Update

langgraph-cli-install
2026-08-20 06:35:46 +02:00 · 2025-03-07 10:49:59 -08:00 · 2025-03-07 10:21:20 -08:00
772 changed files with 98911 additions and 87182 deletions
@@ -1,6 +1,6 @@
 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: [pending,bug]
+labels: ["02 Bug Report"]
 body:
  - type: markdown
    attributes:
@@ -1,4 +1,4 @@
-blank_issues_enabled: true
+blank_issues_enabled: false
 version: 2.1
 contact_links:
  - name: 🤔 Question or Problem
@@ -1,7 +1,7 @@
 name: Documentation
 description: Report an issue related to the LangGraph documentation.
 title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [documentation]
+labels: [03 - Documentation]

 body:
 - type: textarea
@@ -0,0 +1,88 @@
+# 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') }}
@@ -1,11 +0,0 @@
-# Please see the documentation for all configuration options:
-# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
-# and
-# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
-
-version: 2
-updates:
-  - package-ecosystem: "github-actions"
-    directory: "/"
-    schedule:
-      interval: "weekly"
@@ -3,6 +3,9 @@ name: CLI integration test
 on:
  workflow_call:

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -22,14 +25,13 @@ jobs:
        uses: Ana06/get-changed-files@v2.3.0
        with:
          filter: "libs/cli/**"
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
        if: steps.changed-files.outputs.all
-        uses: astral-sh/setup-uv@v6
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: "cli-integration-test"
-          ignore-nothing-to-cache: true
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: integration-test-cli
      - name: Setup env
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples
@@ -69,3 +71,4 @@ jobs:
        working-directory: libs/cli/js-examples
        run: |
          langgraph build -t langgraph-test-e
+  
@@ -9,6 +9,8 @@ on:
        description: "From which folder this pipeline executes"

 env:
+  POETRY_VERSION: "1.7.1"
+
  # This env var allows us to get inline annotations when ruff has complaints.
  RUFF_OUTPUT_FORMAT: github

@@ -34,28 +36,48 @@ jobs:
        uses: Ana06/get-changed-files@v2.3.0
        with:
          filter: "${{ inputs.working-directory }}/**"
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
        if: steps.changed-files.outputs.all
-        uses: astral-sh/setup-uv@v6
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: lint-${{ inputs.working-directory }}
+          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
+
+      - name: Check lock file
+        if: steps.changed-files.outputs.all
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: poetry lock --check

      - 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: uv sync --frozen --group dev
+        run: poetry install --with dev

      - name: Get .mypy_cache to speed up mypy
        if: steps.changed-files.outputs.all
-        uses: actions/cache@v4
+        uses: actions/cache@v3
        env:
          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
        with:
          path: |
            ${{ inputs.working-directory }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}

      - name: Analysing package code with our lint
        if: steps.changed-files.outputs.all
@@ -70,18 +92,27 @@ 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: uv sync --group dev
+        run: |
+          poetry install --with dev

      - name: Get .mypy_cache_test to speed up mypy
        if: steps.changed-files.outputs.all
-        uses: actions/cache@v4
+        uses: actions/cache@v3
        env:
          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
        with:
          path: |
            ${{ inputs.working-directory }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}

      - name: Analysing tests with our lint
        if: steps.changed-files.outputs.all
@@ -8,6 +8,9 @@ on:
        type: string
        description: "From which folder this pipeline executes"

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -23,12 +26,12 @@ jobs:
    name: "test #${{ matrix.python-version }}"
    steps:
      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: test-${{ inputs.working-directory }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: test-${{ inputs.working-directory }}
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -39,12 +42,14 @@ jobs:
      - name: Install dependencies
        shell: bash
        working-directory: ${{ inputs.working-directory }}
-        run: uv sync --frozen --group dev
+        run: |
+          poetry install --with 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
@@ -3,6 +3,9 @@ name: test
 on:
  workflow_call:

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  build:
    runs-on: ubuntu-latest
@@ -21,12 +24,12 @@ jobs:
    name: "test #${{ matrix.python-version }}"
    steps:
      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ matrix.python-version }}
-          enable-cache: true
-          cache-suffix: "test-langgraph"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: test-langgraph
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -36,11 +39,13 @@ jobs:

      - name: Install dependencies
        shell: bash
-        run: uv sync --frozen --group dev
+        run: |
+          poetry install --with 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
@@ -9,6 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
+  POETRY_VERSION: "1.7.1"
  PYTHON_VERSION: "3.10"

 jobs:
@@ -23,12 +24,12 @@ jobs:
    steps:
      - uses: actions/checkout@v4

-      - name: Set up Python $${ env.PYTHON_VERSION }}
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release

      # We want to keep this build stage *separate* from the release stage,
      # so that there's no sharing of permissions between them.
@@ -42,7 +43,7 @@ jobs:
      # > from the publish job.
      # https://github.com/pypa/gh-action-pypi-publish#non-goals
      - name: Build project for distribution
-        run: uv build
+        run: poetry build
        working-directory: ${{ inputs.working-directory }}

      - name: Upload build
@@ -56,8 +57,8 @@ jobs:
        shell: bash
        working-directory: ${{ inputs.working-directory }}
        run: |
-          echo pkg-name=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
-          echo version=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
+          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
+          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT

  publish:
    needs:
@@ -0,0 +1,57 @@
+name: test
+
+on:
+  workflow_call:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+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'
@@ -7,6 +7,9 @@ on:
    paths:
      - "libs/**"

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  benchmark:
    runs-on: ubuntu-latest
@@ -16,14 +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
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "bench"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: bench
      - name: Install dependencies
-        run: uv sync --group dev
+        run: poetry install --with dev
      - name: Run benchmarks
        run: OUTPUT=out/benchmark-baseline.json make -s benchmark
      - name: Save outputs
@@ -5,6 +5,9 @@ on:
    paths:
      - "libs/**"

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  benchmark:
    runs-on: ubuntu-latest
@@ -18,14 +21,14 @@ jobs:
        uses: Ana06/get-changed-files@v2.3.0
        with:
          format: json
-      - name: Set up Python 3.11
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python 3.11 + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "bench"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: bench
      - name: Install dependencies
-        run: uv sync --group dev
+        run: poetry install --with dev
      - name: Download baseline
        uses: actions/cache/restore@v4
        with:
@@ -40,7 +43,7 @@ jobs:
        run: |
          {
            echo 'OUTPUT<<EOF'
-            make -s benchmark-fast
+            make -s benchmark
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Compare benchmarks
@@ -50,7 +53,7 @@ jobs:
            echo 'OUTPUT<<EOF'
            mv out/benchmark-baseline.json out/main.json
            mv out/benchmark.json out/changes.json
-            uv run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
+            poetry run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Annotation
@@ -16,6 +16,9 @@ concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  changes:
    runs-on: ubuntu-latest
@@ -35,6 +38,7 @@ jobs:
              - 'libs/checkpoint/**'
              - 'libs/checkpoint-sqlite/**'
              - 'libs/checkpoint-postgres/**'
+              - 'libs/scheduler-kafka/**'
              - 'libs/prebuilt/**'
            sdk-js:
              - 'libs/sdk-js/**'
@@ -52,7 +56,7 @@ jobs:
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
-
+            "libs/scheduler-kafka",
            "libs/prebuilt",
          ]
    if: needs.changes.outputs.python == 'true'
@@ -88,6 +92,14 @@ jobs:
    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'
@@ -113,26 +125,26 @@ jobs:
          - "3.11"
    steps:
      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "schema-check-cli"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: schema-check-cli
      - name: Install CLI dependencies
        run: |
          cd libs/cli
-          uv sync
+          poetry install
      - 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
-          uv run python generate_schema.py
+          poetry 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 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
+            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."
            diff schemas/schema.json schemas/schema.current.json
            exit 1
          fi
@@ -157,9 +169,9 @@ jobs:
      run:
        working-directory: ${{ matrix.working-directory }}
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
        with:
          node-version: "20"
          cache: "yarn"
@@ -183,9 +195,9 @@ jobs:
      run:
        working-directory: ${{ matrix.working-directory }}
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
        with:
          node-version: "20"
          cache: "yarn"
@@ -203,6 +215,7 @@ jobs:
        lint-js,
        test,
        test-langgraph,
+        test-scheduler-kafka,
        check-sdk-methods,
        check-schema,
        integration-test,
@@ -9,6 +9,9 @@ on:
      - main
  workflow_dispatch:

+env:
+  POETRY_VERSION: "1.7.1"
+
 permissions:
  contents: read
  pages: write
@@ -35,13 +38,12 @@ 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 }}
+  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
@@ -54,23 +56,42 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Set up Python
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: "3.12"
-          enable-cache: true
-          cache-suffix: "docs"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: docs
+
+      - name: Use Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: "22"
+          cache: "yarn"
+          cache-dependency-path: docs/yarn.lock

      - name: Install dependencies
        run: |
          yarn
-          uv sync --all-groups
+          poetry install --with test --with docs --no-root
+          poetry run pip install -U \
+            pytest \
+            pytest-check-links \
+            GitPython \
+            "git+https://github.com/benjamincburns/markdown-exec.git@cc0d39d737e5ffd4b83d23cd8729d7ea16e363c8"
+
          # we run this installation only for internal PRs
          # as GITHUB_TOKEN is not available for PRs from outside contributors
          if [ -n "${GITHUB_TOKEN}" ]; then
-            uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
+            poetry run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
          fi

+          poetry run jupyter kernelspec list
+          poetry run python3 -m ipykernel install --user --name=python3
+          npm install -g tslab
+          poetry run tslab install --python=python3
+          poetry run jupyter kernelspec list
+
      - name: Run unit tests
        # Run unit tests on the docs build pipeline
        run: make tests
@@ -81,14 +102,7 @@ jobs:
      - name: Build llms-text
        run: make llms-text
      - name: Build site
-        run: |
-          # If this is main branch, then we want to download stats. we do this 
-          # with the env variable DOWNLOAD_STATS=true
-          if [ "${{ github.ref }}" == "refs/heads/main" ]; then
-            DOWNLOAD_STATS=true make build-docs
-          else
-            make build-docs
-          fi
+        run: make build-docs
        env:
          MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
@@ -96,11 +110,10 @@ jobs:
      - name: Check links in notebooks
        env:
          LANGCHAIN_API_KEY: test
-        if: github.event_name == 'schedule'
        run: |
-          if [ "${{ github.event_name }}" == "schedule" ]; then
+          if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
            echo "Running link check on all HTML files matching notebooks in docs directory..."
-            uv run pytest -v \
+            poetry 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/.*" \
@@ -114,7 +127,6 @@ jobs:
              --check-links-ignore "https://openai\.com/.*" \
              --check-links-ignore "https://www\.uber\.com/.*" \
              --check-links-ignore "https://pepy\.tech/.*" \
-              --check-links-ignore "docs/docs/static/wordmark_*" \
              --check-links $(find site -name "index.html" | grep -v 'storm/index.html')
              
          else
@@ -125,7 +137,7 @@ jobs:
            echo "Changed files: ${CHANGED_FILES}"
            if [ -n "${CHANGED_FILES}" ]; then
              echo "Running link check on HTML files matching changed notebook files..."
-              uv run pytest -v \
+              poetry 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/.*" \
@@ -135,7 +147,6 @@ jobs:
                --check-links-ignore "https://twitter.com/.*" \
                --check-links-ignore "https://github\.com/.*" \
                --check-links-ignore "/.*\.(ipynb|html)$" \
-                --check-links-ignore "docs/docs/static/wordmark_*" \
                --check-links ${CHANGED_FILES} \
                || ([ $? = 5 ] && exit 0 || exit $?)
            else
@@ -145,7 +156,7 @@ jobs:

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

      - name: Upload Pages Artifact
        # if: github.ref == 'refs/heads/main'
@@ -11,6 +11,9 @@ on:
    - cron: "0 5 * * *"
  workflow_dispatch:

+env:
+  POETRY_VERSION: "1.7.1"
+
 jobs:
  markdown-link-check:
    runs-on: ubuntu-latest
@@ -39,8 +42,8 @@ jobs:

      - name: Check README.md is in sync
        run: |
-          if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
-          echo "README.md is out of sync with libs/langgraph/README.md"
-          diff -C 3 README.md libs/langgraph/README.md
-          exit 1
-          fi
+            if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
+            echo "README.md is out of sync with libs/langgraph/README.md"
+            diff -C 3 README.md libs/langgraph/README.md
+            exit 1
+            fi
@@ -10,6 +10,7 @@ on:

 env:
  PYTHON_VERSION: "3.11"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -25,12 +26,12 @@ jobs:
    steps:
      - uses: actions/checkout@v4

-      - name: Set up Python
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release

      # We want to keep this build stage *separate* from the release stage,
      # so that there's no sharing of permissions between them.
@@ -44,7 +45,7 @@ jobs:
      # > from the publish job.
      # https://github.com/pypa/gh-action-pypi-publish#non-goals
      - name: Build project for distribution
-        run: uv build
+        run: poetry build
        working-directory: ${{ inputs.working-directory }}

      - name: Upload build
@@ -58,8 +59,8 @@ jobs:
        shell: bash
        working-directory: ${{ inputs.working-directory }}
        run: |
-          PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
-          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
+          PKG_NAME="$(poetry version | cut -d ' ' -f 1)"
+          VERSION="$(poetry version --short)"
          SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
          if [ -z $SHORT_PKG_NAME ]; then
            TAG="$VERSION"
@@ -162,11 +163,11 @@ jobs:
      # - The package is published, and it breaks on the missing dependency when
      #   used in the real world.

-      - name: Set up Python
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
+          poetry-version: ${{ env.POETRY_VERSION }}

      - name: Import published package
        shell: bash
@@ -184,18 +185,18 @@ jobs:
        # - attempt install again after 5 seconds if it fails because there is
        #   sometimes a delay in availability on test pypi
        run: |
-          uv run pip install \
+          poetry run pip install \
            --extra-index-url https://test.pypi.org/simple/ \
            "$PKG_NAME==$VERSION" || \
          ( \
            sleep 5 && \
-            uv run pip install \
+            poetry run pip install \
              --extra-index-url https://test.pypi.org/simple/ \
              "$PKG_NAME==$VERSION" \
          )

          if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            uv run pip install langgraph
+            poetry run pip install langgraph
          fi

          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
@@ -208,10 +209,10 @@ jobs:
            IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
          fi

-          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"

      - name: Import test dependencies
-        run: uv sync --group dev
+        run: poetry install --with dev
        working-directory: ${{ inputs.working-directory }}

      # Overwrite the local version of the package with the test PyPI version.
@@ -222,7 +223,7 @@ jobs:
          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
          VERSION: ${{ needs.build.outputs.version }}
        run: |
-          uv run pip install \
+          poetry run pip install \
            --extra-index-url https://test.pypi.org/simple/ \
            "$PKG_NAME==$VERSION"

@@ -252,12 +253,12 @@ jobs:
    steps:
      - uses: actions/checkout@v4

-      - name: Set up Python
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release

      - uses: actions/download-artifact@v4
        with:
@@ -293,12 +294,12 @@ jobs:
    steps:
      - uses: actions/checkout@v4

-      - name: Set up Python
-        uses: astral-sh/setup-uv@v6
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
        with:
          python-version: ${{ env.PYTHON_VERSION }}
-          enable-cache: true
-          cache-suffix: "release"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release

      - uses: actions/download-artifact@v4
        with:
@@ -22,7 +22,7 @@ jobs:
      - uses: actions/checkout@v4
      # JS Build
      - name: Use Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
        with:
          node-version: "20"
          cache: "yarn"
@@ -9,7 +9,7 @@ on:
        type: string
        description: "JSON string of changed files"
  schedule:
-    - cron: "0 13 * * *"
+    - cron: '0 13 * * *'

 defaults:
  run:
@@ -27,43 +27,43 @@ jobs:
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python + Poetry
-        uses: astral-sh/setup-uv@v6
+        uses: "./.github/actions/poetry_setup"
        with:
-          python-version: "3.11"
-          enable-cache: true
-          cache-suffix: "test-langgraph-notebooks"
+          python-version: 3.11
+          poetry-version: 1.7.1
+          cache-key: test-langgraph-notebooks

      - name: Install dependencies
        run: |
-          uv sync --group test
-          uv run pip install jupyter
+          poetry install --with test
+          poetry run pip install jupyter

      - name: Start services
        run: make start-services

      - name: Pre-download tiktoken files
        run: |
-          uv run python _scripts/download_tiktoken.py
+          poetry run python _scripts/download_tiktoken.py

      - name: Prepare notebooks
        run: |
          if [ "${{ matrix.lib-version }}" = "development" ]; then
-            uv run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
+            poetry run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
          else
-            uv run python _scripts/prepare_notebooks_for_ci.py
+            poetry run python _scripts/prepare_notebooks_for_ci.py
          fi

      - name: Run notebooks
        env:
          # these won't actually be used because of the VCR cassettes
          # but need to set them to avoid triggering getpass()
-          OPENAI_API_KEY: "very-secret-key"
-          ANTHROPIC_API_KEY: "very-secret-key"
-          TAVILY_API_KEY: "very-secret-key"
-          LANGSMITH_API_KEY: "very-secret-key"
-          NOMIC_API_KEY: "very-secret-key"
-          COHERE_API_KEY: "very-secret-key"
-          FIREWORKS_API_KEY: "very-secret-key"
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
        run: |
          if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
            echo "Running all notebooks"
@@ -0,0 +1,29 @@
+name: Check File Size
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  file-size-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+      - name: Filter by size
+        # TODO: roll back the web voyager hack
+        run: |
+          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M | grep -v "web_voyager" || true)
+          if [ -n "$large_added_files" ]; then
+            echo "Large files added: $large_added_files"
+            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
+            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
+            exit 1
+          fi
@@ -180,5 +180,3 @@ Chinook.db

 .vercel
 .turbo
-.editorconfig
-.scratch
@@ -1,55 +0,0 @@
-# AGENTS Instructions
-
-This repository is a monorepo. Each library lives in a subdirectory under `libs/`.
-
-When you modify code in any library, run the following commands in that library's directory before creating a pull request:
-
- `make format` – run code formatters
- `make lint` – run the linter
- `make test` – execute the test suite
-
-To run a particular test file or to pass additional pytest options you can specify the `TEST` variable:
-
-```
-TEST=path/to/test.py make test
-```
-
-Other pytest arguments can also be supplied inside the `TEST` variable.
-
-## Libraries
-
-The repository contains several Python and JavaScript/TypeScript libraries.
-Below is a high-level overview:
-
- **checkpoint** – base interfaces for LangGraph checkpointers.
- **checkpoint-postgres** – Postgres implementation of the checkpoint saver.
- **checkpoint-sqlite** – SQLite implementation of the checkpoint saver.
- **cli** – official command-line interface for LangGraph.
- **langgraph** – core framework for building stateful, multi-actor agents.
- **prebuilt** – high-level APIs for creating and running agents and tools.
- **sdk-js** – JS/TS SDK for interacting with the LangGraph REST API.
- **sdk-py** – Python SDK for the LangGraph Platform API.
-
-### Dependency map
-
-The diagram below lists downstream libraries for each production dependency as
-declared in that library's `pyproject.toml` (or `package.json`).
-
-```text
-checkpoint
-├── checkpoint-postgres
-├── checkpoint-sqlite
-├── prebuilt
-└── langgraph
-
-prebuilt
-└── langgraph
-
-sdk-py
-├── langgraph
-└── cli
-
-sdk-js (standalone)
-```
-
-Changes to a library may impact all of its dependents shown above.
@@ -109,7 +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 the way they do.
+impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.


 To quote the Diataxis website:
@@ -153,7 +153,7 @@ 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 actual need.
+We generally do not merge new tutorials from outside contributors without an actue need.
 We welcome updates as well as new integration docs, how-tos, and references.

 ### Avoid duplication
@@ -227,7 +227,6 @@ 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
 ```
@@ -1,58 +0,0 @@
-# Define the directories containing projects
-LIBS_DIRS := $(wildcard libs/*)
-
-# Default target
-.PHONY: all
-all: lint format lock test
-
-# Install dependencies for all projects
-.PHONY: install
-install:
-	@echo "Creating virtual environment..."
-	@uv venv
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/pyproject.toml ]; then \
-			echo "Installing dependencies for $$dir"; \
-			uv pip install -e $$dir; \
-		fi; \
-	done
-
-# Lint all projects
-.PHONY: lint
-lint:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lint in $$dir"; \
-			$(MAKE) -C $$dir lint; \
-		fi; \
-	done
-
-# Format all projects
-.PHONY: format
-format:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running format in $$dir"; \
-			$(MAKE) -C $$dir format; \
-		fi; \
-	done
-
-# Lock all projects
-.PHONY: lock
-lock:
-	@for dir in $(LIBS_DIRS); do \
-		if [ -f $$dir/Makefile ]; then \
-			echo "Running lock in $$dir"; \
-			(cd $$dir && uv lock); \
-		fi; \
-	done
-
-# 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
@@ -1,83 +1,339 @@
-<picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
-</picture>
+# 🦜🕸️LangGraph

-<div>
-<br>
-</div>
-
-[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
+![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)

-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.
+⚡ Building language agents as graphs ⚡

-## Get started
+> [!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/).

-Install LangGraph:
+## Overview

-```
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
+
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+
+### Why use LangGraph?
+
+LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
+
+- **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**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
+
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
+
+### LangGraph Platform
+
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
+- **Background runs**: Runs agents asynchronously in the background
+- **Support for long running agents**: Infrastructure that can handle long running processes
+- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
+- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
+
+## Installation
+
+```shell
 pip install -U langgraph
 ```

-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+## Example

-```python
-# pip install -qU "langchain[anthropic]" to call the model
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!

-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}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
-
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+```shell
+pip install langchain-anthropic
 ```

-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/).
+```shell
+export ANTHROPIC_API_KEY=sk-...
+```

-## Core benefits
+Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.

-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:
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=lsv2_sk_...
+```

- [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.
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:

-## LangGraph’s ecosystem
+<details open>
+  <summary>High-level implementation</summary>

-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:
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool

- [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/).
- [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."

-> [!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
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)

- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()

-## Acknowledgements
+app = create_react_agent(model, tools, checkpointer=checkpointer)

-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
+
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
+> [!TIP]
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
+
+<details>
+<summary>Low-level implementation</summary>
+
+```python
+from typing import Literal
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
+
+
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
+
+
+tools = [search]
+
+tool_node = ToolNode(tools)
+
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
+
+# Define the function that determines whether to continue or not
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state['messages']
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user)
+    return END
+
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    messages = state['messages']
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define a new graph
+workflow = StateGraph(MessagesState)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.add_edge(START, "agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("tools", 'agent')
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable.
+# Note that we're (optionally) passing the memory when compiling the graph
+app = workflow.compile(checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+<b>Step-by-step Breakdown</b>:
+
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
+  </li>
+</ul>
+</details>
+
+<details>
+<summary>Initialize graph with state.</summary>
+
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
+
+<details>
+<summary>Define graph nodes.</summary>
+
+There are two main nodes we need:
+
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
+
+<details>
+<summary>Define entry point and graph edges.</summary>
+
+First, we need to set the entry point for graph execution - <code>agent</code> node.
+
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
+
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
+
+<details>
+<summary>Compile the graph.</summary>
+
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
+
+<details>
+<summary>Execute the graph.</summary>
+
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
+
+</details>
+
+## Documentation
+
+* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
+* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
+* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
+
+## Resources
+
+* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+
+## Contributing
+
+For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
@@ -10,32 +10,26 @@ build-prebuilt:
 	# Use to create an update to date prebuilt page.
 	# Looks up download stats for each of the prebuilt packages and
 	# generates the final prebuilt page.
-	@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
-		set -x; \
-		uv run python -m _scripts.third_party_page.get_download_stats stats.yml; \
-		set +x; \
-	else \
-		set -x; \
-		uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
-		set +x; \
-	fi
-	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
+	poetry run python -m _scripts.third_party_page.get_download_stats stats.yml
+	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/prebuilt.md --language python

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

 llms-text:
-	uv run python -m _scripts.generate_llms_text docs/llms-full.txt
+	poetry run python -m _scripts.generate_llms_text docs/llms-full.txt

 install-vercel-deps:
-	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
+	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

 tests:
 	# Run unit tests
-	uv run pytest tests/unit_tests
+	poetry run pytest tests/unit_tests


 vercel-build-docs: install-vercel-deps
@@ -43,10 +37,10 @@ vercel-build-docs: install-vercel-deps


 serve-clean-docs: clean-docs
-	uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
+	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph

 serve-docs: build-typedoc
-	uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
+	poetry 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
@@ -54,13 +48,13 @@ clean-docs:

 ## Run format against the project documentation.
 format-docs:
-	uv run ruff format docs
-	uv run ruff check --fix docs
+	poetry run ruff format docs
+	poetry run ruff check --fix docs

 # Check the docs for linting violations
 lint-docs:
-	uv run ruff format --check docs
-	uv run ruff check docs
+	poetry run ruff format --check docs
+	poetry run ruff check docs

 codespell:
 	./codespell_notebooks.sh .
@@ -3,7 +3,7 @@
 To setup requirements for building docs you can run:

 ```bash
-uv sync --group test
+poetry install --with test
 ```

 ## Serving documentation locally
@@ -14,8 +14,6 @@ To run the documentation server locally you can run:
 make serve-docs
 ```

-This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
-
 ## Execute notebooks

 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
@@ -58,4 +56,4 @@ To delete cassettes for a notebook, you can run:

 ```bash
 rm cassettes/<notebook_name>*
-```
+```
@@ -1,154 +1,157 @@
-"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
+"""Add typescript translation to a given markdown file."""

 import argparse
+import re

 import requests
 from langchain_anthropic import ChatAnthropic

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

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


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


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

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

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

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


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

-    if args.translate_only and args.consolidate_only:
-        raise ValueError(
-            "Cannot use both --translate-only and --consolidate-only at the same time."
-        )
-
-    main(
-        args.file_path,
-        translate_only=args.translate_only,
-        consolidate_only=args.consolidate_only,
-    )
+    main(args.file_path)
@@ -8,7 +8,7 @@ execute_notebook() {
    file="$1"
    echo "Starting execution of $file"
    start_time=$(date +%s)
-    if ! output=$(time uv run jupyter execute "$file" 2>&1); then
+    if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
        end_time=$(date +%s)
        execution_time=$((end_time - start_time))
        echo "Error in $file. Execution time: $execution_time seconds"
@@ -22,12 +22,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
        "create_react_agent",
        "prebuilt",
    ),
-    (
-        [],
-        "langgraph.prebuilt.chat_agent_executor",
-        "AgentState",
-        "prebuilt",
-    ),
    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
    (
        ["langgraph.prebuilt"],
@@ -51,8 +45,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
    (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
    (["langgraph.constants"], "langgraph.types", "Command", "types"),
-    (["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
-    (["langgraph.config"], "langgraph.config", "get_store", "config"),
    (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
    (["langgraph.func"], "langgraph.func", "task", "func"),
    (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
@@ -64,23 +56,10 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
    ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
    ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
-    ([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
    ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
-    # other prebuilts
-    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
-    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
-    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
-    ([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
-    ([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
-    ([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
-    ([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
 ]

 WELL_KNOWN_LANGGRAPH_OBJECTS = {
@@ -162,9 +141,7 @@ def get_imports(code: str, path: str) -> List[ImportInformation]:
    for found_import in found_imports:
        module = found_import["source"]

-        if module.startswith("langchain_mcp_adapters"):
-            package_ecosystem = "langgraph"
-        elif module.startswith("langchain"):
+        if module.startswith("langchain"):
            # Handles things like `langchain` or `langchain_anthropic`
            package_ecosystem = "langchain"
        elif module.startswith("langgraph"):
@@ -237,7 +214,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
        path: The path of the file where the markdown content originated.

    Returns:
-        Updated markdown with API reference links prepended to Python code blocks.
+        Updated markdown with API reference links appended to Python code blocks.

    Example:
        Given a markdown with a Python code block:
@@ -260,7 +237,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
            match (re.Match): The regex match object containing the code block.

        Returns:
-            str: The modified code block with API reference links prepended if applicable.
+            str: The modified code block with API reference links appended if applicable.
        """
        indent = match.group("indent")
        code_block = match.group("code")
@@ -276,8 +253,8 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
        api_links = " | ".join(
            f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
        )
-        # Return the code block with prepended API reference links
-        return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
+        # Return the code block with appended API reference links
+        return f"{original_code_block}\n\n{indent}API Reference: {api_links}"

    # Apply the replace_code_block function to all matches in the markdown
    updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
@@ -1,82 +1,38 @@
 """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,
-    _apply_conditional_rendering,
-)
+from _scripts.notebook_hooks import _on_page_markdown_with_config

 HERE = os.path.dirname(os.path.abspath(__file__))
 # Get source directory (parent of HERE / docs)
 SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))


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

    Args:
        output_file: Path to output the consolidated text file
    """
    # Collect all markdown and notebook files
-    all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
+    relative_paths = [
+        # Files relative to docs/docs/
+        "tutorials/introduction.ipynb",
+    ]
+    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
+
+    all_files.extend(
+        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)
    )
@@ -86,14 +42,30 @@ async def generate_full_llms_text(output_file: str) -> None:

    all_content = []

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

-    # Combine results with file paths
-    for file_path, processed_content in zip(all_files, results):
+        # Create File and Page objects to match mkdocs structure
+        file_obj = File(
+            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
+        )
+        page = Page(
+            title="",
+            file=file_obj,
+            config={},
+        )
+
+        # Read raw content
+        with open(file_path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Convert to markdown without logic to resolve API references
+        processed_content = _on_page_markdown_with_config(
+            content, page, add_api_references=False, remove_base64_images=True
+        )
        if processed_content:
-            rel_path = os.path.relpath(file_path, SOURCE_DIR)
            # Add file name
            all_content.append(f"---\n{rel_path}\n---")
            # Add content
@@ -104,170 +76,6 @@ async def generate_full_llms_text(output_file: str) -> None:
        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

@@ -277,23 +85,6 @@ 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()
-    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)
+    _make_llms_text(args.output_file)
@@ -1,5 +0,0 @@
-JS_LINK_MAP = {
-    "langgraph.types.interrupt": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph.interrupt-2.html",
-    "create_react_agent": "https://langchain-ai.github.io/langgraphjs/reference/functions/langgraph_prebuilt.createReactAgent.html",
-    "langgraph.types.Command": "https://langchain-ai.github.io/langgraphjs/reference/classes/langgraph.Command.html",
-}
@@ -1,6 +1,7 @@
 import ast
 import os
 import re
+from pathlib import Path
 from typing import Literal

 import nbformat
@@ -25,7 +26,7 @@ def _uses_input(source: str) -> bool:


 def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.
+    """Process a code block that uses cell magic.:w

    - Lines starting with "%%capture" are ignored.
    - Lines starting with "%pip" are rewritten by removing the leading "%" character.
@@ -51,14 +52,10 @@ def _rewrite_cell_magic(code: str) -> str:
        if stripped.startswith("%%capture"):
            continue
        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%") or stripped.startswith("!"):
-            # Drop the leading '%' character and then drop all leading whitespace
-            stripped = stripped.lstrip("%! \t")
-            # Check if the line starts with "pip"
-            if stripped.startswith("pip"):
-                rewritten_lines.append(stripped)
-            else:
-                raise NotImplementedError(f"Unhandled line: {line}")
+        elif stripped.startswith("%pip"):
+            # Drop the leading '%' character
+            rewritten_lines.append(stripped[1:])
+        # Anything else is not supported
        else:
            raise NotImplementedError(f"Unhandled line: {line}")

@@ -220,24 +217,6 @@ def _convert_links_in_markdown(markdown: str) -> str:
    )


-class HideCellTagPreprocessor(Preprocessor):
-    """
-    Removes cells that have '# hide-cell' at the beginning of the cell content.
-    This allows authors to include cells in the notebook that should not
-    appear in the generated markdown output.
-    """
-
-    def preprocess(self, nb, resources):
-        # Filter out cells with the '# hide-cell' comment at the beginning
-        nb.cells = [
-            cell
-            for cell in nb.cells
-            if not (cell.source.strip().startswith("# hide-cell"))
-        ]
-
-        return nb, resources
-
-
 class EscapePreprocessor(Preprocessor):
    def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
@@ -268,10 +247,13 @@ class EscapePreprocessor(Preprocessor):
            )
            cell.metadata["exec"] = is_exec

-            # For markdown exec migration we'll re-write cell magic as bash commands
-            if source.startswith("%%"):
-                cell.source = _rewrite_cell_magic(source)
-                cell.metadata["language"] = "shell"
+            if self.markdown_exec_migration:
+                # For markdown exec migration we'll re-write cell magic as bash commands
+                if source.startswith("%%"):
+                    cell.source = _rewrite_cell_magic(source)
+                    cell.metadata["language"] = "shell"
+
+                cell.metadata["has_output"] = _has_output(source)

            # Remove noqa comments
            cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
@@ -359,7 +341,6 @@ class ExtractAttachmentsPreprocessor(Preprocessor):

 exporter = MarkdownExporter(
    preprocessors=[
-        HideCellTagPreprocessor,
        EscapePreprocessor,
        ExtractAttachmentsPreprocessor,
    ],
@@ -371,7 +352,7 @@ exporter = MarkdownExporter(


 def convert_notebook(
-    notebook_path: str,
+    notebook_path: Path,
    mode: Literal["markdown", "exec"] = "markdown",
 ) -> str:
    with open(notebook_path) as f:
@@ -1,18 +1,5 @@
 {% extends 'markdown/index.md.j2' %}

-{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
-```{%- if 'language' in cell.metadata -%}
-{{ cell.metadata.language }}
-{%- elif 'magics_language' in cell.metadata -%}
-{{ cell.metadata.magics_language }}
-{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
-{{ nb.metadata.language_info.name }}
-{%- endif %}
-{{ cell.source }}
-```
-{% endblock input %}
-
-
 {%- block traceback_line -%}
 ```output
 {{ line.rstrip() | strip_ansi }}
@@ -21,13 +8,13 @@

 {%- block stream -%}
 ```output
-{{ output.text.rstrip() | strip_ansi }}
+{{ output.text.rstrip() }}
 ```
 {%- endblock stream -%}

 {%- block data_text scoped -%}
 ```output
-{{ output.data['text/plain'].rstrip() | strip_ansi }}
+{{ output.data['text/plain'].rstrip() }}
 ```
 {%- endblock data_text -%}

@@ -38,13 +25,9 @@
 {%- endblock data_html -%}

 {%- block data_jpg scoped -%}
-<p>
-<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
-</p>
+![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
 {%- endblock data_jpg -%}

 {%- block data_png scoped -%}
-<p>
-<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
-</p>
+![](data:image/png;base64,{{ output.data['image/png'] }})
 {%- endblock data_png -%}
@@ -1,22 +1,14 @@
-"""mkdocs hooks for adding custom logic to documentation pipeline.
-
-Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
-"""
-
 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.notebook_convert import convert_notebook
-from _scripts.link_map import JS_LINK_MAP

 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -26,104 +18,19 @@ DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")

 REDIRECT_MAP = {
    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.md",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
+    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
+    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
+    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
+    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
+    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
    "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/async.ipynb": "how-tos/graph-api/#async",
-    # memory how-tos
-    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md",
-    "how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages",
-    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory/add-memory.md#summarize-messages",
-    "how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
-    "agents/memory.ipynb": "how-tos/memory/add-memory.md",
-    # 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",
-    # persistence how-tos
-    "how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production",
-    "how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production",
-    "how-tos/persistence_redis.ipynb": "how-tos/memory/add-memory.md#use-in-production",
-    "how-tos/subgraph-persistence.ipynb": "how-tos/memory/add-memory.md#use-with-subgraphs",
-    "how-tos/cross-thread-persistence.ipynb": "how-tos/memory/add-memory.md#add-long-term-memory",
-    "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",
-    # 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",
    # cloud redirects
-    "cloud/index.md": "index.md",
-    "cloud/how-tos/index.md": "concepts/langgraph_platform",
+    "cloud/index.md": "concepts/index.md#langgraph-platform",
+    "cloud/how-tos/index.md": "how-tos/index.md#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",
-    "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-structured-output.ipynb": "agents/agents.md#structured-output",
-    # Time-travel
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
-    # breakpoints
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
-    # misc
-    "prebuilt.md": "agents/prebuilt.md",
-    "reference/prebuilt.md": "reference/agents.md",
-    "concepts/high_level.md": "index.md",
-    "concepts/index.md": "index.md",
-    "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
-    "how-tos/index.md": "index.md",
-    "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
-    "agents/deployment.md": "tutorials/langgraph-platform/local-server.md",
-    # deployment redirects
-    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
-    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
-    "tutorials/deployment.md": "concepts/deployment_options.md",
-    # assistant redirects
-    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
-    "cloud/concepts/runs.md": "concepts/assistants.md#execution",
-    # hitl redirects
-    "how-tos/wait-user-input-functional.ipynb": "how-tos/use-functional-api.md",
-    "how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
-    "how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
-    "agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
 }


@@ -173,62 +80,6 @@ def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
    return code_block_pattern.sub(replace_code_block_header, markdown)


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

@@ -328,20 +179,6 @@ def _on_page_markdown_with_config(
    # Apply highlight comments to code blocks
    markdown = _highlight_code_blocks(markdown)

-    # Apply conditional rendering for code blocks
-    target_language = kwargs.get("target_language", "python")
-    markdown = _apply_conditional_rendering(markdown, target_language)
-    if target_language == "js":
-        markdown = _resolve_cross_references(markdown, JS_LINK_MAP)
-    elif target_language == "python":
-        # Via a dedicated plugin
-        pass
-    else:
-        raise ValueError(
-            f"Unsupported target language: {target_language}. "
-            "Supported languages are 'python' and 'js'."
-        )
-
    # Add file path as an attribute to code blocks that are executable.
    # This file path is used to associate fixtures with the executable code
    # which can be used in CI to test the docs without making network requests.
@@ -349,7 +186,7 @@ def _on_page_markdown_with_config(

    if remove_base64_images:
        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
+        markdown = re.sub(r"!\[.*?\]\(data:image/+;base64,[^\)]+\)", "", markdown)

    return markdown

@@ -383,7 +220,7 @@ 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)
@@ -399,52 +236,6 @@ def _write_html(site_dir, old_path, new_path):
        f.write(content)


-def _inject_gtm(html: str) -> str:
-    """Inject Google Tag Manager code into the HTML.
-
-    Code to inject Google Tag Manager noscript tag immediately after <body>.
-
-    This is done via hooks rather than via a template because the MkDocs material
-    theme does not seem to allow placing the code immediately after the <body> tag
-    without modifying the template files directly.
-
-    Args:
-        html: The HTML content to modify.
-
-    Returns:
-        The modified HTML content with GTM code injected.
-    """
-    # Code was copied from Google Tag Manager setup instructions.
-    gtm_code = """
-<!-- Google Tag Manager (noscript) -->
-<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T35S4S46"
-height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
-<!-- End Google Tag Manager (noscript) -->
-"""
-    soup = BeautifulSoup(html, "html.parser")
-    body = soup.body
-    if body:
-        # Insert the GTM code as raw HTML at the top of <body>
-        body.insert(0, BeautifulSoup(gtm_code, "html.parser"))
-        return str(soup)
-    else:
-        return html  # fallback if no <body> found
-
-
-def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
-    """Inject Google Tag Manager noscript tag immediately after <body>.
-
-    Args:
-        output: The HTML output of the page.
-        page: The page instance.
-        config: The MkDocs configuration object.
-
-    Returns:
-        modified HTML output with GTM code injected.
-    """
-    return _inject_gtm(output)
-
-
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):
    use_directory_urls = config.get("use_directory_urls")
@@ -461,4 +252,4 @@ def on_post_build(config):
            + hash
            + suffix
        )
-        _write_html(config["site_dir"], old_html_path, new_html_path)
+        write_html(config["site_dir"], old_html_path, new_html_path)
@@ -5,7 +5,6 @@ import os
 import json
 import click
 import nbformat
-import re

 logger = logging.getLogger(__name__)
 NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
@@ -21,6 +20,7 @@ BLOCKLIST_COMMANDS = (

 NOTEBOOKS_NO_CASSETTES = (
    "docs/how-tos/visualization.ipynb",
+    "docs/how-tos/many-tools.ipynb"
 )

 NOTEBOOKS_NO_EXECUTION = [
@@ -49,10 +49,7 @@ NOTEBOOKS_NO_EXECUTION = [
    "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
    "docs/tutorials/tot/tot.ipynb",
    "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/streaming-specific-nodes.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb",
-    "docs/tutorials/customer-support/customer-support.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
-    "docs/how-tos/many-tools.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
+    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
 ]


@@ -89,11 +86,6 @@ 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 MERMAID_PATTERN.sub('print()', code)
-

 def add_vcr_to_notebook(
    notebook: nbformat.NotebookNode, cassette_prefix: str
@@ -107,8 +99,6 @@ 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
@@ -190,20 +180,6 @@ def add_vcr_to_notebook(
    return notebook


-def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
-    for cell in notebook.cells:
-        if cell.cell_type != "code":
-            continue
-
-        cell.source = remove_mermaid(cell.source)
-
-        # skip the cell entirely if it contains PYPPETEER
-        if "PYPPETEER" in cell.source:
-            cell.source = ""
-
-    return notebook
-
-
 def process_notebooks(should_comment_install_cells: bool) -> None:
    for directory in NOTEBOOK_DIRS:
        for root, _, files in os.walk(directory):
@@ -225,8 +201,6 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                            notebook, cassette_prefix=cassette_prefix
                        )

-                    notebook = remove_mermaid_from_notebook(notebook)
-
                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
                        # Add a cell at the beginning to indicate that this notebook should not be executed
                        warning_cell = nbformat.v4.new_markdown_cell(
@@ -9,7 +9,10 @@ import yaml

 MARKDOWN = """\
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# Community Agents
+# 🚀 Prebuilt Agents
+
+LangGraph includes a prebuilt React agent. For more information on how to use it, 
+check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).

 If you’re looking for other prebuilt libraries, explore the community-built options 
 below. These libraries can extend LangGraph's functionality in various ways.
@@ -30,23 +30,10 @@ PACKAGES_FILE = HERE / "packages.yml"
 PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']


-def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
+def _get_weekly_downloads(packages: list[Package]) -> list[ResolvedPackage]:
    """Retrieve the monthly download count for a list of packages from PyPIStats."""
    resolved_packages: list[ResolvedPackage] = []

-    if fake:
-        # To avoid making network requests during testing, return fake download counts
-        for package in packages:
-            resolved_packages.append(
-                {
-                    "name": package["name"],
-                    "repo": package["repo"],
-                    "weekly_downloads": -12345,
-                    "description": package["description"],
-                }
-            )
-        return resolved_packages
-
    for package in packages:
        # First check if package exists on PyPI
        pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
@@ -101,13 +88,13 @@ def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedP



-def main(output_file: str, fake: bool) -> None:
+def main(output_file: str) -> None:
    """Main function to generate package download information.

    Args:
        output_file: Path to the output YAML file.
    """
-    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
+    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES)

    if not output_file.endswith(".yml"):
        raise ValueError("Output file must have a .yml extension")
@@ -128,15 +115,6 @@ if __name__ == "__main__":
            "downloads.yml"
        ),
    )
-    parser.add_argument(
-        "--fake",
-        default=False,
-        action="store_true",
-        help=(
-            "Generate fake download counts for testing purposes. "
-            "This option will not make any network requests."
-        ),
-    )
    args = parser.parse_args()

-    main(args.output_file, args.fake)
+    main(args.output_file)
@@ -30,12 +30,3 @@ packages:
  - 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."
@@ -0,0 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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+++ b/Show More
Author	SHA1	Message	Date
William Fu-Hinthorn	656737009b	Update	2025-03-07 10:49:59 -08:00
William Fu-Hinthorn	ecdf70a2ab	langgraph-cli-install	2025-03-07 10:21:20 -08:00
				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -0,0 +1 @@`
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