Test hitl prebuilt

2026-08-18 05:35:43 +02:00 · 2025-05-21 14:01:48 -07:00
530 changed files with 72593 additions and 49793 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.
-labels: [pending,bug]
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
+labels: ["02 Bug Report"]
 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
 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
@@ -1,7 +1,7 @@
 name: Documentation
 description: Report an issue related to the LangGraph documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [documentation]
+labels: [03 - Documentation]

 body:
 - type: textarea
@@ -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 +0,0 @@
-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
@@ -52,7 +49,7 @@ jobs:

      - name: Get .mypy_cache to speed up mypy
        if: steps.changed-files.outputs.all
-        uses: actions/cache@v4
+        uses: actions/cache@v3
        env:
          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
        with:
@@ -78,7 +75,7 @@ jobs:

      - name: Get .mypy_cache_test to speed up mypy
        if: steps.changed-files.outputs.all
-        uses: actions/cache@v4
+        uses: actions/cache@v3
        env:
          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
        with:
@@ -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'
@@ -0,0 +1,52 @@
+name: test
+
+on:
+  workflow_call:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.11"
+          - "3.12"
+
+    defaults:
+      run:
+        working-directory: libs/scheduler-kafka
+    name: "test #${{ matrix.python-version }}"
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@v6
+        with:
+          python-version: ${{ matrix.python-version }}
+          enable-cache: true
+          cache-suffix: "test-scheduler-kafka"
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        if: ${{ !github.event.pull_request.head.repo.fork }}
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
+
+      - name: Install dependencies
+        shell: bash
+        run: uv sync --frozen --group dev
+
+      - name: Run tests
+        shell: bash
+        run: make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'
@@ -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
@@ -38,10 +35,10 @@ jobs:
              - 'libs/checkpoint/**'
              - 'libs/checkpoint-sqlite/**'
              - 'libs/checkpoint-postgres/**'
+              - 'libs/scheduler-kafka/**'
              - 'libs/prebuilt/**'
-            deps:
-              - '**/pyproject.toml'
-              - '**/uv.lock'
+            sdk-js:
+              - 'libs/sdk-js/**'

  lint:
    needs: changes
@@ -56,10 +53,10 @@ jobs:
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
-
+            "libs/scheduler-kafka",
            "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 +75,7 @@ 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 }}
@@ -87,11 +84,19 @@ 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

+  # NOTE: we're testing scheduler-kafka separately because it requires a different matrix
+  test-scheduler-kafka:
+    needs: changes
+    if: needs.changes.outputs.python == 'true'
+    name: "cd libs/scheduler-kafka"
+    uses: ./.github/workflows/_test_scheduler_kafka.yml
+    secrets: inherit
+
  check-sdk-methods:
    needs: changes
    if: needs.changes.outputs.python == 'true'
@@ -144,21 +149,74 @@ 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@v3
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v3
+        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@v3
+      - name: Setup Node.js (LTS)
+        uses: actions/setup-node@v3
+        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,
+        test-scheduler-kafka,
        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:
@@ -136,7 +145,7 @@ jobs:

      - name: Configure GitHub Pages
        if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v5
+        uses: actions/configure-pages@v4

      - name: Upload Pages Artifact
        # if: github.ref == 'refs/heads/main'
@@ -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@v3
+        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
@@ -181,4 +181,3 @@ Chinook.db
 .vercel
 .turbo
 .editorconfig
-.scratch
@@ -1,55 +0,0 @@
-# AGENTS Instructions
-
-This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
-
-When you modify code in any library, run the following commands in that library's directory before creating a pull request:
-
- `make format` – run code formatters
- `make lint` – run the linter
- `make test` – execute the test suite
-
-To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
-
-```
-TEST=path/to/test.py make test
-```
-
-Other pytest arguments can also be supplied inside the `TEST` variable.
-
-## Libraries
-
-The repository contains several Python and JavaScript/TypeScript libraries.
-Below is a high-level overview:
-
- **checkpoint** – base interfaces for LangGraph checkpointers.
- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
- **cli** – official command-line interface for LangGraph.
- **langgraph** – core framework for building stateful, multi-actor agents.
- **prebuilt** – high-level APIs for creating and running agents and tools.
- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** – Python SDK for the LangGraph Platform API.
-
-### Dependency map
-
-The diagram below lists downstream libraries for each production dependency as
-declared in that library's `pyproject.toml` (or `package.json`).
-
-```text
-checkpoint
-├── checkpoint-postgres
-├── checkpoint-sqlite
-├── prebuilt
-└── langgraph
-
-prebuilt
-└── langgraph
-
-sdk-py
-├── langgraph
-└── cli
-
-sdk-js (standalone)
-```
-
-Changes to a library may impact all of its dependents shown above.
@@ -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:

@@ -109,7 +109,8 @@ Here are some high-level tips on writing a good how-to guide:
 LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
 in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
 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.
+impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
+

 To quote the Diataxis website:

@@ -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
-```
+```
@@ -1,68 +0,0 @@
-# Define the directories containing projects
-LIBS_DIRS := $(wildcard libs/*)
-
-# Default target
-.PHONY: all
-all: lint format lock test
-
-# Install dependencies for all projects
-.PHONY: install
-install:
-	@echo "Creating virtual environment..."
-	@uv venv
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/pyproject.toml ]; then \
-			echo "Installing dependencies for $$dir"; \
-			uv pip install -e $$dir; \
-		fi; \
-	done
-
-# Lint all projects
-.PHONY: lint
-lint:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lint in $$dir"; \
-			$(MAKE) -C $$dir lint; \
-		fi; \
-	done
-
-# Format all projects
-.PHONY: format
-format:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running format in $$dir"; \
-			$(MAKE) -C $$dir format; \
-		fi; \
-	done
-
-# Lock all projects
-.PHONY: lock
-lock:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lock in $$dir"; \
-			(cd $$dir && uv lock); \
-		fi; \
-	done
-
-# 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 $$dir && uv lock --upgrade); \
-		fi; \
-	done
-
-# Test all projects
-.PHONY: test
-test:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running test in $$dir"; \
-			$(MAKE) -C $$dir test; \
-		fi; \
-	done
@@ -12,6 +12,7 @@
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)

 Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.

@@ -63,7 +64,7 @@ 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 +74,11 @@ 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/): 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:
@@ -1,154 +1,157 @@
-"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
+"""Add typescript translation to a given markdown file."""

 import argparse
+import re

 import requests
 from langchain_anthropic import ChatAnthropic

-# Load reference TypeScript snippets
 URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
 response = requests.get(URL)
 response.raise_for_status()
 reference_snippets = response.text

-# Initialize model
-model = ChatAnthropic(model="claude-sonnet-4-0", max_tokens=64_000)
-
-TRANSLATION_PROMPT = (
-    "You are a helpful assistant that translates Python-based technical "
-    "documentation written in Markdown to equivalent TypeScript-based documentation. "
-    "The input is a Markdown file written in mkdocs format. It contains "
-    "Python code snippets embedded in prose. "
-    "Your task is to rewrite the content by translating the Python code to "
-    "idiomatic TypeScript, using the provided TypeScript reference snippets "
-    "to ensure accurate and consistent usage (e.g., correct imports, function "
-    "names, and patterns). "
-    "Remove the original Python code and replace it with the corresponding "
-    "TypeScript version. "
-    "Do not alter the surrounding prose unless a change is necessary to "
-    "reflect differences between Python and TypeScript. "
-    "Preserve the structure and formatting of the original Markdown document. "
-    "Do not make stylistic or structural changes unless they directly support "
-    "the translation. "
-    "Use the reference TypeScript snippets as guidance whenever possible to "
-    "maintain alignment with existing conventions.\n\n"
-    f"Here are the reference TypeScript snippets:\n\n{reference_snippets}\n\n"
-)
-
-CONSOLIDATION_PROMPT = (
-    "You are a helpful assistant that consolidates parallel Python and JavaScript (TypeScript) technical documentation "
-    "written in Markdown into a single unified Markdown document. "
-    "The input consists of two documents: the first is for Python users, and the second is for JavaScript/TypeScript users. "
-    "Your task is to merge these into one Markdown file using language-specific fenced blocks to separate the content where needed. "
-    "Use the following syntax to distinguish content for each language:\n\n"
-    ":::python\n"
-    "# Python-specific content\n"
-    ":::\n\n"
-    ":::js\n"
-    "# JavaScript/TypeScript-specific content\n"
-    ":::\n\n"
-    "Follow these consolidation rules:\n"
-    "- When content (prose or code) is the same or nearly identical in both versions, include it only once—outside of any fenced block.\n"
-    "- When content differs between the Python and JS versions, wrap each version in its corresponding fenced block.\n"
-    "- Prefer **paragraph-level separation** of language-specific content. Do not combine Python and JS snippets or terminology in the same sentence or paragraph using conditional phrases.\n"
-    "  For example, avoid inline constructs like:\n"
-    "  `The :::python add_messages ::: :::js reducer ::: function...`\n"
-    "  Instead, write two distinct paragraphs:\n\n"
-    "  :::python\n"
-    "  The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.\n"
-    "  ::: \n\n"
-    "  :::js\n"
-    "  The `reducer` function in our `StateAnnotation` will append the LLM's response messages to whatever messages are already in the state.\n"
-    "  :::\n\n"
-    "- Preserve the overall structure, ordering, and formatting of the original Markdown documents.\n"
-    "- Do not rephrase or unify content unless it is logically and semantically identical.\n"
-    "- Use the fenced blocks for both prose and code as needed, and ensure output is clean, readable Markdown suitable for tools that parse these directives.\n"
-    "Your goal is to produce a cleanly merged documentation file that serves both Python and JavaScript users without redundancy, while maximizing clarity and separation of language-specific details."
-)
+model = ChatAnthropic(model="claude-3-5-sonnet-latest")


-def translate_python_to_ts(markdown_content: str) -> str:
-    response = model.invoke(
+def _get_tqdm():
+    try:
+        from tqdm import tqdm
+    except ImportError:
+        # If not available return a simple identity function
+        def tqdm(iterable, *args, **kwargs):
+            return iterable
+
+    return tqdm
+
+
+_tqdm = _get_tqdm()
+
+opening_pattern = re.compile(r"^\s*```python(?:\s+.*)?\s*$")
+closing_pattern = re.compile(r"^\s*```\s*$")
+
+
+def extract_python_snippets(markdown: str) -> list[str]:
+    """
+    Extract all python code blocks (including their fence lines) from the markdown content.
+    A python block is defined as any block that starts with a line containing an opening fence
+    with '```python' (optionally with extra parameters) and ends with a closing fence '```'.
+    """
+    snippets = []
+    inside_block = False
+    current_snippet = []
+
+    for line in markdown.splitlines(keepends=True):
+        if not inside_block:
+            if opening_pattern.match(line):
+                inside_block = True
+                current_snippet = [line]
+        else:
+            current_snippet.append(line)
+            if closing_pattern.match(line):
+                inside_block = False
+                snippets.append("".join(current_snippet))
+                current_snippet = []
+    return snippets
+
+
+def translate_snippet(python_snippet: str) -> str:
+    """Translate a python code block into a TypeScript code block using Langchain.
+    The response is expected to be a properly fenced TypeScript code block (i.e.
+    starting with ```typescript and ending with ```).
+    """
+    ai_message = model.invoke(
        [
            {
                "role": "system",
-                "content": TRANSLATION_PROMPT,
-                "cache_control": {"type": "ephemeral"},
+                "content": (
+                    f"You have access to the following up-to-date example TypeScript code "
+                    f"snippets that show examples of building with langgraph "
+                    f"and langchain:\n\n{reference_snippets}\n\n"
+                    "Use this context to translate the following Python code to equivalent "
+                    "TypeScript. Ensure that your output is a valid fenced TypeScript "
+                    "code block (i.e. starts with ```typescript and ends with ```)."
+                ),
            },
-            {"role": "user", "content": markdown_content},
-        ]
-    )
-    return response.content
-
-
-def consolidate_python_and_ts(combined_content: str) -> str:
-    response = model.invoke(
-        [
            {
-                "role": "system",
-                "content": CONSOLIDATION_PROMPT,
-                "cache_control": {"type": "ephemeral"},
+                "role": "user",
+                "content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
            },
-            {"role": "user", "content": combined_content},
        ]
    )
-    return response.content
+
+    # Use a regular expression to search for a TypeScript code block in the response.
+    pattern = r"```typescript\s*(.*?)\s*```"
+    match = re.search(pattern, ai_message.content, re.DOTALL)
+    if match:
+        # Reconstruct the code block with proper fences.
+        typescript_code = match.group(1).strip()
+        return f"```typescript\n{typescript_code}\n```"
+    else:
+        raise ValueError("No TypeScript code block found in the model's response.")


-def main(file_path: str, translate_only: bool, consolidate_only: bool) -> None:
-    with open(file_path, "r", encoding="utf-8") as f:
+def insert_translations_into_markdown(
+    markdown: str, typescript_snippets: list[str]
+) -> str:
+    """Walks through the original markdown content and, after each
+    Python snippet block, inserts the corresponding translated TypeScript snippet.
+    It assumes that the ordering of the Python snippets
+    (from extract_python_snippets) matches the order they appear in the markdown.
+    """
+    output_lines = []
+    lines = markdown.splitlines(keepends=True)
+    inside_block = False
+    snippet_index = 0
+
+    for line in lines:
+        output_lines.append(line)
+        if not inside_block and opening_pattern.match(line):
+            # We've encountered the start of a python code block.
+            inside_block = True
+        elif inside_block:
+            if closing_pattern.match(line):
+                # End of a python snippet block.
+                inside_block = False
+                if snippet_index < len(typescript_snippets):
+                    # Insert an extra newline for clarity, then the translated TypeScript snippet.
+                    output_lines.append("\n")
+                    output_lines.append(typescript_snippets[snippet_index])
+                    output_lines.append("\n")
+                    snippet_index += 1
+    return "".join(output_lines)
+
+
+def main(file_path: str) -> None:
+    # Read the markdown file.
+    with open(file_path, "r") as f:
        markdown_content = f.read()

-    if translate_only:
-        translated = translate_python_to_ts(markdown_content)
-        output_path = file_path.replace(".md", ".translated.md")
-        with open(output_path, "w", encoding="utf-8") as f:
-            f.write(translated)
-        print(f"Translated JS/TS version written to: {output_path}")
+    # 1. Extract all Python snippets.
+    python_snippets = extract_python_snippets(markdown_content)[:1]

-    elif consolidate_only:
-        consolidated = consolidate_python_and_ts(markdown_content)
-        with open(file_path, "w", encoding="utf-8") as f:
-            f.write(consolidated)
-        print(f"Consolidated content written to: {file_path}")
+    # 2. Translate each Python snippet to TypeScript.
+    typescript_snippets = []
+    # Replace with .batch() for faster translation
+    for python_snippet in _tqdm(python_snippets):
+        ts_snippet = translate_snippet(python_snippet)
+        typescript_snippets.append(ts_snippet)

-    else:
-        # Default behavior: translate first, then consolidate both
-        translated = translate_python_to_ts(markdown_content)
-        combined = f"{markdown_content.strip()}\n\n\n{translated.strip()}"
-        consolidated = consolidate_python_and_ts(combined)
-        with open(file_path, "w", encoding="utf-8") as f:
-            f.write(consolidated)
-        print(f"Translated and consolidated content written to: {file_path}")
+    # 3. Insert the TypeScript translations after their respective Python snippets.
+    updated_markdown = insert_translations_into_markdown(
+        markdown_content, typescript_snippets
+    )
+
+    # Overwrite the original markdown file with the updated content.
+    with open(file_path, "w") as f:
+        f.write(updated_markdown)


 if __name__ == "__main__":
    parser = argparse.ArgumentParser(
-        description=(
-            "Translate Python markdown to TypeScript and/or consolidate "
-            "Python-JS markdown into one file."
-        )
+        description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
    )
    parser.add_argument("file_path", type=str, help="Path to the markdown file.")
-    parser.add_argument(
-        "--translate-only",
-        action="store_true",
-        help="Only generate the JS translation.",
-    )
-    parser.add_argument(
-        "--consolidate-only",
-        action="store_true",
-        help="Only consolidate pre-paired Python and JS content.",
-    )
    args = parser.parse_args()

-    if args.translate_only and args.consolidate_only:
-        raise ValueError(
-            "Cannot use both --translate-only and --consolidate-only at the same time."
-        )
-
-    main(
-        args.file_path,
-        translate_only=args.translate_only,
-        consolidate_only=args.consolidate_only,
-    )
+    main(args.file_path)
@@ -1,17 +1,12 @@
 """Experimental script to generate consolidated llms text from the docs."""

-import asyncio
 import glob
 import os
-import re
-from typing import TypedDict, List, Optional
+from typing import TypedDict, List

 import yaml
-from langchain.chat_models import init_chat_model
-from langchain_core.rate_limiters import InMemoryRateLimiter
 from mkdocs.structure.files import File
 from mkdocs.structure.pages import Page
-from pydantic import BaseModel, Field
 from yaml import SafeLoader

 from _scripts.notebook_hooks import _on_page_markdown_with_config
@@ -21,49 +16,7 @@ HERE = os.path.dirname(os.path.abspath(__file__))
 SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))


-async def convert_ipynb_to_md(file_path: str) -> Optional[str]:
-    """Process a file (markdown or notebook) to markdown format.
-
-    Args:
-        file_path: Path to the file to process
-
-    Returns:
-        Processed markdown content if successful, None otherwise
-    """
-    rel_path = os.path.relpath(file_path, SOURCE_DIR)
-
-    # Create File and Page objects to match mkdocs structure
-    file_obj = File(
-        path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
-    )
-    page = Page(
-        title="",
-        file=file_obj,
-        config={},
-    )
-
-    try:
-        # Read raw content
-        with open(file_path, "r", encoding="utf-8") as f:
-            content = f.read()
-
-        # Convert to markdown without logic to resolve API references
-        processed_content = _on_page_markdown_with_config(
-            content, page, add_api_references=False, remove_base64_images=True
-        )
-        # Remove self-closing img tags <img ... />
-        processed_content = re.sub(r"<img[^>]*/>", "", processed_content)
-        # Remove img tags with content <img ...>...</img>
-        processed_content = re.sub(
-            r"<img[^>]*>.*?</img>", "", processed_content, flags=re.DOTALL
-        )
-        return processed_content
-    except Exception as e:
-        print(f"Error processing file {file_path}: {e}")
-        return None
-
-
-async def generate_full_llms_text(output_file: str) -> None:
+def generate_full_llms_text(output_file: str) -> str:
    """Generate a consolidated text file from markdown/notebook files for LLM training.

    Args:
@@ -71,9 +24,11 @@ async def generate_full_llms_text(output_file: str) -> None:
    """
    # Collect all markdown and notebook files
    all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
+
    all_files.extend(
        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
    )
+    # Add all concepts
    all_files.extend(
        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
    )
@@ -83,14 +38,30 @@ async def generate_full_llms_text(output_file: str) -> None:

    all_content = []

-    # Process files concurrently
-    tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
-    results = await asyncio.gather(*tasks)
+    # Process each file
+    for file_path in all_files:
+        print(f"Processing {file_path}")
+        rel_path = os.path.relpath(file_path, SOURCE_DIR)

-    # Combine results with file paths
-    for file_path, processed_content in zip(all_files, results):
+        # Create File and Page objects to match mkdocs structure
+        file_obj = File(
+            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
+        )
+        page = Page(
+            title="",
+            file=file_obj,
+            config={},
+        )
+
+        # Read raw content
+        with open(file_path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Convert to markdown without logic to resolve API references
+        processed_content = _on_page_markdown_with_config(
+            content, page, add_api_references=False, remove_base64_images=True
+        )
        if processed_content:
-            rel_path = os.path.relpath(file_path, SOURCE_DIR)
            # Add file name
            all_content.append(f"---\n{rel_path}\n---")
            # Add content
@@ -115,7 +86,6 @@ class NavItem(TypedDict):
    title: str
    url: str
    hierarchy: tuple[str, ...]
-    description: str


 def _flatten_nav(
@@ -128,14 +98,7 @@ def _flatten_nav(
                new_path = path + (title,)
                if isinstance(node, str):
                    # Leaf page
-                    flat.append(
-                        {
-                            "title": title,
-                            "url": node,
-                            "hierarchy": new_path,
-                            "description": "",
-                        }
-                    )
+                    flat.append({"title": title, "url": node, "hierarchy": new_path})
                elif isinstance(node, list):
                    # Dive in, carrying along the updated path
                    flat.extend(_flatten_nav(node, new_path))
@@ -146,84 +109,14 @@ def _flatten_nav(
        elif isinstance(item, str):
            # Bare string entry → use itself as title, and as URL
            new_path = path + (item,)
-            flat.append(
-                {"title": item, "url": item, "hierarchy": new_path, "description": ""}
-            )
+            flat.append({"title": item, "url": item, "hierarchy": new_path})
        else:
            raise TypeError(f"Unexpected item type {type(item)} in nav")
    return flat


-class PageInfo(BaseModel):
-    title: str = Field(description="The title of the page")
-    description: str = Field(
-        description="A short description of the page no longer than 3 sentences "
-        "explaining the kind of content that can be found in the page."
-    )
-
-
-async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
-    """Open the contents of each nav item and come up with a better title and description."""
-    rate_limiter = InMemoryRateLimiter(requests_per_second=10)
-    model = init_chat_model("gpt-4o-mini", temperature=0.0, rate_limiter=rate_limiter)
-    model = model.with_structured_output(PageInfo)
-
-    async def process_single_item(item: NavItem) -> NavItem:
-        path = item["url"]
-        file_path = os.path.join(SOURCE_DIR, path)
-
-        # Process the file content (handles both markdown and notebooks)
-        if path.endswith(".ipynb"):
-            content = await convert_ipynb_to_md(file_path)
-        else:
-            with open(file_path, "r", encoding="utf-8") as f:
-                content = f.read()
-
-        if not content:
-            return item
-
-        # Generate a better title and description
-        response = await model.ainvoke(
-            [
-                {
-                    "role": "system",
-                    "content": "You are a technical documentation writer. "
-                    "You are given a markdown page of documentation. "
-                    "Please come up with an appropriate title and "
-                    "description for the page. The description should "
-                    "be a short summary of the page content that is "
-                    "no longer than 3 sentences.",
-                },
-                {
-                    "role": "user",
-                    "content": "The markdown page is as follows:\n\n" + content,
-                },
-            ]
-        )
-        return {
-            "title": response.title,
-            "url": item["url"],
-            "hierarchy": item["hierarchy"],
-            "description": response.description,
-        }
-
-    # Remove any items that start with http:// or https:// looking only for
-    # local file at this stages.
-    nav_items = [
-        item
-        for item in nav_items
-        if not item["url"].startswith(("http://", "https://"))
-    ]
-    # Process items in parallel
-    tasks = [process_single_item(item) for item in nav_items]
-    new_nav_items = await asyncio.gather(*tasks)
-    return new_nav_items
-
-
-async def generate_nav_links_text(
-    output_file: str, *, replace_links: bool = False
-) -> None:
-    """Generate llms.txt from mkdocs.yaml."""
+def generate_nav_links_text(output_file: str, *, replace_links: bool = False) -> None:
+    """Generate a text file containing navigation structure and links from mkdocs.yaml."""
    # Get path to mkdocs.yaml relative to this script
    script_dir = os.path.dirname(os.path.abspath(__file__))
    mkdocs_path = os.path.join(os.path.dirname(script_dir), "mkdocs.yml")
@@ -236,15 +129,15 @@ async def generate_nav_links_text(
    nav = config.get("nav", [])
    flattened = _flatten_nav(nav)

-    processed_nav = await process_nav_items(flattened)
-
    with open(output_file, "w") as f:
        current_section = None
-        for item in processed_nav:
+        for item in flattened:
            # Get the top-level section (first item in hierarchy)
            section = item["hierarchy"][0]

-            if section not in {"Guides", "Examples", "Resources"}:
+            if section not in {
+                "Guides", "Examples", "Resources"
+            }:
                continue

            # If we're starting a new section, add a heading
@@ -252,7 +145,15 @@ async def generate_nav_links_text(
                f.write(f"\n# {section}\n\n")
                current_section = section

-            title = item["title"]
+            # Add the item as a bullet point with title and link
+            # Include full hierarchy path in title, separated by " > "
+            hierarchy_path = " > ".join(item["hierarchy"][1:])
+            title = (
+                f"{item['title']} ({hierarchy_path})"
+                if hierarchy_path
+                else item["title"]
+            )
+
            # Process URL based on replace_links flag
            url = item["url"]
            if replace_links:
@@ -262,7 +163,7 @@ async def generate_nav_links_text(
                url = url.rstrip("/") + "/"
                url = f"https://langchain-ai.github.io/langgraph/{url}"

-            f.write(f"- [{title}]({url}): {item['description']}\n")
+            f.write(f"- [{title}]({url})\n")


 if __name__ == "__main__":
@@ -287,10 +188,6 @@ if __name__ == "__main__":

    args = parser.parse_args()
    if args.link_only:
-        coro = generate_nav_links_text(
-            args.output_file, replace_links=args.replace_links
-        )
+        generate_nav_links_text(args.output_file, replace_links=args.replace_links)
    else:
-        coro = generate_full_llms_text(args.output_file)
-
-    asyncio.run(coro)
+        generate_full_llms_text(args.output_file)
@@ -1,5 +0,0 @@
-JS_LINK_MAP = {
-    "langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
-    "create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
-}
@@ -1,22 +1,13 @@
-"""mkdocs hooks for adding custom logic to documentation pipeline.
-
-Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
-"""
-
-import json
 import logging
 import os
 import posixpath
 import re
 from typing import Any, Dict

-from bs4 import BeautifulSoup
-from mkdocs.config.defaults import MkDocsConfig
 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

 logger = logging.getLogger(__name__)
@@ -35,49 +26,43 @@ 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",
-    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory/add-memory.md#summarize-messages",
-    "how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
-    "agents/memory.ipynb": "how-tos/memory/add-memory.md",
+    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
+    "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
+    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
    # 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",
-    "how-tos/persistence_redis.ipynb": "how-tos/memory/add-memory.md#use-in-production",
-    "how-tos/subgraph-persistence.ipynb": "how-tos/memory/add-memory.md#use-with-subgraphs",
-    "how-tos/cross-thread-persistence.ipynb": "how-tos/memory/add-memory.md#add-long-term-memory",
+    "how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
+    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
    "cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
-    "cloud/how-tos/check-thread-status": "cloud/how-tos/use_threads",
-    "cloud/concepts/threads.md": "concepts/persistence.md#threads",
-    "how-tos/persistence.ipynb": "how-tos/memory/add-memory.md",
    # tool calling how-tos
    "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
    "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",
@@ -86,7 +71,7 @@ REDIRECT_MAP = {
    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
    "cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
    "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
-    "concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
+    "concepts/platform_architecture.md": "langgraph/concepts/langgraph_cloud#architecture",
    # cloud streaming redirects
    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
@@ -94,13 +79,16 @@ REDIRECT_MAP = {
    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
-    "cloud/concepts/streaming.md": "concepts/streaming.md",
-    "agents/streaming.md": "how-tos/streaming.md",
-    # prebuilt redirects
+    # prebuit redirects
    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
    "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-hitl.ipynb": "agents/human-in-the-loop.md",
    "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.ipynb",
+    # breakpoints
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
    # misc
    "prebuilt.md": "agents/prebuilt.md",
    "reference/prebuilt.md": "reference/agents.md",
@@ -109,24 +97,12 @@ REDIRECT_MAP = {
    "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
    "how-tos/index.md": "index.md",
    "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
-    "agents/deployment.md": "tutorials/langgraph-platform/local-server.md",
    # deployment redirects
    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
-    "tutorials/deployment.md": "concepts/deployment_options.md",
    # assistant redirects
-    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
-    "cloud/concepts/runs.md": "concepts/assistants.md#execution",
-    # hitl redirects
-    "how-tos/wait-user-input-functional.ipynb": "how-tos/use-functional-api.md",
-    "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",
+    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md"
+
 }


@@ -176,62 +152,6 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
    return code_block_pattern.sub(replace_code_block_header, markdown)


-def _resolve_cross_references(md_text: str, link_map: dict[str, str]) -> str:
-    """Replace [title][identifier] with [title](url) using language-specific link_map.
-
-    Args:
-        md_text: The markdown text to process.
-        link_map: mapping of identifier to URL.
-
-    Returns:
-        The processed markdown text with cross-references resolved.
-    """
-    # Pattern to match [title][identifier]
-    pattern = re.compile(r"\[([^\]]+)\]\[([^\]]+)\]")
-
-    def replace_reference(match: re.Match) -> str:
-        """Replace the matched reference with the corresponding URL."""
-        title, identifier = match.group(1), match.group(2)
-        url = link_map.get(identifier)
-
-        if url:
-            return f"[{title}]({url})"
-        else:
-            # Leave it unchanged if not found
-            return match.group(0)
-
-    return pattern.sub(replace_reference, md_text)
-
-
-def _apply_conditional_rendering(md_text: str, target_language: str) -> str:
-    if target_language not in {"python", "js"}:
-        raise ValueError("target_language must be 'python' or 'js'")
-
-    pattern = re.compile(
-        r"(?P<indent>[ \t]*):::(?P<language>\w+)\s*\n"
-        r"(?P<content>((?:.*\n)*?))"  # Capture the content inside the block
-        r"(?P=indent):::"  # Match closing with the same indentation
-    )
-
-    def replace_conditional_blocks(match: re.Match) -> str:
-        """Keep active conditionals."""
-        language = match.group("language")
-        content = match.group("content")
-
-        if language not in {"python", "js"}:
-            # If the language is not supported, return the original block
-            return match.group(0)
-
-        if language == target_language:
-            return content
-
-        # If the language does not match, return an empty string
-        return ""
-
-    processed = pattern.sub(replace_conditional_blocks, md_text)
-    return processed
-
-
 def _highlight_code_blocks(markdown: str) -> str:
    """Find code blocks with highlight comments and add hl_lines attribute.

@@ -310,12 +230,6 @@ 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,
@@ -337,19 +251,6 @@ def _on_page_markdown_with_config(
    # Apply highlight comments to code blocks
    markdown = _highlight_code_blocks(markdown)

-    # Apply conditional rendering for code blocks
-    markdown = _apply_conditional_rendering(markdown, TARGET_LANGUAGE)
-    if TARGET_LANGUAGE == "js":
-        markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
-    elif TARGET_LANGUAGE == "python":
-        # Via a dedicated plugin
-        pass
-    else:
-        raise ValueError(
-            f"Unsupported target language: {TARGET_LANGUAGE}. "
-            "Supported languages are 'python' and 'js'."
-        )
-
    # Add file path as an attribute to code blocks that are executable.
    # This file path is used to associate fixtures with the executable code
    # which can be used in CI to test the docs without making network requests.
@@ -363,16 +264,12 @@ 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
@@ -395,7 +292,7 @@ Redirecting...
 """


-def _write_html(site_dir, old_path, new_path):
+def write_html(site_dir, old_path, new_path):
    """Write an HTML file in the site_dir with a meta redirect to the new page"""
    # Determine all relevant paths
    old_path_abs = os.path.join(site_dir, old_path)
@@ -411,83 +308,6 @@ def _write_html(site_dir, old_path, new_path):
        f.write(content)


-def _inject_gtm(html: str) -> str:
-    """Inject Google Tag Manager code into the HTML.
-
-    Code to inject Google Tag Manager noscript tag immediately after <body>.
-
-    This is done via hooks rather than via a template because the MkDocs material
-    theme does not seem to allow placing the code immediately after the <body> tag
-    without modifying the template files directly.
-
-    Args:
-        html: The HTML content to modify.
-
-    Returns:
-        The modified HTML content with GTM code injected.
-    """
-    # Code was copied from Google Tag Manager setup instructions.
-    gtm_code = """
-<!-- Google Tag Manager (noscript) -->
-<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T35S4S46"
-height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
-<!-- End Google Tag Manager (noscript) -->
-"""
-    soup = BeautifulSoup(html, "html.parser")
-    body = soup.body
-    if body:
-        # Insert the GTM code as raw HTML at the top of <body>
-        body.insert(0, BeautifulSoup(gtm_code, "html.parser"))
-        return str(soup)
-    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:
-    """Inject Google Tag Manager noscript tag immediately after <body>.
-
-    Args:
-        html: 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)
-
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):
    use_directory_urls = config.get("use_directory_urls")
@@ -504,4 +324,4 @@ def on_post_build(config):
            + hash
            + suffix
        )
-        _write_html(config["site_dir"], old_html_path, new_html_path)
+        write_html(config["site_dir"], old_html_path, new_html_path)
@@ -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) |
@@ -15,7 +15,7 @@ This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable*

 Before you start this tutorial, ensure you have the following:

- An [Anthropic](https://console.anthropic.com/settings/keys) API key 
+- An [Anthropic](https://console.anthropic.com/settings/admin-keys) API key 

 ## 1. Install dependencies

@@ -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.
@@ -180,14 +180,14 @@ ny_response = agent.invoke(
 )
 ```

-1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
+1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities.
 2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.

 When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).

 Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.

-For more information, see [Memory](../how-tos/memory/add-memory.md).
+For more information, see [Memory](./memory.md).

 ## 6. Configure structured output

@@ -1,126 +1,99 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Context

-**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
+Agents often require more than a list of messages to function effectively. They need **context**.

-Context includes *any* data outside the message list that can shape behavior. This can be:
+Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:

 - Information passed at runtime, like a `user_id` or API credentials.
 - 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                 |
-| [**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    |
+| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
+| [**State**](#state-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
+You can use context to:

-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.
+- Adjust the system prompt the model sees
+- Feed tools with necessary inputs
+- Track facts during an ongoing conversation

-!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces `config['configurable']`"
+## Providing Runtime Context

-    The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
+Use this when you need to inject data into an agent at runtime.

-!!! note 
+### Config (static context)

-    Runtime context refers to local context: data and dependencies your code needs to run. It does not refer to:
+Config is for immutable data like user metadata or API keys. Use
+when you have values that don't change mid-run.

-    * 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)!
+agent.invoke(
+    {"messages": [{"role": "user", "content": "hi!"}]},
    # highlight-next-line
-    context={"user_name": "John Smith"} # (3)!
+    config={"configurable": {"user_id": "user_123"}}
 )
 ```

-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.
+### State (mutable context)

-=== "Agent prompt"
-
-    ```python
-    from langchain_core.messages import AnyMessage
-    from langgraph.runtime import get_runtime
-    from langgraph.prebuilt.chat_agent_executor import AgentState
-    from langgraph.prebuilt import create_react_agent
+State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.

+```python
+class CustomState(AgentState):
    # 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}."
-        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
-    )
-
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        context={"user_name": "John Smith"}
-    )
-    ```
-
-    * See [Agents](../agents/agents.md) for details.
-
-=== "Workflow node"
-
-    ```python
-    from langgraph.runtime import Runtime
+    user_name: str

+agent = create_react_agent(
+    # Other agent parameters...
    # highlight-next-line
-    def node(state: State, config: Runtime[ContextSchema]):
-        user_name = runtime.context.user_name
-        ...
-    ```
+    state_schema=CustomState,
+)

-    * See [the Graph API](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#add-runtime-configuration) for details.
+agent.invoke({
+    "messages": "hi!",
+    "user_name": "Jane"
+})
+```

-=== "In a tool"
+!!! tip "Turning on memory"

-    ```python
-    from langgraph.runtime import get_runtime
+    Please see the [memory guide](./memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations.
+    Otherwise, the state is scoped only to a single agent run.

-    @tool
-    # highlight-next-line
-    def get_user_email() -> 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
-    ```

-    See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.

-### Short-term memory (mutable context)
+### Long-Term Memory (cross-conversation context)

-State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
+For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](./memory.md).

-=== "In an agent"
+## Customizing Prompts with Context { #prompts }

-    Example shows how to incorporate state into an agent **prompt**.
+Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.

-    State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
+Common use cases:
+
+- Personalization
+- Role or goal customization
+- Conditional behavior (e.g., user is admin)
+
+=== "Using config"

    ```python
    from langchain_core.messages import AnyMessage
@@ -128,14 +101,47 @@ State acts as [short-term memory](../concepts/memory.md) during a run. It holds
    from langgraph.prebuilt import create_react_agent
    from langgraph.prebuilt.chat_agent_executor import AgentState

-    # highlight-next-line
-    class CustomState(AgentState): # (1)!
+    def prompt(
+        state: AgentState,
+        # highlight-next-line
+        config: RunnableConfig,
+    ) -> list[AnyMessage]:
+        # highlight-next-line
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. User's name is {user_name}"
+        return [{"role": "system", "content": system_msg}] + state["messages"]
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # highlight-next-line
+        prompt=prompt
+    )
+
+    agent.invoke(
+        ...,
+        # highlight-next-line
+        config={"configurable": {"user_name": "John Smith"}}
+    )
+    ```
+
+=== "Using state"
+
+    ```python
+    from langchain_core.messages import AnyMessage
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.prebuilt import create_react_agent
+    from langgraph.prebuilt.chat_agent_executor import AgentState
+
+    class CustomState(AgentState):
+        # highlight-next-line
        user_name: str

    def prompt(
        # highlight-next-line
        state: CustomState
    ) -> list[AnyMessage]:
+        # highlight-next-line
        user_name = state["user_name"]
        system_msg = f"You are a helpful assistant. User's name is {user_name}"
        return [{"role": "system", "content": system_msg}] + state["messages"]
@@ -144,58 +150,87 @@ State acts as [short-term memory](../concepts/memory.md) during a run. It holds
        model="anthropic:claude-3-7-sonnet-latest",
        tools=[...],
        # highlight-next-line
-        state_schema=CustomState, # (2)!
+        state_schema=CustomState,
+        # highlight-next-line
        prompt=prompt
    )

    agent.invoke({
        "messages": "hi!",
+        # highlight-next-line
        "user_name": "John Smith"
    })
    ```

-    1. Define a custom state schema that extends `AgentState` or `MessagesState`.
-    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
+## Accessing Context in Tools { #tools }
+
+Tools can access context through special parameter **annotations**.
+
+* Use `RunnableConfig` for config access
+* Use `Annotated[StateSchema, InjectedState]` for agent state


-=== "In a workflow"
+!!! tip 
+
+    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
+
+=== "Using config"

    ```python
-    from typing_extensions import TypedDict
-    from langchain_core.messages import AnyMessage
-    from langgraph.graph import StateGraph
+    def get_user_info(
+        # highlight-next-line
+        config: RunnableConfig,
+    ) -> str:
+        """Look up user info."""
+        # highlight-next-line
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"

-    # highlight-next-line
-    class CustomState(TypedDict): # (1)!
-        messages: list[AnyMessage]
-        extra_field: int
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_user_info],
+    )

-    # highlight-next-line
-    def node(state: CustomState): # (2)!
-        messages = state["messages"]
-        ...
-        return { # (3)!
-            # highlight-next-line
-            "extra_field": state["extra_field"] + 1
-        }
-
-    builder = StateGraph(State)
-    builder.add_node(node)
-    builder.set_entry_point("node")
-    graph = builder.compile()
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "look up user information"}]},
+        # highlight-next-line
+        config={"configurable": {"user_id": "user_123"}}
+    )
    ```
-    
-    1. Define a custom state
-    2. Access the state in any node or tool
-    3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.

+=== "Using State"

-!!! tip "Turning on memory"
+    ```python
+    from typing import Annotated
+    from langgraph.prebuilt import InjectedState

-    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
+    class CustomState(AgentState):
+        # highlight-next-line
+        user_id: str

-### Long-term memory (cross-conversation context)
+    def get_user_info(
+        # highlight-next-line
+        state: Annotated[CustomState, InjectedState]
+    ) -> str:
+        """Look up user info."""
+        # highlight-next-line
+        user_id = state["user_id"]
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"

-For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). 
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_user_info],
+        # highlight-next-line
+        state_schema=CustomState,
+    )

-For more information, see the [Memory guide](../how-tos/memory/add-memory.md).
+    agent.invoke({
+        "messages": "look up user information",
+        # highlight-next-line
+        "user_id": "user_123"
+    })
+    ```
+
+### Update Context from Tools
+
+Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.
@@ -0,0 +1,92 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Deployment
+
+To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.
+
+Features: 
+
+* 🖥️ Local server for development
+* 🧩 Studio Web UI for visual debugging
+* ☁️ Cloud and 🔧 self-hosted deployment options
+* 📊 LangSmith integration for tracing and observability
+
+!!! info "Requirements" 
+
+    - ✅ You **must** have a [LangSmith account](https://www.langchain.com/langsmith). You can sign up for **free** and get started with the free tier.
+
+## Create a LangGraph app
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+langgraph new path/to/your/app --template new-langgraph-project-python
+```
+
+This will create an empty LangGraph project. You can modify it by replacing the code in `src/agent/graph.py` with your agent code. For example:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+graph = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    prompt="You are a helpful assistant"
+)
+```
+
+### Install dependencies
+
+In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
+
+```shell
+pip install -e .
+```
+
+### Create an `.env` file
+
+You will find a `.env.example` in the root of your new LangGraph app. Create
+a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
+
+```bash
+LANGSMITH_API_KEY=lsv2...
+ANTHROPIC_API_KEY=sk-
+```
+
+## Launch LangGraph server locally
+
+```shell
+langgraph dev
+```
+
+This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
+
+>    Ready!
+> 
+>    - API: [http://localhost:2024](http://localhost:2024/)
+>     
+>    - Docs: http://localhost:2024/docs
+>     
+>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
+
+See this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) to learn more about running LangGraph app locally.
+
+## LangGraph Studio Web UI
+
+LangGraph Studio Web is a specialized UI that you can connect to LangGraph API server to enable visualization, interaction, and debugging of your application locally. Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph dev` command.
+
+>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
+
+## Deployment
+
+Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.
@@ -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}
 ```
@@ -0,0 +1,238 @@
+---
+search:
+  boost: 2
+tags:
+  - human-in-the-loop
+  - hil
+  - agent
+hide:
+  - tags
+---
+
+# Human-in-the-loop
+
+To review, edit and approve tool calls in an agent you can use LangGraph's built-in [Human-In-the-Loop (HIL)](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
+
+LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.
+
+This is possible because the agent state is **checkpointed into a database**, which allows the system to persist execution context and later resume the workflow, continuing from where it left off.
+
+For a deeper dive into the **human-in-the-loop** concept, see the [concept guide](../concepts/human_in_the_loop.md).
+
+<figure markdown="1">
+![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
+<figcaption>
+A human can review and edit the output from the agent before proceeding. This is particularly critical in applications where the tool calls requested may be sensitive or require human oversight.
+</figcaption>
+</figure>
+
+
+## Review tool calls
+
+To add a human approval step to a tool:
+
+1. Use `interrupt()` in the tool to pause execution.
+2. Resume with a `Command(resume=...)` to continue based on human input.
+
+```python
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import interrupt
+from langgraph.prebuilt import create_react_agent
+
+# An example of a sensitive tool that requires human review / approval
+def book_hotel(hotel_name: str):
+    """Book a hotel"""
+    # highlight-next-line
+    response = interrupt(  # (1)!
+        f"Trying to call `book_hotel` with args {{'hotel_name': {hotel_name}}}. "
+        "Please approve or suggest edits."
+    )
+    if response["type"] == "accept":
+        pass
+    elif response["type"] == "edit":
+        hotel_name = response["args"]["hotel_name"]
+    else:
+        raise ValueError(f"Unknown response type: {response['type']}")
+    return f"Successfully booked a stay at {hotel_name}."
+
+# highlight-next-line
+checkpointer = InMemorySaver() # (2)!
+
+agent = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    tools=[book_hotel],
+    # highlight-next-line
+    checkpointer=checkpointer, # (3)!
+)
+```
+
+1. The [`interrupt` function][langgraph.types.interrupt] pauses the agent graph at a specific node. In this case, we call `interrupt()` at the beginning of the tool function, which pauses the graph at the node that executes the tool. The information inside `interrupt()` (e.g., tool calls) can be presented to a human, and the graph can be resumed with the user input (tool call approval, edit or feedback).
+2. The `InMemorySaver` is used to store the agent state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities. In this example, we use `InMemorySaver` to store the agent state in memory. In a production application, the agent state will be stored in a database.
+3. Initialize the agent with the `checkpointer`.
+
+Run the agent with the `stream()` method, passing the `config` object to specify the thread ID. This allows the agent to resume the same conversation on future invocations.
+
+```python
+config = {
+   "configurable": {
+      # highlight-next-line
+      "thread_id": "1"
+   }
+}
+
+for chunk in agent.stream(
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
+    # highlight-next-line
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+> You should see that the agent runs until it reaches the `interrupt()` call, at which point it pauses and waits for human input.
+
+Resume the agent with a `Command(resume=...)` to continue based on human input.
+
+```python
+from langgraph.types import Command
+
+for chunk in agent.stream(
+    # highlight-next-line
+    Command(resume={"type": "accept"}),  # (1)!
+    # Command(resume={"type": "edit", "args": {"hotel_name": "McKittrick Hotel"}}),
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`][langgraph.types.Command] object to resume the graph with a value provided by the human.
+
+## Using with Agent Inbox
+
+You can create a wrapper to add interrupts to *any* tool. 
+
+The example below provides a reference implementation compatible with [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox) and [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui).
+
+```python title="Wrapper that adds human-in-the-loop to any tool"
+from typing import Callable
+from langchain_core.tools import BaseTool, tool as create_tool
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import interrupt 
+from langgraph.prebuilt.interrupt import HumanInterruptConfig, HumanInterrupt
+
+def add_human_in_the_loop(
+    tool: Callable | BaseTool,
+    *,
+    interrupt_config: HumanInterruptConfig = None,
+) -> BaseTool:
+    """Wrap a tool to support human-in-the-loop review.""" 
+    if not isinstance(tool, BaseTool):
+        tool = create_tool(tool)
+
+    if interrupt_config is None:
+        interrupt_config = {
+            "allow_accept": True,
+            "allow_edit": True,
+            "allow_respond": True,
+        }
+
+    @create_tool(  # (1)!
+        tool.name,
+        description=tool.description,
+        args_schema=tool.args_schema
+    )
+    def call_tool_with_interrupt(config: RunnableConfig, **tool_input):
+        request: HumanInterrupt = {
+            "action_request": {
+                "action": tool.name,
+                "args": tool_input
+            },
+            "config": interrupt_config,
+            "description": "Please review the tool call"
+        }
+        # highlight-next-line
+        response = interrupt([request])[0]  # (2)!
+        # approve the tool call
+        if response["type"] == "accept":
+            tool_response = tool.invoke(tool_input, config)
+        # update tool call args
+        elif response["type"] == "edit":
+            tool_input = response["args"]["args"]
+            tool_response = tool.invoke(tool_input, config)
+        # respond to the LLM with user feedback
+        elif response["type"] == "response":
+            user_feedback = response["args"]
+            tool_response = user_feedback
+        else:
+            raise ValueError(f"Unsupported interrupt response type: {response['type']}")
+
+        return tool_response
+
+    return call_tool_with_interrupt
+```
+
+1. This wrapper creates a new tool that calls `interrupt()` **before** executing the wrapped tool.
+2. `interrupt()` is using special input and output format that's expected by [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox):
+    - a list of [`HumanInterrupt`][langgraph.prebuilt.interrupt.HumanInterrupt] objects is sent to `AgentInbox` render interrupt information to the end user
+    - resume value is provided by `AgentInbox` as a list (i.e., `Command(resume=[...])`)
+
+You can use the `add_human_in_the_loop` wrapper to add `interrupt()` to any tool without having to add it *inside* the tool:
+
+```python
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.prebuilt import create_react_agent
+
+# highlight-next-line
+checkpointer = InMemorySaver()
+
+def book_hotel(hotel_name: str):
+   """Book a hotel"""
+   return f"Successfully booked a stay at {hotel_name}."
+
+
+agent = create_react_agent(
+    model="anthropic:claude-3-5-sonnet-latest",
+    tools=[
+        # highlight-next-line
+        add_human_in_the_loop(book_hotel), # (1)!
+    ],
+    # highlight-next-line
+    checkpointer=checkpointer,
+)
+
+config = {"configurable": {"thread_id": "1"}}
+
+# Run the agent
+for chunk in agent.stream(
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
+    # highlight-next-line
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+1. The `add_human_in_the_loop` wrapper is used to add `interrupt()` to the tool. This allows the agent to pause execution and wait for human input before proceeding with the tool call.
+
+> You should see that the agent runs until it reaches the `interrupt()` call, 
+>  at which point it pauses and waits for human input.
+
+Resume the agent with a `Command(resume=...)`  to continue based on human input.
+
+```python
+from langgraph.types import Command 
+
+for chunk in agent.stream(
+    # highlight-next-line
+    Command(resume=[{"type": "accept"}]),
+    # Command(resume=[{"type": "edit", "args": {"args": {"hotel_name": "McKittrick Hotel"}}}]),
+    config
+):
+    print(chunk)
+    print("\n")
+```
+
+## Additional resources
+
+* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)
@@ -7,125 +7,57 @@ hide:
  - tags
 ---

-# Use MCP
+# MCP Integration

-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"
+# highlight-next-line
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langgraph.prebuilt import create_react_agent

-    ```python title="Agent using tools defined on MCP servers"
-    # highlight-next-line
-    from langchain_mcp_adapters.client import MultiServerMCPClient
-    from langgraph.prebuilt import create_react_agent
-
-    # highlight-next-line
-    client = MultiServerMCPClient(
-        {
-            "math": {
-                "command": "python",
-                # Replace with absolute path to your math_server.py file
-                "args": ["/path/to/math_server.py"],
-                "transport": "stdio",
-            },
-            "weather": {
-                # Ensure you start your weather server on port 8000
-                "url": "http://localhost:8000/mcp",
-                "transport": "streamable_http",
-            }
+# highlight-next-line
+client = MultiServerMCPClient(
+    {
+        "math": {
+            "command": "python",
+            # Replace with absolute path to your math_server.py file
+            "args": ["/path/to/math_server.py"],
+            "transport": "stdio",
+        },
+        "weather": {
+            # Ensure your start your weather server on port 8000
+            "url": "http://localhost:8000/mcp",
+            "transport": "streamable_http",
        }
-    )
+    }
+)
+# highlight-next-line
+tools = await client.get_tools()
+agent = create_react_agent(
+    "anthropic:claude-3-7-sonnet-latest",
    # highlight-next-line
-    tools = await client.get_tools()
-    agent = create_react_agent(
-        "anthropic:claude-3-7-sonnet-latest",
-        # highlight-next-line
-        tools
-    )
-    math_response = await agent.ainvoke(
-        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
-    )
-    weather_response = await agent.ainvoke(
-        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
-    )
-    ```
-
-=== "In a workflow"
-
-    ```python title="Workflow using MCP tools with ToolNode"
-    from langchain_mcp_adapters.client import MultiServerMCPClient
-    from langchain.chat_models import init_chat_model
-    from langgraph.graph import StateGraph, MessagesState, START, END
-    from langgraph.prebuilt import ToolNode
-
-    # Initialize the model
-    model = init_chat_model("anthropic:claude-3-5-sonnet-latest")
-
-    # Set up MCP client
-    client = MultiServerMCPClient(
-        {
-            "math": {
-                "command": "python",
-                # Make sure to update to the full absolute path to your math_server.py file
-                "args": ["./examples/math_server.py"],
-                "transport": "stdio",
-            },
-            "weather": {
-                # make sure you start your weather server on port 8000
-                "url": "http://localhost:8000/mcp/",
-                "transport": "streamable_http",
-            }
-        }
-    )
-    tools = await client.get_tools()
-
-    # Bind tools to model
-    model_with_tools = model.bind_tools(tools)
-
-    # Create ToolNode
-    tool_node = ToolNode(tools)
-
-    def should_continue(state: MessagesState):
-        messages = state["messages"]
-        last_message = messages[-1]
-        if last_message.tool_calls:
-            return "tools"
-        return END
-
-    # Define call_model function
-    async def call_model(state: MessagesState):
-        messages = state["messages"]
-        response = await model_with_tools.ainvoke(messages)
-        return {"messages": [response]}
-
-    # Build the graph
-    builder = StateGraph(MessagesState)
-    builder.add_node("call_model", call_model)
-    builder.add_node("tools", tool_node)
-
-    builder.add_edge(START, "call_model")
-    builder.add_conditional_edges(
-        "call_model",
-        should_continue,
-    )
-    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?"}]}
-    )
-    ```
-
-
+    tools
+)
+math_response = await agent.ainvoke(
+    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+)
+weather_response = await agent.ainvoke(
+    {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+)
+```

 ## Custom MCP servers

@@ -174,5 +106,4 @@ if __name__ == "__main__":
 ## Additional resources

 - [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)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
@@ -0,0 +1,423 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Memory
+
+LangGraph supports two types of memory essential for building conversational agents:
+
+- **[Short-term memory](#short-term-memory)**: Tracks the ongoing conversation by maintaining message history within a session.
+- **[Long-term memory](#long-term-memory)**: Stores user-specific or application-level data across sessions.
+
+This guide demonstrates how to use both memory types with agents in LangGraph. For a deeper
+understanding of memory concepts, refer to the [LangGraph memory documentation](../concepts/memory.md).
+
+<figure markdown="1">
+![image](./assets/memory.png){: style="max-height:400px"}
+<figcaption>Both <strong>short-term</strong> and <strong>long-term</strong> memory require persistent storage to maintain continuity across LLM interactions. In production environments, this data is typically stored in a database.</figcaption>
+</figure>
+
+!!! note "Terminology"
+
+    In LangGraph:
+
+    - *Short-term memory* is also referred to as **thread-level memory**.
+    - *Long-term memory* is also called **cross-thread memory**.
+
+    A [thread](../concepts/persistence.md#threads) represents a sequence of related runs
+    grouped by the same `thread_id`.
+
+## Short-term memory
+
+Short-term memory enables agents to track multi-turn conversations. To use it, you must:
+
+1. Provide a `checkpointer` when creating the agent. The `checkpointer` enables [persistence](../concepts/persistence.md) of the agent's state.
+2. Supply a `thread_id` in the config when running the agent. The `thread_id` is a unique identifier for the conversation session.
+
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import InMemorySaver
+
+# highlight-next-line
+checkpointer = InMemorySaver() # (1)!
+
+
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
+
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    # highlight-next-line
+    checkpointer=checkpointer # (2)!
+)
+
+# Run the agent
+config = {
+    "configurable": {
+        # highlight-next-line
+        "thread_id": "1"  # (3)!
+    }
+}
+
+sf_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    # highlight-next-line
+    config
+)
+
+# Continue the conversation using the same thread_id
+ny_response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what about new york?"}]},
+    # highlight-next-line
+    config # (4)!
+)
+```
+
+1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
+2. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations. Please note that 
+3. A unique `thread_id` is provided in the config. This ID is used to identify the conversation session. The value is controlled by the user and can be any string.
+4. The agent will continue the conversation using the same `thread_id`. This will allow the agent to infer that the user is asking specifically about the **weather** in New York. 
+
+When the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, allowing the agent to infer that the user is asking specifically about the **weather** in New York.
+
+!!! Note "LangGraph Platform providers a production-ready checkpointer"
+
+    If you're using [LangGraph Platform](./deployment.md), during deployment your checkpointer will be automatically configured to use a production-ready database.
+
+### Manage message history
+
+Long conversations can exceed the LLM's context window. Common solutions are:
+
+* [Summarization](#summarize-message-history): Maintain a running summary of the conversation
+* [Trimming](#trim-message-history): Remove first or last N messages in the history
+
+This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+
+To manage message history, specify `pre_model_hook` — a function ([node](../concepts/low_level.md#nodes)) that will always run before calling the language model.
+
+#### Summarize message history
+
+<figure markdown="1">
+![image](./assets/summary.png){: style="max-height:400px"}
+<figcaption>Long conversations can exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+</figcaption>
+</figure>
+
+To summarize message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with a prebuilt [`SummarizationNode`](https://langchain-ai.github.io/langmem/reference/short_term/#langmem.short_term.SummarizationNode):
+
+```python
+from langchain_anthropic import ChatAnthropic
+from langmem.short_term import SummarizationNode
+from langchain_core.messages.utils import count_tokens_approximately
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.checkpoint.memory import InMemorySaver
+from typing import Any
+
+model = ChatAnthropic(model="claude-3-7-sonnet-latest")
+
+summarization_node = SummarizationNode( # (1)!
+    token_counter=count_tokens_approximately,
+    model=model,
+    max_tokens=384,
+    max_summary_tokens=128,
+    output_messages_key="llm_input_messages",
+)
+
+class State(AgentState):
+    # NOTE: we're adding this key to keep track of previous summary information
+    # to make sure we're not summarizing on every LLM call
+    # highlight-next-line
+    context: dict[str, Any]  # (2)!
+
+
+checkpointer = InMemorySaver() # (3)!
+
+agent = create_react_agent(
+    model=model,
+    tools=tools,
+    # highlight-next-line
+    pre_model_hook=summarization_node, # (4)!
+    # highlight-next-line
+    state_schema=State, # (5)!
+    checkpointer=checkpointer,
+)
+```
+
+1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
+2. The `context` key is added to the agent's state. The key contains book-keeping information for the summarization node. It is used to keep track of the last summary information and ensure that the agent doesn't summarize on every LLM call, which can be inefficient.
+3. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations.
+4. The `pre_model_hook` is set to the `SummarizationNode`. This node will summarize the message history before sending it to the LLM. The summarization node will automatically handle the summarization process and update the agent's state with the new summary. You can replace this with a custom implementation if you prefer. Please see the [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] API reference for more details.
+5. The `state_schema` is set to the `State` class, which is the custom state that contains an extra `context` key.
+
+#### Trim message history
+
+To trim message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with [`trim_messages`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html) function:
+
+```python
+# highlight-next-line
+from langchain_core.messages.utils import (
+    # highlight-next-line
+    trim_messages,
+    # highlight-next-line
+    count_tokens_approximately
+# highlight-next-line
+)
+from langgraph.prebuilt import create_react_agent
+
+# This function will be called every time before the node that calls LLM
+def pre_model_hook(state):
+    trimmed_messages = trim_messages(
+        state["messages"],
+        strategy="last",
+        token_counter=count_tokens_approximately,
+        max_tokens=384,
+        start_on="human",
+        end_on=("human", "tool"),
+    )
+    # highlight-next-line
+    return {"llm_input_messages": trimmed_messages}
+
+checkpointer = InMemorySaver()
+agent = create_react_agent(
+    model,
+    tools,
+    # highlight-next-line
+    pre_model_hook=pre_model_hook,
+    checkpointer=checkpointer,
+)
+```
+
+To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
+
+### Read in tools { #read-short-term }
+
+LangGraph allows agent to access its short-term memory (state) inside the tools.
+
+```python
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, create_react_agent
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_id: str
+
+def get_user_info(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Look up user info."""
+    # highlight-next-line
+    user_id = state["user_id"]
+    return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    state_schema=CustomState,
+)
+
+agent.invoke({
+    "messages": "look up user information",
+    # highlight-next-line
+    "user_id": "user_123"
+})
+```
+
+See the [Context](./context.md#__tabbed_2_2) guide for more information.
+
+### Write from tools { #write-short-term }
+
+To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```python
+from typing import Annotated
+from langchain_core.tools import InjectedToolCallId
+from langchain_core.runnables import RunnableConfig
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.types import Command
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+def update_user_info(
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    config: RunnableConfig
+) -> Command:
+    """Look up and update user info."""
+    user_id = config["configurable"].get("user_id")
+    name = "John Smith" if user_id == "user_123" else "Unknown user"
+    # highlight-next-line
+    return Command(update={
+        # highlight-next-line
+        "user_name": name,
+        # update the message history
+        "messages": [
+            ToolMessage(
+                "Successfully looked up user information",
+                tool_call_id=tool_call_id
+            )
+        ]
+    })
+
+def greet(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Use this to greet the user once you found their info."""
+    user_name = state["user_name"]
+    return f"Hello {user_name}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[update_user_info, greet],
+    # highlight-next-line
+    state_schema=CustomState
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "greet the user"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+For more details, see [how to update state from tools](../how-tos/tool-calling.ipynb#update).
+
+## Long-term memory
+
+Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
+
+To use long-term memory, you need to:
+
+1. [Configure a store](../how-tos/persistence.ipynb#add-long-term-memory) to persist data across invocations.
+2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
+
+### Read { #read-long-term }
+
+```python title="A tool the agent can use to look up user information"
+from langchain_core.runnables import RunnableConfig
+from langgraph.config import get_store
+from langgraph.prebuilt import create_react_agent
+from langgraph.store.memory import InMemoryStore
+
+# highlight-next-line
+store = InMemoryStore() # (1)!
+
+# highlight-next-line
+store.put(  # (2)!
+    ("users",),  # (3)!
+    "user_123",  # (4)!
+    {
+        "name": "John Smith",
+        "language": "English",
+    } # (5)!
+)
+
+def get_user_info(config: RunnableConfig) -> str:
+    """Look up user info."""
+    # Same as that provided to `create_react_agent`
+    # highlight-next-line
+    store = get_store() # (6)!
+    user_id = config["configurable"].get("user_id")
+    # highlight-next-line
+    user_info = store.get(("users",), user_id) # (7)!
+    return str(user_info.value) if user_info else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    store=store # (8)!
+)
+
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "look up user information"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+2. For this example, we write some sample data to the store using the `put` method. Please see the [BaseStore.put][langgraph.store.base.BaseStore.put] API reference for more details.
+3. The first argument is the namespace. This is used to group related data together. In this case, we are using the `users` namespace to group user data.
+4. A key within the namespace. This example uses a user ID for the key.
+5. The data that we want to store for the given user.
+6. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
+7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
+8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
+
+### Write { #write-long-term }
+
+```python title="Example of a tool that updates user information"
+from typing_extensions import TypedDict
+
+from langgraph.config import get_store
+from langgraph.prebuilt import create_react_agent
+from langgraph.store.memory import InMemoryStore
+
+store = InMemoryStore() # (1)!
+
+class UserInfo(TypedDict): # (2)!
+    name: str
+
+def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
+    """Save user info."""
+    # Same as that provided to `create_react_agent`
+    # highlight-next-line
+    store = get_store() # (4)!
+    user_id = config["configurable"].get("user_id")
+    # highlight-next-line
+    store.put(("users",), user_id, user_info) # (5)!
+    return "Successfully saved user info."
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[save_user_info],
+    # highlight-next-line
+    store=store
+)
+
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}} # (6)!
+)
+
+# You can access the store directly to get the value
+store.get(("users",), "user_123").value
+```
+
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+2. The `UserInfo` class is a `TypedDict` that defines the structure of the user information. The LLM will use this to format the response according to the schema.
+3. The `save_user_info` function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
+4. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
+5. The `put` method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
+6. The `user_id` is passed in the config. This is used to identify the user whose information is being updated.
+
+### Semantic search
+
+LangGraph also allows you to [search](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/#using-in-create-react-agent) for items in long-term memory by semantic similarity.
+
+### Prebuilt memory tools
+
+**LangMem** is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the [LangMem documentation](https://langchain-ai.github.io/langmem/) for usage examples.
+
+
+## Additional resources
+
+* [Memory in LangGraph](../concepts/memory.md)
@@ -1,78 +1,233 @@
+---
+search:
+  boost: 2
+tags:
+  - anthropic
+  - openai
+  - agent
+hide:
+  - tags
+---
+
 # Models

-LangGraph provides built-in support for [LLMs (language models)](https://python.langchain.com/docs/concepts/chat_models/) via the LangChain library. This makes it easy to integrate various LLMs into your agents and workflows.
+This page describes how to configure the chat model used by an agent.
+
+## Tool calling support
+
+To enable tool-calling agents, the underlying LLM must support [tool calling](https://python.langchain.com/docs/concepts/tool_calling/).
+
+Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
+
+## Specifying a model by name
+
+You can configure an agent with a model name string:
+
+=== "OpenAI"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="openai:gpt-4.1",
+        # other parameters
+    )
+    ```
+
+=== "Anthropic"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="anthropic:claude-3-7-sonnet-latest",
+        # other parameters
+    )
+    ```
+
+=== "Azure"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="azure_openai:gpt-4.1",
+        # other parameters
+    )
+    ```
+
+=== "Google Gemini"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="google_genai:gemini-2.0-flash",
+        # other parameters
+    )
+    ```
+
+=== "AWS Bedrock"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="bedrock_converse:anthropic.claude-3-5-sonnet-20240620-v1:0",
+        # other parameters
+    )
+    ```


-## Initialize a model
+## Using `init_chat_model`

-Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
+The [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) utility simplifies model initialization with configurable parameters:

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

-### Instantiate a model directly
+    ```
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    model = init_chat_model(
+        "openai:gpt-4.1",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Anthropic"
+
+    ```
+    pip install -U "langchain[anthropic]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    model = init_chat_model(
+        "anthropic:claude-3-5-sonnet-latest",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Azure"
+
+    ```
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    model = init_chat_model(
+        "azure_openai:gpt-4.1",
+        azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Google Gemini"
+
+    ```
+    pip install -U "langchain[google-genai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    model = init_chat_model(
+        "google_genai:gemini-2.0-flash",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "AWS Bedrock"
+
+    ```
+    pip install -U "langchain[aws]"
+    ```
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    model = init_chat_model(
+        "anthropic.claude-3-5-sonnet-20240620-v1:0",
+        model_provider="bedrock_converse",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+
+Refer to the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for advanced options.
+
+## Using provider-specific LLMs 

 If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:

-
 ```python
-# Anthropic is already supported by `init_chat_model`,
-# but you can also instantiate it directly.
 from langchain_anthropic import ChatAnthropic
+from langgraph.prebuilt import create_react_agent

 model = ChatAnthropic(
-  model="claude-3-7-sonnet-latest",
-  temperature=0,
-  max_tokens=2048
+    model="claude-3-7-sonnet-latest",
+    temperature=0,
+    max_tokens=2048
+)
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model,
+    # other parameters
 )
 ```

-!!! important "Tool calling support"
+!!! note "Illustrative example" 

-    If you are building an agent or workflow that requires the model to call external tools, ensure that the underlying
-    language model supports [tool calling](../concepts/tools.md). Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
+    The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.

-
-## Use in an agent
-
-When using `create_react_agent` you can specify the model by its name string, which is a shorthand for initializing the model using `init_chat_model`. This allows you to use the model without needing to import or instantiate it directly.
-
-=== "model name"
-
-
-      ```python
-      from langgraph.prebuilt import create_react_agent
-
-      create_react_agent(
-         # highlight-next-line
-         model="anthropic:claude-3-7-sonnet-latest",
-         # other parameters
-      )
-      ```
-
-=== "model instance"
-
-      ```python
-      from langchain_anthropic import ChatAnthropic
-      from langgraph.prebuilt import create_react_agent
-
-      model = ChatAnthropic(
-          model="claude-3-7-sonnet-latest",
-          temperature=0,
-          max_tokens=2048
-      )
-      # Alternatively
-      # model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
-
-      agent = create_react_agent(
-        # highlight-next-line
-        model=model,
-        # other parameters
-      )
-      ```
-
-## Advanced model configuration
-
-### Disable streaming
+## Disable streaming

 To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:

@@ -102,7 +257,7 @@ To disable streaming of the individual LLM tokens, set `disable_streaming=True`

 Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`

-### Add model fallbacks
+## Adding model fallbacks

 You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:

@@ -137,43 +292,7 @@ You can add a fallback to a different model or a different LLM provider using `m

 See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.

-### Use the built-in rate limiter
-
-Langchain includes a built-in in-memory rate limiter. This rate limiter is thread safe and can be shared by multiple threads in the same process.
-
-```python
-from langchain_core.rate_limiters import InMemoryRateLimiter
-from langchain_anthropic import ChatAnthropic
-
-rate_limiter = InMemoryRateLimiter(
-    requests_per_second=0.1,  # <-- Super slow! We can only make a request once every 10 seconds!!
-    check_every_n_seconds=0.1,  # Wake up every 100 ms to check whether allowed to make a request,
-    max_bucket_size=10,  # Controls the maximum burst size.
-)
-
-model = ChatAnthropic(
-   model_name="claude-3-opus-20240229", 
-   rate_limiter=rate_limiter
-)
-```
-
-See the LangChain docs for more information on how to [handle rate limiting](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/).
-
-## Bring your own model
-
-If your desired LLM isn't officially supported by LangChain, consider these options:
-
-1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
-
-2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
-   Refer to the [custom streaming documentation](../how-tos/streaming.md#use-with-any-llm) for guidance. This approach suits custom workflows where prebuilt agent integration is not necessary.
-
- 
 ## Additional resources

- [Multimodal inputs](https://python.langchain.com/docs/how_to/multimodal_inputs/)
- [Structured outputs](https://python.langchain.com/docs/how_to/structured_output/)
 - [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
- [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
- [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
- [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)
+- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
@@ -9,7 +9,7 @@ hide:

 # Multi-agent

-A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and compose them into a [multi-agent system](../concepts/multi_agent.md).
+A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).

 In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.

@@ -8,9 +8,9 @@ hide:
  - tags
 ---

-# Agent development using prebuilt components
+# Agent development with LangGraph

-LangGraph provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the prebuilt, ready-to-use components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
+**LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.

 ## What is an agent?

@@ -27,12 +27,12 @@ The LLM operates in a loop. In each iteration, it selects a tool to invoke, prov

 LangGraph includes several capabilities essential for building robust, production-ready agentic systems:

- [**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.
+- [**Memory integration**](./memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
+- [**Human-in-the-loop control**](./human-in-the-loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
+- [**Streaming support**](./streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
+- [**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.
+    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for production.

 ## High-level building blocks

@@ -50,142 +50,6 @@ The high-level components are organized into several packages, each with a speci
 | `langgraph-supervisor`                     | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `pip install -U langgraph-supervisor`   |
 | `langgraph-swarm`                          | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm`        |
 | `langchain-mcp-adapters`                   | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
-| `langmem`                                  | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md)        | `pip install -U langmem`                |
+| `langmem`                                  | Agent memory management: [**short-term and long-term**](./memory.md)        | `pip install -U langmem`                |
 | `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |

-## Visualize an agent graph
-
-Use the following tool to visualize the graph generated by
-[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
-and to view an outline of the corresponding code.
-It allows you to explore the infrastructure of the agent as defined by the presence of:
-
-* [`tools`](../how-tos/tool-calling.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`.
-
-<div class="agent-layout">
-  <div class="agent-graph-features-container">
-    <div class="agent-graph-features">
-      <h3 class="agent-section-title">Features</h3>
-      <label><input type="checkbox" id="tools" checked> <code>tools</code></label>
-      <label><input type="checkbox" id="pre_model_hook"> <code>pre_model_hook</code></label>
-      <label><input type="checkbox" id="post_model_hook"> <code>post_model_hook</code></label>
-      <label><input type="checkbox" id="response_format"> <code>response_format</code></label>
-    </div>
-  </div>
-
-  <div class="agent-graph-container">
-    <h3 class="agent-section-title">Graph</h3>
-    <img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
-  </div>
-</div>
-
-
-The following code snippet shows how to create the above agent (and underlying graph) with
-[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
-
-<div class="language-python">
-  <pre><code id="agent-code" class="language-python"></code></pre>
-</div>
-
-
-<script>
-function getCheckedValue(id) {
-  return document.getElementById(id).checked ? "1" : "0";
-}
-
-function getKey() {
-  return [
-    getCheckedValue("response_format"),
-    getCheckedValue("post_model_hook"),
-    getCheckedValue("pre_model_hook"),
-    getCheckedValue("tools")
-  ].join("");
-}
-
-function generateCodeSnippet({ tools, pre, post, response }) {
-  const lines = [
-    "from langgraph.prebuilt import create_react_agent",
-    "from langchain_openai import ChatOpenAI"
-  ];
-
-  if (response) lines.push("from pydantic import BaseModel");
-
-  lines.push("", 'model = ChatOpenAI("o4-mini")', "");
-
-  if (tools) {
-    lines.push(
-      "def tool() -> None:",
-      '    """Testing tool."""',
-      "    ...",
-      ""
-    );
-  }
-
-  if (pre) {
-    lines.push(
-      "def pre_model_hook() -> None:",
-      '    """Pre-model hook."""',
-      "    ...",
-      ""
-    );
-  }
-
-  if (post) {
-    lines.push(
-      "def post_model_hook() -> None:",
-      '    """Post-model hook."""',
-      "    ...",
-      ""
-    );
-  }
-
-  if (response) {
-    lines.push(
-      "class ResponseFormat(BaseModel):",
-      '    """Response format for the agent."""',
-      "    result: str",
-      ""
-    );
-  }
-
-  lines.push("agent = create_react_agent(");
-  lines.push("    model,");
-
-  if (tools) lines.push("    tools=[tool],");
-  if (pre) lines.push("    pre_model_hook=pre_model_hook,");
-  if (post) lines.push("    post_model_hook=post_model_hook,");
-  if (response) lines.push("    response_format=ResponseFormat,");
-
-  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");
-
-  return lines.join("\n");
-}
-
-async function render() {
-  const key = getKey();
-  document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
-
-  const state = {
-    tools: document.getElementById("tools").checked,
-    pre: document.getElementById("pre_model_hook").checked,
-    post: document.getElementById("post_model_hook").checked,
-    response: document.getElementById("response_format").checked
-  };
-
-  document.getElementById("agent-code").textContent = generateCodeSnippet(state);
-}
-
-function initializeWidget() {
-  render(); // no need for `await` here
-  document.querySelectorAll(".agent-graph-features input").forEach((input) => {
-    input.addEventListener("change", render);
-  });
-}
-
-// Init for both full reload and SPA nav (used by MkDocs Material)
-window.addEventListener("DOMContentLoaded", initializeWidget);
-document$.subscribe(initializeWidget);
-</script>
@@ -1,10 +1,10 @@
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# Community Agents
+# Community agents

 If you’re looking for other prebuilt libraries, explore the community-built options 
 below. These libraries can extend LangGraph's functionality in various ways.

-## 📚 Available Libraries
+## 📚 Available libraries

 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
 | Name | GitHub URL | Description | Weekly Downloads | Stars |
@@ -23,7 +23,7 @@ below. These libraries can extend LangGraph's functionality in various ways.
 | **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
 | **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)

-## ✨ Contributing Your Library
+## ✨ Contributing your library

 Have you built an awesome open-source library using LangGraph? We'd love to feature 
 your project on the official LangGraph documentation pages! 🏆
@@ -10,7 +10,7 @@ hide:
 # Running agents


-Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .ainvoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](../how-tos/streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
+Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .ainvoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.


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

 !!! tip

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

 ## Max iterations

@@ -0,0 +1,223 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Streaming
+
+Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
+
+1. [**Agent progress**](#agent-progress) — get updates after each node in the agent graph is executed.
+2. [**LLM tokens**](#llm-tokens) — stream tokens as they are generated by the language model.
+3. [**Custom updates**](#tool-updates) — emit custom data from tools during execution (e.g., "Fetched 10/100 records")
+
+You can stream [more than one type of data](#stream-multiple-modes) at a time. 
+
+
+<figure markdown="1">
+![image](./assets/fast_parrot.png){: style="max-height:300px"}
+<figcaption>
+Waiting is for pigeons.
+</figcaption>
+</figure>
+
+## Agent progress
+
+To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with [`stream_mode="updates"`](https://langchain-ai.github.io/langgraph/how-tos/streaming/#updates). This emits an event after every agent step.
+
+For example, if you have an agent that calls a tool once, you should see the following updates:
+
+* **LLM node**: AI message with tool call requests
+* **Tool node**: Tool message with execution result
+* **LLM node**: Final AI response
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="updates"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+## LLM tokens
+
+To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    for token, metadata in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+    # highlight-next-line
+    async for token, metadata in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="messages"
+    ):
+        print("Token", token)
+        print("Metadata", metadata)
+        print("\n")
+    ```
+
+## Tool updates
+
+To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
+
+=== "Sync"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    # highlight-next-line
+    from langgraph.config import get_stream_writer
+
+    def get_weather(city: str) -> str:
+        """Get weather for a given city."""
+        # highlight-next-line
+        writer = get_stream_writer()
+        # stream any arbitrary data
+        # highlight-next-line
+        writer(f"Looking up data for city: {city}")
+        return f"It's always sunny in {city}!"
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode="custom"
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+!!! Note
+    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
+
+## Stream multiple modes
+
+You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
+
+=== "Sync"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    for stream_mode, chunk in agent.stream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+=== "Async"
+
+    ```python
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+    )
+
+    async for stream_mode, chunk in agent.astream(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        stream_mode=["updates", "messages", "custom"]
+    ):
+        print(chunk)
+        print("\n")
+    ```
+
+## Disable streaming
+
+In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
+
+See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
+
+## Additional resources
+
+* [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)
@@ -0,0 +1,296 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
+# Tools
+
+[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
+
+You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
+
+## Define simple tools
+
+You can pass a vanilla function to `create_react_agent` to use as a tool:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+create_react_agent(
+    model="anthropic:claude-3-7-sonnet",
+    tools=[multiply]
+)
+```
+
+`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
+
+## Customize tools
+
+For more control over tool behavior, use the `@tool` decorator:
+
+```python
+# highlight-next-line
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool("multiply_tool", parse_docstring=True)
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+
+    Args:
+        a: First operand
+        b: Second operand
+    """
+    return a * b
+```
+
+You can also define a custom input schema using Pydantic:
+
+```python
+from pydantic import BaseModel, Field
+
+class MultiplyInputSchema(BaseModel):
+    """Multiply two numbers"""
+    a: int = Field(description="First operand")
+    b: int = Field(description="Second operand")
+
+# highlight-next-line
+@tool("multiply_tool", args_schema=MultiplyInputSchema)
+def multiply(a: int, b: int) -> int:
+    return a * b
+```
+
+For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
+
+## Hide arguments from the model
+
+Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
+
+You can put these arguments in the `state` or `config` of the agent, and access
+this information inside the tool:
+
+```python
+from langgraph.prebuilt import InjectedState
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langchain_core.runnables import RunnableConfig
+
+def my_tool(
+    # This will be populated by an LLM
+    tool_arg: str,
+    # access information that's dynamically updated inside the agent
+    # highlight-next-line
+    state: Annotated[AgentState, InjectedState],
+    # access static data that is passed at agent invocation
+    # highlight-next-line
+    config: RunnableConfig,
+) -> str:
+    """My tool."""
+    do_something_with_state(state["messages"])
+    do_something_with_config(config)
+    ...
+```
+
+## Disable parallel tool calling
+
+Some model providers support executing multiple tools in parallel, but
+allow users to disable this feature.
+
+For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
+
+```python
+from langchain.chat_models import init_chat_model
+
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers."""
+    return a * b
+
+model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
+tools = [add, multiply]
+agent = create_react_agent(
+    # disable parallel tool calls
+    # highlight-next-line
+    model=model.bind_tools(tools, parallel_tool_calls=False),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
+)
+```
+
+## Return tool results directly
+
+Use `return_direct=True` to return tool results immediately and stop the agent loop:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[add]
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
+)
+```
+
+## Force tool use
+
+To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
+
+```python
+from langchain_core.tools import tool
+
+# highlight-next-line
+@tool(return_direct=True)
+def greet(user_name: str) -> int:
+    """Greet user."""
+    return f"Hello {user_name}!"
+
+tools = [greet]
+
+agent = create_react_agent(
+    # highlight-next-line
+    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
+    tools=tools
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
+)
+```
+
+!!! Warning "Avoid infinite loops"
+
+    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
+
+    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
+    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
+
+## Handle tool errors
+
+By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
+
+=== "Enable error handling (default)"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # Run with error handling (default)
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[multiply]
+    )
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+=== "Disable error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=False  # (1)!
+    )
+    agent_no_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_no_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+=== "Custom error handling"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent, ToolNode
+
+    def multiply(a: int, b: int) -> int:
+        """Multiply two numbers."""
+        if a == 42:
+            raise ValueError("The ultimate error")
+        return a * b
+
+    # highlight-next-line
+    tool_node = ToolNode(
+        [multiply],
+        # highlight-next-line
+        handle_tool_errors=(
+            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
+        )
+    )
+    agent_custom_error_handling = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=tool_node
+    )
+    agent_custom_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
+    ```
+
+    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
+
+See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
+
+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
+
+* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
+* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
+
+## Prebuilt tools
+
+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](./human-in-the-loop.md#using-with-agent-inbox):

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

@@ -1,19 +0,0 @@
-{
-    "0000": "graph TD;\n\t__start__ --> agent;\n\tagent --> __end__;",
-    "0001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\ttools --> agent;",
-    "0010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tpre_model_hook --> agent;\n\tagent --> __end__;",
-    "0011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
-    "0100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> __end__;",
-    "0101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;",
-    "0110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpre_model_hook --> agent;\n\tpost_model_hook --> __end__;",
-    "0111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
-    "1000": "graph TD;\n\t__start__ --> agent;\n\tagent --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
-    "1001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
-    "1010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
-    "1011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;",
-    "1100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
-    "1101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
-    "1110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
-    "1111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;"
-}
-  
@@ -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 +1,5 @@
+# Runs
+
+A run is an invocation of an [assistant](../../concepts/assistants.md). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./threads.md).
+
+The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.
@@ -0,0 +1,138 @@
+# Streaming
+
+Streaming is critical for making LLM applications feel responsive to end users.  
+When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
+
+## Supported streaming modes
+
+LangGraph Platform supports the following streaming modes:
+
+| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
+| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
+| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
+| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
+| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
+| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
+
+✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
+
+## Stateless runs
+
+If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    async for chunk in client.runs.stream(
+        # highlight-next-line
+        None,  # (1)!
+        assistant_id,
+        input=inputs,
+        stream_mode="updates"
+    ):
+        print(chunk.data)
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // create a streaming run
+    // highlight-next-line
+    const streamResponse = client.runs.stream(
+      // highlight-next-line
+      null,  // (1)!
+      assistantID,
+      {
+        input,
+        streamMode: "updates"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk.data);
+    }
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <inputs>,
+      \"stream_mode\": \"updates\"
+    }"
+    ```
+
+## Join and stream
+
+LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    # highlight-next-line
+    async for chunk in client.runs.join_stream(
+        thread_id,
+        # highlight-next-line
+        run_id,  # (1)!
+    ):
+        print(chunk)
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // highlight-next-line
+    const streamResponse = client.runs.joinStream(
+      threadID,
+      // highlight-next-line
+      runId  // (1)!
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk);
+    }
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+=== "cURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    ```
+
+!!! warning "Outputs not buffered"
+
+    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
+
+## API Reference
+
+For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
+
@@ -0,0 +1,12 @@
+# Threads
+
+A thread contains the accumulated state of a sequence of [runs](./runs.md). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
+
+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.
@@ -62,15 +62,6 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.

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

 Interrupting a revision will stop deployment of the revision.
@@ -16,4 +16,4 @@ Users can add an array of additional lines to add to the Dockerfile following th
 }
 ```

-This would install the system packages required to use Pillow if we were working with `jpeg` or `png` image formats. 
+This would install the system packages required to use Pillow if we were working with `jpeq` or `png` image formats. 
@@ -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.
@@ -2,8 +2,8 @@

 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.
+!!! important "Beta"
+    The Self-Hosted Control Plane deployment option is currently in beta stage.

 ## 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.
@@ -32,17 +30,18 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
    1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
    1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
    1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
-1. Two additional images will be used by the chart. Use the images that are specified in the latest release.
+1. Two additional images will be used by the chart.

        hostBackendImage:
          repository: "docker.io/langchain/hosted-langserve-backend"
          pullPolicy: IfNotPresent
+          tag: "0.9.80"
        operatorImage:
          repository: "docker.io/langchain/langgraph-operator"
          pullPolicy: IfNotPresent
+          tag: "aa9dff4"

-1. In your config file for langsmith (usually `langsmith_config.yaml`, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
-
+1. In your `values.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
        config:
          langgraphPlatform:
            enabled: true
@@ -2,8 +2,8 @@

 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.
+!!! important "Beta"
+    The Self-Hosted Data Plane deployment option is currently in beta stage.

 ## Prerequisites

@@ -15,15 +15,11 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
 ### Prerequisites
 1. `KEDA` is installed on your cluster.

-        helm repo add kedacore https://kedacore.github.io/charts
+        helm repo add kedacore https://kedacore.github.io/charts 
        helm install keda kedacore/keda --namespace keda --create-namespace

 1. A valid `Ingress` controller is installed on your cluster.
 1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
-1. You will need to enable egress to two control plane URLs. The listener polls these endpoints for deployments:
-
-        https://api.host.langchain.com
-        https://api.smith.langchain.com

 ### Setup

@@ -35,6 +31,7 @@ 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
@@ -95,7 +95,7 @@ my-app/

 ## Define Graphs

-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).

 Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):

@@ -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")
@@ -129,6 +129,9 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```

+!!! warning "Assign `CompiledGraph` to Variable"
+    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
+
 Example file directory:

 ```bash
@@ -155,6 +155,10 @@ const workflow = new StateGraph(MessagesAnnotation)
 export const graph = workflow.compile();
 ```

+!!! info "Assign `CompiledGraph` to Variable"
+
+    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
+
 Example file directory:

 ```bash
@@ -108,7 +108,7 @@ my-app/

 ## Define Graphs

-Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledStateGraph][langgraph.graph.state.CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
+Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each [CompiledGraph][langgraph.graph.graph.CompiledGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).

 Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example-pyproject) to see their implementation):

@@ -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")
@@ -142,6 +142,9 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```

+!!! warning "Assign `CompiledGraph` to Variable"
+    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
+
 Example file directory:

 ```bash
@@ -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)

@@ -1,8 +1,38 @@
-# Human-in-the-loop using Server API
+# Human-in-the-loop

-To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
+LangGraph supports robust **human-in-the-loop (HIL)** workflows, enabling human intervention at any point in an automated process. This is especially useful in large language model (LLM)-driven applications where model output may require validation, correction, or additional context.

-## Dynamic interrupts
+Please see [the overview of LangGraph human-in-the-loop](../../concepts/human_in_the_loop.md) features for more information.
+
+## `interrupt`
+
+The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+
+The graph is resumed using a [`Command`][langgraph.types.Command] object that provides the human's response.
+
+**Graph node with `interrupt`:**
+
+```python
+# highlight-next-line
+from langgraph.types import interrupt, Command
+
+def human_node(state: State):
+    # highlight-next-line
+    value = interrupt( # (1)!
+        {
+            "text_to_revise": state["some_text"] # (2)!
+        }
+    )
+    return {
+        "some_text": value # (3)!
+    }
+```
+
+1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
+2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
+3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
+
+**LangGraph API invoke & resume:**

 === "Python"

@@ -30,7 +60,9 @@ 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 +233,9 @@ 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'
        # >     }
        # > ]

@@ -301,186 +335,8 @@ 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. 
- [Common patterns](../../how-tos/human_in_the_loop/add-human-in-the-loop.md#common-patterns): learn how to implement patterns like approving/rejecting actions, requesting user input, tool call review, and validating human input.
+- [**LangGraph human-in-the-loop overview**](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features. 
+- [**Design patterns**](../../how-tos/human_in_the_loop/add-human-in-the-loop.md#design-patterns): learn how to implement patterns like approving/rejecting actions, requesting user input, and more.
+- [**How to review tool calls**](./human_in_the_loop_review_tool_calls.md): detailed examples of how to review and approve/edit tool calls or provide feedback to the tool-calling LLM.
@@ -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

@@ -211,7 +212,6 @@ We have now created an assistant called "Open AI Assistant" that has `model_name

 Output:

-    ```
    Receiving event of type: metadata
    {'run_id': '1ef6746e-5893-67b1-978a-0f1cd4060e16'}

@@ -219,7 +219,6 @@ Output:

    Receiving event of type: updates
    {'agent': {'messages': [{'content': 'I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-e1a6b25c-8416-41f2-9981-f9cfe043f414', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-    ```

 ### LangGraph Platform UI

@@ -232,11 +231,9 @@ Inside your deployment, select the "Assistants" tab. For the assistant you would
 To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.AssistantsClient.update) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#update) SDK reference docs for more information.

 !!! note "Note"
-
-    You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previous versions.
+You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previous versions.

 For example, to update your assistant's system prompt:
-
 === "Python"

    ```python
@@ -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 +1,184 @@
+# Breakpoints
+
+[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.
+
+## Set breakpoints
+
+=== "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>
+        }"
+        ```
+
+!!! tip
+
+    This example shows how to add **static** breakpoints. See [this guide](../../how-tos/human_in_the_loop/breakpoints.ipynb) for more options for how to add 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\"
+    }"
+    ```
+
+## Learn more
+
+- [**LangGraph breakpoints guide**](../../how-tos/human_in_the_loop/breakpoints.ipynb): learn more about adding breakpoints in LangGraph.
@@ -0,0 +1,549 @@
+# How to review tool calls
+
+!!! tip "Prerequisites"
+
+    This guide assumes familiarity with the following concepts:
+
+    * [Tool calling](https://python.langchain.com/docs/concepts/tool_calling/)
+    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:
+
+- A tool call to execute SQL, which will then be run by the tool
+- A tool call to generate a summary, which will then be saved to the State of the graph
+
+Note that using tool calls is common **whether actually calling tools or not**.
+
+There are typically a few different interactions you may want to do here:
+
+1. Approve the tool call and continue
+2. Modify the tool call manually and then continue
+3. Give natural language feedback, and then pass that back to the agent
+
+
+We can implement these in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input:
+
+
+```python
+def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
+    # this is the value we'll be providing via Command(resume=<human_review>)
+    human_review = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface tool calls for review
+            "tool_call": tool_call
+        }
+    )
+
+    review_action, review_data = human_review
+
+    # Approve the tool call and continue
+    if review_action == "continue":
+        return Command(goto="run_tool")
+
+    # Modify the tool call manually and then continue
+    elif review_action == "update":
+        ...
+        updated_msg = get_updated_msg(review_data)
+        return Command(goto="run_tool", update={"messages": [updated_message]})
+
+    # Give natural language feedback, and then pass that back to the agent
+    elif review_action == "feedback":
+        ...
+        feedback_msg = get_feedback_msg(review_data)
+        return Command(goto="call_llm", update={"messages": [feedback_msg]})
+
+```
+
+## Setup
+
+We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb). Once this graph is hosted, we are ready to invoke it and wait for user input. 
+
+### SDK initialization
+
+First, we need to setup our client so that we can communicate with our hosted graph:
+
+
+=== "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"
+    thread = await client.threads.create()
+    ```
+
+=== "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";
+    const thread = await client.threads.create();
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+      --url <DEPLOYMENT_URL>/threads \
+      --header 'Content-Type: application/json' \
+      --data '{}'
+    ```
+
+## Example of approving tool
+
+First, let's run the agent with an input that requires tool calls with approval:
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        input=input,
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+Output:
+
+    {'call_llm': {'messages': [{'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01Tdfufy4nZYXMbVZvgyNbhc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 379, 'output_tokens': 66}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-a33434b2-f5ca-40c6-98e2-6288d349d4ce-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 379, 'output_tokens': 66, 'total_tokens': 445, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:9caf42cf-1371-7213-a331-e6fe5d026be8'], 'when': 'during'}]}
+
+To approve the tool call, we need to let `human_review_node` know what value to use for the `human_review` variable we defined inside the node. We can provide this value by invoking the graph with a `Command(resume=<human_review>)` input.  Since we're approving the tool call, we'll provide `resume` value of `{"action": "continue"}` to navigate to `run_tool` node:
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+Output:
+
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01142G3woscA8JjFTLdqymtn'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01JJE9AtT4a9Lob91RRiW9rU', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 458, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-5e8d80b5-c46a-4aad-af37-b01f8bb15963-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 458, 'output_tokens': 18, 'total_tokens': 476, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+
+## Edit Tool Call
+
+Let's now say we want to edit the tool call. E.g. change some of the parameters (or even the tool called!) but then execute that tool.
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        input=input,
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "updates",
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+To do this, we will use `Command` with a different resume value of `{"action": "update", "data": <tool call args>}`. This will do the following:
+
+* combine existing tool call with user-provided tool call arguments and update the existing AI message with the new tool call
+* navigate to `run_tool` node with the updated AI message and continue execution
+
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(
+            # highlight-next-line
+            resume={"action": "update", "data": {"city": "San Francisco, USA"}}
+            # highlight-next-line
+        ),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: {
+          // highlight-next-line
+          resume: { "action": "update", "data": { "city": "San Francisco, USA" } }
+          // highlight-next-line
+        },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"update\", \"data\": { \"city\": \"San Francisco, USA\" } }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+Output:
+
+    {'human_review_node': {'messages': [{'role': 'ai', 'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'tool_calls': [{'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}}], 'id': 'run-b07f0c35-4e93-43a5-9b48-363767ada3ca-0'}]}}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01De5HurjNUMwMUpfRtMLbX1', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 460, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-85e2aaaa-6f61-4fa0-b594-b6e57129d7e7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 460, 'output_tokens': 18, 'total_tokens': 478, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+
+## Give feedback to a tool call
+
+Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert this feedback as a mock **RESULT** of the tool call.
+
+There are multiple ways to do this:
+
+1. You could add a new message to the state (representing the "result" of a tool call)
+2. You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback
+
+Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_review_node` and how it handles different types of messages.
+
+For this example we will just add a single tool call representing the feedback (see `human_review_node` implementation). Let's see this in action!
+
+=== "Python"
+
+    ```python
+    input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        input=input,
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const input = { "messages": [{ "role": "user", "content": "what's the weather in sf?" }] };
+
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        input: input,
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+To do this, we will use `Command` with a different resume value of `{"action": "feedback", "data": <feedback string>}`. This will do the following:
+
+* create a new tool message that combines existing tool call from LLM with the with user-provided feedback as content
+* navigate to `call_llm` node with the updated tool message and continue execution
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(
+            resume={
+                "action": "feedback", 
+                "data": "User requested changes: use <city, country> format for location"
+            }
+        ),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: {
+          resume: {
+            "action": "feedback", 
+            "data": "User requested changes: use <city, country> format for location"
+          }
+        },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"feedback\", \"data\": \"User requested changes: use <city, country> format for location\" }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+
+Output:
+
+    {'human_review_node': {'messages': [{'role': 'tool', 'content': 'User requested changes: use <city, country> format for location', 'name': 'weather_search', 'tool_call_id': 'toolu_01RkPHCjpfoUvPAktaq4Cqhm'}]}}
+    {'call_llm': {'messages': [{'content': [{'text': 'Let me try that again with the correct format:', 'type': 'text'}, {'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01EBan969yY5f6iGk6sPgKcj', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 469, 'output_tokens': 68}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-64bbc255-d126-4db0-8ae5-3197cf29bed1-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 469, 'output_tokens': 68, 'total_tokens': 537, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:e9856878-e28c-5dd1-d353-4d83aa1a3a2b'], 'when': 'during'}]}
+
+We can see that we now get to another interrupt - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue.
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+Output:
+
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01Rdrag6cVufHZG26BwVaiE7'}]}}
+    {'call_llm': {'messages': [{'content': 'The weather in San Francisco is sunny!', 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_013WTDHhbg8WiYLiQ9n2CaTk', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 550, 'output_tokens': 12}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-b6c815f0-989a-47cf-b150-33e3bbc4eab7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 550, 'output_tokens': 12, 'total_tokens': 562, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
@@ -1,8 +1,10 @@
-# Time travel using Server API
+# Time travel

-LangGraph provides the [**time travel**](../../concepts/time-travel.md) functionality to resume execution from a prior checkpoint, either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a new fork in the history.
+LangGraph provides [**time travel**](../../concepts/time-travel.md) functionality to **resume execution from a prior checkpoint** — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a **new fork** in the history.

-To time travel using the LangGraph Server API (via the LangGraph SDK):
+## Use time travel
+
+To use time-travel in LangGraph:

 1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs.
 2. **Identify a checkpoint in an existing thread**: Use [`client.threads.get_history`][langgraph_sdk.client.ThreadsClient.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
@@ -10,7 +12,7 @@ To time travel using the LangGraph Server API (via the LangGraph SDK):
 3. **(Optional) modify the graph state**: Use the [`client.threads.update_state`][langgraph_sdk.client.ThreadsClient.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
 4. **Resume execution from the checkpoint**: Use the [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.

-## Use time travel in a workflow
+## Example

 ??? example "Example graph"

@@ -235,4 +237,4 @@ To time travel using the LangGraph Server API (via the LangGraph SDK):

 ## Learn more

- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.md): learn more about using time travel in LangGraph.
+- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.ipynb): learn more about using time travel in LangGraph.
@@ -247,7 +247,5 @@ Verify that the original, interrupted run was interrupted

 Output:

-    ```
    'interrupted'
-    ```

@@ -3,7 +3,7 @@
 !!!info  "Prerequisites"
    - [Running agents](../../agents/run_agents.md#running-agents)

-This guide shows how to submit a [run](../../concepts/assistants.md#execution) to your application.
+This guide shows how to submit a [run](../concepts/runs.md) to your application.

 ## Graph mode

@@ -29,11 +29,11 @@ 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

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

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

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

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

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

 Basic usage example:

@@ -837,121 +833,3 @@ To stream all events, including the state of the graph:
      \"stream_mode\": \"events\"
    }"
    ```
-
-## Stateless runs
-
-If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    async for chunk in client.runs.stream(
-        # highlight-next-line
-        None,  # (1)!
-        assistant_id,
-        input=inputs,
-        stream_mode="updates"
-    ):
-        print(chunk.data)
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // create a streaming run
-    // highlight-next-line
-    const streamResponse = client.runs.stream(
-      // highlight-next-line
-      null,  // (1)!
-      assistantID,
-      {
-        input,
-        streamMode: "updates"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk.data);
-    }
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "cURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <inputs>,
-      \"stream_mode\": \"updates\"
-    }"
-    ```
-
-## Join and stream
-
-LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    # highlight-next-line
-    async for chunk in client.runs.join_stream(
-        thread_id,
-        # highlight-next-line
-        run_id,  # (1)!
-    ):
-        print(chunk)
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // highlight-next-line
-    const streamResponse = client.runs.joinStream(
-      threadID,
-      // highlight-next-line
-      runId  // (1)!
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk);
-    }
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-=== "cURL"
-
-    ```bash
-    curl --request GET \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    ```
-
-!!! warning "Outputs not buffered"
-
-    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
-
-## API Reference
-
-For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
@@ -2,7 +2,7 @@

 !!! info "Prerequisites"

-    - [Assistants Overview](../../../concepts/assistants.md)
+    - [Assistants Overview](../../concepts/assistants.md)

 LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.

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

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

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

 ## Local development server

@@ -73,11 +73,9 @@ langgraph dev --debug-port 5678
 Then attach your preferred debugger:

 === "VS Code"
-
-    Add this configuration to `launch.json`:
-
-    ```json
-    {
+Add this configuration to `launch.json`:
+`json
+      {
        "name": "Attach to LangGraph",
        "type": "debugpy",
        "request": "attach",
@@ -85,16 +83,11 @@ Then attach your preferred debugger:
          "host": "0.0.0.0",
          "port": 5678
        }
-    }
-    ```
+      }
+    `
+Specify the port number you chose in the previous step.

-=== "PyCharm" 
-
-    1. Go to Run → Edit Configurations 
-    2. Click + and select "Python Debug Server" 
-    3. Set IDE host name: `localhost` 
-    4. Set port: `5678` (or the port number you chose in the previous step) 
-    5. Click "OK" and start debugging
+=== "PyCharm" 1. Go to Run → Edit Configurations 2. Click + and select "Python Debug Server" 3. Set IDE host name: `localhost` 4. Set port: `5678` (or the port number you chose in the previous step) 5. Click "OK" and start debugging

 ## Troubleshooting

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

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

 ## View threads

@@ -1,8 +1,8 @@
 # How to integrate LangGraph into your React application

-!!! info "Prerequisites"
+!!! info "Prerequisites" 

-    - [LangGraph Platform](../../concepts/langgraph_platform.md)
+    - [LangGraph Platform](../../concepts/langgraph_platform.md) 
    - [LangGraph Server](../../concepts/langgraph_server.md)

 The `useStream()` React hook provides a seamless way to integrate LangGraph into your React applications. It handles all the complexities of streaming, state management, and branching logic, letting you focus on building great chat experiences.
@@ -113,115 +113,6 @@ export default function App() {
 }
 ```

-### Resume a stream after page refresh
-
-The `useStream()` hook can automatically resume an ongoing run upon mounting by setting `reconnectOnMount: true`. This is useful for continuing a stream after a page refresh, ensuring no messages and events generated during the downtime are lost.
-
-```tsx
-const thread = useStream<{ messages: Message[] }>({
-  apiUrl: "http://localhost:2024",
-  assistantId: "agent",
-  reconnectOnMount: true,
-});
-```
-
-By default the ID of the created run is stored in `window.sessionStorage`, which can be swapped by passing a custom storage in `reconnectOnMount` instead. The storage is used to persist the in-flight run ID for a thread (under `lg:stream:${threadId}` key).
-
-```tsx
-const thread = useStream<{ messages: Message[] }>({
-  apiUrl: "http://localhost:2024",
-  assistantId: "agent",
-  reconnectOnMount: () => window.localStorage,
-});
-```
-
-You can also manually manage the resuming process by using the run callbacks to persist the run metadata and the `joinStream` function to resume the stream. Make sure to pass `streamResumable: true` when creating the run; otherwise some events might be lost.
-
-````tsx
-import type { Message } from "@langchain/langgraph-sdk";
-import { useStream } from "@langchain/langgraph-sdk/react";
-import { useCallback, useState, useEffect, useRef } from "react";
-
-export default function App() {
-  const [threadId, onThreadId] = useSearchParam("threadId");
-
-  const thread = useStream<{ messages: Message[] }>({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-
-    threadId,
-    onThreadId,
-
-    onCreated: (run) => {
-      window.sessionStorage.setItem(`resume:${run.thread_id}`, run.run_id);
-    },
-    onFinish: (_, run) => {
-      window.sessionStorage.removeItem(`resume:${run?.thread_id}`);
-    },
-  });
-
-  // Ensure that we only join the stream once per thread.
-  const joinedThreadId = useRef<string | null>(null);
-  useEffect(() => {
-    if (!threadId) return;
-
-    const resume = window.sessionStorage.getItem(`resume:${threadId}`);
-    if (resume && joinedThreadId.current !== threadId) {
-      thread.joinStream(resume);
-      joinedThreadId.current = threadId;
-    }
-  }, [threadId]);
-
-  return (
-    <form
-      onSubmit={(e) => {
-        e.preventDefault();
-        const form = e.target as HTMLFormElement;
-        const message = new FormData(form).get("message") as string;
-        thread.submit(
-          { messages: [{ type: "human", content: message }] },
-          { streamResumable: true }
-        );
-      }}
-    >
-      <div>
-        {thread.messages.map((message) => (
-          <div key={message.id}>{message.content as string}</div>
-        ))}
-      </div>
-      <input type="text" name="message" />
-      <button type="submit">Send</button>
-    </form>
-  );
-}
-
-// Utility method to retrieve and persist data in URL as search param
-function useSearchParam(key: string) {
-  const [value, setValue] = useState<string | null>(() => {
-    const params = new URLSearchParams(window.location.search);
-    return params.get(key) ?? null;
-  });
-
-  const update = useCallback(
-    (value: string | null) => {
-      setValue(value);
-
-      const url = new URL(window.location.href);
-      if (value == null) {
-        url.searchParams.delete(key);
-      } else {
-        url.searchParams.set(key, value);
-      }
-
-      window.history.pushState({}, "", url.toString());
-    },
-    [key]
-  );
-
-  return [value, update] as const;
-}
-```
-
 ### Thread Management

 Keep track of conversations with built-in thread management. You can access the current thread ID and get notified when new threads are created:
@@ -236,7 +127,7 @@ const thread = useStream<{ messages: Message[] }>({
  threadId: threadId,
  onThreadId: setThreadId,
 });
-````
+```

 We recommend storing the `threadId` in your URL's query parameters to let users resume conversations after page refreshes.

@@ -503,74 +394,6 @@ 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.
@@ -1,6 +1,10 @@
 # Use threads

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

 ## Create a thread

@@ -8,15 +8,15 @@ Currently, the SDK does not provide built-in support for defining webhook endpoi

 The following API endpoints accept a `webhook` parameter:

-| Operation            | HTTP Method | Endpoint                          |
-|----------------------|-------------|-----------------------------------|
-| Create Run           | `POST`      | `/thread/{thread_id}/runs`        |
-| Create Thread Cron   | `POST`      | `/thread/{thread_id}/runs/crons`  |
-| Stream Run           | `POST`      | `/thread/{thread_id}/runs/stream` |
-| Wait Run             | `POST`      | `/thread/{thread_id}/runs/wait`   |
-| Create Cron          | `POST`      | `/runs/crons`                     |
-| Stream Run Stateless | `POST`      | `/runs/stream`                    |
-| Wait Run Stateless   | `POST`      | `/runs/wait`                      |
+| Operation | HTTP Method | Endpoint |
+|-----------|------------|----------|
+| Create Run | `POST` | `/thread/{thread_id}/runs` |
+| Create Thread Cron | `POST` | `/thread/{thread_id}/runs/crons` |
+| Stream Run | `POST` | `/thread/{thread_id}/runs/stream` |
+| Wait Run | `POST` | `/thread/{thread_id}/runs/wait` |
+| Create Cron | `POST` | `/runs/crons` |
+| Stream Run Stateless | `POST` | `/runs/stream` |
+| Wait Run Stateless | `POST` | `/runs/wait` |

 In this guide, we’ll show how to trigger a webhook after streaming a run.

@@ -25,39 +25,36 @@ In this guide, we’ll show how to trigger a webhook after streaming a run.
 Before making API calls, set up your assistant and thread.

 === "Python"
+```python
+from langgraph_sdk import get_client

-    ```python
-    from langgraph_sdk import get_client
-
-    client = get_client(url=<DEPLOYMENT_URL>)
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    print(thread)
-    ```
+client = get_client(url=<DEPLOYMENT_URL>)
+assistant_id = "agent"
+thread = await client.threads.create()
+print(thread)
+```

 === "JavaScript"
+```js
+import { Client } from "@langchain/langgraph-sdk";

-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    const assistantID = "agent";
-    const thread = await client.threads.create();
-    console.log(thread);
-    ```
+const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+const assistantID = "agent";
+const thread = await client.threads.create();
+console.log(thread);
+```

 === "CURL"
-
-    ```bash
-    curl --request POST \
-        --url <DEPLOYMENT_URL>/assistants/search \
-        --header 'Content-Type: application/json' \
-        --data '{ "limit": 10, "offset": 0 }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
-    curl --request POST \
-        --url <DEPLOYMENT_URL>/threads \
-        --header 'Content-Type: application/json' \
-        --data '{}'
-    ```
+```bash
+curl --request POST \
+    --url <DEPLOYMENT_URL>/assistants/search \
+    --header 'Content-Type: application/json' \
+    --data '{ "limit": 10, "offset": 0 }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
+curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+```

 Example response:

@@ -80,55 +77,52 @@ To use a webhook, specify the `webhook` parameter in your API request. When the
 For example, if your server listens for webhook events at `https://my-server.app/my-webhook-endpoint`, include this in your request:

 === "Python"
+```python
+input = { "messages": [{ "role": "user", "content": "Hello!" }] }

-    ```python
-    input = { "messages": [{ "role": "user", "content": "Hello!" }] }
-
-    async for chunk in client.runs.stream(
-        thread_id=thread["thread_id"],
-        assistant_id=assistant_id,
-        input=input,
-        stream_mode="events",
-        webhook="https://my-server.app/my-webhook-endpoint"
-    ):
-        pass
-    ```
+async for chunk in client.runs.stream(
+    thread_id=thread["thread_id"],
+    assistant_id=assistant_id,
+    input=input,
+    stream_mode="events",
+    webhook="https://my-server.app/my-webhook-endpoint"
+):
+    pass
+```

 === "JavaScript"
+```js
+const input = { messages: [{ role: "human", content: "Hello!" }] };

-    ```js
-    const input = { messages: [{ role: "human", content: "Hello!" }] };
+const streamResponse = client.runs.stream(
+  thread["thread_id"],
+  assistantID,
+  {
+    input: input,
+    webhook: "https://my-server.app/my-webhook-endpoint"
+  }
+);

-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantID,
-      {
-        input: input,
-        webhook: "https://my-server.app/my-webhook-endpoint"
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      // Handle stream output
-    }
-    ```
+for await (const chunk of streamResponse) {
+  // Handle stream output
+}
+```

 === "CURL"
-
-    ```bash
-    curl --request POST \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-        --header 'Content-Type: application/json' \
-        --data '{
-            "assistant_id": <ASSISTANT_ID>,
-            "input": {"messages": [{"role": "user", "content": "Hello!"}]},
-            "webhook": "https://my-server.app/my-webhook-endpoint"
-        }'
-    ```
+```bash
+curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --data '{
+        "assistant_id": <ASSISTANT_ID>,
+        "input": {"messages": [{"role": "user", "content": "Hello!"}]},
+        "webhook": "https://my-server.app/my-webhook-endpoint"
+    }'
+```

 ## Webhook payload

-LangGraph Platform sends webhook notifications in the format of a [Run](../../concepts/assistants.md#execution). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.
+LangGraph Platform sends webhook notifications in the format of a [Run](../../cloud/concepts/runs.md). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.

 ## Secure webhooks

@@ -140,22 +134,6 @@ 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
-}'
+}'  
 ```
--- a/Show More
+++ b/Show More