feat(prebuilt): add support for handle_tool_errors with single exception type (#6021)

This PR extends the `ToolNode` `handle_tool_errors` property to also
accept a single exception type: `type[Exception]`.

Example:
```python
tool_node_with_error_handling = ToolNode(
    [tool1, tool2, tool3], handle_tool_errors=ValueError
).invoke(...)
"""A tool node that will only retry a failed tool call if a ValueError is raised"""
```

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,29 +1,29 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
-labels: ["02 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]
 body:
   - type: markdown
     attributes:
-      value: >
+      value: |
         Thank you for taking the time to file a bug report. 
         
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
+        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
         
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
         if there's another way to solve your problem:
         
-        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
-        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
-        [GitHub search](https://github.com/langchain-ai/langgraph),
+        * [LangChain Forum](https://forum.langchain.com/),
+        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        * [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
+        * [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        * [GitHub search](https://github.com/langchain-ai/langgraph),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
       description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
       options:
-        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
+        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
           required: true
         - label: I added a clear and detailed title that summarizes the issue.
           required: true
@@ -38,7 +38,7 @@ body:
     attributes:
       label: Example Code
       description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -78,7 +78,7 @@ body:
     attributes:
       label: System Info
       description: |
-        python -m langchain_core.sys_info
+        Run on your machine: `python -m langchain_core.sys_info`
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,15 +1,6 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - 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
+  - name: LangChain Forum
+    url:  https://forum.langchain.com/
+    about: General community discussions, support, and feature requests

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,7 +1,7 @@
 name: Documentation
 description: Report an issue related to the LangGraph documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [03 - Documentation]
+labels: [documentation]
 
 body:
 - type: textarea

.github/ISSUE_TEMPLATE/privileged.yml

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

.github/PULL_REQUEST_TEMPLATE.md

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

.github/actions/poetry_setup/action.yml

@@ -1,88 +0,0 @@
-# An action for setting up poetry install with caching.
-# Using a custom action since the default action does not
-# take poetry install groups into account.
-# Action code from:
-# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
-name: poetry-install-with-caching
-description: Poetry install with support for caching of dependency groups.
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-
-  poetry-version:
-    description: Poetry version
-    required: true
-
-  cache-key:
-    description: Cache key to use for manual handling of caching
-    required: true
-
-runs:
-  using: composite
-  steps:
-    - uses: actions/setup-python@v5
-      name: Setup python ${{ inputs.python-version }}
-      id: setup-python
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - uses: actions/cache@v3
-      id: cache-bin-poetry
-      name: Cache Poetry binary - Python ${{ inputs.python-version }}
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
-      with:
-        path: |
-          /opt/pipx/venvs/poetry
-        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
-        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
-
-    - name: Refresh shell hashtable and fixup softlinks
-      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      run: |
-        set -eux
-
-        # Refresh the shell hashtable, to ensure correct `which` output.
-        hash -r
-
-        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
-        # Delete and recreate the softlinks pipx expects to have.
-        rm /opt/pipx/venvs/poetry/bin/python
-        cd /opt/pipx/venvs/poetry/bin
-        ln -s "$(which "python$PYTHON_VERSION")" python
-        chmod +x python
-        cd /opt/pipx_bin/
-        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
-        chmod +x poetry
-
-        # Ensure everything got set up correctly.
-        /opt/pipx/venvs/poetry/bin/python --version
-        /opt/pipx_bin/poetry --version
-
-    - name: Install poetry
-      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
-      shell: bash
-      env:
-        POETRY_VERSION: ${{ inputs.poetry-version }}
-        PYTHON_VERSION: ${{ inputs.python-version }}
-      # Install poetry using the python version installed by setup-python step.
-      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
-
-    - name: Restore pip and poetry cached dependencies
-      uses: actions/cache@v3
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
-      with:
-        path: |
-          ~/.cache/pip
-          ~/.cache/pypoetry/virtualenvs
-          ~/.cache/pypoetry/cache
-          ~/.cache/pypoetry/artifacts
-          ./.venv
-        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('./poetry.lock') }}

.github/dependabot.yml

@@ -0,0 +1,18 @@
+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"

.github/workflows/_integration_test.yml

@@ -3,8 +3,8 @@ name: CLI integration test
 on:
   workflow_call:
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   build:
@@ -25,13 +25,14 @@ jobs:
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "libs/cli/**"
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }}
         if: steps.changed-files.outputs.all
-        uses: "./.github/actions/poetry_setup"
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: integration-test-cli
+          enable-cache: true
+          cache-suffix: "cli-integration-test"
+          ignore-nothing-to-cache: true
       - name: Setup env
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples

.github/workflows/_lint.yml

@@ -8,9 +8,10 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
+env:
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github
 
@@ -36,42 +37,28 @@ jobs:
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "${{ inputs.working-directory }}/**"
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+      - name: Set up Python ${{ matrix.python-version }}
         if: steps.changed-files.outputs.all
-        uses: "./.github/actions/poetry_setup"
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: lint-${{ inputs.working-directory }}
-
-      - name: Check Poetry File
-        if: steps.changed-files.outputs.all
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: poetry check
+          enable-cache: true
+          cache-suffix: lint-${{ inputs.working-directory }}
 
       - name: Install dependencies
         if: steps.changed-files.outputs.all
-        # Also installs dev/lint/test/typing dependencies, to ensure we have
-        # type hints for as many of our libraries as possible.
-        # This helps catch errors that require dependencies to be spotted, for example:
-        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
-        #
-        # If you change this configuration, make sure to change the `cache-key`
-        # in the `poetry_setup` action above to stop using the old cache.
-        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
-        run: poetry install --with dev
+        run: uv sync --frozen --group dev
 
       - name: Get .mypy_cache to speed up mypy
         if: steps.changed-files.outputs.all
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ inputs.working-directory }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
 
       - name: Analysing package code with our lint
         if: steps.changed-files.outputs.all
@@ -86,27 +73,18 @@ jobs:
 
       - name: Install test dependencies
         if: steps.changed-files.outputs.all
-        # Also installs dev/lint/test/typing dependencies, to ensure we have
-        # type hints for as many of our libraries as possible.
-        # This helps catch errors that require dependencies to be spotted, for example:
-        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
-        #
-        # If you change this configuration, make sure to change the `cache-key`
-        # in the `poetry_setup` action above to stop using the old cache.
-        # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry install --with dev
+        run: uv sync --group dev
 
       - name: Get .mypy_cache_test to speed up mypy
         if: steps.changed-files.outputs.all
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ inputs.working-directory }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
 
       - name: Analysing tests with our lint
         if: steps.changed-files.outputs.all

.github/workflows/_test.yml

@@ -8,8 +8,8 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   build:
@@ -26,12 +26,12 @@ jobs:
     name: "test #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: test-${{ inputs.working-directory }}
+          enable-cache: true
+          cache-suffix: test-${{ inputs.working-directory }}
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -42,14 +42,12 @@ jobs:
       - name: Install dependencies
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry install --with dev
+        run: uv sync --frozen --group dev
 
       - name: Run tests
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: |
-          make test
+        run: make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_langgraph.yml

@@ -3,8 +3,8 @@ name: test
 on:
   workflow_call:
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   build:
@@ -24,12 +24,12 @@ jobs:
     name: "test #${{ matrix.python-version }}"
     steps:
       - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: test-langgraph
+          enable-cache: true
+          cache-suffix: "test-langgraph"
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -39,13 +39,11 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: |
-          poetry install --with dev
+        run: uv sync --frozen --group dev
 
       - name: Run tests
         shell: bash
-        run: |
-          make test_parallel
+        run: make test_parallel
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_release.yml

@@ -9,9 +9,11 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
   PYTHON_VERSION: "3.10"
 
+permissions:
+  contents: read
+
 jobs:
   build:
     if: github.ref == 'refs/heads/main'
@@ -24,12 +26,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python $${ env.PYTHON_VERSION }}
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -43,7 +45,7 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: poetry build
+        run: uv build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
@@ -57,8 +59,8 @@ jobs:
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
+          echo pkg-name=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
+          echo version=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
 
   publish:
     needs:

.github/workflows/_test_scheduler_kafka.yml

@@ -1,57 +0,0 @@
-name: test
-
-on:
-  workflow_call:
-
-env:
-  POETRY_VERSION: "2.1.2"
-
-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 }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: 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: ${{ secrets.DOCKERHUB_RO_TOKEN }}
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          poetry install --with dev
-
-      - name: Run tests
-        shell: bash
-        run: |
-          make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(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'

.github/workflows/baseline.yml

@@ -7,8 +7,8 @@ on:
     paths:
       - "libs/**"
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   benchmark:
@@ -19,14 +19,14 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - run: SHA=$(git rev-parse HEAD) && echo "SHA=$SHA" >> $GITHUB_ENV
-      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python 3.11
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: bench
+          enable-cache: true
+          cache-suffix: "bench"
       - name: Install dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
       - name: Run benchmarks
         run: OUTPUT=out/benchmark-baseline.json make -s benchmark
       - name: Save outputs

.github/workflows/bench.yml

@@ -5,8 +5,8 @@ on:
     paths:
       - "libs/**"
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   benchmark:
@@ -21,14 +21,14 @@ jobs:
         uses: Ana06/get-changed-files@v2.3.0
         with:
           format: json
-      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python 3.11
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: bench
+          enable-cache: true
+          cache-suffix: "bench"
       - name: Install dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
       - name: Download baseline
         uses: actions/cache/restore@v4
         with:
@@ -53,7 +53,7 @@ jobs:
             echo 'OUTPUT<<EOF'
             mv out/benchmark-baseline.json out/main.json
             mv out/benchmark.json out/changes.json
-            poetry run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
+            uv run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
             echo EOF
           } >> "$GITHUB_OUTPUT"
       - name: Annotation

.github/workflows/ci.yml

@@ -3,9 +3,13 @@ name: CI
 
 on:
   push:
-    branches: [main]
+    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.
 #
@@ -16,15 +20,12 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
       python: ${{ steps.filter.outputs.python }}
-      sdk-js: ${{ steps.filter.outputs.sdk-js }}
+      deps: ${{ steps.filter.outputs.deps }}
     steps:
       - uses: actions/checkout@v4
       - uses: dorny/paths-filter@v3
@@ -38,10 +39,10 @@ jobs:
               - 'libs/checkpoint/**'
               - 'libs/checkpoint-sqlite/**'
               - 'libs/checkpoint-postgres/**'
-              - 'libs/scheduler-kafka/**'
               - 'libs/prebuilt/**'
-            sdk-js:
-              - 'libs/sdk-js/**'
+            deps:
+              - '**/pyproject.toml'
+              - '**/uv.lock'
 
   lint:
     needs: changes
@@ -56,10 +57,10 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
-            "libs/scheduler-kafka",
+
             "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -78,7 +79,7 @@ jobs:
             "libs/checkpoint-postgres",
             "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -87,19 +88,11 @@ jobs:
   # NOTE: we're testing langgraph separately because it requires a different matrix
   test-langgraph:
     needs: changes
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == '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'
@@ -125,26 +118,26 @@ jobs:
           - "3.11"
     steps:
       - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: schema-check-cli
+          enable-cache: true
+          cache-suffix: "schema-check-cli"
       - name: Install CLI dependencies
         run: |
           cd libs/cli
-          poetry install
+          uv sync
       - name: Generate schema and check for changes
         run: |
           cd libs/cli
           # Create a temporary copy of the current schema
           cp schemas/schema.json schemas/schema.current.json
           # Generate new schema
-          poetry run python generate_schema.py
+          uv run python generate_schema.py
           # Compare the new schema with the original
           if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
-            echo "Error: Langgraph.json configuration schema has changed. Please run 'poetry run python generate_schema.py' in the libs/cli directory and commit the changes."
+            echo "Error: Langgraph.json configuration schema has changed. Please run 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
             diff schemas/schema.json schemas/schema.current.json
             exit 1
           fi
@@ -152,74 +145,21 @@ jobs:
 
   integration-test:
     needs: changes
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == '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()

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

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

.github/workflows/codespell.yml

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

.github/workflows/deploy_docs.yml

@@ -9,9 +9,6 @@ on:
       - main
   workflow_dispatch:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 permissions:
   contents: read
   pages: write
@@ -38,16 +35,7 @@ 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:
@@ -57,21 +45,21 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.12"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: docs
+          enable-cache: true
+          cache-suffix: "docs"
 
       - name: Install dependencies
         run: |
           yarn
-          poetry install --with test --with docs --no-root
+          uv sync --all-groups
           # we run this installation only for internal PRs
           # as GITHUB_TOKEN is not available for PRs from outside contributors
           if [ -n "${GITHUB_TOKEN}" ]; then
-            poetry run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
+            uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
           fi
 
       - name: Run unit tests
@@ -103,7 +91,7 @@ jobs:
         run: |
           if [ "${{ github.event_name }}" == "schedule" ]; then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
-            poetry run pytest -v \
+            uv run pytest -v \
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
@@ -128,7 +116,7 @@ jobs:
             echo "Changed files: ${CHANGED_FILES}"
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
-              poetry run pytest -v \
+              uv run pytest -v \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
                 --check-links-ignore "https://academy\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
@@ -148,7 +136,7 @@ jobs:
 
       - name: Configure GitHub Pages
         if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v4
+        uses: actions/configure-pages@v5
 
       - name: Upload Pages Artifact
         # if: github.ref == 'refs/heads/main'

.github/workflows/link_check.yml

@@ -11,8 +11,8 @@ on:
     - cron: "0 5 * * *"
   workflow_dispatch:
 
-env:
-  POETRY_VERSION: "2.1.2"
+permissions:
+  contents: read
 
 jobs:
   markdown-link-check:

.github/workflows/pr_lint.yml

@@ -0,0 +1,45 @@
+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

.github/workflows/release.yml

@@ -8,9 +8,11 @@ on:
         type: string
         default: "libs/langgraph"
 
+permissions:
+  contents: read
+
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "2.1.2"
 
 jobs:
   build:
@@ -26,12 +28,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -45,7 +47,7 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: poetry build
+        run: uv build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
@@ -59,8 +61,8 @@ jobs:
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
-          PKG_NAME="$(poetry version | cut -d ' ' -f 1)"
-          VERSION="$(poetry version --short)"
+          PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
+          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
           SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
           if [ -z $SHORT_PKG_NAME ]; then
             TAG="$VERSION"
@@ -135,7 +137,9 @@ jobs:
     needs:
       - build
       - release-notes
-    permissions: write-all
+    permissions:
+      contents: read
+      id-token: write
     uses: ./.github/workflows/_test_release.yml
     with:
       working-directory: ${{ inputs.working-directory }}
@@ -163,11 +167,11 @@ jobs:
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
+          enable-cache: true
 
       - name: Import published package
         shell: bash
@@ -185,18 +189,18 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          poetry run pip install \
+          uv run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION" || \
           ( \
             sleep 5 && \
-            poetry run pip install \
+            uv run pip install \
               --extra-index-url https://test.pypi.org/simple/ \
               "$PKG_NAME==$VERSION" \
           )
 
           if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            poetry run pip install langgraph
+            uv run pip install langgraph
           fi
 
           if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
@@ -209,10 +213,10 @@ jobs:
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
           fi
 
-          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the test PyPI version.
@@ -223,7 +227,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          poetry run pip install \
+          uv run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION"
 
@@ -253,12 +257,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       - uses: actions/download-artifact@v4
         with:
@@ -294,12 +298,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       - uses: actions/download-artifact@v4
         with:

.github/workflows/release_js.yml

@@ -1,38 +0,0 @@
-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

.github/workflows/run_notebooks.yml

@@ -11,6 +11,9 @@ on:
   schedule:
     - cron: "0 13 * * *"
 
+permissions:
+  contents: read
+
 defaults:
   run:
     working-directory: docs
@@ -27,30 +30,30 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python + Poetry
-        uses: "./.github/actions/poetry_setup"
+        uses: astral-sh/setup-uv@v6
         with:
-          python-version: 3.11
-          poetry-version: 2.1.2
-          cache-key: test-langgraph-notebooks
+          python-version: "3.11"
+          enable-cache: true
+          cache-suffix: "test-langgraph-notebooks"
 
       - name: Install dependencies
         run: |
-          poetry install --with test --no-root
-          poetry run pip install jupyter
+          uv sync --group test
+          uv run pip install jupyter
 
       - name: Start services
         run: make start-services
 
       - name: Pre-download tiktoken files
         run: |
-          poetry run python _scripts/download_tiktoken.py
+          uv run python _scripts/download_tiktoken.py
 
       - name: Prepare notebooks
         run: |
           if [ "${{ matrix.lib-version }}" = "development" ]; then
-            poetry run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
+            uv run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
           else
-            poetry run python _scripts/prepare_notebooks_for_ci.py
+            uv run python _scripts/prepare_notebooks_for_ci.py
           fi
 
       - name: Run notebooks

.github/workflows/uv_lock_ugprade.yml

@@ -0,0 +1,45 @@
+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

.gitignore

@@ -181,3 +181,4 @@ Chinook.db
 .vercel
 .turbo
 .editorconfig
+.scratch

AGENTS.md

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

CONTRIBUTING.md

@@ -9,7 +9,7 @@ Here are some things to keep in mind for all types of contributions:
 - Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
 - Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
 - Ensure your PR passes formatting, linting, and testing checks before requesting a review.
-  - If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
+  - If you would like comments or feedback, please tag a maintainer.
 - Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
 - Look for duplicate PRs or issues that have already been opened before opening a new one.
 - Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
@@ -20,7 +20,7 @@ For bug fixes, please open up an issue before proposing a fix to ensure the prop
 
 ### New features
 
-For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
+For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
 
 ## Contribute Documentation
 
@@ -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-agent/)
+- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
 
 Here are some high-level tips on writing a good tutorial:
 
@@ -109,8 +109,7 @@ 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 they way they do.
-
+impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
 
 To quote the Diataxis website:
 
@@ -153,7 +152,7 @@ Each category serves a distinct purpose and requires a specific approach to writ
 
 Here are some other guidelines you should think about when writing and organizing documentation.
 
-We generally do not merge new tutorials from outside contributors without an actue need.
+We generally do not merge new tutorials from outside contributors without an actual need.
 We welcome updates as well as new integration docs, how-tos, and references.
 
 ### Avoid duplication
@@ -187,9 +186,9 @@ Be concise, including in code samples.
 
 ## Setup
 
-LangChain documentation consists of two components:
+LangGraph documentation consists of two components:
 
-1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
+1. Main Documentation: Hosted at [https://langchain-ai.github.io/langgraph/](https://langchain-ai.github.io/langgraph/),
 this comprehensive resource serves as the primary user-facing documentation.
 It covers a wide array of topics, including tutorials, use cases, integrations,
 and more, offering extensive guidance on building with LangGraph.
@@ -227,6 +226,7 @@ see a preview of the documentation on the pull request page.
 
 From the **monorepo root**, run the following command to install the dependencies:
 
+<!-- TODO -->
 ```bash
 poetry install --with docs --no-root
 ```
@@ -249,17 +249,17 @@ make serve-docs
 
 #### Linting
 
-The documentation is linted from the **monorepo root**. To lint it, run the following from there:
+To spell check the docs, run the following from the `docs` directory:
 
 ```bash
-make spellcheck
+codespell --skip="*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.css.map,*.js.map" --ignore-words-list="infor,thead,stdio,nd,jupyter,lets,lite,uis,deque" .
 ```
 
 ### ️In-code Documentation
 
 The in-code documentation is autogenerated from docstrings.
 
-For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
+For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangGraph because the API reference is the primary resource for developers to understand how to use the codebase.
 
 We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
 
@@ -290,4 +290,4 @@ def my_function(arg1: int, arg2: str) -> float:
         This is a description of the return value.
     """
     return 3.14
-```
+```

Makefile

@@ -0,0 +1,68 @@
+# 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

README.md

@@ -13,60 +13,71 @@
 [![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/)
 
-> [!NOTE]
-> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
-
-LangGraph is a low-level orchestration framework for building controllable agents. While [LangChain](https://python.langchain.com/docs/introduction/) provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+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.
 
 ## Get started
 
-First, install LangGraph:
+Install LangGraph:
 
 ```
 pip install -U langgraph
 ```
 
-There are two ways to get started with LangGraph:
+Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
 
-- [Use prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): Construct agentic systems quickly and reliably without the need to implement orchestration, memory, or human feedback handling from scratch.
-- [Use LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Customize your architectures, use long-term memory, and implement human-in-the-loop to reliably handle complex tasks.
+```python
+# pip install -qU "langchain[anthropic]" to call the model
 
-Once you have a LangGraph application and are ready to move into production, use [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/) to test, debug, and deploy your application.
+from langgraph.prebuilt import create_react_agent
 
-## What LangGraph provides
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
 
-LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    prompt="You are a helpful assistant"
+)
 
-### Persistence
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)
+```
 
-LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:
+For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
 
-- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.
+## Core benefits
 
-### Streaming
+LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-LangGraph provides support for [streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](https://langchain-ai.github.io/langgraph/how-tos/streaming.ipynb#updates)) and [tokens from LLM calls](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens.ipynb) embedded in an application.
-
-### Debugging and deployment
-
-LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 
+- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
+- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 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]
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
 ## Additional resources
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/examples/): Guided examples on getting started with LangGraph.
+- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
-- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements
 

docs/.gitignore

@@ -1,4 +1,3 @@
 site/
-docs/cloud/reference/sdk/js_ts_sdk_ref.md
 
 .vercel

docs/Makefile

@@ -1,10 +1,4 @@
-.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
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell llms-text build-prebuilt tests
 
 build-prebuilt:
 	# Use to create an update to date prebuilt page.
@@ -12,32 +6,33 @@ build-prebuilt:
 	# generates the final prebuilt page.
 	@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
 		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats stats.yml; \
+		uv run python -m _scripts.third_party_page.get_download_stats stats.yml; \
 		set +x; \
 	else \
 		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
+		uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
 		set +x; \
 	fi
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
+	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md
 
-build-docs: build-typedoc build-prebuilt
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
+build-docs: build-prebuilt
+	TARGET_LANGUAGE=python uv run python -m mkdocs build --clean -f mkdocs.yml --strict
+
+build-docs-js: build-prebuilt
+	TARGET_LANGUAGE=js uv run python -m mkdocs build --clean -f mkdocs.yml --strict
 
 llms-text:
-	poetry run python -m _scripts.generate_llms_text docs/llms-full.txt
+	uv run python -m _scripts.generate_llms_text docs/llms-full.txt
 
 install-vercel-deps:
-	dnf install -y python3.11
-	curl -sSL https://install.python-poetry.org | python3 -
-	poetry self update 1.8.5
-	# don't use vercel's python - it wasn't compiled with sqlite support, and it fails when installing ipython's kernel
-	poetry env use /usr/bin/python3.11
-	poetry install --with docs --with test --no-root
+	curl -sL "https://astral.sh/uv/install.sh" | bash -s
+	export PATH="${HOME}/.cargo/bin:${PATH}"
+	uv venv --python 3.11
+	uv sync --all-groups
 
 tests:
 	# Run unit tests
-	poetry run pytest tests/unit_tests
+	uv run pytest tests/unit_tests
 
 
 vercel-build-docs: install-vercel-deps
@@ -45,10 +40,10 @@ vercel-build-docs: install-vercel-deps
 
 
 serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
+	uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
 
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
+serve-docs:
+	uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
 
 clean-docs:
 	find ./docs -name "*.ipynb" -type f -delete
@@ -56,13 +51,13 @@ clean-docs:
 
 ## Run format against the project documentation.
 format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
+	uv run ruff format docs
+	uv run ruff check --fix docs
 
 # Check the docs for linting violations
 lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
+	uv run ruff format --check docs
+	uv run ruff check docs
 
 codespell:
 	./codespell_notebooks.sh .

docs/README.md

@@ -3,7 +3,7 @@
 To setup requirements for building docs you can run:
 
 ```bash
-poetry install --with test
+uv sync --group test
 ```
 
 ## Serving documentation locally

docs/_scripts/add_translation.py

@@ -1,157 +0,0 @@
-"""Add typescript translation to a given markdown file."""
-
-import argparse
-import re
-
-import requests
-from langchain_anthropic import ChatAnthropic
-
-URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
-response = requests.get(URL)
-response.raise_for_status()
-reference_snippets = response.text
-
-model = ChatAnthropic(model="claude-3-5-sonnet-latest")
-
-
-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": (
-                    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": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
-            },
-        ]
-    )
-
-    # 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 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()
-
-    # 1. Extract all Python snippets.
-    python_snippets = extract_python_snippets(markdown_content)[:1]
-
-    # 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)
-
-    # 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 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.")
-    args = parser.parse_args()
-
-    main(args.file_path)

docs/_scripts/execute_notebooks.sh

@@ -8,7 +8,7 @@ execute_notebook() {
     file="$1"
     echo "Starting execution of $file"
     start_time=$(date +%s)
-    if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
+    if ! output=$(time uv run jupyter execute "$file" 2>&1); then
         end_time=$(date +%s)
         execution_time=$((end_time - start_time))
         echo "Error in $file. Execution time: $execution_time seconds"

docs/_scripts/generate_llms_text.py

@@ -1,10 +1,18 @@
 """Experimental script to generate consolidated llms text from the docs."""
 
+import asyncio
 import glob
 import os
+import re
+from typing import TypedDict, List, Optional
 
+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
 
@@ -13,7 +21,49 @@ HERE = os.path.dirname(os.path.abspath(__file__))
 SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
 
 
-def _make_llms_text(output_file: str) -> str:
+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:
     """Generate a consolidated text file from markdown/notebook files for LLM training.
 
     Args:
@@ -21,11 +71,9 @@ def _make_llms_text(output_file: str) -> str:
     """
     # 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)
     )
@@ -35,30 +83,14 @@ def _make_llms_text(output_file: str) -> str:
 
     all_content = []
 
-    # Process each file
-    for file_path in all_files:
-        print(f"Processing {file_path}")
-        rel_path = os.path.relpath(file_path, SOURCE_DIR)
+    # Process files concurrently
+    tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
+    results = await asyncio.gather(*tasks)
 
-        # 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
-        )
+    # Combine results with file paths
+    for file_path, processed_content in zip(all_files, results):
         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
@@ -69,6 +101,170 @@ def _make_llms_text(output_file: str) -> str:
         f.write("\n\n".join(all_content))
 
 
+def no_op_constructor(*args):
+    """No-op"""
+
+
+SafeLoader.add_multi_constructor(
+    "tag:yaml.org,2002:python/name",
+    no_op_constructor,
+)
+
+
+class NavItem(TypedDict):
+    title: str
+    url: str
+    hierarchy: tuple[str, ...]
+    description: str
+
+
+def _flatten_nav(
+    nav: list[dict[str, str | list] | str], path: tuple[str, ...] = ()
+) -> list[NavItem]:
+    flat: List[NavItem] = []
+    for item in nav:
+        if isinstance(item, dict):
+            for title, node in item.items():
+                new_path = path + (title,)
+                if isinstance(node, str):
+                    # Leaf page
+                    flat.append(
+                        {
+                            "title": title,
+                            "url": node,
+                            "hierarchy": new_path,
+                            "description": "",
+                        }
+                    )
+                elif isinstance(node, list):
+                    # Dive in, carrying along the updated path
+                    flat.extend(_flatten_nav(node, new_path))
+                else:
+                    raise TypeError(
+                        f"Unexpected node type {type(node)} under {title!r}"
+                    )
+        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": ""}
+            )
+        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."""
+    # 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")
+
+    # Load and parse yaml
+    with open(mkdocs_path, "r") as f:
+        config = yaml.safe_load(f)
+
+    # Extract nav section
+    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:
+            # Get the top-level section (first item in hierarchy)
+            section = item["hierarchy"][0]
+
+            if section not in {"Guides", "Examples", "Resources"}:
+                continue
+
+            # If we're starting a new section, add a heading
+            if section != current_section:
+                f.write(f"\n# {section}\n\n")
+                current_section = section
+
+            title = item["title"]
+            # Process URL based on replace_links flag
+            url = item["url"]
+            if replace_links:
+                # Remove .md extension and ensure single trailing slash
+                url = url.removesuffix(".md")
+                url = url.removesuffix(".ipynb")
+                url = url.rstrip("/") + "/"
+                url = f"https://langchain-ai.github.io/langgraph/{url}"
+
+            f.write(f"- [{title}]({url}): {item['description']}\n")
+
+
 if __name__ == "__main__":
     import argparse
 
@@ -78,6 +274,23 @@ if __name__ == "__main__":
         )
     )
     parser.add_argument("output_file", help="Path to output the consolidated text file")
+    parser.add_argument(
+        "--link-only",
+        action="store_true",
+        help="Only include link references in the output",
+    )
+    parser.add_argument(
+        "--replace-links",
+        action="store_true",
+        help="Replace markdown links with full URLs in the output",
+    )
 
     args = parser.parse_args()
-    _make_llms_text(args.output_file)
+    if args.link_only:
+        coro = generate_nav_links_text(
+            args.output_file, replace_links=args.replace_links
+        )
+    else:
+        coro = generate_full_llms_text(args.output_file)
+
+    asyncio.run(coro)

docs/_scripts/handle_auto_links.py

@@ -0,0 +1,181 @@
+"""Logic to identify and transform cross-reference links in markdown files.
+
+This module allows supporting custom markdown syntax for "autolinks". These are links
+that will be transformed based on the current scope context, such as "global", "python",
+or "js" into an appropriate markdown link format.
+
+For example,
+
+```markdown
+@[StateGraph]
+```
+
+May be transformed into:
+
+```markdown
+[StateGraph](some_path/api-reference/state-graph.md)
+```
+
+The transformation value depends on the scope in which the link is used.
+"""
+
+import logging
+import re
+from typing import Optional
+
+from _scripts.link_map import SCOPE_LINK_MAPS
+
+logger = logging.getLogger(__name__)
+
+
+def _transform_link(
+    link_name: str, scope: str, file_path: str, line_number: int, custom_title: Optional[str] = None
+) -> Optional[str]:
+    """Transform a cross-reference link based on the current scope.
+
+    Args:
+        link_name: The name of the link to transform (e.g., "StateGraph").
+        scope: The current scope context ("global", "python", "js", etc.).
+        file_path: The file path for error reporting.
+        line_number: The line number for error reporting.
+        custom_title: Optional custom title for the link. If None, uses link_name.
+
+    Returns:
+        A formatted markdown link if the link is found in the scope mapping,
+        None otherwise.
+
+    Example:
+        >>> _transform_link("StateGraph", "python", "file.md", 5)
+        "[StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
+
+        >>> _transform_link("StateGraph", "python", "file.md", 5, "Custom Title")
+        "[Custom Title](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.StateGraph)"
+
+        >>> _transform_link("unknown-link", "python", "file.md", 5)
+        None
+    """
+    if scope == "global":
+        # Special scope that is composed of both Python and JS links
+        # For now, we will substitute in the python scope!
+        # But we need to add support for handling both scopes.
+        scope = "python"
+        logger.error(
+            "Encountered unhandled 'global' scope. Defaulting to 'python'."
+            "In file: %s, line %d, link_name: %s",
+            file_path,
+            line_number,
+            link_name,
+        )
+    link_map = SCOPE_LINK_MAPS.get(scope, {})
+    url = link_map.get(link_name)
+
+    if url:
+        title = custom_title if custom_title is not None else link_name
+        return f"[{title}]({url})"
+    else:
+        # Log error with file location information
+        logger.info(
+            # Using %s
+            "Link '%s' not found in scope '%s'. "
+            "In file: %s, line %d. Available links in scope: %s",
+            link_name,
+            scope,
+            file_path,
+            line_number,
+            list(link_map.keys() if link_map else []),
+        )
+        return None
+
+
+CONDITIONAL_FENCE_PATTERN = re.compile(
+    r"""
+    ^                       # Start of line
+    (?P<indent>[ \t]*)      # Optional indentation (spaces or tabs)
+    :::                     # Literal fence marker
+    (?P<language>\w+)?      # Optional language identifier (named group: language)
+    \s*                     # Optional trailing whitespace
+    $                       # End of line
+    """,
+    re.VERBOSE,
+)
+CROSS_REFERENCE_PATTERN = re.compile(
+    r"""
+    (?:                     # Non-capturing group for two possible formats:
+        @\[                 # @ symbol followed by opening bracket for title
+        (?P<title>[^\]]+)   # Custom title - one or more non-bracket characters
+        \]                  # Closing bracket for title
+        \[                  # Opening bracket for link name
+        (?P<link_name_with_title>[^\]]+)  # Link name - one or more non-bracket characters
+        \]                  # Closing bracket for link name
+        |                   # OR
+        @\[                 # @ symbol followed by opening bracket
+        (?P<link_name>[^\]]+)   # Link name - one or more non-bracket characters
+        \]                  # Closing bracket
+    )
+    """,
+    re.VERBOSE,
+)
+
+
+def _replace_autolinks(markdown: str, file_path: str, *, default_scope: str = "python") -> str:
+    """Preprocess markdown lines to handle @[links] with conditional fence scopes.
+
+    This function processes markdown content to transform @[link_name] references
+    based on the current conditional fence scope. Conditional fences use the
+    syntax :::language to define scope boundaries.
+
+    Args:
+        markdown: The markdown content to process.
+        file_path: The file path for error reporting.
+        default_scope: The default scope to use if no scope is matched.
+
+    Returns:
+        Processed markdown content with @[references] transformed to proper
+        markdown links or left unchanged if not found.
+
+    Example:
+        Input:
+            "@[StateGraph]\\n:::python\\n@[Command]\\n:::\\n"
+        Output:
+            "[StateGraph](url)\\n:::python\\n[Command](url)\\n:::\\n"
+    """
+    # Track the current scope context
+    current_scope = default_scope
+    lines = markdown.splitlines(keepends=True)
+    processed_lines = []
+
+    for line_number, line in enumerate(lines, 1):
+        line_stripped = line.strip()
+
+        # Check if this line defines a new conditional fence scope
+        fence_match = CONDITIONAL_FENCE_PATTERN.match(line_stripped)
+        if fence_match:
+            language = fence_match.group("language")
+            # Set scope to the specified language, or reset to global if no language
+            current_scope = language.lower() if language else default_scope
+            processed_lines.append(line)
+            continue
+
+        # Transform all @[link_name] references in this line based on current scope
+        def replace_cross_reference(match: re.Match[str]) -> str:
+            """Replace a single @[link_name] with the scoped equivalent."""
+            # Check if this is the @[title][ref] format or @[ref] format
+            title = match.group("title")
+            if title is not None:
+                # This is @[title][ref] format
+                link_name = match.group("link_name_with_title")
+                custom_title = title
+            else:
+                # This is @[ref] format
+                link_name = match.group("link_name")
+                custom_title = None
+            
+            transformed = _transform_link(
+                link_name, current_scope, file_path, line_number, custom_title
+            )
+            return transformed if transformed is not None else match.group(0)
+
+        transformed_line = CROSS_REFERENCE_PATTERN.sub(replace_cross_reference, line)
+        processed_lines.append(transformed_line)
+
+    return "".join(processed_lines)

docs/_scripts/js_translation/add_translation.py

@@ -0,0 +1,236 @@
+"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
+
+import argparse
+
+import requests
+from langchain_anthropic import ChatAnthropic
+from textwrap import dedent
+
+
+# Load reference TypeScript snippets
+URL = "https://gist.githubusercontent.com/dqbd/b35d49e2ceec80e654fe1c5ab61ec477/raw/f4768aeedb67628190a4e06d063a938afc8e7672/snippets.md"
+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)
+
+
+FLUENT_INTERFACE_PROMPT = (
+    "CRITICAL: Always use method chaining (fluent interface) for StateGraph operations in TypeScript. "
+    "Never create separate variables for the graph builder or call methods individually. "
+    "The fluent interface provides better type safety and is the preferred pattern.\n\n"
+    "CORRECT examples with fluent interface:\n"
+    + dedent(
+        """
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('node1', node1)
+          .addNode('node2', node2)
+          .addEdge(START, 'node1')
+          .addEdge('node1', 'node2')
+          .addEdge('node2', END)
+          .compile()
+        ```
+        
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('chatbot', chatbot)
+          .addEdge(START, 'chatbot')
+          .addEdge('chatbot', END)
+          .compile()
+        ```
+        
+        ```typescript
+        const graph = new StateGraph(MyState)
+          .addNode('chatbot', chatbot)
+          .addEdge(START, 'chatbot')
+          .addEdge('chatbot', END)
+          .compile()
+        ```
+        """
+    )
+    + "\n"
+    + "INCORRECT examples to avoid:\n"
+    + dedent(
+        """
+        ```typescript
+        // WRONG: Creating separate builder variable
+        const graphBuilder = new StateGraph(MyState)
+        graphBuilder.addNode('node1', node1)
+        graphBuilder.addEdge(START, 'node1')
+        const graph = graphBuilder.compile()
+        ```
+        
+        ```typescript
+        // WRONG: Using Python-style method names
+        const workflow = new StateGraph(MyState)
+        workflow.add_node('node1', node1)
+        workflow.add_edge(START, 'node1')
+        const graph = workflow.compile()
+        ```
+        
+        ```typescript
+        // WRONG: Calling methods individually
+        const graphBuilder = new StateGraph(MyState)
+        graphBuilder.addNode('chatbot', chatbot)
+        graphBuilder.addEdge(START, 'chatbot')
+        graphBuilder.addEdge('chatbot', END)
+        const graph = graphBuilder.compile()
+        ```
+        """
+    )
+    + "\n"
+    + "Key rules:\n"
+    + "- Always chain methods directly on the StateGraph constructor\n"
+    + "- Use camelCase method names (addNode, addEdge, not add_node, add_edge)\n"
+    + "- Always end with .compile()\n"
+    + "- Never store the builder in a separate variable\n"
+)
+
+
+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"
+    "IMPORTANT REQUIREMENTS:\n"
+    "- Use Zod for state definition for StateGraph. Avoid using Annotation since it will be deprecated in the future.\n"
+    "- ALWAYS use fluent interface (method chaining) for StateGraph operations - this is CRITICAL\n"
+    "- Never create separate variables for graph builders\n"
+    "- Always chain methods directly on the StateGraph constructor and end with .compile()\n\n"
+    f"{FLUENT_INTERFACE_PROMPT}\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."
+)
+
+
+def translate_python_to_ts(markdown_content: str) -> str:
+    response = model.invoke(
+        [
+            {
+                "role": "system",
+                "content": TRANSLATION_PROMPT,
+                "cache_control": {"type": "ephemeral"},
+            },
+            {"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": combined_content},
+        ]
+    )
+    return response.content
+
+
+def main(file_path: str, translate_only: bool, consolidate_only: bool) -> None:
+    with open(file_path, "r", encoding="utf-8") 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}")
+
+    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}")
+
+    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}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=(
+            "Translate Python markdown to TypeScript and/or consolidate "
+            "Python-JS markdown into one file."
+        )
+    )
+    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,
+    )

docs/_scripts/js_translation/codeblocks/.gitkeep

@@ -0,0 +1,6 @@
+.prettierrc
+.eslint.config.mjs
+package.json
+README.md
+tsconfig.json
+yarn.lock

docs/_scripts/js_translation/codeblocks/.prettierrc

@@ -0,0 +1,19 @@
+{
+  "$schema": "https://json.schemastore.org/prettierrc",
+  "printWidth": 80,
+  "tabWidth": 2,
+  "useTabs": false,
+  "semi": true,
+  "singleQuote": false,
+  "quoteProps": "as-needed",
+  "jsxSingleQuote": false,
+  "trailingComma": "es5",
+  "bracketSpacing": true,
+  "arrowParens": "always",
+  "requirePragma": false,
+  "insertPragma": false,
+  "proseWrap": "preserve",
+  "htmlWhitespaceSensitivity": "css",
+  "vueIndentScriptAndStyle": false,
+  "endOfLine": "lf"
+}

docs/_scripts/js_translation/codeblocks/README.md

@@ -0,0 +1 @@
+# \_codeblocks

docs/_scripts/js_translation/codeblocks/eslint.config.mjs

@@ -0,0 +1,14 @@
+import js from "@eslint/js";
+import globals from "globals";
+import tseslint from "typescript-eslint";
+import { defineConfig } from "eslint/config";
+
+export default defineConfig([
+  {
+    files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
+    plugins: { js },
+    extends: ["js/recommended"],
+    languageOptions: { globals: globals.browser },
+  },
+  tseslint.configs.recommended,
+]);

docs/_scripts/js_translation/codeblocks/package.json

@@ -0,0 +1,27 @@
+{
+  "name": "_codeblocks",
+  "packageManager": "yarn@4.6.0",
+  "scripts": {
+    "lint": "eslint .",
+    "lint:fix": "eslint . --fix",
+    "format": "prettier --write .",
+    "format:fix": "prettier --write . --fix"
+  },
+  "dependencies": {
+    "@langchain/anthropic": "^0.3.24",
+    "@langchain/core": "^0.3.66",
+    "@langchain/langgraph": "^0.3.11",
+    "@langchain/langgraph-api": "^0.0.52",
+    "@langchain/langgraph-sdk": "^0.0.102",
+    "@langchain/openai": "^0.6.3",
+    "zod": "^4.0.10"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.32.0",
+    "eslint": "^9.32.0",
+    "globals": "^16.3.0",
+    "jiti": "^2.5.1",
+    "typescript": "^5.8.3",
+    "typescript-eslint": "^8.38.0"
+  }
+}

docs/_scripts/js_translation/codeblocks/tsconfig.json

@@ -0,0 +1,114 @@
+{
+  "compilerOptions": {
+    /* Visit https://aka.ms/tsconfig to read more about this file */
+
+    /* Projects */
+    // "incremental": true,                              /* Save .tsbuildinfo files to allow for incremental compilation of projects. */
+    // "composite": true,                                /* Enable constraints that allow a TypeScript project to be used with project references. */
+    // "tsBuildInfoFile": "./.tsbuildinfo",              /* Specify the path to .tsbuildinfo incremental compilation file. */
+    // "disableSourceOfProjectReferenceRedirect": true,  /* Disable preferring source files instead of declaration files when referencing composite projects. */
+    // "disableSolutionSearching": true,                 /* Opt a project out of multi-project reference checking when editing. */
+    // "disableReferencedProjectLoad": true,             /* Reduce the number of projects loaded automatically by TypeScript. */
+
+    /* Language and Environment */
+    "target": "esnext",                                  /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */
+    // "lib": [],                                        /* Specify a set of bundled library declaration files that describe the target runtime environment. */
+    // "jsx": "preserve",                                /* Specify what JSX code is generated. */
+    // "libReplacement": true,                           /* Enable lib replacement. */
+    // "experimentalDecorators": true,                   /* Enable experimental support for legacy experimental decorators. */
+    // "emitDecoratorMetadata": true,                    /* Emit design-type metadata for decorated declarations in source files. */
+    // "jsxFactory": "",                                 /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */
+    // "jsxFragmentFactory": "",                         /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */
+    // "jsxImportSource": "",                            /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */
+    // "reactNamespace": "",                             /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */
+    // "noLib": true,                                    /* Disable including any library files, including the default lib.d.ts. */
+    // "useDefineForClassFields": true,                  /* Emit ECMAScript-standard-compliant class fields. */
+    // "moduleDetection": "auto",                        /* Control what method is used to detect module-format JS files. */
+
+    /* Modules */
+    "module": "nodenext",                                /* Specify what module code is generated. */
+    // "rootDir": "./",                                  /* Specify the root folder within your source files. */
+    "moduleResolution": "nodenext",                     /* Specify how TypeScript looks up a file from a given module specifier. */
+    // "baseUrl": "./",                                  /* Specify the base directory to resolve non-relative module names. */
+    // "paths": {},                                      /* Specify a set of entries that re-map imports to additional lookup locations. */
+    // "rootDirs": [],                                   /* Allow multiple folders to be treated as one when resolving modules. */
+    // "typeRoots": [],                                  /* Specify multiple folders that act like './node_modules/@types'. */
+    // "types": [],                                      /* Specify type package names to be included without being referenced in a source file. */
+    // "allowUmdGlobalAccess": true,                     /* Allow accessing UMD globals from modules. */
+    // "moduleSuffixes": [],                             /* List of file name suffixes to search when resolving a module. */
+    // "allowImportingTsExtensions": true,               /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */
+    // "rewriteRelativeImportExtensions": true,          /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */
+    // "resolvePackageJsonExports": true,                /* Use the package.json 'exports' field when resolving package imports. */
+    // "resolvePackageJsonImports": true,                /* Use the package.json 'imports' field when resolving imports. */
+    // "customConditions": [],                           /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */
+    // "noUncheckedSideEffectImports": true,             /* Check side effect imports. */
+    // "resolveJsonModule": true,                        /* Enable importing .json files. */
+    // "allowArbitraryExtensions": true,                 /* Enable importing files with any extension, provided a declaration file is present. */
+    // "noResolve": true,                                /* Disallow 'import's, 'require's or '<reference>'s from expanding the number of files TypeScript should add to a project. */
+
+    /* JavaScript Support */
+    // "allowJs": true,                                  /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */
+    // "checkJs": true,                                  /* Enable error reporting in type-checked JavaScript files. */
+    // "maxNodeModuleJsDepth": 1,                        /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */
+
+    /* Emit */
+    // "declaration": true,                              /* Generate .d.ts files from TypeScript and JavaScript files in your project. */
+    // "declarationMap": true,                           /* Create sourcemaps for d.ts files. */
+    // "emitDeclarationOnly": true,                      /* Only output d.ts files and not JavaScript files. */
+    // "sourceMap": true,                                /* Create source map files for emitted JavaScript files. */
+    // "inlineSourceMap": true,                          /* Include sourcemap files inside the emitted JavaScript. */
+    // "noEmit": true,                                   /* Disable emitting files from a compilation. */
+    // "outFile": "./",                                  /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */
+    // "outDir": "./",                                   /* Specify an output folder for all emitted files. */
+    // "removeComments": true,                           /* Disable emitting comments. */
+    // "importHelpers": true,                            /* Allow importing helper functions from tslib once per project, instead of including them per-file. */
+    // "downlevelIteration": true,                       /* Emit more compliant, but verbose and less performant JavaScript for iteration. */
+    // "sourceRoot": "",                                 /* Specify the root path for debuggers to find the reference source code. */
+    // "mapRoot": "",                                    /* Specify the location where debugger should locate map files instead of generated locations. */
+    // "inlineSources": true,                            /* Include source code in the sourcemaps inside the emitted JavaScript. */
+    // "emitBOM": true,                                  /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */
+    // "newLine": "crlf",                                /* Set the newline character for emitting files. */
+    // "stripInternal": true,                            /* Disable emitting declarations that have '@internal' in their JSDoc comments. */
+    // "noEmitHelpers": true,                            /* Disable generating custom helper functions like '__extends' in compiled output. */
+    // "noEmitOnError": true,                            /* Disable emitting files if any type checking errors are reported. */
+    // "preserveConstEnums": true,                       /* Disable erasing 'const enum' declarations in generated code. */
+    // "declarationDir": "./",                           /* Specify the output directory for generated declaration files. */
+
+    /* Interop Constraints */
+    // "isolatedModules": true,                          /* Ensure that each file can be safely transpiled without relying on other imports. */
+    // "verbatimModuleSyntax": true,                     /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */
+    // "isolatedDeclarations": true,                     /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */
+    // "erasableSyntaxOnly": true,                       /* Do not allow runtime constructs that are not part of ECMAScript. */
+    // "allowSyntheticDefaultImports": true,             /* Allow 'import x from y' when a module doesn't have a default export. */
+    "esModuleInterop": true,                             /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */
+    // "preserveSymlinks": true,                         /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */
+    "forceConsistentCasingInFileNames": true,            /* Ensure that casing is correct in imports. */
+
+    /* Type Checking */
+    "strict": false,                                      /* Enable all strict type-checking options. */
+    // "noImplicitAny": true,                            /* Enable error reporting for expressions and declarations with an implied 'any' type. */
+    // "strictNullChecks": true,                         /* When type checking, take into account 'null' and 'undefined'. */
+    // "strictFunctionTypes": true,                      /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */
+    // "strictBindCallApply": true,                      /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */
+    // "strictPropertyInitialization": true,             /* Check for class properties that are declared but not set in the constructor. */
+    // "strictBuiltinIteratorReturn": true,              /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */
+    // "noImplicitThis": true,                           /* Enable error reporting when 'this' is given the type 'any'. */
+    // "useUnknownInCatchVariables": true,               /* Default catch clause variables as 'unknown' instead of 'any'. */
+    // "alwaysStrict": true,                             /* Ensure 'use strict' is always emitted. */
+    // "noUnusedLocals": true,                           /* Enable error reporting when local variables aren't read. */
+    // "noUnusedParameters": true,                       /* Raise an error when a function parameter isn't read. */
+    // "exactOptionalPropertyTypes": true,               /* Interpret optional property types as written, rather than adding 'undefined'. */
+    // "noImplicitReturns": true,                        /* Enable error reporting for codepaths that do not explicitly return in a function. */
+    // "noFallthroughCasesInSwitch": true,               /* Enable error reporting for fallthrough cases in switch statements. */
+    // "noUncheckedIndexedAccess": true,                 /* Add 'undefined' to a type when accessed using an index. */
+    // "noImplicitOverride": true,                       /* Ensure overriding members in derived classes are marked with an override modifier. */
+    // "noPropertyAccessFromIndexSignature": true,       /* Enforces using indexed accessors for keys declared using an indexed type. */
+    // "allowUnusedLabels": true,                        /* Disable error reporting for unused labels. */
+    // "allowUnreachableCode": true,                     /* Disable error reporting for unreachable code. */
+
+    /* Completeness */
+    // "skipDefaultLibCheck": true,                      /* Skip type checking .d.ts files that are included with TypeScript. */
+    "skipLibCheck": true                                 /* Skip type checking all .d.ts files. */
+    ""
+  }
+}

docs/_scripts/js_translation/extract_codeblocks.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+"""Extracts typescript code blocks from a markdown file."""
+
+import argparse
+import json
+import re
+import os
+from typing import List, TypedDict, Literal
+
+
+class CodeBlock(TypedDict):
+    """A code block extracted from a markdown file."""
+    starting_line: int
+    """The line number where the code block starts in the source file"""
+    ending_line: int
+    """The line number where the code block ends in the source file"""
+    indentation: int
+    """Number of spaces/tabs used for indentation of the code block"""
+    source_file: str
+    """Path to the markdown file containing this code block"""
+    frontmatter: str
+    """Any metadata or frontmatter specified after the opening code fence"""
+    code: str
+    """The actual code content within the code block"""
+    language: str
+    """The language of the code block (e.g. typescript, javascript)"""
+
+
+def extract_code_blocks(markdown_content: str, source_file: str) -> List[CodeBlock]:
+    """Extracts code blocks from a markdown file.
+
+    Args:
+        markdown_content: The content of the markdown file.
+        source_file: The path to the markdown file.
+
+    Returns:
+        A list of TypedDicts, where each dict represents a code block.
+    """
+    # Regex to find code blocks with specified languages, capturing indentation
+    # and frontmatter.
+    pattern = re.compile(
+        r"^(?P<indentation>\s*)```(?P<language>typescript|javascript|ts|js)(?P<frontmatter>[^\n]*)\n(?P<code>.*?)\n^(?P=indentation)```\s*$",
+        re.DOTALL | re.MULTILINE,
+    )
+
+    code_blocks: List[CodeBlock] = []
+    for match in pattern.finditer(markdown_content):
+        start_pos = match.start()
+
+        # Calculate line numbers
+        starting_line = markdown_content.count("\n", 0, start_pos) + 1
+        ending_line = starting_line + match.group(0).count("\n")
+
+        indentation_str = match.group("indentation")
+
+        code_block: CodeBlock = {
+            "starting_line": starting_line,
+            "ending_line": ending_line,
+            "indentation": len(indentation_str),
+            "source_file": source_file,
+            "frontmatter": match.group("frontmatter").strip(),
+            "code": match.group("code"),
+            "language": match.group("language"),
+        }
+        code_blocks.append(code_block)
+
+    return code_blocks
+
+def dump_code_blocks(input_file: str, output_file: str, format: Literal["json", "inline"]) -> None:
+    """Function to extract and save code blocks from a markdown file.
+
+    Args:
+        input_file: Path to the input markdown file.
+        output_file: Path to the output JSON file for the extracted code blocks.
+        format: Output format - either "json" or "inline"
+    """
+    with open(input_file, "r", encoding="utf-8") as f:
+        markdown_content = f.read()
+
+    extracted_code = extract_code_blocks(markdown_content, input_file)
+
+    if len(extracted_code) == 0:
+        print(f"No code blocks found in {input_file}")
+        return
+    
+    if format == "json":
+        with open(output_file, "w", encoding="utf-8") as f:
+            json.dump(extracted_code, f, indent=2)
+    elif format == "inline":
+        with open(output_file, "w", encoding="utf-8") as f:
+            for code_block in extracted_code:
+                f.write(f"// {json.dumps({k:v for k,v in code_block.items() if k != 'code'})}\n")
+                f.write("\n")
+                f.write(code_block["code"])
+                f.write("\n")
+    print(f"Extracted {len(extracted_code)} code blocks from {input_file} to {output_file}")
+
+def main(input_path: str, output_path: str, format: Literal["json", "inline"]) -> None:
+    """Main function to extract code blocks from a markdown file.
+
+    Args:
+        input_file: Path to the input markdown file.
+        output_file: Path to the output JSON file for the extracted code blocks.
+        format: Output format - either "json" or "inline"
+    """
+    # Check if input path is a directory
+    if os.path.isdir(input_path):
+        if os.path.isfile(output_path):
+            raise ValueError("If input_path is a directory, output_path must also be a directory")
+        if not os.path.isdir(output_path):
+            os.makedirs(output_path, exist_ok=True)
+        
+        # Process each markdown file in the directory recursively
+        for root, _, files in os.walk(input_path):
+            for filename in files:
+                if filename.endswith(".md"):
+                    # Get relative path to maintain directory structure
+                    rel_path = os.path.relpath(root, input_path)
+                    input_file = os.path.join(root, filename)
+                    # Create output directory if it doesn't exist
+                    output_dir = os.path.join(output_path, rel_path)
+                    os.makedirs(output_dir, exist_ok=True)
+                    output_file = os.path.join(output_dir, filename.replace(".md", ".ts"))
+                    dump_code_blocks(input_file, output_file, format)
+    else:
+        # Process single file
+        dump_code_blocks(input_path, output_path, format)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Extract typescript code blocks from a markdown file."
+    )
+    parser.add_argument(
+        "input_file",
+        help="Path to the input markdown file.",
+    )
+    parser.add_argument(
+        "output_file",
+        help="Path to the output JSON file for the extracted code blocks.",
+    )
+    parser.add_argument(
+        "--format",
+        choices=["json", "inline"],
+        default="json",
+        help="Output format - either 'json' or 'inline'",
+    )
+    args = parser.parse_args()
+
+    main(args.input_file, args.output_file, args.format)

docs/_scripts/link_map.py

@@ -0,0 +1,127 @@
+"""Link mapping for cross-reference resolution across different scopes.
+
+This module provides link mappings for different language/framework scopes
+to resolve @[link_name] references to actual URLs.
+"""
+
+# Python-specific link mappings
+PYTHON_LINK_MAP = {
+    "StateGraph": "reference/graphs/#langgraph.graph.StateGraph",
+    "add_conditional_edges": "reference/graphs/#langgraph.graph.state.StateGraph.add_conditional_edges",
+    "add_edge": "reference/graphs/#langgraph.graph.state.StateGraph.add_edge",
+    "add_node": "reference/graphs/#langgraph.graph.state.StateGraph.add_node",
+    "add_messages": "reference/graphs/#langgraph.graph.message.add_messages",
+    "ToolNode": "reference/agents/#langgraph.prebuilt.tool_node.ToolNode",
+    "CompiledStateGraph.astream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.astream",
+    "Pregel.astream": "reference/pregel/#langgraph.pregel.Pregel.astream",
+    "AsyncPostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.aio.AsyncPostgresSaver",
+    "AsyncSqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver",
+    "BaseCheckpointSaver": "reference/checkpoints/#langgraph.checkpoint.base.BaseCheckpointSaver",
+    "BaseStore": "reference/store/#langgraph.store.base.BaseStore",
+    "BaseStore.put": "reference/store/#langgraph.store.base.BaseStore.put",
+    "BinaryOperatorAggregate": "reference/pregel/#langgraph.pregel.Pregel--advanced-channels-context-and-binaryoperatoraggregate",
+    "CipherProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.CipherProtocol",
+    "client.runs.stream": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.stream",
+    "client.runs.wait": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.RunsClient.wait",
+    "client.threads.get_history": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.get_history",
+    "client.threads.update_state": "cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.update_state",
+    "Command": "reference/types/#langgraph.types.Command",
+    "CompiledStateGraph": "reference/graphs/#langgraph.graph.state.CompiledStateGraph",
+    "create_react_agent": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
+    "create_supervisor": "reference/supervisor/#langgraph_supervisor.supervisor.create_supervisor",
+    "EncryptedSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer",
+    "entrypoint.final": "reference/func/#langgraph.func.entrypoint.final",
+    "entrypoint": "reference/func/#langgraph.func.entrypoint",
+    "from_pycryptodome_aes": "reference/checkpoints/#langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes",
+    "get_state_history": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.get_state_history",
+    "get_stream_writer": "reference/config/#langgraph.config.get_stream_writer",
+    "HumanInterrupt": "reference/prebuilt/#langgraph.prebuilt.interrupt.HumanInterrupt",
+    "InjectedState": "reference/agents/#langgraph.prebuilt.tool_node.InjectedState",
+    "InMemorySaver": "reference/checkpoints/#langgraph.checkpoint.memory.InMemorySaver",
+    "interrupt": "reference/types/#langgraph.types.Interrupt",
+    "CompiledStateGraph.invoke": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.invoke",
+    "JsonPlusSerializer": "reference/checkpoints/#langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer",
+    "langgraph.json": "cloud/reference/cli/#configuration-file",
+    "LastValue": "reference/channels/#langgraph.channels.LastValue",
+    "PostgresSaver": "reference/checkpoints/#langgraph.checkpoint.postgres.PostgresSaver",
+    "Pregel": "reference/pregel/",
+    "Pregel.stream": "reference/pregel/#langgraph.pregel.Pregel.stream",
+    "pre_model_hook": "reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent",
+    "protocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
+    "Send": "reference/types/#langgraph.types.Send",
+    "SerializerProtocol": "reference/checkpoints/#langgraph.checkpoint.serde.base.SerializerProtocol",
+    "SqliteSaver": "reference/checkpoints/#langgraph.checkpoint.sqlite.SqliteSaver",
+    "START": "reference/constants/#langgraph.constants.START",
+    "CompiledStateGraph.stream": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.stream",
+    "task": "reference/func/#langgraph.func.task",
+    "Topic": "reference/channels/#langgraph.channels.Topic",
+    "update_state": "reference/graphs/#langgraph.graph.state.CompiledStateGraph.update_state",
+}
+
+# JavaScript-specific link mappings
+JS_LINK_MAP = {
+    "Auth": "reference/classes/sdk_auth.Auth.html",
+    "StateGraph": "reference/classes/langgraph.StateGraph.html",
+    "add_conditional_edges": "/reference/classes/langgraph.StateGraph.html#addConditionalEdges",
+    "add_edge": "reference/classes/langgraph.StateGraph.html#addEdge",
+    "add_node": "reference/classes/langgraph.StateGraph.html#addNode",
+    "add_messages": "reference/modules/langgraph.html#addMessages",
+    "ToolNode": "reference/classes/langgraph_prebuilt.ToolNode.html",
+    "BaseCheckpointSaver": "reference/classes/checkpoint.BaseCheckpointSaver.html",
+    "BaseStore": "reference/classes/checkpoint.BaseStore.html",
+    "BaseStore.put": "reference/classes/checkpoint.BaseStore.html#put",
+    "BinaryOperatorAggregate": "reference/classes/langgraph.BinaryOperatorAggregate.html",
+    "client.runs.stream": "reference/classes/sdk_client.RunsClient.html#stream",
+    "client.runs.wait": "reference/classes/sdk_client.RunsClient.html#wait",
+    "client.threads.get_history": "reference/classes/sdk_client.ThreadsClient.html#getHistory",
+    "client.threads.update_state": "reference/classes/sdk_client.ThreadsClient.html#updateState",
+    "Command": "reference/classes/langgraph.Command.html",
+    "CompiledStateGraph": "reference/classes/langgraph.CompiledStateGraph.html",
+    "create_react_agent": "reference/functions/langgraph_prebuilt.createReactAgent.html",
+    "create_supervisor": "reference/functions/langgraph_supervisor.createSupervisor.html",
+    "entrypoint.final": "reference/functions/langgraph.entrypoint.html#final",
+    "entrypoint": "reference/functions/langgraph.entrypoint.html",
+    "getContextVariable": "https://v03.api.js.langchain.com/functions/_langchain_core.context.getContextVariable.html",
+    "get_state_history": "reference/classes/langgraph.CompiledStateGraph.html#getStateHistory",
+    "HumanInterrupt": "reference/interfaces/langgraph_prebuilt.HumanInterrupt.html",
+    "interrupt": "reference/functions/langgraph.interrupt-2.html",
+    "CompiledStateGraph.invoke": "reference/classes/langgraph.CompiledStateGraph.html#invoke",
+    "langgraph.json": "cloud/reference/cli/#configuration-file",
+    "MemorySaver": "reference/classes/checkpoint.MemorySaver.html",
+    "messagesStateReducer": "reference/functions/langgraph.messagesStateReducer.html",
+    "PostgresSaver": "reference/classes/checkpoint_postgres.PostgresSaver.html",
+    "Pregel": "reference/classes/langgraph.Pregel.html",
+    "Pregel.stream": "reference/classes/langgraph.Pregel.html#stream",
+    "pre_model_hook": "reference/functions/langgraph_prebuilt.createReactAgent.html",
+    "protocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
+    "Send": "reference/classes/langgraph.Send.html",
+    "SerializerProtocol": "reference/interfaces/checkpoint.SerializerProtocol.html",
+    "SqliteSaver": "reference/classes/checkpoint_sqlite.SqliteSaver.html",
+    "START": "reference/variables/langgraph.START.html",
+    "CompiledStateGraph.stream": "reference/classes/langgraph.CompiledStateGraph.html#stream",
+    "task": "reference/functions/langgraph.task.html",
+    ## TODO (hntrl): export Topic from langgraphjs
+    # "Topic": "reference/classes/langgraph_channels.Topic.html",
+    "update_state": "reference/classes/langgraph.CompiledStateGraph.html#updateState",
+}
+
+# TODO: Allow updating these to localhost for local development
+PY_REFERENCE_HOST = "https://langchain-ai.github.io/langgraph/"
+JS_REFERENCE_HOST = "https://langchain-ai.github.io/langgraphjs/"
+
+for key, value in PYTHON_LINK_MAP.items():
+    # Ensure the link is absolute
+    if not value.startswith("http"):
+        PYTHON_LINK_MAP[key] = f"{PY_REFERENCE_HOST}{value}"
+
+for key, value in JS_LINK_MAP.items():
+    # Ensure the link is absolute
+    if not value.startswith("http"):
+        JS_LINK_MAP[key] = f"{JS_REFERENCE_HOST}{value}"
+
+# Global scope is assembled from the Python and JS mappings
+# Combined mapping by scope
+SCOPE_LINK_MAPS = {
+    "python": PYTHON_LINK_MAP,
+    "js": JS_LINK_MAP,
+}

docs/_scripts/notebook_hooks.py

@@ -1,13 +1,22 @@
+"""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.handle_auto_links import _replace_autolinks
 from _scripts.notebook_convert import convert_notebook
 
 logger = logging.getLogger(__name__)
@@ -26,64 +35,72 @@ 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#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/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",
     # memory how-tos
-    "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",
+    "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",
     # subgraph how-tos
-    "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",
+    "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",
     # persistence how-tos
-    "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",
+    "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",
+    "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.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",
+    "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",
     # cloud redirects
     "cloud/index.md": "index.md",
     "cloud/how-tos/index.md": "concepts/langgraph_platform",
     "cloud/concepts/api.md": "concepts/langgraph_server.md",
     "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
     "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",
     # 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",
-    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
-    "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",
-    # prebuit redirects
+    "cloud/how-tos/stream_values.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_updates.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_messages.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_events.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_debug.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/how-tos/stream_multiple.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/concepts/streaming.md": "concepts/streaming.md",
+    "agents/streaming.md": "how-tos/streaming.md",
+    # prebuilt 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",
@@ -92,10 +109,103 @@ 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"
+    "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",
 
+    # LGP mintlify migration redirects
+    "tutorials/auth/getting_started.md": "https://docs.langchain.com/langgraph-platform/auth",
+    "tutorials/auth/resource_auth.md": "https://docs.langchain.com/langgraph-platform/resource-auth",
+    "tutorials/auth/add_auth_server.md": "https://docs.langchain.com/langgraph-platform/add-auth-server",
+    "how-tos/use-remote-graph.md": "https://docs.langchain.com/langgraph-platform/use-remote-graph",
+    "how-tos/autogen-integration.md": "https://docs.langchain.com/langgraph-platform/autogen-integration",
+    "cloud/how-tos/use_stream_react.md": "https://docs.langchain.com/langgraph-platform/use-stream-react",
+    "cloud/how-tos/generative_ui_react.md": "https://docs.langchain.com/langgraph-platform/generative-ui-react",
+    "concepts/langgraph_platform.md": "https://docs.langchain.com/langgraph-platform/index",
+    "concepts/langgraph_components.md": "https://docs.langchain.com/langgraph-platform/components",
+    "concepts/langgraph_server.md": "https://docs.langchain.com/langgraph-platform/langgraph-server",
+    "concepts/langgraph_data_plane.md": "https://docs.langchain.com/langgraph-platform/data-plane",
+    "concepts/langgraph_control_plane.md": "https://docs.langchain.com/langgraph-platform/control-plane",
+    "concepts/langgraph_cli.md": "https://docs.langchain.com/langgraph-platform/langgraph-cli",
+    "concepts/langgraph_studio.md": "https://docs.langchain.com/langgraph-platform/langgraph-studio",
+    "cloud/how-tos/studio/quick_start.md": "https://docs.langchain.com/langgraph-platform/quick-start-studio",
+    "cloud/how-tos/invoke_studio.md": "https://docs.langchain.com/langgraph-platform/invoke-studio",
+    "cloud/how-tos/studio/manage_assistants.md": "https://docs.langchain.com/langgraph-platform/manage-assistants-studio",
+    "cloud/how-tos/threads_studio.md": "https://docs.langchain.com/langgraph-platform/threads-studio",
+    "cloud/how-tos/iterate_graph_studio.md": "https://docs.langchain.com/langgraph-platform/iterate-graph-studio",
+    "cloud/how-tos/studio/run_evals.md": "https://docs.langchain.com/langgraph-platform/run-evals-studio",
+    "cloud/how-tos/clone_traces_studio.md": "https://docs.langchain.com/langgraph-platform/clone-traces-studio",
+    "cloud/how-tos/datasets_studio.md": "https://docs.langchain.com/langgraph-platform/datasets-studio",
+    "concepts/sdk.md": "https://docs.langchain.com/langgraph-platform/sdk",
+    "concepts/plans.md": "https://docs.langchain.com/langgraph-platform/plans",
+    "concepts/application_structure.md": "https://docs.langchain.com/langgraph-platform/application-structure",
+    "concepts/scalability_and_resilience.md": "https://docs.langchain.com/langgraph-platform/scalability-and-resilience",
+    "concepts/auth.md": "https://docs.langchain.com/langgraph-platform/auth",
+    "how-tos/auth/custom_auth.md": "https://docs.langchain.com/langgraph-platform/custom-auth",
+    "how-tos/auth/openapi_security.md": "https://docs.langchain.com/langgraph-platform/openapi-security",
+    "concepts/assistants.md": "https://docs.langchain.com/langgraph-platform/assistants",
+    "cloud/how-tos/configuration_cloud.md": "https://docs.langchain.com/langgraph-platform/configuration-cloud",
+    "cloud/how-tos/use_threads.md": "https://docs.langchain.com/langgraph-platform/use-threads",
+    "cloud/how-tos/background_run.md": "https://docs.langchain.com/langgraph-platform/background-run",
+    "cloud/how-tos/same-thread.md": "https://docs.langchain.com/langgraph-platform/same-thread",
+    "cloud/how-tos/stateless_runs.md": "https://docs.langchain.com/langgraph-platform/stateless-runs",
+    "cloud/how-tos/configurable_headers.md": "https://docs.langchain.com/langgraph-platform/configurable-headers",
+    "concepts/double_texting.md": "https://docs.langchain.com/langgraph-platform/double-texting",
+    "cloud/how-tos/interrupt_concurrent.md": "https://docs.langchain.com/langgraph-platform/interrupt-concurrent",
+    "cloud/how-tos/rollback_concurrent.md": "https://docs.langchain.com/langgraph-platform/rollback-concurrent",
+    "cloud/how-tos/reject_concurrent.md": "https://docs.langchain.com/langgraph-platform/reject-concurrent",
+    "cloud/how-tos/enqueue_concurrent.md": "https://docs.langchain.com/langgraph-platform/enqueue-concurrent",
+    "cloud/concepts/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
+    "cloud/how-tos/webhooks.md": "https://docs.langchain.com/langgraph-platform/use-webhooks",
+    "cloud/concepts/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
+    "cloud/how-tos/cron_jobs.md": "https://docs.langchain.com/langgraph-platform/cron-jobs",
+    "how-tos/http/custom_lifespan.md": "https://docs.langchain.com/langgraph-platform/custom-lifespan",
+    "how-tos/http/custom_middleware.md": "https://docs.langchain.com/langgraph-platform/custom-middleware",
+    "how-tos/http/custom_routes.md": "https://docs.langchain.com/langgraph-platform/custom-routes",
+    "cloud/concepts/data_storage_and_privacy.md": "https://docs.langchain.com/langgraph-platform/data-storage-and-privacy",
+    "cloud/deployment/semantic_search.md": "https://docs.langchain.com/langgraph-platform/semantic-search",
+    "how-tos/ttl/configure_ttl.md": "https://docs.langchain.com/langgraph-platform/configure-ttl",
+    "concepts/deployment_options.md": "https://docs.langchain.com/langgraph-platform/deployment-options",
+    "cloud/quick_start.md": "https://docs.langchain.com/langgraph-platform/deployment-quickstart",
+    "cloud/deployment/setup.md": "https://docs.langchain.com/langgraph-platform/setup-app-requirements-txt",
+    "cloud/deployment/setup_pyproject.md": "https://docs.langchain.com/langgraph-platform/setup-pyproject",
+    "cloud/deployment/setup_javascript.md": "https://docs.langchain.com/langgraph-platform/setup-javascript",
+    "cloud/deployment/custom_docker.md": "https://docs.langchain.com/langgraph-platform/custom-docker",
+    "cloud/deployment/graph_rebuild.md": "https://docs.langchain.com/langgraph-platform/graph-rebuild",
+    "concepts/langgraph_cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
+    "concepts/langgraph_self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/hybrid",
+    "concepts/langgraph_self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/self-hosted",
+    "concepts/langgraph_standalone_container.md": "https://docs.langchain.com/langgraph-platform/self-hosted#data-plane-only",
+    "cloud/deployment/cloud.md": "https://docs.langchain.com/langgraph-platform/cloud",
+    "cloud/deployment/self_hosted_data_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-hybrid",
+    "cloud/deployment/self_hosted_control_plane.md": "https://docs.langchain.com/langgraph-platform/deploy-self-hosted-full-platform",
+    "cloud/deployment/standalone_container.md": "https://docs.langchain.com/langgraph-platform/deploy-data-plane-only",
+    "concepts/server-mcp.md": "https://docs.langchain.com/langgraph-platform/server-mcp",
+    "cloud/how-tos/human_in_the_loop_time_travel.md": "https://docs.langchain.com/langgraph-platform/human-in-the-loop-time-travel",
+    "cloud/how-tos/add-human-in-the-loop.md": "https://docs.langchain.com/langgraph-platform/add-human-in-the-loop",
+    "cloud/deployment/egress.md": "https://docs.langchain.com/langgraph-platform/env-var",
+    "cloud/how-tos/streaming.md": "https://docs.langchain.com/langgraph-platform/streaming",
+    "cloud/reference/api/api_ref.md": "https://docs.langchain.com/langgraph-platform/server-api-ref",
+    "cloud/reference/langgraph_server_changelog.md": "https://docs.langchain.com/langgraph-platform/langgraph-server-changelog",
+    "cloud/reference/api/api_ref_control_plane.md": "https://docs.langchain.com/langgraph-platform/api-ref-control-plane",
+    "cloud/reference/cli.md": "https://docs.langchain.com/langgraph-platform/cli",
+    "cloud/reference/env_var.md": "https://docs.langchain.com/langgraph-platform/env-var",
+    "troubleshooting/studio.md": "https://docs.langchain.com/langgraph-platform/troubleshooting-studio",
 }
 
 
@@ -145,6 +255,38 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
     return code_block_pattern.sub(replace_code_block_header, markdown)
 
 
+# Compiled regex patterns for better performance and readability
+
+
+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)[ \t]*:::"  # Match closing with the same indentation + any additional whitespace
+    )
+
+    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.
 
@@ -208,7 +350,7 @@ def _highlight_code_blocks(markdown: str) -> str:
             opening_fence += f" {attributes}"
 
         if highlighted_lines:
-            opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
+            opening_fence += f' hl_lines="{" ".join(highlighted_lines)}"'
 
         return (
             # The indent and opening fence
@@ -223,6 +365,21 @@ def _highlight_code_blocks(markdown: str) -> str:
     return markdown
 
 
+def _save_page_output(markdown: str, output_path: str):
+    """Save markdown content to a file, creating parent directories if needed.
+
+    Args:
+        markdown: The markdown content to save
+        output_path: The file path to save to
+    """
+    # Create parent directories recursively if they don't exist
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    
+    # Write the markdown content to the file
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write(markdown)
+
+
 def _on_page_markdown_with_config(
     markdown: str,
     page: Page,
@@ -238,12 +395,23 @@ def _on_page_markdown_with_config(
         # logger.info("Processing Jupyter notebook: %s", page.file.src_path)
         markdown = convert_notebook(page.file.abs_src_path)
 
+    target_language = kwargs.get(
+        "target_language",
+        os.environ.get("TARGET_LANGUAGE", "python")
+    )
+
+    # Apply cross-reference preprocessing to all markdown content
+    markdown = _replace_autolinks(markdown, page.file.src_path, default_scope=target_language)
+
     # Append API reference links to code blocks
     if add_api_references:
         markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
     # Apply highlight comments to code blocks
     markdown = _highlight_code_blocks(markdown)
 
+    # Apply conditional rendering for code blocks
+    markdown = _apply_conditional_rendering(markdown, target_language)
+
     # 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.
@@ -257,12 +425,20 @@ def _on_page_markdown_with_config(
 
 
 def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
+    finalized_markdown = _on_page_markdown_with_config(
         markdown,
         page,
         add_api_references=True,
         **kwargs,
     )
+    page.meta["original_markdown"] = finalized_markdown
+
+    output_path = os.environ.get("MD_OUTPUT_PATH")
+    if output_path:
+        file_path = os.path.join(output_path, page.file.src_path)
+        _save_page_output(finalized_markdown, file_path)
+
+    return finalized_markdown
 
 
 # redirects
@@ -285,7 +461,7 @@ 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)
@@ -301,20 +477,135 @@ 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")
     for page_old, page_new in REDIRECT_MAP.items():
+        # Convert .ipynb to .md for path calculation
         page_old = page_old.replace(".ipynb", ".md")
-        page_new = page_new.replace(".ipynb", ".md")
-        page_new_before_hash, hash, suffix = page_new.partition("#")
-        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
-            os.sep, "/"
-        )
-        new_html_path = File(page_new_before_hash, "", "", True).url
-        new_html_path = (
-            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-            + hash
-            + suffix
-        )
-        write_html(config["site_dir"], old_html_path, new_html_path)
+        
+        # Calculate the HTML path for the old page (whether it exists or not)
+        if use_directory_urls:
+            # With directory URLs: /path/to/page/ becomes /path/to/page/index.html
+            if page_old.endswith(".md"):
+                old_html_path = page_old[:-3] + "/index.html"
+            else:
+                old_html_path = page_old + "/index.html"
+        else:
+            # Without directory URLs: /path/to/page.md becomes /path/to/page.html
+            if page_old.endswith(".md"):
+                old_html_path = page_old[:-3] + ".html"
+            else:
+                old_html_path = page_old + ".html"
+        
+        if isinstance(page_new, str) and page_new.startswith("http"):
+            # Handle external redirects
+            _write_html(config["site_dir"], old_html_path, page_new)
+        else:
+            # Handle internal redirects
+            page_new = page_new.replace(".ipynb", ".md")
+            page_new_before_hash, hash, suffix = page_new.partition("#")
+            
+            # Try to get the new path using File class, but fallback to manual calculation
+            try:
+                new_html_path = File(page_new_before_hash, "", "", True).url
+                new_html_path = (
+                    posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
+                    + hash
+                    + suffix
+                )
+            except:
+                # Fallback: calculate relative path manually
+                if use_directory_urls:
+                    if page_new_before_hash.endswith(".md"):
+                        new_html_path = page_new_before_hash[:-3] + "/"
+                    else:
+                        new_html_path = page_new_before_hash + "/"
+                else:
+                    if page_new_before_hash.endswith(".md"):
+                        new_html_path = page_new_before_hash[:-3] + ".html"
+                    else:
+                        new_html_path = page_new_before_hash + ".html"
+                new_html_path += hash + suffix
+            
+            _write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -5,6 +5,7 @@ import os
 import json
 import click
 import nbformat
+import re
 
 logger = logging.getLogger(__name__)
 NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
@@ -88,12 +89,10 @@ def has_blocklisted_command(code: str, metadata: dict) -> bool:
             return True
     return False
 
+MERMAID_PATTERN = re.compile(r'display\(Image\((\w+)\.get_graph\(\)\.draw_mermaid_png\(\)\)\)')
+
 def remove_mermaid(code: str) -> str:
-    return code.replace(
-        "display(Image(graph.get_graph().draw_mermaid_png()))",
-        # replace with a dummy statement
-        "print()"
-    )
+    return MERMAID_PATTERN.sub('print()', code)
 
 
 def add_vcr_to_notebook(
@@ -108,6 +107,8 @@ def add_vcr_to_notebook(
             continue
 
         lines = cell.source.splitlines()
+        # remove the special tag for hidden cells
+        lines = [line for line in lines if not line.strip().startswith("# hide-cell")]
         # skip if empty cell
         if not lines:
             continue
@@ -195,6 +196,11 @@ def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.No
             continue
 
         cell.source = remove_mermaid(cell.source)
+
+        # skip the cell entirely if it contains PYPPETEER
+        if "PYPPETEER" in cell.source:
+            cell.source = ""
+
     return notebook
 
 

docs/_scripts/third_party_page/create_third_party_page.py

@@ -15,9 +15,10 @@ If you’re looking for other prebuilt libraries, explore the community-built op
 below. These libraries can extend LangGraph's functionality in various ways.
 
 ## 📚 Available Libraries
-
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-{library_list}
+
+:::python
+{python_library_list}
 
 ## ✨ Contributing Your Library
 
@@ -28,16 +29,39 @@ To share your project, simply open a Pull Request adding an entry for your packa
 
 **Guidelines**
 
-- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
-  for JavaScript/TypeScript, etc.) 📦
+- Your repo must be distributed as an installable package on PyPI 📦
 - The repo should either use the Graph API (exposing a `StateGraph` instance) or 
   the Functional API (exposing an `entrypoint`).
 - The package must include documentation (e.g., a `README.md` or docs site) 
   explaining how to use it.
-  
+
 We'll review your contribution and merge it in!
 
 Thanks for contributing! 🚀
+:::
+
+:::js
+{js_library_list}
+
+## ✨ 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! 🏆
+
+To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
+
+**Guidelines**
+
+- Your repo must be distributed as an installable package on npm 📦
+- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
+  the Functional API (exposing an `entrypoint`).
+- The package must include documentation (e.g., a `README.md` or docs site) 
+  explaining how to use it.
+
+We'll review your contribution and merge it in!
+
+Thanks for contributing! 🚀
+:::
 """
 
 
@@ -46,36 +70,18 @@ class ResolvedPackage(TypedDict):
     """The name of the package."""
     repo: str
     """Repository ID within github. Format is: [orgname]/[repo_name]."""
+    monorepo_path: str | None
+    """Optional: The path to the package in the monorepo. Must be relative to the root of the monorepo."""
+    language: str
+    """The language of the package. (either 'python' or 'js')"""
     weekly_downloads: int | None
     """The weekly download count of the package."""
     description: str
     """A brief description of what the package does."""
 
-
-def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
-    """Generate the markdown content for the third party page.
-
-    Args:
-        resolved_packages: A list of resolved package information.
-        language: str
-
-    Returns:
-        The markdown content as a string.
+def generate_package_table(resolved_packages: List[ResolvedPackage]) -> str:
+    """Generate the package table for the third party page.
     """
-    # Update the URL to the actual file once the initial version is merged
-    if language == "python":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraph/blob/main/docs"
-            "/_scripts/third_party_page/packages.yml"
-        )
-    elif language == "js":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
-            "/_scripts/third_party/packages.yml"
-        )
-    else:
-        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
-
     sorted_packages = sorted(
         resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
     )
@@ -85,7 +91,15 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
     ]
     for package in sorted_packages:
         name = f"**{package['name']}**"
-        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
+        
+        monorepo_path = package.get("monorepo_path", "")
+        if monorepo_path:
+            monorepo_path = monorepo_path[1:] if monorepo_path.startswith('/') else monorepo_path
+            repo_url_suffix = f"/tree/main/{monorepo_path}"
+        else:
+            repo_url_suffix = ""
+        repo_url = f"https://github.com/{package['repo']}{repo_url_suffix}"
+
         stars_badge = (
             f"https://img.shields.io/github/stars/{package['repo']}?style=social"
         )
@@ -93,13 +107,39 @@ def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -
         downloads = package["weekly_downloads"] or "-"
         row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
         rows.append(row)
+    return "\n".join(rows)
+
+def generate_markdown(resolved_packages: List[ResolvedPackage]) -> str:
+    """Generate the markdown content for the third party page.
+
+    Args:
+        resolved_packages: A list of resolved package information.
+
+    Returns:
+        The markdown content as a string.
+    """
+    # Update the URL to the actual file once the initial version is merged
+    langgraph_url = (
+        "https://github.com/langchain-ai/langgraph/blob/main/docs"
+        "/_scripts/third_party_page/packages.yml"
+    )
+
+    python_library_list = generate_package_table(
+        [p for p in resolved_packages if p["language"] == "python"]
+    )
+    js_library_list = generate_package_table(
+        [p for p in resolved_packages if p["language"] == "js"]
+    )
+    
     markdown_content = MARKDOWN.format(
-        library_list="\n".join(rows), langgraph_url=langgraph_url
+        python_library_list=python_library_list,
+        js_library_list=js_library_list,
+        langgraph_url=langgraph_url,
     )
     return markdown_content
 
 
-def main(input_file: str, output_file: str, language: str) -> None:
+def main(input_file: str, output_file: str) -> None:
     """Main function to create the third party page.
 
     Args:
@@ -111,7 +151,7 @@ def main(input_file: str, output_file: str, language: str) -> None:
     with open(input_file, "r") as f:
         resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
 
-    markdown_content = generate_markdown(resolved_packages, language)
+    markdown_content = generate_markdown(resolved_packages)
 
     # Write the markdown content to the output file
     with open(output_file, "w", encoding="utf-8") as f:
@@ -127,12 +167,6 @@ if __name__ == "__main__":
     parser.add_argument(
         "output_file", help="Path to the output file for the third party page."
     )
-    parser.add_argument(
-        "--language",
-        choices=["python", "js"],
-        default="python",
-        help="The language for which to generate the third party page. Defaults to 'python'.",
-    )
     args = parser.parse_args()
 
-    main(args.input_file, args.output_file, args.language)
+    main(args.input_file, args.output_file)

docs/_scripts/third_party_page/get_download_stats.py

@@ -11,101 +11,146 @@ import yaml
 
 
 class Package(TypedDict):
-    """A TypedDict representing a package"""
-
     name: str
     """The name of the package."""
     repo: str
     """Repository ID within github. Format is: [orgname]/[repo_name]."""
+    monorepo_path: str | None
+    """The path to the package in the monorepo. Only used for JS packages."""
     description: str
     """A brief description of what the package does."""
 
-
 class ResolvedPackage(Package):
     weekly_downloads: int | None
-
+    """The weekly download count of the package."""
+    language: str
+    """The language of the package. (either 'python' or 'js')"""
 
 HERE = pathlib.Path(__file__).parent
 PACKAGES_FILE = HERE / "packages.yml"
-PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
+PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())["packages"]
 
+def _get_pypi_downloads(package: Package) -> int:
+    """Retrieve the weekly download count for a package from PyPIStats."""
 
-def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
-    """Retrieve the monthly download count for a list of packages from PyPIStats."""
+    # First check if package exists on PyPI
+    pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
+    try:
+        pypi_response = requests.get(pypi_url)
+        pypi_response.raise_for_status()
+    except requests.exceptions.HTTPError:
+        raise AssertionError(f"Package {package['name']} does not exist on PyPI")
+
+    # Get first release date
+    pypi_data = pypi_response.json()
+    releases = pypi_data["releases"]
+    first_release_date = None
+    for version_releases in releases.values():
+        if version_releases:  # Some versions may be empty lists
+            upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
+            if first_release_date is None or upload_time < first_release_date:
+                first_release_date = upload_time
+
+    if first_release_date is None:
+        raise AssertionError(f"Package {package['name']} has no releases yet")
+
+    # If package was published in last 48 hours, skip download stats
+    if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
+        url = f"https://pypistats.org/api/packages/{package['name']}/overall"
+
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+
+        sorted_data = sorted(
+            data["data"],
+            key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
+            reverse=True,
+        )
+
+        # Sum the last 7 days of downloads
+        return sum(entry["downloads"] for entry in sorted_data[:7])
+    else:
+        return None
+    
+def _get_npm_downloads(package: Package) -> int:
+    """Retrieve the weekly download count for a package on the npm registry."""
+
+    # Check if package exists on the npm registry
+    npm_url = f"https://registry.npmjs.org/{package['name']}"
+    try:
+        npm_response = requests.get(npm_url)
+        npm_response.raise_for_status()
+    except requests.exceptions.HTTPError:
+        raise AssertionError(f"Package {package['name']} does not exist on npm registry")
+
+    npm_data = npm_response.json()
+
+    # Retrieve the first publish date using the 'created' timestamp from the 'time' field.
+    created_str = npm_data.get("time", {}).get("created")
+    if created_str is None:
+        raise AssertionError(f"Package {package['name']} has no creation time in registry data")
+    # Remove the trailing 'Z' if present and parse the ISO format timestamp
+    first_publish_date = datetime.fromisoformat(created_str.rstrip("Z"))
+
+    # If package was published more than 48 hours ago, fetch download stats.
+    if (datetime.now() - first_publish_date).total_seconds() >= 48 * 3600:
+        stats_url = f"https://api.npmjs.org/downloads/point/last-week/{package['name']}"
+        stats_response = requests.get(stats_url)
+        stats_response.raise_for_status()
+        stats_data = stats_response.json()
+        return stats_data.get("downloads", None)
+    else:
+        return None
+
+def _get_weekly_downloads(packages: dict[str, list[Package]], fake: bool) -> list[ResolvedPackage]:
+    """Retrieve the weekly download count for a dictionary of python or js packages."""
     resolved_packages: list[ResolvedPackage] = []
 
     if fake:
         # To avoid making network requests during testing, return fake download counts
-        for package in packages:
+        for language, package_list in packages.items():
+            for package in package_list:
+                resolved_packages.append(
+                    {
+                        "name": package["name"],
+                        "repo": package["repo"],
+                        "monorepo_path": package.get("monorepo_path", None),
+                        "language": language,
+                        "description": package["description"],
+                        "weekly_downloads": -12345,
+                    }
+                )
+        return resolved_packages
+
+    for language, package_list in packages.items():
+        for package in package_list:
+            if language == "python":
+                num_downloads = _get_pypi_downloads(package)
+            elif language == "js":
+                num_downloads = _get_npm_downloads(package)
+            else:
+                num_downloads = None
+            
             resolved_packages.append(
                 {
                     "name": package["name"],
                     "repo": package["repo"],
-                    "weekly_downloads": -12345,
+                    "monorepo_path": package.get("monorepo_path", None),
+                    "language": language,
                     "description": package["description"],
+                    "weekly_downloads": num_downloads,
                 }
             )
-        return resolved_packages
-
-    for package in packages:
-        # First check if package exists on PyPI
-        pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
-        try:
-            pypi_response = requests.get(pypi_url)
-            pypi_response.raise_for_status()
-        except requests.exceptions.HTTPError:
-            raise AssertionError(f"Package {package['name']} does not exist on PyPI")
-
-        # Get first release date
-        pypi_data = pypi_response.json()
-        releases = pypi_data["releases"]
-        first_release_date = None
-        for version_releases in releases.values():
-            if version_releases:  # Some versions may be empty lists
-                upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
-                if first_release_date is None or upload_time < first_release_date:
-                    first_release_date = upload_time
-
-        if first_release_date is None:
-            raise AssertionError(f"Package {package['name']} has no releases yet")
-
-        # If package was published in last 48 hours, skip download stats
-        if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
-            url = f"https://pypistats.org/api/packages/{package['name']}/overall"
-
-            response = requests.get(url)
-            response.raise_for_status()
-            data = response.json()
-
-            sorted_data = sorted(
-                data["data"],
-                key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
-                reverse=True,
-            )
-
-            # Sum the last 7 days of downloads
-            num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
-        else:
-            num_downloads = None
-
-        resolved_packages.append(
-            {
-                "name": package["name"],
-                "repo": package["repo"],
-                "weekly_downloads": num_downloads,
-                "description": package["description"],
-            }
-        )
 
     return resolved_packages
 
-
-
 def main(output_file: str, fake: bool) -> None:
     """Main function to generate package download information.
 
     Args:
         output_file: Path to the output YAML file.
+        fake: If True, use fake download counts for testing purposes.
     """
     resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
 

docs/_scripts/third_party_page/packages.yml

@@ -1,41 +1,58 @@
 #A list of third-party packages to surface on the third-party page.
 packages:
-  - name: "trustcall"
-    repo: "hinthornw/trustcall"
-    description: "Tenacious tool calling built on LangGraph."
-  - name: "breeze-agent"
-    repo: "andrestorres123/breeze-agent"
-    description: "A streamlined research system built inspired on STORM and built on LangGraph."
-  - name: "langgraph-supervisor"
-    repo: "langchain-ai/langgraph-supervisor-py"
-    description: "Build supervisor multi-agent systems with LangGraph."
-  - name: "langmem"
-    repo: "langchain-ai/langmem"
-    description: "Build agents that learn and adapt from interactions over time."
-  - name: "langchain-mcp-adapters"
-    repo: "langchain-ai/langchain-mcp-adapters"
-    description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-  - name: "open-deep-research"
-    repo: "langchain-ai/open_deep_research"
-    description: "Open source assistant for iterative web research and report writing."
-  - name: "langgraph-swarm"
-    repo: "langchain-ai/langgraph-swarm-py"
-    description: "Build swarm-style multi-agent systems using LangGraph."
-  - name: "delve-taxonomy-generator"
-    repo: "andrestorres123/delve"
-    description: "A taxonomy generator for unstructured data"
-  - name: "nodeology"
-    repo: "xyin-anl/Nodeology"
-    description: "Enable researcher to build scientific workflows easily with simplified interface."
-  - name: "langgraph-bigtool"
-    repo: "langchain-ai/langgraph-bigtool"
-    description: "Build LangGraph agents with large numbers of tools."
-  - name: "ai-data-science-team"
-    repo: "business-science/ai-data-science-team"
-    description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
-  - name: "langgraph-reflection"
-    repo: "langchain-ai/langgraph-reflection"
-    description: "LangGraph agent that runs a reflection step."
-  - name: "langgraph-codeact"
-    repo: "langchain-ai/langgraph-codeact"
-    description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
+  python:
+    - name: "trustcall"
+      repo: "hinthornw/trustcall"
+      description: "Tenacious tool calling built on LangGraph."
+    - name: "breeze-agent"
+      repo: "andrestorres123/breeze-agent"
+      description: "A streamlined research system built inspired on STORM and built on LangGraph."
+    - name: "langgraph-supervisor"
+      repo: "langchain-ai/langgraph-supervisor-py"
+      description: "Build supervisor multi-agent systems with LangGraph."
+    - name: "langmem"
+      repo: "langchain-ai/langmem"
+      description: "Build agents that learn and adapt from interactions over time."
+    - name: "langchain-mcp-adapters"
+      repo: "langchain-ai/langchain-mcp-adapters"
+      description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
+    - name: "open-deep-research"
+      repo: "langchain-ai/open_deep_research"
+      description: "Open source assistant for iterative web research and report writing."
+    - name: "langgraph-swarm"
+      repo: "langchain-ai/langgraph-swarm-py"
+      description: "Build swarm-style multi-agent systems using LangGraph."
+    - name: "delve-taxonomy-generator"
+      repo: "andrestorres123/delve"
+      description: "A taxonomy generator for unstructured data"
+    - name: "nodeology"
+      repo: "xyin-anl/Nodeology"
+      description: "Enable researcher to build scientific workflows easily with simplified interface."
+    - name: "langgraph-bigtool"
+      repo: "langchain-ai/langgraph-bigtool"
+      description: "Build LangGraph agents with large numbers of tools."
+    - name: "ai-data-science-team"
+      repo: "business-science/ai-data-science-team"
+      description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
+    - name: "langgraph-reflection"
+      repo: "langchain-ai/langgraph-reflection"
+      description: "LangGraph agent that runs a reflection step."
+    - name: "langgraph-codeact"
+      repo: "langchain-ai/langgraph-codeact"
+      description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
+  js:
+    - name: "@langchain/mcp-adapters"
+      repo: "langchain-ai/langchainjs"
+      description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
+    - name: "@langchain/langgraph-supervisor"
+      repo: "langchain-ai/langgraphjs"
+      monorepo_path: "libs/langgraph-supervisor"
+      description: "Build supervisor multi-agent systems with LangGraph"
+    - name: "@langchain/langgraph-swarm"
+      repo: "langchain-ai/langgraphjs"
+      monorepo_path: "libs/langgraph-swarm"
+      description: "Build multi-agent swarms with LangGraph"
+    - name: "@langchain/langgraph-cua"
+      repo: "langchain-ai/langgraphjs"
+      monorepo_path: "libs/langgraph-cua"
+      description: "Build computer use agents with LangGraph"

docs/cassettes/graph-api_04924627-9326-438b-aa52-91cea76f4477.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/graph-api_a6033c2a-3b56-46f5-9c3f-79310e31e545.msgpack.zlib

@@ -0,0 +1 @@
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

docs/cassettes/persistence-functional_08ae8246-11d5-40e1-8567-361e5bef8917.msgpack.zlib

@@ -1 +1 @@
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docs/cassettes/time-travel_f0bab935-6d9b-4cfd-afe8-bfcf4638847e.msgpack.zlib

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docs/docs/additional-resources/index.md

@@ -0,0 +1,11 @@
+# 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.

docs/docs/adopters.md

@@ -8,24 +8,41 @@ 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 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) |
+| [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) |
 | [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 | [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 | [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) |
 | [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 | [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 | [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) |
 | [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 | [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/) |
+| [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/) |
 | [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) |

docs/docs/agents/agents.md

@@ -15,23 +15,40 @@ 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/admin-keys) API key 
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key
 
 ## 1. Install dependencies
 
 If you haven't already, install LangGraph and LangChain:
 
+:::python
+
 ```
 pip install -U langgraph "langchain[anthropic]"
 ```
 
-!!! info 
+!!! info
 
-    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+    `langchain[anthropic]` is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+
+:::
+
+:::js
+
+```bash
+npm install @langchain/langgraph @langchain/core @langchain/anthropic
+```
+
+!!! info
+
+    `@langchain/core` `@langchain/anthropic` are installed so the agent can call the [model](https://js.langchain.com/docs/integrations/chat/).
+
+:::
 
 ## 2. Create an agent
 
-To create an agent, use [`create_react_agent`](langgraph.prebuilt.chat_agent_executor.create_react_agent):
+:::python
+To create an agent, use @[`create_react_agent`][create_react_agent]:
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -52,13 +69,56 @@ 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](./tools.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](../how-tos/tool-calling.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.
+   :::
+
+:::js
+To create an agent, use [`createReactAgent`](https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html):
+
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const getWeather = tool(
+  // (1)!
+  async ({ city }) => {
+    return `It's always sunny in ${city}!`;
+  },
+  {
+    name: "get_weather",
+    description: "Get weather for a given city.",
+    schema: z.object({
+      city: z.string().describe("The city to get weather for"),
+    }),
+  }
+);
+
+const agent = createReactAgent({
+  llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }), // (2)!
+  tools: [getWeather], // (3)!
+  stateModifier: "You are a helpful assistant", // (4)!
+});
+
+// Run the agent
+await agent.invoke({
+  messages: [{ role: "user", content: "what is the weather in sf" }],
+});
+```
+
+1. Define a tool for the agent to use. Tools can be defined using the `tool` function. 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.
+   :::
 
 ## 3. Configure an LLM
 
+:::python
 To configure an LLM with specific parameters, such as temperature, use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):
 
 ```python
@@ -79,19 +139,45 @@ agent = create_react_agent(
 )
 ```
 
+:::
+
+:::js
+To configure an LLM with specific parameters, such as temperature, use a model instance:
+
+```typescript
+import { ChatAnthropic } from "@langchain/anthropic";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+// highlight-next-line
+const model = new ChatAnthropic({
+  model: "claude-3-5-sonnet-latest",
+  // highlight-next-line
+  temperature: 0,
+});
+
+const agent = createReactAgent({
+  // highlight-next-line
+  llm: model,
+  tools: [getWeather],
+});
+```
+
+:::
+
 For more information on how to configure LLMs, see [Models](./models.md).
 
 ## 4. Add a custom prompt
 
 Prompts instruct the LLM how to behave. Add one of the following types of prompts:
 
-* **Static**: A string is interpreted as a **system message**.
-* **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
+- **Static**: A string is interpreted as a **system message**.
+- **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
 
 === "Static prompt"
 
     Define a fixed prompt string or list of messages:
 
+    :::python
     ```python
     from langgraph.prebuilt import create_react_agent
 
@@ -107,9 +193,30 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
         {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
     )
     ```
+    :::
+
+    :::js
+    ```typescript
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { ChatAnthropic } from "@langchain/anthropic";
+
+    const agent = createReactAgent({
+      llm: new ChatAnthropic({ model: "anthropic:claude-3-5-sonnet-latest" }),
+      tools: [getWeather],
+      // A static prompt that never changes
+      // highlight-next-line
+      stateModifier: "Never answer questions about the weather."
+    });
+
+    await agent.invoke({
+      messages: [{ role: "user", content: "what is the weather in sf" }]
+    });
+    ```
+    :::
 
 === "Dynamic prompt"
 
+    :::python
     Define a function that returns a message list based on the agent's state and configuration:
 
     ```python
@@ -144,12 +251,52 @@ Prompts instruct the LLM how to behave. Add one of the following types of prompt
         - Internal agent state updated during a multi-step reasoning process (using `state`).
 
         Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
+    :::
+
+    :::js
+    Define a function that returns messages based on the agent's state and configuration:
+
+    ```typescript
+    import { type BaseMessageLike } from "@langchain/core/messages";
+    import { type RunnableConfig } from "@langchain/core/runnables";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+    // highlight-next-line
+    const dynamicPrompt = (state: { messages: BaseMessageLike[] }, config: RunnableConfig): BaseMessageLike[] => {  // (1)!
+      const userName = config.configurable?.user_name;
+      const systemMsg = `You are a helpful assistant. Address the user as ${userName}.`;
+      return [{ role: "system", content: systemMsg }, ...state.messages];
+    };
+
+    const agent = createReactAgent({
+      llm: "anthropic:claude-3-5-sonnet-latest",
+      tools: [getWeather],
+      // highlight-next-line
+      stateModifier: dynamicPrompt
+    });
+
+    await agent.invoke(
+      { messages: [{ role: "user", content: "what is the weather in sf" }] },
+      // highlight-next-line
+      { configurable: { user_name: "John Smith" } }
+    );
+    ```
+
+    1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
+
+        - Information passed at runtime, like a `user_id` or API credentials (using `config`).
+        - Internal agent state updated during a multi-step reasoning process (using `state`).
+
+        Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
+    :::
 
 For more information, see [Context](./context.md).
 
 ## 5. Add memory
 
-To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
+To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a checkpointer when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
+
+:::python
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -180,17 +327,60 @@ 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](./memory.md#short-term-memory) and [human-in-the-loop](./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](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
 2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
+   :::
 
+:::js
+
+```typescript
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { MemorySaver } from "@langchain/langgraph";
+
+// highlight-next-line
+const checkpointer = new MemorySaver();
+
+const agent = createReactAgent({
+  llm: "anthropic:claude-3-5-sonnet-latest",
+  tools: [getWeather],
+  // highlight-next-line
+  checkpointSaver: checkpointer, // (1)!
+});
+
+// Run the agent
+// highlight-next-line
+const config = { configurable: { thread_id: "1" } };
+const sfResponse = await agent.invoke(
+  { messages: [{ role: "user", content: "what is the weather in sf" }] },
+  // highlight-next-line
+  config // (2)!
+);
+const nyResponse = await agent.invoke(
+  { messages: [{ role: "user", content: "what about new york?" }] },
+  // highlight-next-line
+  config
+);
+```
+
+1. `checkpointSaver` 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.
+2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
+   :::
+
+:::python
 When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
+:::
+
+:::js
+When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `MemorySaver`).
+:::
 
 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](./memory.md).
+For more information, see [Memory](../how-tos/memory/add-memory.md).
 
 ## 6. Configure structured output
 
+:::python
 To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.
 
 ```python
@@ -215,9 +405,43 @@ response = agent.invoke(
 response["structured_response"]
 ```
 
-1. When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
+1.  When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
 
-    To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
+        To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
+
+    :::
+
+:::js
+To produce structured responses conforming to a schema, use the `responseFormat` parameter. The schema can be defined with a `Zod` schema. The result will be accessible via the `structuredResponse` field.
+
+```typescript
+import { z } from "zod";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+const WeatherResponse = z.object({
+  conditions: z.string(),
+});
+
+const agent = createReactAgent({
+  llm: "anthropic:claude-3-5-sonnet-latest",
+  tools: [getWeather],
+  // highlight-next-line
+  responseFormat: WeatherResponse, // (1)!
+});
+
+const response = await agent.invoke({
+  messages: [{ role: "user", content: "what is the weather in sf" }],
+});
+
+// highlight-next-line
+response.structuredResponse;
+```
+
+1.  When `responseFormat` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
+
+        To provide a system prompt to this LLM, use an object `{ prompt, schema }`, e.g., `responseFormat: { prompt, schema: WeatherResponse }`.
+
+    :::
 
 !!! Note "LLM post-processing"
 

docs/docs/agents/context.md

@@ -1,147 +1,176 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Context
 
-Agents often require more than a list of messages to function effectively. They need **context**.
+**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that an AI application can accomplish a task. Context can be characterized along two key dimensions:
 
-Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
+1. By **mutability**:
+    - **Static context**: Immutable data that doesn't change during execution (e.g., user metadata, database connections, tools)
+    - **Dynamic context**: Mutable data that evolves as the application runs (e.g., conversation history, intermediate results, tool call observations)
+2. By **lifetime**:
+    - **Runtime context**: Data scoped to a single run or invocation
+    - **Cross-conversation context**: Data that persists across multiple conversations or sessions
 
-- 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.
+!!! tip "Runtime context vs LLM context"
 
-LangGraph provides **three** primary ways to supply context:
+    Runtime context refers to local context: data and dependencies your code needs to run. It does **not** refer to:
 
-| Type                                                                         | Description                                   | Mutable? | Lifetime                |
-|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
-| [**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    |
+    * 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 can use context to:
+    Runtime context can be used to optimize the LLM context. For example, you can use user metadata
+    in the runtime context to fetch user preferences and feed them into the context window.
 
-- Adjust the system prompt the model sees
-- Feed tools with necessary inputs
-- Track facts during an ongoing conversation
+LangGraph provides three ways to manage context, which combines the mutability and lifetime dimensions:
 
-## Providing Runtime Context
+:::python
 
-Use this when you need to inject data into an agent at runtime.
+| Context type                                                                                | Description                                            | Mutability | Lifetime           | Access method                           |
+| ------------------------------------------------------------------------------------------- | ------------------------------------------------------ | ---------- | ------------------ | --------------------------------------- |
+| [**Static runtime context**](#static-runtime-context)                                       | User metadata, tools, db connections passed at startup | Static     | Single run         | `context` argument to `invoke`/`stream` |
+| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state)                       | Mutable data that evolves during a single run          | Dynamic    | Single run         | LangGraph state object                  |
+| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations            | Dynamic    | Cross-conversation | LangGraph store                         |
 
-### Config (static context)
+## Static runtime context
 
-Config is for immutable data like user metadata or API keys. Use
-when you have values that don't change mid-run.
+**Static runtime context** represents immutable data like user metadata, tools, and database connections that are passed to an application at the start of a run via the `context` argument to `invoke`/`stream`. This data does not change during execution.
 
-Specify configuration using a key called **"configurable"** which is reserved
-for this purpose:
+!!! version-added "New in LangGraph v0.6: `context` replaces `config['configurable']`"
+
+    Runtime context is now passed to the `context` argument of `invoke`/`stream`,
+    which replaces the previous pattern of passing application configuration to `config['configurable']`.
 
 ```python
-agent.invoke(
-    {"messages": [{"role": "user", "content": "hi!"}]},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-### State (mutable context)
-
-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
+@dataclass
+class ContextSchema:
     user_name: str
 
-agent = create_react_agent(
-    # Other agent parameters...
+graph.invoke( # (1)!
+    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    state_schema=CustomState,
+    context={"user_name": "John Smith"} # (3)!
 )
-
-agent.invoke({
-    "messages": "hi!",
-    "user_name": "Jane"
-})
 ```
 
-!!! tip "Turning on memory"
+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.
 
-    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.
-
-
-
-### Long-Term Memory (cross-conversation context)
-
-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).
-
-## Customizing Prompts with Context { #prompts }
-
-Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
-
-Common use cases:
-
-- Personalization
-- Role or goal customization
-- Conditional behavior (e.g., user is admin)
-
-=== "Using config"
+=== "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
-    from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
+    from langgraph.runtime import get_runtime
     from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent
 
-    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}"
+    # 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],
-        # highlight-next-line
-        prompt=prompt
+        prompt=prompt,
+        context_schema=ContextSchema
     )
 
     agent.invoke(
-        ...,
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
-        config={"configurable": {"user_name": "John Smith"}}
+        context={"user_name": "John Smith"}
     )
     ```
 
-=== "Using state"
+    * See [Agents](../agents/agents.md) for details.
 
+=== "Workflow node"
+
+    ```python
+    from langgraph.runtime import Runtime
+
+    # highlight-next-line
+    def node(state: State, config: Runtime[ContextSchema]):
+        user_name = runtime.context.user_name
+        ...
+    ```
+
+    * See [the Graph API](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#add-runtime-configuration) for details.
+
+=== "In a tool"
+
+    ```python
+    from langgraph.runtime import get_runtime
+
+    @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.
+
+!!! tip
+
+    The `Runtime` object can be used to access static context and other utilities like the active store and stream writer.
+    See the [Runtime][langgraph.runtime.Runtime] documentation for details.
+
+:::
+
+:::js
+
+| Context type                                                                                | Description                                   | Mutability | Lifetime           |
+| ------------------------------------------------------------------------------------------- | --------------------------------------------- | ---------- | ------------------ |
+| [**Config**](#config-static-context)                                                        | data passed at the start of a run             | Static     | Single run         |
+| [**Dynamic runtime context (state)**](#dynamic-runtime-context-state)                       | Mutable data that evolves during a single run | Dynamic    | Single run         |
+| [**Dynamic cross-conversation context (store)**](#dynamic-cross-conversation-context-store) | Persistent data shared across conversations   | Dynamic    | Cross-conversation |
+
+## Config (static context)
+
+Config is for immutable data like user metadata or API keys. Use this when you have values that don't change mid-run.
+
+Specify configuration using a key called **"configurable"** which is reserved for this purpose.
+
+```typescript
+await graph.invoke(
+  // (1)!
+  { messages: [{ role: "user", content: "hi!" }] }, // (2)!
+  // highlight-next-line
+  { configurable: { user_id: "user_123" } } // (3)!
+);
+```
+
+:::
+
+## Dynamic runtime context (state)
+
+**Dynamic runtime context** represents mutable data that can evolve during a single run and is managed through the LangGraph state object. This includes conversation history, intermediate results, and values derived from tools or LLM outputs. In LangGraph, the state object acts as [short-term memory](../concepts/memory.md) during a run.
+
+=== "In an agent"
+
+    Example shows how to incorporate state into an agent **prompt**.
+
+    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.
+
+    :::python
     ```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
+    # highlight-next-line
+    class CustomState(AgentState): # (1)!
         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"]
@@ -150,87 +179,130 @@ Common use cases:
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[...],
         # highlight-next-line
-        state_schema=CustomState,
-        # highlight-next-line
+        state_schema=CustomState, # (2)!
         prompt=prompt
     )
 
     agent.invoke({
         "messages": "hi!",
-        # highlight-next-line
         "user_name": "John Smith"
     })
     ```
 
-## Accessing Context in Tools { #tools }
+    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.
+    :::
 
-Tools can access context through special parameter **annotations**.
+    :::js
+    ```typescript
+    import type { BaseMessage } from "@langchain/core/messages";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+    import { MessagesZodState } from "@langchain/langgraph";
+    import { z } from "zod";
 
-* Use `RunnableConfig` for config access
-* Use `Annotated[StateSchema, InjectedState]` for agent state
+    // highlight-next-line
+    const CustomState = z.object({ // (1)!
+      messages: MessagesZodState.shape.messages,
+      userName: z.string(),
+    });
 
+    const prompt = (
+      // highlight-next-line
+      state: z.infer<typeof CustomState>
+    ): BaseMessage[] => {
+      const userName = state.userName;
+      const systemMsg = `You are a helpful assistant. User's name is ${userName}`;
+      return [{ role: "system", content: systemMsg }, ...state.messages];
+    };
 
-!!! tip 
+    const agent = createReactAgent({
+      llm: model,
+      tools: [...],
+      // highlight-next-line
+      stateSchema: CustomState, // (2)!
+      stateModifier: prompt,
+    });
 
-    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
-
-=== "Using config"
-
-    ```python
-    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"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-    )
-
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "look up user information"}]},
-        # highlight-next-line
-        config={"configurable": {"user_id": "user_123"}}
-    )
+    await agent.invoke({
+      messages: [{ role: "user", content: "hi!" }],
+      userName: "John Smith",
+    });
     ```
 
-=== "Using State"
+    1. Define a custom state schema that extends `MessagesZodState` or creates a new schema.
+    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
+    :::
 
+=== "In a workflow"
+
+    :::python
     ```python
-    from typing import Annotated
-    from langgraph.prebuilt import InjectedState
+    from typing_extensions import TypedDict
+    from langchain_core.messages import AnyMessage
+    from langgraph.graph import StateGraph
 
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_id: str
+    # highlight-next-line
+    class CustomState(TypedDict): # (1)!
+        messages: list[AnyMessage]
+        extra_field: int
 
-    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"
+    # highlight-next-line
+    def node(state: CustomState): # (2)!
+        messages = state["messages"]
+        ...
+        return { # (3)!
+            # highlight-next-line
+            "extra_field": state["extra_field"] + 1
+        }
 
-    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"
-    })
+    builder = StateGraph(State)
+    builder.add_node(node)
+    builder.set_entry_point("node")
+    graph = builder.compile()
     ```
 
-### Update Context from Tools
+    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.
+    :::
 
-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.
+    :::js
+    ```typescript
+    import type { BaseMessage } from "@langchain/core/messages";
+    import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+    import { z } from "zod";
+
+    // highlight-next-line
+    const CustomState = z.object({ // (1)!
+      messages: MessagesZodState.shape.messages,
+      extraField: z.number(),
+    });
+
+    const builder = new StateGraph(CustomState)
+      .addNode("node", async (state) => { // (2)!
+        const messages = state.messages;
+        // ...
+        return { // (3)!
+          // highlight-next-line
+          extraField: state.extraField + 1,
+        };
+      })
+      .addEdge(START, "node");
+
+    const graph = builder.compile();
+    ```
+
+    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.
+    :::
+
+!!! tip "Turning on memory"
+
+    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.
+
+## Dynamic cross-conversation context (store)
+
+**Dynamic cross-conversation context** represents persistent, mutable data that spans across multiple conversations or sessions and is managed through the LangGraph store. This includes user profiles, preferences, and historical interactions. The LangGraph store acts as [long-term memory](../concepts/memory.md#long-term-memory) across multiple runs. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions).
+
+For more information, see the [Memory guide](../how-tos/memory/add-memory.md).

docs/docs/agents/deployment.md

@@ -1,92 +0,0 @@
----
-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 Cloud or self-hosted options. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

docs/docs/agents/evals.md

@@ -11,25 +11,62 @@ hide:
 
 To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:
 
+:::python
+
 ```python
 def evaluator(*, outputs: dict, reference_outputs: dict):
     # compare agent outputs against reference outputs
     output_messages = outputs["messages"]
-    reference_messages = reference["messages"]
+    reference_messages = reference_outputs["messages"]
     score = compare_messages(output_messages, reference_messages)
     return {"key": "evaluator_score", "score": score}
 ```
 
+:::
+
+:::js
+
+```typescript
+type EvaluatorParams = {
+  outputs: Record<string, any>;
+  referenceOutputs: Record<string, any>;
+};
+
+function evaluator({ outputs, referenceOutputs }: EvaluatorParams) {
+  // compare agent outputs against reference outputs
+  const outputMessages = outputs.messages;
+  const referenceMessages = referenceOutputs.messages;
+  const score = compareMessages(outputMessages, referenceMessages);
+  return { key: "evaluator_score", score: score };
+}
+```
+
+:::
+
 To get started, you can use prebuilt evaluators from `AgentEvals` package:
 
+:::python
+
 ```bash
 pip install -U agentevals
 ```
 
+:::
+
+:::js
+
+```bash
+npm install agentevals
+```
+
+:::
+
 ## Create evaluator
 
 A common way to evaluate agent performance is by comparing its trajectory (the order in which it calls its tools) against a reference trajectory:
 
+:::python
+
 ```python
 import json
 # highlight-next-line
@@ -80,8 +117,63 @@ result = evaluator(
 )
 ```
 
-1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)
+:::
 
+:::js
+
+```typescript
+import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
+
+const outputs = [
+  {
+    role: "assistant",
+    tool_calls: [
+      {
+        function: {
+          name: "get_weather",
+          arguments: JSON.stringify({ city: "san francisco" }),
+        },
+      },
+      {
+        function: {
+          name: "get_directions",
+          arguments: JSON.stringify({ destination: "presidio" }),
+        },
+      },
+    ],
+  },
+];
+
+const referenceOutputs = [
+  {
+    role: "assistant",
+    tool_calls: [
+      {
+        function: {
+          name: "get_weather",
+          arguments: JSON.stringify({ city: "san francisco" }),
+        },
+      },
+    ],
+  },
+];
+
+// Create the evaluator
+const evaluator = createTrajectoryMatchEvaluator({
+  // Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: strict, unordered and subset
+  trajectoryMatchMode: "superset", // (1)!
+});
+
+// Run the evaluator
+const result = evaluator({
+  outputs: outputs,
+  referenceOutputs: referenceOutputs,
+});
+```
+
+:::
+
+1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)
 
 As a next step, learn more about how to [customize trajectory match evaluator](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#agent-trajectory-match).
 
@@ -89,6 +181,8 @@ As a next step, learn more about how to [customize trajectory match evaluator](h
 
 You can use LLM-as-a-judge evaluator that uses an LLM to compare the trajectory against the reference outputs and output a score:
 
+:::python
+
 ```python
 import json
 from agentevals.trajectory.llm import (
@@ -103,6 +197,24 @@ evaluator = create_trajectory_llm_as_judge(
 )
 ```
 
+:::
+
+:::js
+
+```typescript
+import {
+  createTrajectoryLlmAsJudge,
+  TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
+} from "agentevals/trajectory/llm";
+
+const evaluator = createTrajectoryLlmAsJudge({
+  prompt: TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
+  model: "openai:o3-mini",
+});
+```
+
+:::
+
 ## Run evaluator
 
 To run an evaluator, you will first need to create a [LangSmith dataset](https://docs.smith.langchain.com/evaluation/concepts#datasets). To use the prebuilt AgentEvals evaluators, you will need a dataset with the following schema:
@@ -110,6 +222,8 @@ To run an evaluator, you will first need to create a [LangSmith dataset](https:/
 - **input**: `{"messages": [...]}` input messages to call the agent with.
 - **output**: `{"messages": [...]}` expected message history in the agent output. For trajectory evaluation, you can choose to keep only assistant messages.
 
+:::python
+
 ```python
 from langsmith import Client
 from langgraph.prebuilt import create_react_agent
@@ -125,4 +239,27 @@ experiment_results = client.evaluate(
     data="<Name of your dataset>",
     evaluators=[evaluator]
 )
-```
+```
+
+:::
+
+:::js
+
+```typescript
+import { Client } from "langsmith";
+import { createReactAgent } from "@langchain/langgraph/prebuilt";
+import { createTrajectoryMatchEvaluator } from "agentevals/trajectory/match";
+
+const client = new Client();
+const agent = createReactAgent({...});
+const evaluator = createTrajectoryMatchEvaluator({...});
+
+const experimentResults = await client.evaluate(
+    (inputs) => agent.invoke(inputs),
+    // replace with your dataset name
+    { data: "<Name of your dataset>" },
+    { evaluators: [evaluator] }
+);
+```
+
+:::

docs/docs/agents/human-in-the-loop.md

@@ -1,238 +0,0 @@
----
-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`](../reference/types.md#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)

docs/docs/agents/mcp.md

@@ -7,47 +7,64 @@ hide:
   - tags
 ---
 
-# MCP Integration
+# Use MCP
 
 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
 
 ![MCP](./assets/mcp.png)
 
+:::python
 Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
 
 ```bash
 pip install langchain-mcp-adapters
 ```
 
+:::
+
+:::js
+Install the `@langchain/mcp-adapters` library to use MCP tools in LangGraph:
+
+```bash
+npm install langchain-mcp-adapters
+```
+
+:::
+
 ## Use MCP tools
 
+:::python
 The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
 
-```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
+=== "In an agent"
 
-# highlight-next-line
-async with 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/sse",
-            "transport": "sse",
+    ```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",
+            }
         }
-    }
-) as client:
+    )
+    # highlight-next-line
+    tools = await client.get_tools()
     agent = create_react_agent(
         "anthropic:claude-3-7-sonnet-latest",
         # highlight-next-line
-        client.get_tools()
+        tools
     )
     math_response = await agent.ainvoke(
         {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
@@ -55,10 +72,185 @@ async with MultiServerMCPClient(
     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?"}]}
+    )
+    ```
+
+:::
+
+:::js
+The `@langchain/mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
+
+=== "In an agent"
+
+    ```typescript title="Agent using tools defined on MCP servers"
+    // highlight-next-line
+    import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
+    import { ChatAnthropic } from "@langchain/langgraph/prebuilt";
+    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+    // highlight-next-line
+    const client = new MultiServerMCPClient({
+      math: {
+        command: "node",
+        // Replace with absolute path to your math_server.js file
+        args: ["/path/to/math_server.js"],
+        transport: "stdio",
+      },
+      weather: {
+        // Ensure you start your weather server on port 8000
+        url: "http://localhost:8000/mcp",
+        transport: "streamable_http",
+      },
+    });
+
+    // highlight-next-line
+    const tools = await client.getTools();
+    const agent = createReactAgent({
+      llm: new ChatAnthropic({ model: "claude-3-7-sonnet-latest" }),
+      // highlight-next-line
+      tools,
+    });
+
+    const mathResponse = await agent.invoke({
+      messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
+    });
+
+    const weatherResponse = await agent.invoke({
+      messages: [{ role: "user", content: "what is the weather in nyc?" }],
+    });
+    ```
+
+=== "In a workflow"
+
+    ```typescript
+    import { MultiServerMCPClient } from "langchain-mcp-adapters/client";
+    import { StateGraph, MessagesZodState, START } from "@langchain/langgraph";
+    import { ToolNode } from "@langchain/langgraph/prebuilt";
+    import { ChatOpenAI } from "@langchain/openai";
+    import { AIMessage } from "@langchain/core/messages";
+    import { z } from "zod";
+
+    const model = new ChatOpenAI({ model: "gpt-4" });
+
+    const client = new MultiServerMCPClient({
+      math: {
+        command: "node",
+        // Make sure to update to the full absolute path to your math_server.js file
+        args: ["./examples/math_server.js"],
+        transport: "stdio",
+      },
+      weather: {
+        // make sure you start your weather server on port 8000
+        url: "http://localhost:8000/mcp/",
+        transport: "streamable_http",
+      },
+    });
+
+    const tools = await client.getTools();
+
+    const builder = new StateGraph(MessagesZodState)
+      .addNode("callModel", async (state) => {
+        const response = await model.bindTools(tools).invoke(state.messages);
+        return { messages: [response] };
+      })
+      .addNode("tools", new ToolNode(tools))
+      .addEdge(START, "callModel")
+      .addConditionalEdges("callModel", (state) => {
+        const lastMessage = state.messages.at(-1) as AIMessage | undefined;
+        if (!lastMessage?.tool_calls?.length) {
+          return "__end__";
+        }
+        return "tools";
+      })
+      .addEdge("tools", "callModel");
+
+    const graph = builder.compile();
+
+    const mathResponse = await graph.invoke({
+      messages: [{ role: "user", content: "what's (3 + 5) x 12?" }],
+    });
+
+    const weatherResponse = await graph.invoke({
+      messages: [{ role: "user", content: "what is the weather in nyc?" }],
+    });
+    ```
+
+:::
 
 ## Custom MCP servers
 
+:::python
 To create your own MCP servers, you can use the `mcp` library. This library provides a simple way to define tools and run them as servers.
 
 Install the MCP library:
@@ -66,8 +258,24 @@ Install the MCP library:
 ```bash
 pip install mcp
 ```
+
+:::
+
+:::js
+To create your own MCP servers, you can use the `@modelcontextprotocol/sdk` library. This library provides a simple way to define tools and run them as servers.
+
+Install the MCP SDK:
+
+```bash
+npm install @modelcontextprotocol/sdk
+```
+
+:::
+
 Use the following reference implementations to test your agent with MCP tool servers.
 
+:::python
+
 ```python title="Example Math Server (stdio transport)"
 from mcp.server.fastmcp import FastMCP
 
@@ -87,7 +295,116 @@ if __name__ == "__main__":
     mcp.run(transport="stdio")
 ```
 
-```python title="Example Weather Server (SSE transport)"
+:::
+
+:::js
+
+```typescript title="Example Math Server (stdio transport)"
+import { Server } from "@modelcontextprotocol/sdk/server/index.js";
+import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
+import {
+  CallToolRequestSchema,
+  ListToolsRequestSchema,
+} from "@modelcontextprotocol/sdk/types.js";
+
+const server = new Server(
+  {
+    name: "math-server",
+    version: "0.1.0",
+  },
+  {
+    capabilities: {
+      tools: {},
+    },
+  }
+);
+
+server.setRequestHandler(ListToolsRequestSchema, async () => {
+  return {
+    tools: [
+      {
+        name: "add",
+        description: "Add two numbers",
+        inputSchema: {
+          type: "object",
+          properties: {
+            a: {
+              type: "number",
+              description: "First number",
+            },
+            b: {
+              type: "number",
+              description: "Second number",
+            },
+          },
+          required: ["a", "b"],
+        },
+      },
+      {
+        name: "multiply",
+        description: "Multiply two numbers",
+        inputSchema: {
+          type: "object",
+          properties: {
+            a: {
+              type: "number",
+              description: "First number",
+            },
+            b: {
+              type: "number",
+              description: "Second number",
+            },
+          },
+          required: ["a", "b"],
+        },
+      },
+    ],
+  };
+});
+
+server.setRequestHandler(CallToolRequestSchema, async (request) => {
+  switch (request.params.name) {
+    case "add": {
+      const { a, b } = request.params.arguments as { a: number; b: number };
+      return {
+        content: [
+          {
+            type: "text",
+            text: String(a + b),
+          },
+        ],
+      };
+    }
+    case "multiply": {
+      const { a, b } = request.params.arguments as { a: number; b: number };
+      return {
+        content: [
+          {
+            type: "text",
+            text: String(a * b),
+          },
+        ],
+      };
+    }
+    default:
+      throw new Error(`Unknown tool: ${request.params.name}`);
+  }
+});
+
+async function main() {
+  const transport = new StdioServerTransport();
+  await server.connect(transport);
+  console.error("Math MCP server running on stdio");
+}
+
+main();
+```
+
+:::
+
+:::python
+
+```python title="Example Weather Server (Streamable HTTP transport)"
 from mcp.server.fastmcp import FastMCP
 
 mcp = FastMCP("Weather")
@@ -98,10 +415,103 @@ async def get_weather(location: str) -> str:
     return "It's always sunny in New York"
 
 if __name__ == "__main__":
-    mcp.run(transport="sse")
+    mcp.run(transport="streamable-http")
 ```
 
+:::
+
+:::js
+
+```typescript title="Example Weather Server (HTTP transport)"
+import { Server } from "@modelcontextprotocol/sdk/server/index.js";
+import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";
+import {
+  CallToolRequestSchema,
+  ListToolsRequestSchema,
+} from "@modelcontextprotocol/sdk/types.js";
+import express from "express";
+
+const app = express();
+app.use(express.json());
+
+const server = new Server(
+  {
+    name: "weather-server",
+    version: "0.1.0",
+  },
+  {
+    capabilities: {
+      tools: {},
+    },
+  }
+);
+
+server.setRequestHandler(ListToolsRequestSchema, async () => {
+  return {
+    tools: [
+      {
+        name: "get_weather",
+        description: "Get weather for location",
+        inputSchema: {
+          type: "object",
+          properties: {
+            location: {
+              type: "string",
+              description: "Location to get weather for",
+            },
+          },
+          required: ["location"],
+        },
+      },
+    ],
+  };
+});
+
+server.setRequestHandler(CallToolRequestSchema, async (request) => {
+  switch (request.params.name) {
+    case "get_weather": {
+      const { location } = request.params.arguments as { location: string };
+      return {
+        content: [
+          {
+            type: "text",
+            text: `It's always sunny in ${location}`,
+          },
+        ],
+      };
+    }
+    default:
+      throw new Error(`Unknown tool: ${request.params.name}`);
+  }
+});
+
+app.post("/mcp", async (req, res) => {
+  const transport = new SSEServerTransport("/mcp", res);
+  await server.connect(transport);
+});
+
+const PORT = process.env.PORT || 8000;
+app.listen(PORT, () => {
+  console.log(`Weather MCP server running on port ${PORT}`);
+});
+```
+
+:::
+
+:::python
+
 ## Additional resources
 
 - [MCP documentation](https://modelcontextprotocol.io/introduction)
-- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)
+  :::
+
+:::js
+
+## Additional resources
+
+- [MCP documentation](https://modelcontextprotocol.io/introduction)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [`@langchain/mcp-adapters`](https://npmjs.com/package/@langchain/mcp-adapters)
+  :::