contacts an external service.
-
-## Studio
-
-[LangGraph Studio](../../concepts/langgraph_studio.md) is a graphical interface for interacting with your LangGraph server. It does not persist any private data (the data you send to your server is not sent to LangSmith). Though the studio interface is served at [smith.langchain.com](https://smith.langchain.com), it is run in your browser and connects directly to your local LangGraph server so that no data needs to be sent to LangSmith.
-
-If you are logged in, LangSmith does collect some usage analytics to help improve studio's user experience. This includes:
-
- Page visits and navigation patterns
- User actions (button clicks)
- Browser type and version
- Screen resolution and viewport size
-
-Importantly, no application data or code (or other sensitive configuration details) are collected. All of that is stored in the persistence layer of your LangGraph server. When using Studio anonymously, no account creation is required and usage analytics are not collected.
-
-## Quick reference
-
-In summary, you can opt-out of server-side telemetry by turning off CLI analytics and disabling tracing.
-
-| Variable                       | Purpose                   | Default                          |
-| ------------------------------ | ------------------------- | -------------------------------- |
-| `LANGGRAPH_CLI_NO_ANALYTICS=1` | Disable CLI analytics     | Analytics enabled                |
-| `LANGSMITH_API_KEY`            | Enable LangSmith tracing  | Tracing disabled                 |
-| `LANGSMITH_TRACING=false`      | Disable LangSmith tracing | Depends on environment           |
@@ -0,0 +1,12 @@
+# Threads
+
+A thread contains the accumulated state of a sequence of [runs](../../concepts/assistants.md#execution). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
+
+A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
+
+The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints are persisted and can be used to restore the state of a thread at a later time.
+
+## Learn more
+
+* For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+* The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.
@@ -1,119 +0,0 @@
-# Egress for Subscription Metrics and Operational Metadata
-
-> **Important: Self Hosted Only** 
-> This section only applies to customers who are not running in offline mode and assumes you are using a self-hosted LangGraph Platform instance.
-> This does not apply to SaaS or Hybrid deployments.
-
-Self-Hosted LangGraph Platform instances store all information locally and will never send sensitive information outside of your network. We currently only track platform usage for billing purposes according to the entitlements in your order. In order to better remotely support our customers, we do require egress to `https://beacon.langchain.com`.
-
-In the future, we will be introducing support diagnostics to help us ensure that the LangGraph Platform is running at an optimal level within your environment.
-
-> **Warning**  
-> **This will require egress to `https://beacon.langchain.com` from your network.**
-> **If using an API key, you will also need to allow egress to `https://api.smith.langchain.com` or `https://eu.api.smith.langchain.com` for API key verification.**
-
-Generally, data that we send to Beacon can be categorized as follows:
-
- **Subscription Metrics**
-  - Subscription metrics are used to determine level of access and utilization of LangSmith. This includes, but are not limited to:
-    - Nodes Executed
-    - Runs Executed
-    - License Key Verification
- **Operational Metadata**
-  - This metadata will contain and collect the above subscription metrics to assist with remote support, allowing the LangChain team to diagnose and troubleshoot performance issues more effectively and proactively.
-
-## Example Payloads
-
-In an effort to maximize transparency, we provide sample payloads here:
-
-### License Verification (If using an Enterprise License)
-
-**Endpoint:**
-
-`POST beacon.langchain.com/v1/beacon/verify`
-
-**Request:**
-
-```json
-{
-  "license": "<YOUR_LICENSE_KEY>"
-}
-```
-
-**Response:**
-
-```json
-{
-  "token": "Valid JWT" // Short-lived JWT token to avoid repeated license checks
-}
-```
-
-### Api Key Verification (If using a LangSmith API Key)
-
-**Endpoint:**
-`POST api.smith.langchain.com/auth`
-
-**Request:**
-
-```json
-"Headers": {
-  X-Api-Key: <YOUR_API_KEY>
-}
-```
-
-**Response:**
-
-```json
-{
-  "org_config": {
-    "org_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
-    ... // Additional organization details
-  }
-}
-```
-
-### Usage Reporting
-
-**Endpoint:**
-
-`POST beacon.langchain.com/v1/metadata/submit`
-
-**Request:**
-
-```json
-{
-  "license": "<YOUR_LICENSE_KEY>",
-  "from_timestamp": "2025-01-06T09:00:00Z",
-  "to_timestamp": "2025-01-06T10:00:00Z",
-  "tags": {
-    "langgraph.python.version": "0.1.0",
-    "langgraph_api.version": "0.2.0",
-    "langgraph.platform.revision": "abc123",
-    "langgraph.platform.variant": "standard",
-    "langgraph.platform.host": "host-1",
-    "langgraph.platform.tenant_id": "3a1c2b6f-4430-4b92-8a5b-79b8b567bbc1",
-    "langgraph.platform.project_id": "c5b5f53a-4716-4326-8967-d4f7f7799735",
-    "langgraph.platform.plan": "enterprise",
-    "user_app.uses_indexing": "true",
-    "user_app.uses_custom_app": "false",
-    "user_app.uses_custom_auth": "true",
-    "user_app.uses_thread_ttl": "true",
-    "user_app.uses_store_ttl": "false"
-  },
-  "measures": {
-    "langgraph.platform.runs": 150,
-    "langgraph.platform.nodes": 450
-  },
-  "logs": []
-}
-```
-
-**Response:**
-
-```json
-"204 No Content"
-```
-
-## Our Commitment
-
-LangChain will not store any sensitive information in the Subscription Metrics or Operational Metadata. Any data collected will not be shared with a third party. If you have any concerns about the data being sent, please reach out to your account team.
@@ -20,7 +20,7 @@ my-app/
 |-- openai_agent.py     # code for your graph
 ```

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

 ### No rebuild

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

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

 model = ChatOpenAI(temperature=0)

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

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

-See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
+See more info on LangGraph API configuration file [here](../reference/cli.md#configuration-file)
@@ -3,7 +3,7 @@
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.

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

 ## Prerequisites

@@ -23,8 +23,6 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane

        kubectl get storageclass

-1. Egress to `https://beacon.langchain.com` from your network. This is required for license verification and usage reporting if not running in air-gapped mode. See the [Egress documentation](../../cloud/deployment/egress.md) for more details.
-
 ## Setup

 1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
@@ -3,7 +3,7 @@
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.

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

 ## Prerequisites

@@ -35,6 +35,7 @@ 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
@@ -108,11 +108,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state

-# Define the runtime context
-class GraphContext(TypedDict):
+# Define the config
+class GraphConfig(TypedDict):
    model_name: Literal["anthropic", "openai"]

-workflow = StateGraph(AgentState, context_schema=GraphContext)
+workflow = StateGraph(AgentState, config_schema=GraphConfig)
 workflow.add_node("agent", call_model)
 workflow.add_node("action", tool_node)
 workflow.add_edge(START, "agent")
@@ -121,11 +121,11 @@ from langgraph.graph import StateGraph, END, START
 from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
 from my_agent.utils.state import AgentState # import state

-# Define the runtime context
-class GraphContext(TypedDict):
+# Define the config
+class GraphConfig(TypedDict):
    model_name: Literal["anthropic", "openai"]

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

 ## Kubernetes (Helm)

@@ -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.

-## Dynamic interrupts
+## LangGraph API invoke & resume

 === "Python"

@@ -30,7 +30,9 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
    # > [
    # >     {
    # >         'value': {'text_to_revise': 'original text'},
-    # >         'id': '...',
+    # >         'resumable': True,
+    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+    # >         'when': 'during'
    # >     }
    # > ]

@@ -201,7 +203,9 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
        # > [
        # >     {
        # >         'value': {'text_to_revise': 'original text'},
-        # >         'id': '...',
+        # >         'resumable': True,
+        # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+        # >         'when': 'during'
        # >     }
        # > ]

@@ -301,185 +305,6 @@ 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. 
@@ -2,20 +2,21 @@

 In this guide we will show how to create, configure, and manage an [assistant](../../concepts/assistants.md).

-First, as a brief refresher on the concept of runtime context, consider the following simple `call_model` node and context schema. Observe that this node tries to read and use the `model_provider` as defined by the `Runtime` object's `context` property.
+First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.

 === "Python"

    ```python
-    @dataclass
-    class ContextSchema:
-        llm_provider: str = "anthropic"

-    builder = StateGraph(AgentState, context_schema=ContextSchema)
+    class ConfigSchema(TypedDict):
+        model_name: str

-    def call_model(state, runtime: Runtime[ContextSchema]):
+    builder = StateGraph(AgentState, config_schema=ConfigSchema)
+
+    def call_model(state, config):
        messages = state["messages"]
-        model = _get_model(runtime.context.llm_provider)
+        model_name = config.get('configurable', {}).get("model_name", "anthropic")
+        model = _get_model(model_name)
        response = model.invoke(messages)
        # We return a list, because this will get added to the existing list
        return {"messages": [response]}
@@ -43,7 +44,7 @@ First, as a brief refresher on the concept of runtime context, consider the foll
    }
    ```

-For more information on runtime context, [see here](../../concepts/low_level.md#runtime-context).
+For more information on configurations, [see here](../../concepts/low_level.md#configuration).

 ## Create an assistant

@@ -30,33 +30,17 @@ export default {

 Next, define your UI components in your `langgraph.json` configuration:

-=== "Python agent"
-
-    ```json title="langgraph.json"
-    {
-      "node_version": "20",
-      "graphs": {
-        "agent": "./src/agent.py:graph"
-      },
-      "ui": {
-        "agent": "./src/agent/ui.tsx"
-      }
-    }
-    ```
-
-=== "JS agent"
-
-    ```json title="langgraph.json"
-    {
-      "node_version": "20",
-      "graphs": {
-        "agent": "./src/agent/index.ts:graph"
-      },
-      "ui": {
-        "agent": "./src/agent/ui.tsx"
-      }
-    }
-    ```
+```json
+{
+  "node_version": "20",
+  "graphs": {
+    "agent": "./src/agent/index.ts:graph"
+  },
+  "ui": {
+    "agent": "./src/agent/ui.tsx"
+  }
+}
+```

 The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.

@@ -0,0 +1,185 @@
+# 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\"
+    }"
+    ```
@@ -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/human_in_the_loop.md).
+For more information on breakpoints see [here](../../concepts/breakpoints.md).

 ### Submit run

@@ -1,4 +1,4 @@
-# How to integrate LangGraph into your React application
+How to integrate LangGraph into your React application# How to integrate LangGraph into your React application

 !!! info "Prerequisites"

@@ -503,74 +503,6 @@ const handleSubmit = (text: string) => {
 };
 ```

-### Cached Thread Display
-
-Use the `initialValues` option to display cached thread data immediately while the history is being loaded from the server. This improves user experience by showing cached data instantly when navigating to existing threads.
-
-```tsx
-import { useStream } from "@langchain/langgraph-sdk/react";
-
-const CachedThreadExample = ({ threadId, cachedThreadData }) => {
-  const stream = useStream({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-    threadId,
-    // Show cached data immediately while history loads
-    initialValues: cachedThreadData?.values,
-    messagesKey: "messages",
-  });
-
-  return (
-    <div>
-      {stream.messages.map((message) => (
-        <div key={message.id}>{message.content as string}</div>
-      ))}
-    </div>
-  );
-};
-```
-
-### Optimistic Thread Creation
-
-Use the `threadId` option in `submit` function to enable optimistic UI patterns where you need to know the thread ID before the thread is actually created.
-
-```tsx
-import { useState } from "react";
-import { useStream } from "@langchain/langgraph-sdk/react";
-
-const OptimisticThreadExample = () => {
-  const [threadId, setThreadId] = useState<string | null>(null);
-  const [optimisticThreadId] = useState(() => crypto.randomUUID());
-
-  const stream = useStream({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-    threadId,
-    onThreadId: setThreadId, // (3) Updated after thread has been created.
-    messagesKey: "messages",
-  });
-
-  const handleSubmit = (text: string) => {
-    // (1) Perform a soft navigation to /threads/${optimisticThreadId}
-    // without waiting for thread creation.
-    window.history.pushState({}, "", `/threads/${optimisticThreadId}`);
-
-    // (2) Submit message to create thread with the predetermined ID.
-    stream.submit(
-      { messages: [{ type: "human", content: text }] },
-      { threadId: optimisticThreadId }
-    );
-  };
-
-  return (
-    <div>
-      <p>Thread ID: {threadId ?? optimisticThreadId}</p>
-      {/* Rest of component */}
-    </div>
-  );
-};
-```
-
 ### TypeScript

 The `useStream()` hook is friendly for apps written in TypeScript and you can specify types for the state to get better type safety and IDE support.
@@ -140,22 +140,6 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN

 Your server should extract and validate this token before processing requests.

-## Disable webhooks
-
-As of `langgraph-api>=0.2.78`, developers can disable webhooks in the `langgraph.json` file:
-
-```json
-{
-  "http": {
-    "disable_webhooks": true
-  }
-}
-```
-
-This feature is primarily intended for self-hosted deployments, where platform administrators or developers may prefer to disable webhooks to simplify their security posture—especially if they are not configuring firewall rules or other network controls. Disabling webhooks helps prevent untrusted payloads from being sent to internal endpoints.
-
-For full configuration details, refer to the [configuration file reference](https://langchain-ai.github.io/langgraph/cloud/reference/cli/?h=disable_webhooks#configuration-file).
-
 ## Test webhooks

 You can test your webhook using online services like:
@@ -154,9 +154,8 @@ You can now test the API:

    ```bash
    curl -s --request POST \
-        --url <DEPLOYMENT_URL>/runs/stream \
+        --url <DEPLOYMENT_URL> \
        --header 'Content-Type: application/json' \
-        --header "X-Api-Key: <LANGSMITH API KEY> \
        --data "{
            \"assistant_id\": \"agent\",
            \"input\": {
@@ -1,12 +1,12 @@
-# LangGraph Server API Reference
+# API Reference

-The LangGraph Server API reference is available within each deployment at the `/docs` endpoint (e.g. `http://localhost:8124/docs`).
+The LangGraph Platform API reference is available with each deployment at the `/docs` URL path (e.g. `http://localhost:8124/docs`).

 Click <a href="/langgraph/cloud/reference/api/api_ref.html" target="_blank">here</a> to view the API reference.

 ## Authentication

-For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Server. The value of the header should be set to a valid LangSmith API key for the organization where the LangGraph Server is deployed.
+For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Platform API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.

 Example `curl` command:
 ```shell
@@ -18,5 +18,5 @@ curl --request POST \
  "metadata": {},
  "limit": 10,
  "offset": 0
-}'
+}'  
 ```
@@ -1,247 +0,0 @@
-# LangGraph Control Plane API Reference
-
-The LangGraph Control Plane API is used to programmatically create and manage LangGraph Server deployments. For example, the APIs can be orchestrated to create custom CI/CD workflows.
-
-Click <a href="https://api.host.langchain.com/docs" target="_blank">here</a> to view the API reference.
-
-## Host
-
-LangGraph Control Plane hosts for Cloud SaaS data regions:
-
-| US | EU |
-|----|----|
-| `https://api.host.langchain.com` | `https://eu.api.host.langchain.com` |
-
-**Note**: Self-hosted deployments of LangGraph Platform will have a custom host for the LangGraph Control Plane.
-
-## Authentication
-
-To authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key.
-
-Example `curl` command:
-```shell
-curl --request GET \
-  --url http://localhost:8124/v2/deployments \
-  --header 'X-Api-Key: LANGSMITH_API_KEY'
-```
-
-## Versioning
-
-Each endpoint path is prefixed with a version (e.g. `v1`, `v2`).
-
-## Quick Start
-
-1. Call `POST /v2/deployments` to create a new Deployment. The response body contains the Deployment ID (`id`) and the ID of the latest (and first) revision (`latest_revision_id`).
-1. Call `GET /v2/deployments/{deployment_id}` to retrieve the Deployment. Set `deployment_id` in the URL to the value of Deployment ID (`id`).
-1. Poll for revision `status` until `status` is `DEPLOYED` by calling `GET /v2/deployments/{deployment_id}/revisions/{latest_revision_id}`.
-1. Call `PATCH /v2/deployments/{deployment_id}` to update the deployment.
-
-## Example Code
-Below is example Python code that demonstrates how to orchestrate the LangGraph Control Plane APIs to create a deployment, update the deployment, and delete the deployment.
-```python
-import os
-import time
-
-import requests
-from dotenv import load_dotenv
-
-
-load_dotenv()
-
-# required environment variables
-CONTROL_PLANE_HOST = os.getenv("CONTROL_PLANE_HOST")
-LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
-INTEGRATION_ID = os.getenv("INTEGRATION_ID")
-MAX_WAIT_TIME = 1800  # 30 mins
-
-
-def get_headers() -> dict:
-    """Return common headers for requests to LangGraph Control Plane API."""
-    return {
-        "X-Api-Key": LANGSMITH_API_KEY,
-    }
-
-
-def create_deployment() -> str:
-    """Create deployment. Return deployment ID."""
-    headers = get_headers()
-    headers["Content-Type"] = "application/json"
-
-    deployment_name = "my_deployment"
-
-    request_body = {
-        "name": deployment_name,
-        "source": "github",
-        "source_config": {
-            "integration_id": INTEGRATION_ID,
-            "repo_url": "https://github.com/langchain-ai/langgraph-example",
-            "deployment_type": "dev",
-            "build_on_push": False,
-            "custom_url": None,
-            "resource_spec": None,
-        },
-        "source_revision_config": {
-            "repo_ref": "main",
-            "langgraph_config_path": "langgraph.json",
-            "image_uri": None,
-        },
-        "secrets": [
-            {
-                "name": "OPENAI_API_KEY",
-                "value": "test_openai_api_key",
-            },
-            {
-                "name": "ANTHROPIC_API_KEY",
-                "value": "test_anthropic_api_key",
-            },
-            {
-                "name": "TAVILY_API_KEY",
-                "value": "test_tavily_api_key",
-            },
-        ],
-    }
-
-    response = requests.post(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments",
-        headers=headers,
-        json=request_body,
-    )
-
-    if response.status_code != 201:
-        raise Exception(f"Failed to create deployment: {response.text}")
-
-    deployment_id = response.json()["id"]
-    print(f"Created deployment {deployment_name} ({deployment_id})")
-    return deployment_id
-
-
-def get_deployment(deployment_id: str) -> dict:
-    """Get deployment."""
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to get deployment ID {deployment_id}: {response.text}")
-
-    return response.json()
-
-
-def list_revisions(deployment_id: str) -> list[dict]:
-    """List revisions.
-
-    Return list is sorted by created_at in descending order (latest first).
-    """
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(
-            f"Failed to list revisions for deployment ID {deployment_id}: {response.text}"
-        )
-
-    return response.json()
-
-
-def get_revision(
-    deployment_id: str,
-    revision_id: str,
-) -> dict:
-    """Get revision."""
-    response = requests.get(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}/revisions/{revision_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to get revision ID {revision_id}: {response.text}")
-
-    return response.json()
-
-
-def patch_deployment(deployment_id: str) -> None:
-    """Patch deployment."""
-    headers = get_headers()
-    headers["Content-Type"] = "application/json"
-
-    response = requests.patch(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=headers,
-        json={
-            "source_config": {
-                "build_on_push": True,
-            },
-            "source_revision_config": {
-                "repo_ref": "main",
-                "langgraph_config_path": "langgraph.json",
-            },
-        },
-    )
-
-    if response.status_code != 200:
-        raise Exception(f"Failed to patch deployment: {response.text}")
-
-    print(f"Patched deployment ID {deployment_id}")
-
-
-def wait_for_deployment(deployment_id: str, revision_id: str) -> None:
-    """Wait for revision status to be DEPLOYED."""
-    start_time = time.time()
-    revision, status = None, None
-    while time.time() - start_time < MAX_WAIT_TIME:
-        revision = get_revision(deployment_id, revision_id)
-        status = revision["status"]
-        if status == "DEPLOYED":
-            break
-        elif "FAILED" in status:
-            raise Exception(f"Revision ID {revision_id} failed: {revision}")
-
-        print(f"Waiting for revision ID {revision_id} to be DEPLOYED...")
-        time.sleep(60)
-
-    if status != "DEPLOYED":
-        raise Exception(
-            f"Timeout waiting for revision ID {revision_id} to be DEPLOYED: {revision}"
-        )
-
-
-def delete_deployment(deployment_id: str) -> None:
-    """Delete deployment."""
-    response = requests.delete(
-        url=f"{CONTROL_PLANE_HOST}/v2/deployments/{deployment_id}",
-        headers=get_headers(),
-    )
-
-    if response.status_code != 204:
-        raise Exception(
-            f"Failed to delete deployment ID {deployment_id}: {response.text}"
-        )
-
-    print(f"Deployment ID {deployment_id} deleted")
-
-
-if __name__ == "__main__":
-    # create deployment and get the latest revision
-    deployment_id = create_deployment()
-    revisions = list_revisions(deployment_id)
-    latest_revision = revisions["resources"][0]
-    latest_revision_id = latest_revision["id"]
-
-    # wait for latest revision to be DEPLOYED
-    wait_for_deployment(deployment_id, latest_revision_id)
-
-    # patch the deployment and get the latest revision
-    patch_deployment(deployment_id)
-    revisions = list_revisions(deployment_id)
-    latest_revision = revisions["resources"][0]
-    latest_revision_id = latest_revision["id"]
-
-    # wait for latest revision to be DEPLOYED
-    wait_for_deployment(deployment_id, latest_revision_id)
-
-    # delete the deployment
-    delete_deployment(deployment_id)
-```
@@ -51,10 +51,9 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
    | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
    | <span style="white-space: nowrap;">`pip_installer`</span> | _(Added in v0.3)_ Optional. Python package installer selector. It can be set to `"auto"`, `"pip"`, or `"uv"`. From version&nbsp;0.3 onward the default strategy is to run `uv pip`, which typically delivers faster builds while remaining a drop-in replacement. In the uncommon situation where `uv` cannot handle your dependency graph or the structure of your `pyproject.toml`, specify `"pip"` here to revert to the earlier behaviour. |
-    | <span style="white-space: nowrap;">`keep_pkg_tools`</span> | _(Added in v0.3.4)_ Optional. Control whether to retain Python packaging tools (`pip`, `setuptools`, `wheel`) in the final image. Accepted values: <ul><li><code>true</code> : Keep all three tools (skip uninstall).</li><li><code>false</code> / omitted : Uninstall all three tools (default behaviour).</li><li><code>list[str]</code> : Names of tools <strong>to retain</strong>. Each value must be one of "pip", "setuptools", "wheel".</li></ul>. By default, all three tools are uninstalled. |
    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
    | <span style="white-space: nowrap;">`checkpointer`</span>   | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
-    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_ui`: Disable `/ui` routes</li><li>`disable_webhooks`: Disable webhooks calls on run completion in all routes</li><li>`mount_prefix`: Prefix for mounted routes (e.g., "/my-deployment/api")</li></ul> |
+    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`disable_mcp`: Disable `/mcp` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li></ul> |

 === "JS"

@@ -396,7 +395,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](

 === "Python"

-    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform closed beta. Requires a license key for production use.

    **Usage**

@@ -409,8 +408,6 @@ 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.           |
    | `--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.                                                                       |
@@ -425,7 +422,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](

 === "JS"

-    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform. Requires a license key for production use.
+    Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Platform closed beta. Requires a license key for production use.

    **Usage**

@@ -438,8 +435,6 @@ 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;">`--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.                                    |
@@ -10,10 +10,6 @@ This environment variable should be set to `True` if the implementation of a gra

 Defaults to `False`.

-## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
-
-Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `180` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
-
 ## `BG_JOB_TIMEOUT_SECS`

 The timeout of a background run can be increased. However, the infrastructure for a Cloud SaaS deployment enforces a 1 hour timeout limit for API requests. This means the connection between client and server will timeout after 1 hour. This is not configurable.
@@ -22,15 +18,16 @@ A background run can execute for longer than 1 hour, but a client must reconnect

 Defaults to `3600`.

+## `BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS`
+
+Specifies, in seconds, how long the server will wait for background jobs to finish after the queue receives a shutdown signal. After this period, the server will force termination. Defaults to `3600` seconds. Set this to ensure jobs have enough time to complete cleanly during shutdown. Added in `langgraph-api==0.2.16`.
+
 ## `DD_API_KEY`

 Specify `DD_API_KEY` (your [Datadog API Key](https://docs.datadoghq.com/account_management/api-app-keys/)) to automatically enable Datadog tracing for the deployment. Specify other [`DD_*` environment variables](https://ddtrace.readthedocs.io/en/stable/configuration.html) to configure the tracing instrumentation.

 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`.
@@ -43,14 +40,6 @@ Type of authentication for the LangGraph Server deployment. Valid values: `langs

 For deployments to LangGraph Platform, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to `noop`.

-## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`
-
-Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool (per replica) can be controlled using the `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` environment variable. By setting this variable, you can determine the upper bound on the number of simultaneous connections the server will establish with the Postgres database.
-
-For example, if a deployment is scaled up to 10 replicas and `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` is configured to `150`, then up to `1500` connections to Postgres can be established. This is particularly useful for deployments where database resources are limited (or more available) or where you need to tune connection behavior for performance or scaling reasons.
-
-Defaults to `150` connections.
-
 ## `LANGSMITH_RUNS_ENDPOINTS`

 For deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
@@ -65,10 +54,6 @@ Set `LANGSMITH_TRACING` to `false` to disable tracing to LangSmith.

 Defaults to `true`.

-## `LOG_COLOR`
-
-This is mainly relevant in the context of using the dev server via the `langgraph dev` command. Set `LOG_COLOR` to `true` to enable ANSI-colored console output when using the default console renderer. Disabling color output by setting this variable to `false` produces monochrome logs. Defaults to `true`.
-
 ## `LOG_LEVEL`

 Configure [log level](https://docs.python.org/3/library/logging.html#logging-levels). Defaults to `INFO`.
@@ -77,14 +62,9 @@ Configure [log level](https://docs.python.org/3/library/logging.html#logging-lev

 Set `LOG_JSON` to `true` to render all log messages as JSON objects using the configured `JSONRenderer`. This produces structured logs that can be easily parsed or ingested by log management systems. Defaults to `false`.

-## `MOUNT_PREFIX`
+## `LOG_COLOR`

-!!! info "Only Allowed in Self-Hosted Deployments"
-    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
-
-Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
-
-For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.
+This is mainly relevant in the context of using the dev server via the `langgraph dev` command. Set `LOG_COLOR` to `true` to enable ANSI-colored console output when using the default console renderer. Disabling color output by setting this variable to `false` produces monochrome logs. Defaults to `true`.

 ## `N_JOBS_PER_WORKER`

@@ -114,14 +94,16 @@ Database Connectivity:

 - The custom Postgres instance must be accessible by the LangGraph Server. The user is responsible for ensuring connectivity.

-## `REDIS_CLUSTER`
+## `LANGGRAPH_POSTGRES_POOL_MAX_SIZE`

-!!! info "Only Allowed in Self-Hosted Deployments"
-    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Platform SaaS will provision a redis instance for you by default.
+Beginning with langgraph-api version `0.2.12`, the maximum size of the Postgres connection pool can be controlled using the `LANGGRAPH_POSTGRES_POOL_MAX_SIZE` environment variable. By setting this variable, you can determine the upper bound on the number of simultaneous connections the server will establish with the Postgres database. This is particularly useful for deployments where database resources are limited (or more available) or where you need to tune connection behavior for performance or scaling reasons. If not specified, the pool size defaults to 150 connections.

-Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
+## `REDIS_URI_CUSTOM`

-Defaults to `False`.
+!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
+    Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
+
+Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).

 ## `REDIS_KEY_PREFIX`

@@ -132,19 +114,20 @@ Specify a prefix for Redis keys. This allows multiple LangGraph Server instances

 Defaults to `''`.

-## `REDIS_URI_CUSTOM`
+## `REDIS_CLUSTER`

-!!! info "Only for Self-Hosted Data Plane and Self-Hosted Control Plane"
-    Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.
+!!! info "Only Allowed in Self-Hosted Deployments"
+    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Platform SaaS will provision a redis instance for you by default.

-Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
+Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.

-## `RESUMABLE_STREAM_TTL_SECONDS`
+Defaults to `False`.

-Time-to-live in seconds for resumable stream data in Redis.
+## `MOUNT_PREFIX`

-When a run is created and the output is streamed, the stream can be configured to be resumable (e.g. `stream_resumable=True`). If a stream is resumable, output from the stream is temporarily stored in Redis. The TTL for this data can be configured by setting `RESUMABLE_STREAM_TTL_SECONDS`.
+!!! info "Only Allowed in Self-Hosted Deployments"
+    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.

-See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream) and [JS/TS](https://langchain-ai.github.io/langgraphjs/reference/classes/sdk_client.RunsClient.html#stream) SDKs for more details on how to implement resumable streams.
+Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.

-Defaults to `120` seconds.
+For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.
@@ -1,225 +0,0 @@
-# LangGraph Server Changelog
-
-[LangGraph Server](../../concepts/langgraph_server.md) is an API platform for creating and managing agent-based applications. It provides built-in persistence, a task queue, and supports deploying, configuring, and running assistants (agentic workflows) at scale. This changelog documents all notable updates, features, and fixes to LangGraph Server releases.
-
---
-
-## v0.2.109 (2025-07-28)
- Fixed an issue where missing config schema occurred when `config_type` was not set.
-
-## v0.2.108 (2025-07-28)
- Added compatibility for langgraph v0.6, including new context API support and a migration to enhance context handling in assistant operations.
-
-## v0.2.107 (2025-07-27)
- Implemented caching for authentication processes to improve performance.
- Merged count and select queries to improve database query efficiency.
-
-## v0.2.106 (2025-07-27)
- Log whether run uses resumable streams.
-
-## v0.2.105 (2025-07-27)
- Added a `/heapdump` endpoint to capture and save JS process heap data.
-
-## v0.2.103 (2025-07-25)
- Corrected the metadata endpoint to ensure accurate data retrieval.
-
-## v0.2.102 (2025-07-24)
- Captured interrupt events in the wait method to preserve legacy behavior and stream updates by default.
- Added support for SDK structlog in the JavaScript environment, enhancing logging capabilities.
-
-## v0.2.101 (2025-07-24)
- Used the correct metadata endpoint for self-hosted environments, resolving an access issue.
-
-## v0.2.99 (2025-07-22)
- Improved license validation by adding an in-memory cache and handling Redis connection errors more effectively.
- Automatically remove agents from memory that are removed from `langgraph.json` to prevent persistence issues.
- Ensured the UI namespace for generated UI is a valid JavaScript property name to prevent errors.
- Raised a 422 error for improved request validation feedback.
-
-## v0.2.98 (2025-07-19)
- Added langgraph node context for improved log filtering and trace visibility.
-
-## v0.2.97 (2025-07-19)
- Fixed scheduling issue with ckpt ingestion worker that occurred on isolated background loops.
- Ensured queue worker starts only after all migrations have completed.
- Added more detailed error messages for thread state issues and improved response handling when state updates fail.
- Exposed interrupt ID while retrieving thread state for enhanced API response details.
-
-## v0.2.96 (2025-07-17)
- Added a fallback mechanism for configurable header patterns to handle exclude/include settings more effectively.
-
-## v0.2.95 (2025-07-17)
- Avoided setting the future if it is already done to prevent redundant operations.
- Resolved compatibility errors in CI by switching from `typing.TypedDict` to `typing_extensions.TypedDict` for Python versions below 3.12.
-
-## v0.2.94 (2025-07-16)
- Improved performance by omitting pending sends for langgraph versions 0.5 and above.
- Improved server startup logs to provide clearer warnings when the DD_API_KEY environment variable is set.
-
-## v0.2.93 (2025-07-16)
- Removed the GIN index for run metadata to improve performance.
-
-## v0.2.92 (2025-07-16)
- Enabled copying functionality for blobs and checkpoints, improving data management flexibility.
-
-## v0.2.91 (2025-07-16)
- Reduced writes to the `checkpoint_blobs` table by inlining small values (null, numeric, str, etc.). This means we don't need to store extra values for channels that haven't been updated.
-
-## v0.2.90 (2025-07-16)
- Improve checkpoint writes via node-local background queueing.
-
-
-## v0.2.89 (2025-07-15)
- Decoupled checkpoint writing from thread/run state by removing foreign keys and updated logger to prevent timeout-related failures.
-
-## v0.2.88 (2025-07-14)
- Removed the foreign key constraint for `thread` in the `run` table to simplify database schema.
-
-## 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.
-
-## v0.2.84 (2025-07-09)
- Removed unnecessary status updates to streamline thread handling and updated version to 0.2.84.
-
-## v0.2.83 (2025-07-09)
- Reduced the default time-to-live for resumable streams to 2 minutes.
- Enhanced data submission logic to send data to both Beacon and LangSmith instance based on license configuration.
- Enabled submission of self-hosted data to a Langsmith instance when the endpoint is configured.
-
-## v0.2.82 (2025-07-03)
- Addressed a race condition in background runs by implementing a lock using join, ensuring reliable execution across CTEs.
-
-## v0.2.81 (2025-07-03)
- Optimized run streams by reducing initial wait time to improve responsiveness for older or non-existent runs.
-
-## v0.2.80 (2025-07-03)
- Corrected parameter passing in the `logger.ainfo()` API call to resolve a TypeError.
-
-## v0.2.79 (2025-07-02)
- Fixed a JsonDecodeError in checkpointing with remote graph by correcting JSON serialization to handle trailing slashes properly.
- Introduced a configuration flag to disable webhooks globally across all routes.
-
-## v0.2.78 (2025-07-02)
- Added timeout retries to webhook calls to improve reliability.
- Added HTTP request metrics, including a request count and latency histogram, for enhanced monitoring capabilities.
-
-## v0.2.77 (2025-07-02)
- Added HTTP metrics to improve performance monitoring.
- Changed the Redis cache delimiter to reduce conflicts with subgraph message names and updated caching behavior.
-
-## v0.2.76 (2025-07-01)
- Updated Redis cache delimiter to prevent conflicts with subgraph messages.
-
-## v0.2.74 (2025-06-30)
- Scheduled webhooks in an isolated loop to ensure thread-safe operations and prevent errors with PYTHONASYNCIODEBUG=1.
-
-## v0.2.73 (2025-06-27)
- Fixed an infinite frame loop issue and removed the dict_parser due to structlog's unexpected behavior.
- Throw a 409 error on deadlock occurrence during run cancellations to handle lock conflicts gracefully.
-
-## v0.2.72 (2025-06-27)
- Ensured compatibility with future langgraph versions.
- Implemented a 409 response status to handle deadlock issues during cancellation.
-
-## v0.2.71 (2025-06-26)
- Improved logging for better clarity and detail regarding log types.
-
-## v0.2.70 (2025-06-26)
- Improved error handling to better distinguish and log TimeoutErrors caused by users from internal run timeouts.
-
-## v0.2.69 (2025-06-26)
- Added sorting and pagination to the crons API and updated schema definitions for improved accuracy.
-
-## v0.2.66 (2025-06-26)
- Fixed a 404 error when creating multiple runs with the same thread_id using `on_not_exist="create"`.
-
-## v0.2.65 (2025-06-25)
- Ensured that only fields from `assistant_versions` are returned when necessary.
- Ensured consistent data types for in-memory and PostgreSQL users, improving internal authentication handling.
-
-## v0.2.64 (2025-06-24)
- Added descriptions to version entries for better clarity.
-
-## v0.2.62 (2025-06-23)
- Improved user handling for custom authentication in the JS Studio.
- Added Prometheus-format run statistics to the metrics endpoint for better monitoring.
- Added run statistics in Prometheus format to the metrics endpoint.
-
-## v0.2.61 (2025-06-20)
- Set a maximum idle time for Redis connections to prevent unnecessary open connections.
-
-## v0.2.60 (2025-06-20)
- Enhanced error logging to include traceback details for dictionary operations.
- Added a `/metrics` endpoint to expose queue worker metrics for monitoring.
-
-## v0.2.57 (2025-06-18)
- Removed CancelledError from retriable exceptions to allow local interrupts while maintaining retriability for workers.
- Introduced middleware to gracefully shut down the server after completing in-flight requests upon receiving a SIGINT.
- Reduced metadata stored in checkpoint to only include necessary information.
- Improved error handling in join runs to return error details when present.
-
-## v0.2.56 (2025-06-17)
- Improved application stability by adding a handler for SIGTERM signals.
-
-## v0.2.55 (2025-06-17)
- Improved the handling of cancellations in the queue entrypoint.
- Improved cancellation handling in the queue entry point.
-
-## v0.2.54 (2025-06-16)
- Enhanced error message for LuaLock timeout during license validation.
- Fixed the $contains filter in custom auth by requiring an explicit ::text cast and updated tests accordingly.
- Ensured project and tenant IDs are formatted as UUIDs for consistency.
-
-## v0.2.53 (2025-06-13)
- Resolved a timing issue to ensure the queue starts only after the graph is registered.
- Improved performance by setting thread and run status in a single query and enhanced error handling during checkpoint writes.
- Reduced the default background grace period to 3 minutes.
-
-## v0.2.52 (2025-06-12)
- Now logging expected graphs when one is omitted to improve traceability.
- Implemented a time-to-live (TTL) feature for resumable streams.
- Improved query efficiency and consistency by adding a unique index and optimizing row locking.
-
-## v0.2.51 (2025-06-12)
- Handled `CancelledError` by marking tasks as ready to retry, improving error management in worker processes.
- Added LG API version and request ID to metadata and logs for better tracking.
- Added LG API version and request ID to metadata and logs to improve traceability.
- Improved database performance by creating indexes concurrently.
- Ensured postgres write is committed only after the Redis running marker is set to prevent race conditions.
- Enhanced query efficiency and reliability by adding a unique index on thread_id/running, optimizing row locks, and ensuring deterministic run selection.
- Resolved a race condition by ensuring Postgres updates only occur after the Redis running marker is set.
-
-## v0.2.46 (2025-06-07)
- Introduced a new connection for each operation while preserving transaction characteristics in Threads state `update()` and `bulk()` commands.
-
-## v0.2.45 (2025-06-05)
- Enhanced streaming feature by incorporating tracing contexts.
- Removed an unnecessary query from the Crons.search function.
- Resolved connection reuse issue when scheduling next run for multiple cron jobs.
- Removed an unnecessary query in the Crons.search function to improve efficiency.
- Resolved an issue with scheduling the next cron run by improving connection reuse.
-
-## v0.2.44 (2025-06-04)
- Enhanced the worker logic to exit the pipeline before continuing when the Redis message limit is reached.
- Introduced a ceiling for Redis message size with an option to skip messages larger than 128 MB for improved performance.
- Ensured the pipeline always closes properly to prevent resource leaks.
-
-## v0.2.43 (2025-06-04)
- Improved performance by omitting logs in metadata calls and ensuring output schema compliance in value streaming.
- Ensured the connection is properly closed after use.
- Aligned output format to strictly adhere to the specified schema.
- Stopped sending internal logs in metadata requests to improve privacy.
-
-## v0.2.42 (2025-06-04)
- Added timestamps to track the start and end of a request's run.
- Added tracer information to the configuration settings.
- Added support for streaming with tracing contexts.
-
-## v0.2.41 (2025-06-03)
- Added locking mechanism to prevent errors in pipelined executions.
@@ -97,7 +97,7 @@ Parallel processing is vital for efficient multi-agent systems and complex tasks
 - Implementation of map-reduce-like operations
 - Efficient handling of independent subtasks

-For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.md#map-reduce-and-the-send-api)
+For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api.ipynb#map-reduce-and-the-send-api)

 ### Subgraphs

@@ -107,7 +107,7 @@ For practical implementation, see our [map-reduce tutorial](../how-tos/graph-api
 - Hierarchical organization of agent teams
 - Controlled communication between agents and the main system

-Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.md).
+Subgraphs communicate with the parent graph through overlapping keys in the state schema. This enables flexible, modular agent design. For implementation details, refer to our [subgraph how-to guide](../how-tos/subgraph.ipynb).

 ### Reflection

@@ -48,7 +48,7 @@ Below are examples of directory structures for Python and JavaScript application
    │   ├── utils # utilities for your graph
    │   │   ├── __init__.py
    │   │   ├── tools.py # tools for your graph
-    │   │   ├── nodes.py # node functions for your graph
+    │   │   ├── nodes.py # node functions for you graph
    │   │   └── state.py # state definition of your graph
    │   ├── __init__.py
    │   └── agent.py # code for constructing your graph
@@ -64,7 +64,7 @@ Below are examples of directory structures for Python and JavaScript application
    ├── src # all project code lies within here
    │   ├── utils # optional utilities for your graph
    │   │   ├── tools.ts # tools for your graph
-    │   │   ├── nodes.ts # node functions for your graph
+    │   │   ├── nodes.ts # node functions for you graph
    │   │   └── state.ts # state definition of your graph
    │   └── agent.ts # code for constructing your graph
    ├── package.json # package dependencies
@@ -1,6 +1,6 @@
 # Assistants

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

 For example, imagine a general-purpose writing agent built on a common graph architecture. While the structure remains the same, different writing styles—such as blog posts and tweets—require tailored configurations to optimize performance. To support these variations, you can create multiple assistants (e.g., one for blogs and another for tweets) that share the underlying graph but differ in model selection and system prompt.

@@ -14,8 +14,8 @@ The LangGraph Cloud API provides several endpoints for creating and managing ass

 ## Configuration

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

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

@@ -26,6 +26,6 @@ Once you've created an assistant, subsequent edits to that assistant will create

 ## Execution

-A **run** is an invocation of an assistant. Each run may have its own input, configuration, context, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).
+A **run** is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./persistence.md#threads).

 The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
@@ -143,54 +143,6 @@ The returned user information is available:
    In many of our tutorials, we will just show the "authorization" parameter to be concise, but you can opt to accept more information as needed
    to implement your custom authentication scheme.

-### Agent authentication
-
-Custom authentication permits delegated access. The values you return in  `@auth.authenticate` are added to the run context, giving agents user-scoped credentials lets them access resources on the user’s behalf.
-
-```mermaid
-sequenceDiagram
-  %% Actors
-  participant ClientApp as Client
-  participant AuthProv  as Auth Provider
-  participant LangGraph as LangGraph Backend
-  participant SecretStore as Secret Store
-  participant ExternalService as External Service
-
-  %% Platform login / AuthN
-  ClientApp  ->> AuthProv: 1. Login (username / password)
-  AuthProv   -->> ClientApp: 2. Return token
-  ClientApp  ->> LangGraph: 3. Request with token
-
-  Note over LangGraph: 4. Validate token (@auth.authenticate)
-  LangGraph  -->> AuthProv: 5. Fetch user info
-  AuthProv   -->> LangGraph: 6. Confirm validity
-
-  %% Fetch user tokens from secret store
-  LangGraph  ->> SecretStore: 6a. Fetch user tokens
-  SecretStore -->> LangGraph: 6b. Return tokens
-
-  Note over LangGraph: 7. Apply access control (@auth.on.*)
-
-  %% External Service round-trip
-  LangGraph  ->> ExternalService: 8. Call external service (with header)
-  Note over ExternalService: 9. External service validates header and executes action
-  ExternalService  -->> LangGraph: 10. Service response
-
-  %% Return to caller
-  LangGraph  -->> ClientApp: 11. Return resources 
-```
-
-After authentication, the platform creates a special configuration object that is passed to your graph and all nodes via the configurable context.
-This object contains information about the current user, including any custom fields you return from your [`@auth.authenticate`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.authenticate) handler.
-
-To enable an agent to act on behalf of the user, use [custom authentication middleware](../how-tos/auth/custom_auth.md). This will allow the agent to interact with external systems like MCP servers, external databases, and even other agents on behalf of the user.
-
-For more information, see the [Use custom auth](../how-tos/auth/custom_auth.md#enable-agent-authentication) guide.
-
-### Agent authentication with MCP
-
-For information on how to authenticate an agent to an MCP server, see the [MCP conceptual guide](../concepts/mcp.md).
-
 ## Authorization

 After authentication, LangGraph calls your [`@auth.on`](../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth.on) handlers to control access to specific resources (e.g., threads, assistants, crons). These handlers can:
@@ -0,0 +1,18 @@
+---
+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).
@@ -10,7 +10,7 @@ search:
 There are two free options for deploying LangGraph applications via the LangGraph Server:

 1. [Local](../tutorials/langgraph-platform/local-server.md): Deploy for local testing and development. 
-1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more than 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.
+1. [Standalone Container (Lite)](../concepts/langgraph_standalone_container.md): A limited version of Standalone Container for deployments unlikely to see more that 1 million node executions per year and that do not need crons and other enterprise features. Standalone Container (Lite) deployment option is free with a LangSmith API key.

 ## Production deployment

@@ -18,9 +18,9 @@ There are 4 main options for deploying with the [LangGraph Platform](langgraph_p

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

-1. [Self-Hosted Data Plane](#self-hosted-data-plane)
+1. [Self-Hosted Data Plane<sup>(Beta)</sup>](#self-hosted-data-plane)

-1. [Self-Hosted Control Plane](#self-hosted-control-plane)
+1. [Self-Hosted Control Plane<sup>(Beta)</sup>](#self-hosted-control-plane)

 1. [Standalone Container](#standalone-container)

@@ -50,7 +50,7 @@ For more information, please see:
 ## Self-Hosted Data Plane

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

 The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.

@@ -66,7 +66,7 @@ For more information, please see:
 ## Self-Hosted Control Plane

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

 The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option gives you full control and responsibility of the control plane and data plane infrastructure.

@@ -48,7 +48,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
    from typing_extensions import TypedDict
    import uuid

-    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.memory import MemorySaver
    from langgraph.graph import StateGraph, START, END
    import requests

@@ -74,7 +74,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
    builder.add_edge("call_api", END)

    # Specify a checkpointer
-    checkpointer = InMemorySaver()
+    checkpointer = MemorySaver()

    # Compile the graph with the checkpointer
    graph = builder.compile(checkpointer=checkpointer)
@@ -94,7 +94,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
    from typing_extensions import TypedDict
    import uuid

-    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.checkpoint.memory import MemorySaver
    from langgraph.func import task
    from langgraph.graph import StateGraph, START, END
    import requests
@@ -129,7 +129,7 @@ If a [node](./low_level.md#nodes) contains multiple operations, you may find it
    builder.add_edge("call_api", END)

    # Specify a checkpointer
-    checkpointer = InMemorySaver()
+    checkpointer = MemorySaver()

    # Compile the graph with the checkpointer
    graph = builder.compile(checkpointer=checkpointer)
@@ -47,7 +47,7 @@ LangGraph is a stateful, orchestration framework that brings added control to ag

 No. LangGraph Platform is proprietary software.

-There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option and the Self-Hosted deployment options are paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.
+There is a free, self-hosted version of LangGraph Platform with access to basic features. The Cloud SaaS deployment option is free while in beta, but will eventually be a paid service. We will always give ample notice before charging for a service and reward our early adopters with preferential pricing. The Self-Hosted deployment options are paid services. [Contact our sales team](https://www.langchain.com/contact-sales) to learn more.

 For more information, see our [LangGraph Platform pricing page](https://www.langchain.com/pricing-langgraph-platform).

@@ -39,7 +39,7 @@ Here are some key differences:
 Below we demonstrate a simple application that writes an essay and [interrupts](human_in_the_loop.md) to request human review.

 ```python
-from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.memory import MemorySaver
 from langgraph.func import entrypoint, task
 from langgraph.types import interrupt

@@ -50,7 +50,7 @@ def write_essay(topic: str) -> str:
    time.sleep(1) # A placeholder for a long-running task.
    return f"An essay about topic: {topic}"

-@entrypoint(checkpointer=InMemorySaver())
+@entrypoint(checkpointer=MemorySaver())
 def workflow(topic: str) -> dict:
    """A simple workflow that writes an essay and asks for a review."""
    essay = write_essay("cat").result()
@@ -79,54 +79,51 @@ def workflow(topic: str) -> dict:
    ```python
    import time
    import uuid
+
    from langgraph.func import entrypoint, task
    from langgraph.types import interrupt
-    from langgraph.checkpoint.memory import InMemorySaver
-
+    from langgraph.checkpoint.memory import MemorySaver

    @task
    def write_essay(topic: str) -> str:
        """Write an essay about the given topic."""
-        time.sleep(1)  # This is a placeholder for a long-running task.
+        time.sleep(1) # This is a placeholder for a long-running task.
        return f"An essay about topic: {topic}"

-    @entrypoint(checkpointer=InMemorySaver())
+    @entrypoint(checkpointer=MemorySaver())
    def workflow(topic: str) -> dict:
        """A simple workflow that writes an essay and asks for a review."""
        essay = write_essay("cat").result()
-        is_approved = interrupt(
-            {
-                # Any json-serializable payload provided to interrupt as argument.
-                # It will be surfaced on the client side as an Interrupt when streaming data
-                # from the workflow.
-                "essay": essay,  # The essay we want reviewed.
-                # We can add any additional information that we need.
-                # For example, introduce a key called "action" with some instructions.
-                "action": "Please approve/reject the essay",
-            }
-        )
+        is_approved = interrupt({
+            # Any json-serializable payload provided to interrupt as argument.
+            # It will be surfaced on the client side as an Interrupt when streaming data
+            # from the workflow.
+            "essay": essay, # The essay we want reviewed.
+            # We can add any additional information that we need.
+            # For example, introduce a key called "action" with some instructions.
+            "action": "Please approve/reject the essay",
+        })
+
        return {
-            "essay": essay,  # The essay that was generated
-            "is_approved": is_approved,  # Response from HIL
+            "essay": essay, # The essay that was generated
+            "is_approved": is_approved, # Response from HIL
        }

-
    thread_id = str(uuid.uuid4())
-    config = {"configurable": {"thread_id": thread_id}}
+
+    config = {
+        "configurable": {
+            "thread_id": thread_id
+        }
+    }
+
    for item in workflow.stream("cat", config):
        print(item)
-    # > {'write_essay': 'An essay about topic: cat'}
-    # > {
-    # >     '__interrupt__': (
-    # >        Interrupt(
-    # >            value={
-    # >                'essay': 'An essay about topic: cat',
-    # >                'action': 'Please approve/reject the essay'
-    # >            },
-    # >            id='b9b2b9d788f482663ced6dc755c9e981'
-    # >        ),
-    # >    )
-    # > }
+    ```
+
+    ```pycon
+    {'write_essay': 'An essay about topic: cat'}
+    {'__interrupt__': (Interrupt(value={'essay': 'An essay about topic: cat', 'action': 'Please approve/reject the essay'}, resumable=True, ns=['workflow:f7b8508b-21c0-8b4c-5958-4e8de74d2684'], when='during'),)}
    ```

    An essay has been written and is ready for review. Once the review is provided, we can resume the workflow:
@@ -23,18 +23,9 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'

 ## Key capabilities

-* **Persistent execution state**: Interrupts use LangGraph's [persistence](./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.
+* **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.

-    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 pre-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.
+* **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.

 ## Patterns

@@ -26,7 +26,7 @@ The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com

 ## Control Plane API

-This section describes the data model of the control plane API. The API is used to create, update, and delete deployments. See the [control plane API reference](../cloud/reference/api/api_ref_control_plane.md) for more details.
+This section describes data model of the control plane API. The API is used to create, update, and delete deployments. However, they are not publicly accessible.

 ### Deployment

@@ -34,7 +34,11 @@ A deployment is an instance of a LangGraph Server. A single deployment can have

 ### Revision

-A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update secrets for a deployment, a new revision must be created.
+A revision is an iteration of a deployment. When a new deployment is created, an initial revision is automatically created. To deploy code changes or update environment variables for a deployment, a new revision must be created.
+
+### Environment Variable
+
+Environment variables are set for a deployment. All environment variables are stored as secrets (i.e. saved in a secrets store).

 ## Control Plane Features

@@ -46,40 +50,21 @@ For simplicity, the control plane offers two deployment types with different res

 | **Deployment Type** | **CPU/Memory**  | **Scaling**         | **Database**                                                                     |
 |---------------------|-----------------|---------------------|----------------------------------------------------------------------------------|
-| Development         | 1 CPU, 1 GB RAM | Up to 1 replica   | 10 GB disk, no backups                                                           |
-| Production          | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
+| Development         | 1 CPU, 1 GB RAM | Up to 1 container   | 10 GB disk, no backups                                                           |
+| Production          | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |

-CPU and memory resources are per replica.
+CPU and memory resources are per container.

 !!! warning "Immutable Deployment Type"

    Once a deployment is created, the deployment type cannot be changed.

-!!! info "Self-Hosted Deployment"
-    Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized. Deployment types are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
+!!! info "Resource Customization"
+    For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.

-#### Production
+    For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.

-`Production` type deployments are suitable for "production" workloads. For example, select `Production` for customer-facing applications in the critical path.
-
-Resources for `Production` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
-
-#### Development
-
-`Development` type deployments are suitable development and testing. For example, select `Development` for internal testing environments. `Development` type deployments are not suitable for "production" workloads.
-
-!!! danger "Preemptible Compute Infrastructure"
-    `Development` type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure **may be terminated at any time without notice**. This may result in intermittent...
-
-    - Redis connection timeouts/errors
-    - Postgres connection timeouts/errors
-    - Failed or retrying background runs
-    
-    This behavior is expected. Preemptible compute infrastructure **significantly reduces the cost to provision a `Development` type deployment**. By design, LangGraph Server is fault-tolerant. The implementation will automatically attempt to recover from Redis/Postgres connection errors and retry failed background runs.
-
-    `Production` type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure.
-
-Database disk size for `Development` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
+    Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized.

 ### Database Provisioning

@@ -112,18 +97,11 @@ After a deployment is ready, the control plane monitors the deployment and recor
 - Number of container restarts.
 - Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)).
 - [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage.
- [LangGraph Server queue](../concepts/langgraph_server.md#persistence-and-task-queue) pending/active run count.
- [LangGraph Server API](../concepts/langgraph_server.md) success response count, error response count, and latency.

 These metrics are displayed as charts in the Control Plane UI.

 ### LangSmith Integration

-A [LangSmith](https://docs.smith.langchain.com/) tracing project and LangSmith API key are automatically created for each deployment. The deployment uses the API key to automatically send traces to LangSmith.
+A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.

- The tracing project has the same name as the deployment.
- The API key has the description `LangGraph Platform: <deployment_name>`.
- The API key is never revealed and cannot be deleted manually.
- When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.
-
-When a deployment is deleted, the traces and the tracing project are not deleted. However, the API will be deleted when the deployment is deleted.
+When a deployment is deleted, the traces and the tracing project are not deleted.
@@ -50,15 +50,6 @@ Runs in a LangGraph Server may be retried for specific failures (currently only

 This section describes various features of the data plane.

-### Data Region
-
-!!! info "Only for Cloud SaaS"
-    Data regions are only applicable for [Cloud SaaS](../concepts/langgraph_cloud.md) deployments.
-
-Deployments can be created in 2 data regions: US and EU
-
-The data region for a deployment is implied by the data region of the LangSmith organization where the deployment is created. Deployments and the underlying database for the deployments cannot be migrated between data regions.
-
 ### Autoscaling

 [`Production` type](../concepts/langgraph_control_plane.md#deployment-types) deployments automatically scale up to 10 containers. Scaling is based on 3 metrics:
@@ -3,7 +3,7 @@
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).

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

 ## Requirements

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

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

 ## Requirements

@@ -19,7 +19,7 @@ The Standalone Container deployment option is the least restrictive model for de

 !!! warning

-      LangGraph Platform should not be deployed in serverless environments. Scale to zero may cause task loss and scaling up will not work reliably.
+      LangGraph Platform should not be deployed in serverless environments.

 ## Architecture

@@ -45,9 +45,9 @@ 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 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`).
+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.ipynb#use-pydantic-models-for-graph-state) as your graph state to add **default values** and additional data validation.

-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.
+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.ipynb#define-input-and-output-schemas) for how to use.

 #### Multiple schemas

@@ -56,9 +56,9 @@ Typically, all graph nodes communicate with a single schema. This means that the
 - Internal nodes can pass information that is not required in the graph's input / output.
 - We may also want to use different input / output schemas for the graph. The output might, for example, only contain a single relevant output key.

-It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`. See [this guide](../how-tos/graph-api.md#pass-private-state-between-nodes) for more detail.
+It is possible to have nodes write to private state channels inside the graph for internal node communication. We can simply define a private schema, `PrivateState`. See [this guide](../how-tos/graph-api.ipynb#pass-private-state-between-nodes) for more detail.

-It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.md#define-input-and-output-schemas) for more detail.
+It is also possible to define explicit input and output schemas for a graph. In these cases, we define an "internal" schema that contains _all_ keys relevant to graph operations. But, we also define `input` and `output` schemas that are sub-sets of the "internal" schema to constrain the input and output of the graph. See [this guide](../how-tos/graph-api.ipynb#define-input-and-output-schemas) for more detail.

 Let's look at an example:

@@ -192,48 +192,35 @@ class State(MessagesState):

 ## Nodes

-In LangGraph, nodes are Python functions (either synchronous or asynchronous) that accept the following arguments:
-
-1. `state`: The [state](#state) of the graph
-2. `config`: A `RunnableConfig` object that contains configuration information like `thread_id` and tracing information like `tags`
-3. `runtime`: A `Runtime` object that contains [runtime `context`](#runtime-context) and other information like `store` and `stream_writer`
-    
+In LangGraph, nodes are typically python functions (sync or async) where the **first** positional argument is the [state](#state), and (optionally), the **second** positional argument is a "config", containing optional [configurable parameters](#configuration) (such as a `thread_id`).

 Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:

 ```python
-from dataclasses import dataclass
 from typing_extensions import TypedDict

 from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph
-from langgraph.runtime import Runtime

 class State(TypedDict):
    input: str
    results: str

-@dataclass
-class Context:
-    user_id: str
-
 builder = StateGraph(State)

-def plain_node(state: State):
+
+def my_node(state: State, config: RunnableConfig):
+    print("In node: ", config["configurable"]["user_id"])
+    return {"results": f"Hello, {state['input']}!"}
+
+
+# The second argument is optional
+def my_other_node(state: State):
    return state

-def node_with_runtime(state: State, runtime: Runtime[Context]):
-    print("In node: ", runtime.context.user_id)
-    return {"results": f"Hello, {state['input']}!"}

-def node_with_config(state: State, config: RunnableConfig):
-    print("In node with thread_id: ", config["configurable"]["thread_id"])
-    return {"results": f"Hello, {state['input']}!"}
-
-
-builder.add_node("plain_node", plain_node)
-builder.add_node("node_with_runtime", node_with_runtime)
-builder.add_node("node_with_config", node_with_config)
+builder.add_node("my_node", my_node)
+builder.add_node("other_node", my_other_node)
 ...
 ```

@@ -311,7 +298,7 @@ print(graph.invoke({"x": 5}, stream_mode='updates'))  # (2)!
 [{'expensive_node': {'result': 10}, '__metadata__': {'cached': True}}]
 ```

-1. First run takes two seconds to run (due to mocked expensive computation).
+1. First run takes the full second to run (due to mocked expensive computation).
 2. Second run utilizes cache and returns quickly.

 ## Edges
@@ -419,7 +406,7 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:

    When returning `Command` in your node functions, you must add return type annotations with the list of node names the node is routing to, e.g. `Command[Literal["my_other_node"]]`. This is necessary for the graph rendering and tells LangGraph that `my_node` can navigate to `my_other_node`.

-Check out this [how-to guide](../how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.
+Check out this [how-to guide](../how-tos/graph-api.ipynb#combine-control-flow-and-state-updates-with-command) for an end-to-end example of how to use `Command`.

 ### When should I use Command instead of conditional edges?

@@ -446,17 +433,17 @@ def my_node(state: State) -> Command[Literal["other_subgraph"]]:

 !!! important "State updates with `Command.PARENT`"

-    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/graph-api.md#navigate-to-a-node-in-a-parent-graph).
+    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/graph-api.ipynb#navigate-to-a-node-in-a-parent-graph).

 This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs).

-Check out [this guide](../how-tos/graph-api.md#navigate-to-a-node-in-a-parent-graph) for detail.
+Check out [this guide](../how-tos/graph-api.ipynb#navigate-to-a-node-in-a-parent-graph) for detail.

 ### Using inside tools

 A common use case is updating graph state from inside a tool. For example, in a customer support application you might want to look up customer information based on their account number or ID in the beginning of the conversation.

-Refer to [this guide](../how-tos/graph-api.md#use-inside-tools) for detail.
+Refer to [this guide](../how-tos/graph-api.ipynb#use-inside-tools) for detail.

 ### Human-in-the-loop

@@ -472,47 +459,48 @@ LangGraph can easily handle migrations of graph definitions (nodes, edges, and s
 - State keys that are renamed lose their saved state in existing threads
 - State keys whose types change in incompatible ways could currently cause issues in threads with state from before the change -- if this is a blocker please reach out and we can prioritize a solution.

-## Runtime Context
+## Configuration

-When creating a graph, you can specify a `context_schema` for runtime context passed to nodes. This is useful for passing
-information to nodes that is not part of the graph state. For example, you might want to pass dependencies such as model name or a database connection.
+When creating a graph, you can also mark that certain parts of the graph are configurable. This is commonly done to enable easily switching between models or system prompts. This allows you to create a single "cognitive architecture" (the graph) but have multiple different instance of it.
+
+You can optionally specify a `config_schema` when creating a graph.

 ```python
-@dataclass
-class ContextSchema:
-    llm_provider: str = "openai"
+class ConfigSchema(TypedDict):
+    llm: str

-graph = StateGraph(State, context_schema=ContextSchema)
+graph = StateGraph(State, config_schema=ConfigSchema)
 ```

-You can then pass this context into the graph using the `context` parameter of the `invoke` method.
+You can then pass this configuration into the graph using the `configurable` config field.

 ```python
-graph.invoke(inputs, context={"llm_provider": "anthropic"})
+config = {"configurable": {"llm": "anthropic"}}
+
+graph.invoke(inputs, config=config)
 ```

-You can then access and use this context inside a node or conditional edge:
+You can then access and use this configuration inside a node or conditional edge:

 ```python
-from langgraph.runtime import Runtime
-
-def node_a(state: State, runtime: Runtime[ContextSchema]):
-    llm = get_llm(runtime.context.llm_provider)
+def node_a(state, config):
+    llm_type = config.get("configurable", {}).get("llm", "openai")
+    llm = get_llm(llm_type)
    ...
 ```

-See [this guide](../how-tos/graph-api.md#add-runtime-configuration) for a full breakdown on configuration.
+See [this guide](../how-tos/graph-api.ipynb#add-runtime-configuration) for a full breakdown on configuration.

 ### Recursion Limit

 The recursion limit sets the maximum number of [super-steps](#graphs) the graph can execute during a single execution. Once the limit is reached, LangGraph will raise `GraphRecursionError`. By default this value is set to 25 steps. The recursion limit can be set on any graph at runtime, and is passed to `.invoke`/`.stream` via the config dictionary. Importantly, `recursion_limit` is a standalone `config` key and should not be passed inside the `configurable` key as all other user-defined configuration. See the example below:

 ```python
-graph.invoke(inputs, config={"recursion_limit": 5}, context={"llm": "anthropic"})
+graph.invoke(inputs, config={"recursion_limit": 5, "configurable":{"llm": "anthropic"}})
 ```

 Read [this how-to](https://langchain-ai.github.io/langgraph/how-tos/recursion-limit/) to learn more about how the recursion limit works.

 ## Visualization

-It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.md#visualize-your-graph) for more info.
+It's often nice to be able to visualize graphs, especially as they get more complex. LangGraph comes with several built-in ways to visualize graphs. See [this how-to guide](../how-tos/graph-api.ipynb#visualize-your-graph) for more info.
@@ -1,57 +0,0 @@
-# MCP
-
-[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
-
-![MCP](../agents/assets/mcp.png)
-
-Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
-
-```bash
-pip install langchain-mcp-adapters
-```
-
-## Authenticate to an MCP server
-
-You can set up [custom authentication middleware](../how-tos/auth/custom_auth.md) to authenticate a user with an MCP server to get access to user-scoped tools within your LangGraph Platform deployment. 
-
-!!! note
-    Custom authentication is a LangGraph Platform feature.
-
-An example architecture for this flow:
-
-```mermaid
-sequenceDiagram
-  %% Actors
-  participant ClientApp as Client
-  participant AuthProv  as Auth Provider
-  participant LangGraph as LangGraph Backend
-  participant SecretStore as Secret Store
-  participant MCPServer as MCP Server
-
-  %% Platform login / AuthN
-  ClientApp  ->> AuthProv: 1. Login (username / password)
-  AuthProv   -->> ClientApp: 2. Return token
-  ClientApp  ->> LangGraph: 3. Request with token
-
-  Note over LangGraph: 4. Validate token (@auth.authenticate)
-  LangGraph  -->> AuthProv: 5. Fetch user info
-  AuthProv   -->> LangGraph: 6. Confirm validity
-
-  %% Fetch user tokens from secret store
-  LangGraph  ->> SecretStore: 6a. Fetch user tokens
-  SecretStore -->> LangGraph: 6b. Return tokens
-
-  Note over LangGraph: 7. Apply access control (@auth.on.*)
-
-  %% MCP round-trip
-  Note over LangGraph: 8. Build MCP client with user token
-  LangGraph  ->> MCPServer: 9. Call MCP tool (with header)
-  Note over MCPServer: 10. MCP validates header and runs tool
-  MCPServer  -->> LangGraph: 11. Tool response
-
-  %% Return to caller
-  LangGraph  -->> ClientApp: 12. Return resources / tool output
-```
-
-For more information, see [MCP endpoint in LangGraph Server](../concepts/server-mcp.md#use-user-scoped-mcp-tools-in-your-deployment).
-
@@ -26,7 +26,7 @@ The primary benefits of using multi-agent systems are:
 There are several ways to connect agents in a multi-agent system:

 - **Network**: each agent can communicate with [every other agent](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/). Any agent can decide which other agent to call next.
- **Supervisor**: each agent communicates with a single [supervisor](../tutorials/multi_agent/agent_supervisor.md) agent. Supervisor agent makes decisions on which agent should be called next.
+- **Supervisor**: each agent communicates with a single [supervisor](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) agent. Supervisor agent makes decisions on which agent should be called next.
 - **Supervisor (tool-calling)**: this is a special case of supervisor architecture. Individual agents can be represented as tools. In this case, a supervisor agent uses a tool-calling LLM to decide which of the agent tools to call, as well as the arguments to pass to those agents.
 - **Hierarchical**: you can define a multi-agent system with [a supervisor of supervisors](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/). This is a generalization of the supervisor architecture and allows for more complex control flows.
 - **Custom multi-agent workflow**: each agent communicates with only a subset of agents. Parts of the flow are deterministic, and only some agents can decide which other agents to call next.
@@ -166,7 +166,7 @@ network = builder.compile()

 ### Supervisor

-In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [`Command`](./low_level.md#command) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/graph-api.md#map-reduce-and-the-send-api) pattern.
+In this architecture, we define agents as nodes and add a supervisor node (LLM) that decides which agent nodes should be called next. We use [`Command`](./low_level.md#command) to route execution to the appropriate agent node based on supervisor's decision. This architecture also lends itself well to running multiple agents in parallel or using [map-reduce](../how-tos/graph-api.ipynb#map-reduce-and-the-send-api) pattern.

 ```python
 from typing import Literal
@@ -211,7 +211,7 @@ builder.add_edge(START, "supervisor")
 supervisor = builder.compile()
 ```

-Check out this [tutorial](../tutorials/multi_agent/agent_supervisor.md) for an example of supervisor multi-agent architecture.
+Check out this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/) for an example of supervisor multi-agent architecture.

 ### Supervisor (tool-calling)

@@ -414,5 +414,5 @@ There are two high-level approaches to achieve that:

 An agent might need to have a different state schema from the rest of the agents. For example, a search agent might only need to keep track of queries and retrieved documents. There are two ways to achieve this in LangGraph:

- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it’s important to [add input / output transformations](../how-tos/subgraph.md#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
- Define agent node functions with a [private input state schema](../how-tos/graph-api.md/#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
+- Define [subgraph](./subgraphs.md) agents with a separate state schema. If there are no shared state keys (channels) between the subgraph and the parent graph, it’s important to [add input / output transformations](../how-tos/subgraph.ipynb#different-state-schemas) so that the parent graph knows how to communicate with the subgraphs.
+- Define agent node functions with a [private input state schema](../how-tos/graph-api.ipynb/#pass-private-state-between-nodes) that is distinct from the overall graph state schema. This allows passing information that is only needed for executing that particular agent.
@@ -5,7 +5,7 @@ search:

 # Persistence

-LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile a graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail. 
+LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. Below, we'll discuss each of these concepts in more detail. 

 ![Checkpoints](img/persistence/checkpoints.jpg)

@@ -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/add-human-in-the-loop.md#pause-using-interrupt) 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/breakpoints.md#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.

 Checkpoints are persisted and can be used to restore the state of a thread at a later time.

@@ -78,7 +78,7 @@ After we run the graph, we expect to see exactly 4 checkpoints:
 * checkpoint with the outputs of `node_a` `{'foo': 'a', 'bar': ['a']}` and `node_b` as the next node to be executed
 * checkpoint with the outputs of `node_b` `{'foo': 'b', 'bar': ['a', 'b']}` and no next nodes to be executed

-Note that the `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel.
+Note that we `bar` channel values contain outputs from both nodes as we have a reducer for `bar` channel.

 ### Get state

@@ -487,12 +487,12 @@ If you want to fallback to pickle for objects not currently supported by our msg
 you can use the `pickle_fallback` argument of the `JsonPlusSerializer`:

 ```python
-from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.memory import MemorySaver
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer

 # ... Define the graph ...
 graph.compile(
-    checkpointer=InMemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
+    checkpointer=MemorySaver(serde=JsonPlusSerializer(pickle_fallback=True))
 )
 ```

@@ -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 [the how-to guides](../how-tos/human_in_the_loop/add-human-in-the-loop.md) for 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 [these how-to guides](../how-tos/human_in_the_loop/breakpoints.md) for concrete examples.

 ### Memory

@@ -8,7 +8,8 @@ hide:

 # MCP endpoint in LangGraph Server

-The [Model Context Protocol (MCP)](./mcp.md) is an open protocol for describing tools and data sources in a model-agnostic format, enabling LLMs to discover and use them via a structured API. 
+The **Model Context Protocol (MCP)** is an open protocol for describing tools and data sources in a model-agnostic format, enabling LLMs to discover
+and use them via a structured API. 

 [LangGraph Server](./langgraph_server.md) implements MCP using the [Streamable HTTP transport](https://spec.modelcontextprotocol.io/specification/2025-03-26/basic/transports/#streamable-http). This allows LangGraph **agents** to be exposed as **MCP tools**, making them usable with any MCP-compliant client supporting Streamable HTTP.

@@ -27,6 +28,79 @@ Install them with:
 pip install "langgraph-api>=0.2.3" "langgraph-sdk>=0.1.61"
 ```

+## Exposing an agent as MCP tool
+
+
+When deployed, your agent will appear as a tool in the MCP endpoint
+with this configuration:
+
+- **Tool name**: The agent's name.
+- **Tool description**: The agent's description.
+- **Tool input schema**: The agent's input schema.
+
+### Setting name and description 
+
+You can set the name and description of your agent in `langgraph.json`:
+
+```json
+{
+    "graphs": {
+        "my_agent": {
+            "path": "./my_agent/agent.py:graph",
+            "description": "A description of what the agent does"
+        }
+    },
+    "env": ".env"
+}
+```
+
+After deployment, you can update the name and description using the LangGraph SDK.
+
+### Schema
+
+Define clear, minimal input and output schemas to avoid exposing unnecessary internal complexity to the LLM.
+
+The default [MessagesState](./low_level.md#messagesstate) uses `AnyMessage`, which supports many message types but is too general for direct LLM exposure.
+
+Instead, define **custom agents or workflows** that use explicitly typed input and output structures.
+
+For example, a workflow answering documentation questions might look like this:
+
+```python
+from langgraph.graph import StateGraph, START, END
+from typing_extensions import TypedDict
+
+# Define input schema
+class InputState(TypedDict):
+    question: str
+
+# Define output schema
+class OutputState(TypedDict):
+    answer: str
+
+# Combine input and output
+class OverallState(InputState, OutputState):
+    pass
+
+# Define the processing node
+def answer_node(state: InputState):
+    # Replace with actual logic and do something useful
+    return {"answer": "bye", "question": state["question"]}
+
+# Build the graph with explicit schemas
+builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
+builder.add_node(answer_node)
+builder.add_edge(START, "answer_node")
+builder.add_edge("answer_node", END)
+graph = builder.compile()
+
+# Run the graph
+print(graph.invoke({"question": "hi"}))
+```
+
+For more details, see the [low-level concepts guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#state).
+
+
 ## Usage overview

 To enable MCP:
@@ -127,114 +201,6 @@ Use an MCP-compliant client to connect to the LangGraph server. The following ex
        asyncio.run(main())
    ```

-## Expose an agent as MCP tool
-
-When deployed, your agent will appear as a tool in the MCP endpoint
-with this configuration:
-
- **Tool name**: The agent's name.
- **Tool description**: The agent's description.
- **Tool input schema**: The agent's input schema.
-
-### Setting name and description 
-
-You can set the name and description of your agent in `langgraph.json`:
-
-```json
-{
-    "graphs": {
-        "my_agent": {
-            "path": "./my_agent/agent.py:graph",
-            "description": "A description of what the agent does"
-        }
-    },
-    "env": ".env"
-}
-```
-
-After deployment, you can update the name and description using the LangGraph SDK.
-
-### Schema
-
-Define clear, minimal input and output schemas to avoid exposing unnecessary internal complexity to the LLM.
-
-The default [MessagesState](./low_level.md#messagesstate) uses `AnyMessage`, which supports many message types but is too general for direct LLM exposure.
-
-Instead, define **custom agents or workflows** that use explicitly typed input and output structures.
-
-For example, a workflow answering documentation questions might look like this:
-
-```python
-from langgraph.graph import StateGraph, START, END
-from typing_extensions import TypedDict
-
-# Define input schema
-class InputState(TypedDict):
-    question: str
-
-# Define output schema
-class OutputState(TypedDict):
-    answer: str
-
-# Combine input and output
-class OverallState(InputState, OutputState):
-    pass
-
-# Define the processing node
-def answer_node(state: InputState):
-    # Replace with actual logic and do something useful
-    return {"answer": "bye", "question": state["question"]}
-
-# Build the graph with explicit schemas
-builder = StateGraph(OverallState, input_schema=InputState, output_schema=OutputState)
-builder.add_node(answer_node)
-builder.add_edge(START, "answer_node")
-builder.add_edge("answer_node", END)
-graph = builder.compile()
-
-# Run the graph
-print(graph.invoke({"question": "hi"}))
-```
-
-For more details, see the [low-level concepts guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#state).
-
-## Use user-scoped MCP tools in your deployment
-
-!!! tip "Prerequisites"
-
-    You have added your own [custom auth middleware](https://langchain-ai.github.io/langgraph/how-tos/auth/custom_auth/) that populates the `langgraph_auth_user` object, making it accessible through configurable context for every node in your graph. 
-
-To make user-scoped tools available to your LangGraph Platform deployment, start with implementing a snippet like the following: 
-
-```python
-from langchain_mcp_adapters.client import MultiServerMCPClient
-
-def mcp_tools_node(state, config):
-    user = config["configurable"].get("langgraph_auth_user")
-		 # e.g., user["github_token"], user["email"], etc.
-		
-    client = MultiServerMCPClient({
-        "github": {
-            "transport": "streamable_http", # (1)
-            "url": "https://my-github-mcp-server/mcp", # (2)
-            "headers": {
-                "Authorization": f"Bearer {user['github_token']}" 
-            }
-        }
-    })
-    tools = await client.get_tools() # (3)
-    
-    # Your tool-calling logic here
-    
-    tool_messages = ...
-    return {"messages": tool_messages}
-```
-
-1. MCP only supports adding headers to requests made to `streamable_http` and `sse` `transport` servers.
-2. Your MCP server URL.
-3. Get available tools from your MCP server.
-
-_This can also be done by [rebuilding your graph at runtime](https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/) to have a different configuration for a new run_

 ## Session behavior  

@@ -244,7 +210,7 @@ The current LangGraph MCP implementation does not support sessions. Each `/mcp`

 The `/mcp` endpoint uses the same authentication as the rest of the LangGraph API. Refer to the [authentication guide](./auth.md) for setup details.

-## Disable MCP
+## Disabling MCP

 To disable the MCP endpoint, set `disable_mcp` to `true` in your `langgraph.json` configuration file:

@@ -256,4 +222,4 @@ To disable the MCP endpoint, set `disable_mcp` to `true` in your `langgraph.json
 }
 ```

-This will prevent the server from exposing the `/mcp` endpoint.
+This will prevent the server from exposing the `/mcp` endpoint.
@@ -12,7 +12,7 @@ Some reasons for using subgraphs are:

 The main question when adding subgraphs is how the parent graph and subgraph communicate, i.e. how they pass the [state](./low_level.md#state) between each other during the graph execution. There are two scenarios:

-* parent and subgraph have **shared state keys** in their state [schemas](./low_level.md#state). In this case, you can [include the subgraph as a node in the parent graph](../how-tos/subgraph.md#shared-state-schemas)
+* parent and subgraph have **shared state keys** in their state [schemas](./low_level.md#state). In this case, you can [include the subgraph as a node in the parent graph](../how-tos/subgraph.ipynb#shared-state-schemas)

    ```python
    from langgraph.graph import StateGraph, MessagesState, START
@@ -40,7 +40,7 @@ The main question when adding subgraphs is how the parent graph and subgraph com
    graph.invoke({"messages": [{"role": "user", "content": "hi!"}]})
    ```

-* parent graph and subgraph have **different schemas** (no shared state keys in their state [schemas](./low_level.md#state)). In this case, you have to [call the subgraph from inside a node in the parent graph](../how-tos/subgraph.md#different-state-schemas): this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph
+* parent graph and subgraph have **different schemas** (no shared state keys in their state [schemas](./low_level.md#state)). In this case, you have to [call the subgraph from inside a node in the parent graph](../how-tos/subgraph.ipynb#different-state-schemas): this is useful when the parent graph and the subgraph have different state schemas and you need to transform state before or after calling the subgraph

    ```python
    from typing_extensions import TypedDict, Annotated
@@ -64,7 +64,7 @@ To create a new app from a template, use the `langgraph new` command.
 === "JS"

    ```bash
-    npm create langgraph@latest
+    npx @langchain/langgraph-cli new 
    ```

 ## Next Steps
@@ -1,17 +0,0 @@
-# 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)
-
@@ -1,23 +0,0 @@
-# Examples
-
-The pages in this section provide end-to-end examples for the following topics:
-
-## General 
-
- [Template Applications](../concepts/template_applications.md): Create a LangGraph application from a template.
- [Agentic RAG](../tutorials/rag/langgraph_agentic_rag.md): Build a retrieval agent that can decide when to use a retriever tool.
- [Agent Supervisor](../tutorials/multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents.
- [SQL agent](../tutorials/sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results.
- [Prebuilt chat UI](../agents/ui.md): Use a prebuilt chat UI to interact with any LangGraph agent.
- [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs.
-
-## LangGraph Platform
-
- [Set up custom authentication](../tutorials/auth/getting_started.md): Set up custom authentication for your LangGraph application.
- [Make conversations private](../tutorials/auth/resource_auth.md): Make conversations private by using resource-based authentication.
- [Connect an authentication provider](../tutorials/auth/add_auth_server.md): Connect an authentication provider to your LangGraph application.
- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md): Rebuild a graph at runtime.
- [Use RemoteGraph](../how-tos/use-remote-graph.md): Use RemoteGraph to deploy your LangGraph application to a remote server.
- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md): Deploy CrewAI, AutoGen, and other frameworks with LangGraph.
- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md)
- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)
@@ -1,45 +0,0 @@
-# Guides
-
-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.
- [Functional API](../concepts/functional_api.md): Use Functional API to build workflows using a functional paradigm without thinking about the graph structure.
- [Runtime](../concepts/pregel.md): Pregel implements LangGraph's runtime, managing the execution of LangGraph applications.
-
-## Core capabilities
-
-These capabilities are available in both LangGraph OSS and the LangGraph Platform.
-
- [Streaming](../concepts/streaming.md): Stream outputs from a LangGraph graph.
- [Persistence](../concepts/persistence.md): Persist the state of a LangGraph graph.
- [Durable execution](../concepts/durable_execution.md): Save progress at key points in the graph execution.
- [Memory](../concepts/memory.md): Remember information about previous interactions.
- [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): 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.
- [MCP](../concepts/mcp.md): Use MCP servers in a LangGraph graph.
- [Evaluation](../agents/evals.md): Use LangSmith to evaluate your graph's performance.
-
-## Platform-only capabilities
-
-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 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.
--- a/Show More
+++ b/Show More