feat: [CLI] Add arg to retain build deps (setuptools, pip, wheel) (#5404)

* cli: update file paths to make the examples more robust

* fix: fix the prompt path

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -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: ["02 Bug Report"]
+labels: [pending,bug]
 body:
   - type: markdown
     attributes:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,4 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 version: 2.1
 contact_links:
   - name: 🤔 Question or Problem
@@ -10,6 +10,6 @@ contact_links:
   - name: Show and tell
     about: Show what you built with LangChain
     url: https://github.com/langchain-ai/langgraph/discussions/categories/show-and-tell
-  - name: Slack
-    url:  https://www.langchain.com/join-community
-    about: General community discussions
+  - name: LangChain Forum
+    url:  https://forum.langchain.com/
+    about: General community discussions and support

.github/ISSUE_TEMPLATE/documentation.yml

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

.github/actions/poetry_setup/action.yml

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

.github/dependabot.yml

@@ -0,0 +1,18 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+
+  - package-ecosystem: "pip"
+    directories:
+      - "libs/checkpoint"
+      - "libs/checkpoint-postgres"
+      - "libs/checkpoint-sqlite"
+      - "libs/cli"
+      - "libs/langgraph"
+      - "libs/prebuilt"
+      - "libs/sdk-py"
+    schedule:
+      interval: "weekly"

.github/workflows/_integration_test.yml

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

.github/workflows/_lint.yml

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

.github/workflows/_test.yml

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

.github/workflows/_test_langgraph.yml

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

.github/workflows/_test_release.yml

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

.github/workflows/_test_scheduler_kafka.yml

@@ -1,57 +0,0 @@
-name: test
-
-on:
-  workflow_call:
-
-env:
-  POETRY_VERSION: "2.1.2"
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.11"
-          - "3.12"
-
-    defaults:
-      run:
-        working-directory: libs/scheduler-kafka
-    name: "test #${{ matrix.python-version }}"
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: test-scheduler-kafka
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        if: ${{ !github.event.pull_request.head.repo.fork }}
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          poetry install --with dev
-
-      - name: Run tests
-        shell: bash
-        run: |
-          make test
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/baseline.yml

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

.github/workflows/bench.yml

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

.github/workflows/ci.yml

@@ -6,6 +6,9 @@ on:
     branches: [main]
   pull_request:
 
+permissions:
+  contents: read
+
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
 #
@@ -16,15 +19,12 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
       python: ${{ steps.filter.outputs.python }}
-      sdk-js: ${{ steps.filter.outputs.sdk-js }}
+      deps: ${{ steps.filter.outputs.deps }}
     steps:
       - uses: actions/checkout@v4
       - uses: dorny/paths-filter@v3
@@ -38,10 +38,10 @@ jobs:
               - 'libs/checkpoint/**'
               - 'libs/checkpoint-sqlite/**'
               - 'libs/checkpoint-postgres/**'
-              - 'libs/scheduler-kafka/**'
               - 'libs/prebuilt/**'
-            sdk-js:
-              - 'libs/sdk-js/**'
+            deps:
+              - '**/pyproject.toml'
+              - '**/uv.lock'
 
   lint:
     needs: changes
@@ -56,10 +56,10 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
-            "libs/scheduler-kafka",
+
             "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -78,7 +78,7 @@ jobs:
             "libs/checkpoint-postgres",
             "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -87,19 +87,11 @@ jobs:
   # NOTE: we're testing langgraph separately because it requires a different matrix
   test-langgraph:
     needs: changes
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     name: "cd libs/langgraph"
     uses: ./.github/workflows/_test_langgraph.yml
     secrets: inherit
 
-  # NOTE: we're testing scheduler-kafka separately because it requires a different matrix
-  test-scheduler-kafka:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
-    name: "cd libs/scheduler-kafka"
-    uses: ./.github/workflows/_test_scheduler_kafka.yml
-    secrets: inherit
-
   check-sdk-methods:
     needs: changes
     if: needs.changes.outputs.python == 'true'
@@ -125,26 +117,26 @@ jobs:
           - "3.11"
     steps:
       - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: schema-check-cli
+          enable-cache: true
+          cache-suffix: "schema-check-cli"
       - name: Install CLI dependencies
         run: |
           cd libs/cli
-          poetry install
+          uv sync
       - name: Generate schema and check for changes
         run: |
           cd libs/cli
           # Create a temporary copy of the current schema
           cp schemas/schema.json schemas/schema.current.json
           # Generate new schema
-          poetry run python generate_schema.py
+          uv run python generate_schema.py
           # Compare the new schema with the original
           if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
-            echo "Error: Langgraph.json configuration schema has changed. Please run 'poetry run python generate_schema.py' in the libs/cli directory and commit the changes."
+            echo "Error: Langgraph.json configuration schema has changed. Please run 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
             diff schemas/schema.json schemas/schema.current.json
             exit 1
           fi
@@ -152,74 +144,21 @@ jobs:
 
   integration-test:
     needs: changes
-    if: needs.changes.outputs.python == 'true'
+    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
     name: CLI integration test
     uses: ./.github/workflows/_integration_test.yml
     secrets: inherit
 
-  lint-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        working-directory:
-          - "libs/sdk-js"
-    defaults:
-      run:
-        working-directory: ${{ matrix.working-directory }}
-    steps:
-      - uses: actions/checkout@v3
-      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "yarn"
-          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
-      - name: Install dependencies
-        run: yarn install
-      - name: Run lint
-        run: yarn lint
-      - name: Build
-        run: yarn build
-
-  test-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        working-directory:
-          - "libs/sdk-js"
-    defaults:
-      run:
-        working-directory: ${{ matrix.working-directory }}
-    steps:
-      - uses: actions/checkout@v3
-      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "yarn"
-          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
-      - name: Install dependencies
-        run: yarn install
-      - name: Run tests
-        run: yarn test
-
   ci_success:
     name: "CI Success"
     needs:
       [
         lint,
-        lint-js,
         test,
         test-langgraph,
-        test-scheduler-kafka,
         check-sdk-methods,
         check-schema,
         integration-test,
-        test-js,
       ]
     if: |
       always()

.github/workflows/deploy_docs.yml

@@ -9,9 +9,6 @@ on:
       - main
   workflow_dispatch:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 permissions:
   contents: read
   pages: write
@@ -57,21 +54,21 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: "3.12"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: docs
+          enable-cache: true
+          cache-suffix: "docs"
 
       - name: Install dependencies
         run: |
           yarn
-          poetry install --with test --with docs --no-root
+          uv sync --all-groups
           # we run this installation only for internal PRs
           # as GITHUB_TOKEN is not available for PRs from outside contributors
           if [ -n "${GITHUB_TOKEN}" ]; then
-            poetry run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
+            uv run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
           fi
 
       - name: Run unit tests
@@ -103,7 +100,7 @@ jobs:
         run: |
           if [ "${{ github.event_name }}" == "schedule" ]; then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
-            poetry run pytest -v \
+            uv run pytest -v \
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
@@ -128,7 +125,7 @@ jobs:
             echo "Changed files: ${CHANGED_FILES}"
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
-              poetry run pytest -v \
+              uv run pytest -v \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
                 --check-links-ignore "https://academy\.langchain\.com/.*" \
                 --check-links-ignore "http://localhost:8123/.*" \
@@ -148,7 +145,7 @@ jobs:
 
       - name: Configure GitHub Pages
         if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v4
+        uses: actions/configure-pages@v5
 
       - name: Upload Pages Artifact
         # if: github.ref == 'refs/heads/main'

.github/workflows/link_check.yml

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

.github/workflows/release.yml

@@ -8,9 +8,11 @@ on:
         type: string
         default: "libs/langgraph"
 
+permissions:
+  contents: read
+
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "2.1.2"
 
 jobs:
   build:
@@ -26,12 +28,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       # We want to keep this build stage *separate* from the release stage,
       # so that there's no sharing of permissions between them.
@@ -45,7 +47,7 @@ jobs:
       # > from the publish job.
       # https://github.com/pypa/gh-action-pypi-publish#non-goals
       - name: Build project for distribution
-        run: poetry build
+        run: uv build
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
@@ -59,8 +61,8 @@ jobs:
         shell: bash
         working-directory: ${{ inputs.working-directory }}
         run: |
-          PKG_NAME="$(poetry version | cut -d ' ' -f 1)"
-          VERSION="$(poetry version --short)"
+          PKG_NAME=$(grep -m 1 "^name = " pyproject.toml | cut -d '"' -f 2)
+          VERSION=$(grep -m 1 "^version = " pyproject.toml | cut -d '"' -f 2)
           SHORT_PKG_NAME="$(echo "$PKG_NAME" | sed -e 's/langgraph//g' -e 's/-//g')"
           if [ -z $SHORT_PKG_NAME ]; then
             TAG="$VERSION"
@@ -163,11 +165,11 @@ jobs:
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
+          enable-cache: true
 
       - name: Import published package
         shell: bash
@@ -185,18 +187,18 @@ jobs:
         # - attempt install again after 5 seconds if it fails because there is
         #   sometimes a delay in availability on test pypi
         run: |
-          poetry run pip install \
+          uv run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION" || \
           ( \
             sleep 5 && \
-            poetry run pip install \
+            uv run pip install \
               --extra-index-url https://test.pypi.org/simple/ \
               "$PKG_NAME==$VERSION" \
           )
 
           if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            poetry run pip install langgraph
+            uv run pip install langgraph
           fi
 
           if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
@@ -209,10 +211,10 @@ jobs:
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
           fi
 
-          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          uv run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
 
       - name: Import test dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
         working-directory: ${{ inputs.working-directory }}
 
       # Overwrite the local version of the package with the test PyPI version.
@@ -223,7 +225,7 @@ jobs:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
         run: |
-          poetry run pip install \
+          uv run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION"
 
@@ -253,12 +255,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       - uses: actions/download-artifact@v4
         with:
@@ -294,12 +296,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
+      - name: Set up Python
+        uses: astral-sh/setup-uv@v6
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: release
+          enable-cache: true
+          cache-suffix: "release"
 
       - uses: actions/download-artifact@v4
         with:

.github/workflows/release_js.yml

@@ -1,38 +0,0 @@
-name: JS Release
-
-on:
-  workflow_dispatch:
-
-jobs:
-  publish:
-    # Disallow publishing from branches that aren't `main`.
-    if: github.ref == 'refs/heads/main'
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        working-directory:
-          - "libs/sdk-js"
-
-    defaults:
-      run:
-        working-directory: ${{ matrix.working-directory }}
-
-    steps:
-      - uses: actions/checkout@v4
-      # JS Build
-      - name: Use Node.js
-        uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "yarn"
-          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
-
-      - name: Install dependencies
-        run: yarn install
-      - name: Build
-        run: yarn build
-      - name: Publish package to NPM
-        run: |
-          echo "//registry.npmjs.org/:_authToken=${{ secrets.NPM_TOKEN }}" > .npmrc
-          npm publish

.github/workflows/run_notebooks.yml

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

.github/workflows/uv_lock_ugprade.yml

@@ -0,0 +1,45 @@
+name: UV Lock Upgrade
+
+on:
+  schedule:
+    # run at midnight every Sunday
+    - cron: '0 0 * * 0'
+  # allow manual triggering
+  workflow_dispatch:
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  upgrade-dependencies:
+    runs-on: ubuntu-latest
+    
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          # use minimum supported Python version
+          python-version: "3.9"
+          enable-cache: true
+          cache-suffix: "uv-lock-upgrade"
+          
+      - name: Run uv lock --upgrade in all Python packages
+        run: make lock-upgrade
+          
+      - name: Create Pull Request
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          commit-message: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
+          title: "chore[deps]: upgrade dependencies with `uv lock --upgrade`"
+          body: |
+            This PR updates the dependencies in all Python packages using `uv lock --upgrade`.
+            
+            This is an automated PR created by the UV Lock Upgrade workflow.
+          branch: deps/uv-lock-upgrade
+          delete-branch: true
+          labels: |
+            dependencies

.gitignore

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

AGENTS.md

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

CONTRIBUTING.md

@@ -60,7 +60,7 @@ In LangGraph, these are often higher level guides that show off end-to-end use c
 Some examples include:
 
 - [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
-- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
+- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql/sql-agent/)
 
 Here are some high-level tips on writing a good tutorial:
 
@@ -109,7 +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 they way they do.
+impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work the way they do.
 
 
 To quote the Diataxis website:
@@ -153,7 +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 actue need.
+We generally do not merge new tutorials from outside contributors without an actual need.
 We welcome updates as well as new integration docs, how-tos, and references.
 
 ### Avoid duplication
@@ -227,6 +227,7 @@ see a preview of the documentation on the pull request page.
 
 From the **monorepo root**, run the following command to install the dependencies:
 
+<!-- TODO -->
 ```bash
 poetry install --with docs --no-root
 ```

Makefile

@@ -0,0 +1,68 @@
+# Define the directories containing projects
+LIBS_DIRS := $(wildcard libs/*)
+
+# Default target
+.PHONY: all
+all: lint format lock test
+
+# Install dependencies for all projects
+.PHONY: install
+install:
+	@echo "Creating virtual environment..."
+	@uv venv
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/pyproject.toml ]; then \
+			echo "Installing dependencies for $$dir"; \
+			uv pip install -e $$dir; \
+		fi; \
+	done
+
+# Lint all projects
+.PHONY: lint
+lint:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running lint in $$dir"; \
+			$(MAKE) -C $$dir lint; \
+		fi; \
+	done
+
+# Format all projects
+.PHONY: format
+format:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running format in $$dir"; \
+			$(MAKE) -C $$dir format; \
+		fi; \
+	done
+
+# Lock all projects
+.PHONY: lock
+lock:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running lock in $$dir"; \
+			(cd $$dir && uv lock); \
+		fi; \
+	done
+
+# Lock all projects and upgrade dependencies
+.PHONY: lock-upgrade
+lock-upgrade:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running lock-upgrade in $$dir"; \
+			(cd $$dir && uv lock --upgrade); \
+		fi; \
+	done
+
+# Test all projects
+.PHONY: test
+test:
+	@for dir in $(LIBS_DIRS); do \
+		if [ -f $$dir/Makefile ]; then \
+			echo "Running test in $$dir"; \
+			$(MAKE) -C $$dir test; \
+		fi; \
+	done

README.md

@@ -13,61 +13,71 @@
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
-> [!NOTE]
-> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
-
-LangGraph is a low-level orchestration framework for building controllable agents. While [LangChain](https://python.langchain.com/docs/introduction/) provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
 
 ## Get started
 
-First, install LangGraph:
+Install LangGraph:
 
 ```
 pip install -U langgraph
 ```
 
-There are two ways to get started with LangGraph:
+Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
 
-- [Use prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): Construct agentic systems quickly and reliably without the need to implement orchestration, memory, or human feedback handling from scratch.
-- [Use LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Customize your architectures, use long-term memory, and implement human-in-the-loop to reliably handle complex tasks.
+```python
+# pip install -qU "langchain[anthropic]" to call the model
 
-Once you have a LangGraph application and are ready to move into production, use [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/) to test, debug, and deploy your application.
+from langgraph.prebuilt import create_react_agent
 
-## What LangGraph provides
+def get_weather(city: str) -> str:
+    """Get weather for a given city."""
+    return f"It's always sunny in {city}!"
 
-LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_weather],
+    prompt="You are a helpful assistant"
+)
 
-### Persistence
+# Run the agent
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)
+```
 
-LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:
+For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
 
-- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
-- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.
+## Core benefits
 
-### Streaming
+LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
 
-LangGraph provides support for [streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](https://langchain-ai.github.io/langgraph/how-tos/streaming.ipynb#updates)) and [tokens from LLM calls](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens.ipynb) embedded in an application.
-
-### Debugging and deployment
-
-LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 
+- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
+- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
+- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
+- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
 
 ## LangGraph’s ecosystem
 
 While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
+
+> [!NOTE]
+> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
 ## Additional resources
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/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 powerful, production-ready AI applications.
+- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
 
 ## Acknowledgements
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

docs/.gitignore

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

docs/Makefile

@@ -1,10 +1,4 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
-
-build-typedoc:
-	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
-	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
-	#  Add links to the monorepo
-	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell llms-text build-prebuilt tests
 
 build-prebuilt:
 	# Use to create an update to date prebuilt page.
@@ -12,32 +6,30 @@ build-prebuilt:
 	# generates the final prebuilt page.
 	@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
 		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats stats.yml; \
+		uv run python -m _scripts.third_party_page.get_download_stats stats.yml; \
 		set +x; \
 	else \
 		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
+		uv run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
 		set +x; \
 	fi
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
+	uv run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
 
-build-docs: build-typedoc build-prebuilt
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
+build-docs: build-prebuilt
+	uv run python -m mkdocs build --clean -f mkdocs.yml --strict
 
 llms-text:
-	poetry run python -m _scripts.generate_llms_text docs/llms-full.txt
+	uv run python -m _scripts.generate_llms_text docs/llms-full.txt
 
 install-vercel-deps:
-	dnf install -y python3.11
-	curl -sSL https://install.python-poetry.org | python3 -
-	poetry self update 1.8.5
-	# don't use vercel's python - it wasn't compiled with sqlite support, and it fails when installing ipython's kernel
-	poetry env use /usr/bin/python3.11
-	poetry install --with docs --with test --no-root
+	curl -sL "https://astral.sh/uv/install.sh" | bash -s
+	export PATH="${HOME}/.cargo/bin:${PATH}"
+	uv venv --python 3.11
+	uv sync --all-groups
 
 tests:
 	# Run unit tests
-	poetry run pytest tests/unit_tests
+	uv run pytest tests/unit_tests
 
 
 vercel-build-docs: install-vercel-deps
@@ -45,10 +37,10 @@ vercel-build-docs: install-vercel-deps
 
 
 serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
+	uv run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
 
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
+serve-docs:
+	uv run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
 
 clean-docs:
 	find ./docs -name "*.ipynb" -type f -delete
@@ -56,13 +48,13 @@ clean-docs:
 
 ## Run format against the project documentation.
 format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
+	uv run ruff format docs
+	uv run ruff check --fix docs
 
 # Check the docs for linting violations
 lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
+	uv run ruff format --check docs
+	uv run ruff check docs
 
 codespell:
 	./codespell_notebooks.sh .

docs/README.md

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

docs/_scripts/add_translation.py

@@ -1,157 +1,154 @@
-"""Add typescript translation to a given markdown file."""
+"""Translate Python markdown to TypeScript and/or consolidate Python-JS markdown into a single document."""
 
 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
 
-model = ChatAnthropic(model="claude-3-5-sonnet-latest")
+# 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."
+)
 
 
-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(
+def translate_python_to_ts(markdown_content: str) -> str:
+    response = model.invoke(
         [
             {
                 "role": "system",
-                "content": (
-                    f"You have access to the following up-to-date example TypeScript code "
-                    f"snippets that show examples of building with langgraph "
-                    f"and langchain:\n\n{reference_snippets}\n\n"
-                    "Use this context to translate the following Python code to equivalent "
-                    "TypeScript. Ensure that your output is a valid fenced TypeScript "
-                    "code block (i.e. starts with ```typescript and ends with ```)."
-                ),
-            },
-            {
-                "role": "user",
-                "content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
+                "content": TRANSLATION_PROMPT,
+                "cache_control": {"type": "ephemeral"},
             },
+            {"role": "user", "content": markdown_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.")
+    return response.content
 
 
-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 consolidate_python_and_ts(combined_content: str) -> str:
+    response = model.invoke(
+        [
+            {
+                "role": "system",
+                "content": CONSOLIDATION_PROMPT,
+                "cache_control": {"type": "ephemeral"},
+            },
+            {"role": "user", "content": combined_content},
+        ]
+    )
+    return response.content
 
 
-def main(file_path: str) -> None:
-    # Read the markdown file.
-    with open(file_path, "r") as f:
+def main(file_path: str, translate_only: bool, consolidate_only: bool) -> None:
+    with open(file_path, "r", encoding="utf-8") as f:
         markdown_content = f.read()
 
-    # 1. Extract all Python snippets.
-    python_snippets = extract_python_snippets(markdown_content)[:1]
+    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}")
 
-    # 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)
+    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}")
 
-    # 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)
+    else:
+        # Default behavior: translate first, then consolidate both
+        translated = translate_python_to_ts(markdown_content)
+        combined = f"{markdown_content.strip()}\n\n\n{translated.strip()}"
+        consolidated = consolidate_python_and_ts(combined)
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(consolidated)
+        print(f"Translated and consolidated content written to: {file_path}")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
+        description=(
+            "Translate Python markdown to TypeScript and/or consolidate "
+            "Python-JS markdown into one file."
+        )
     )
     parser.add_argument("file_path", type=str, help="Path to the markdown file.")
+    parser.add_argument(
+        "--translate-only",
+        action="store_true",
+        help="Only generate the JS translation.",
+    )
+    parser.add_argument(
+        "--consolidate-only",
+        action="store_true",
+        help="Only consolidate pre-paired Python and JS content.",
+    )
     args = parser.parse_args()
 
-    main(args.file_path)
+    if args.translate_only and args.consolidate_only:
+        raise ValueError(
+            "Cannot use both --translate-only and --consolidate-only at the same time."
+        )
+
+    main(
+        args.file_path,
+        translate_only=args.translate_only,
+        consolidate_only=args.consolidate_only,
+    )

docs/_scripts/execute_notebooks.sh

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

docs/_scripts/generate_llms_text.py

@@ -1,10 +1,18 @@
 """Experimental script to generate consolidated llms text from the docs."""
 
+import asyncio
 import glob
 import os
+import re
+from typing import TypedDict, List, Optional
 
+import yaml
+from langchain.chat_models import init_chat_model
+from langchain_core.rate_limiters import InMemoryRateLimiter
 from mkdocs.structure.files import File
 from mkdocs.structure.pages import Page
+from pydantic import BaseModel, Field
+from yaml import SafeLoader
 
 from _scripts.notebook_hooks import _on_page_markdown_with_config
 
@@ -13,7 +21,49 @@ HERE = os.path.dirname(os.path.abspath(__file__))
 SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
 
 
-def _make_llms_text(output_file: str) -> str:
+async def convert_ipynb_to_md(file_path: str) -> Optional[str]:
+    """Process a file (markdown or notebook) to markdown format.
+
+    Args:
+        file_path: Path to the file to process
+
+    Returns:
+        Processed markdown content if successful, None otherwise
+    """
+    rel_path = os.path.relpath(file_path, SOURCE_DIR)
+
+    # Create File and Page objects to match mkdocs structure
+    file_obj = File(
+        path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
+    )
+    page = Page(
+        title="",
+        file=file_obj,
+        config={},
+    )
+
+    try:
+        # Read raw content
+        with open(file_path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        # Convert to markdown without logic to resolve API references
+        processed_content = _on_page_markdown_with_config(
+            content, page, add_api_references=False, remove_base64_images=True
+        )
+        # Remove self-closing img tags <img ... />
+        processed_content = re.sub(r"<img[^>]*/>", "", processed_content)
+        # Remove img tags with content <img ...>...</img>
+        processed_content = re.sub(
+            r"<img[^>]*>.*?</img>", "", processed_content, flags=re.DOTALL
+        )
+        return processed_content
+    except Exception as e:
+        print(f"Error processing file {file_path}: {e}")
+        return None
+
+
+async def generate_full_llms_text(output_file: str) -> None:
     """Generate a consolidated text file from markdown/notebook files for LLM training.
 
     Args:
@@ -21,11 +71,9 @@ def _make_llms_text(output_file: str) -> str:
     """
     # Collect all markdown and notebook files
     all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
-
     all_files.extend(
         glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
     )
-    # Add all concepts
     all_files.extend(
         glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
     )
@@ -35,30 +83,14 @@ def _make_llms_text(output_file: str) -> str:
 
     all_content = []
 
-    # Process each file
-    for file_path in all_files:
-        print(f"Processing {file_path}")
-        rel_path = os.path.relpath(file_path, SOURCE_DIR)
+    # Process files concurrently
+    tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
+    results = await asyncio.gather(*tasks)
 
-        # Create File and Page objects to match mkdocs structure
-        file_obj = File(
-            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
-        )
-        page = Page(
-            title="",
-            file=file_obj,
-            config={},
-        )
-
-        # Read raw content
-        with open(file_path, "r", encoding="utf-8") as f:
-            content = f.read()
-
-        # Convert to markdown without logic to resolve API references
-        processed_content = _on_page_markdown_with_config(
-            content, page, add_api_references=False, remove_base64_images=True
-        )
+    # Combine results with file paths
+    for file_path, processed_content in zip(all_files, results):
         if processed_content:
+            rel_path = os.path.relpath(file_path, SOURCE_DIR)
             # Add file name
             all_content.append(f"---\n{rel_path}\n---")
             # Add content
@@ -69,6 +101,170 @@ def _make_llms_text(output_file: str) -> str:
         f.write("\n\n".join(all_content))
 
 
+def no_op_constructor(*args):
+    """No-op"""
+
+
+SafeLoader.add_multi_constructor(
+    "tag:yaml.org,2002:python/name",
+    no_op_constructor,
+)
+
+
+class NavItem(TypedDict):
+    title: str
+    url: str
+    hierarchy: tuple[str, ...]
+    description: str
+
+
+def _flatten_nav(
+    nav: list[dict[str, str | list] | str], path: tuple[str, ...] = ()
+) -> list[NavItem]:
+    flat: List[NavItem] = []
+    for item in nav:
+        if isinstance(item, dict):
+            for title, node in item.items():
+                new_path = path + (title,)
+                if isinstance(node, str):
+                    # Leaf page
+                    flat.append(
+                        {
+                            "title": title,
+                            "url": node,
+                            "hierarchy": new_path,
+                            "description": "",
+                        }
+                    )
+                elif isinstance(node, list):
+                    # Dive in, carrying along the updated path
+                    flat.extend(_flatten_nav(node, new_path))
+                else:
+                    raise TypeError(
+                        f"Unexpected node type {type(node)} under {title!r}"
+                    )
+        elif isinstance(item, str):
+            # Bare string entry → use itself as title, and as URL
+            new_path = path + (item,)
+            flat.append(
+                {"title": item, "url": item, "hierarchy": new_path, "description": ""}
+            )
+        else:
+            raise TypeError(f"Unexpected item type {type(item)} in nav")
+    return flat
+
+
+class PageInfo(BaseModel):
+    title: str = Field(description="The title of the page")
+    description: str = Field(
+        description="A short description of the page no longer than 3 sentences "
+        "explaining the kind of content that can be found in the page."
+    )
+
+
+async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
+    """Open the contents of each nav item and come up with a better title and description."""
+    rate_limiter = InMemoryRateLimiter(requests_per_second=10)
+    model = init_chat_model("gpt-4o-mini", temperature=0.0, rate_limiter=rate_limiter)
+    model = model.with_structured_output(PageInfo)
+
+    async def process_single_item(item: NavItem) -> NavItem:
+        path = item["url"]
+        file_path = os.path.join(SOURCE_DIR, path)
+
+        # Process the file content (handles both markdown and notebooks)
+        if path.endswith(".ipynb"):
+            content = await convert_ipynb_to_md(file_path)
+        else:
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+
+        if not content:
+            return item
+
+        # Generate a better title and description
+        response = await model.ainvoke(
+            [
+                {
+                    "role": "system",
+                    "content": "You are a technical documentation writer. "
+                    "You are given a markdown page of documentation. "
+                    "Please come up with an appropriate title and "
+                    "description for the page. The description should "
+                    "be a short summary of the page content that is "
+                    "no longer than 3 sentences.",
+                },
+                {
+                    "role": "user",
+                    "content": "The markdown page is as follows:\n\n" + content,
+                },
+            ]
+        )
+        return {
+            "title": response.title,
+            "url": item["url"],
+            "hierarchy": item["hierarchy"],
+            "description": response.description,
+        }
+
+    # Remove any items that start with http:// or https:// looking only for
+    # local file at this stages.
+    nav_items = [
+        item
+        for item in nav_items
+        if not item["url"].startswith(("http://", "https://"))
+    ]
+    # Process items in parallel
+    tasks = [process_single_item(item) for item in nav_items]
+    new_nav_items = await asyncio.gather(*tasks)
+    return new_nav_items
+
+
+async def generate_nav_links_text(
+    output_file: str, *, replace_links: bool = False
+) -> None:
+    """Generate llms.txt from mkdocs.yaml."""
+    # Get path to mkdocs.yaml relative to this script
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    mkdocs_path = os.path.join(os.path.dirname(script_dir), "mkdocs.yml")
+
+    # Load and parse yaml
+    with open(mkdocs_path, "r") as f:
+        config = yaml.safe_load(f)
+
+    # Extract nav section
+    nav = config.get("nav", [])
+    flattened = _flatten_nav(nav)
+
+    processed_nav = await process_nav_items(flattened)
+
+    with open(output_file, "w") as f:
+        current_section = None
+        for item in processed_nav:
+            # Get the top-level section (first item in hierarchy)
+            section = item["hierarchy"][0]
+
+            if section not in {"Guides", "Examples", "Resources"}:
+                continue
+
+            # If we're starting a new section, add a heading
+            if section != current_section:
+                f.write(f"\n# {section}\n\n")
+                current_section = section
+
+            title = item["title"]
+            # Process URL based on replace_links flag
+            url = item["url"]
+            if replace_links:
+                # Remove .md extension and ensure single trailing slash
+                url = url.removesuffix(".md")
+                url = url.removesuffix(".ipynb")
+                url = url.rstrip("/") + "/"
+                url = f"https://langchain-ai.github.io/langgraph/{url}"
+
+            f.write(f"- [{title}]({url}): {item['description']}\n")
+
+
 if __name__ == "__main__":
     import argparse
 
@@ -78,6 +274,23 @@ if __name__ == "__main__":
         )
     )
     parser.add_argument("output_file", help="Path to output the consolidated text file")
+    parser.add_argument(
+        "--link-only",
+        action="store_true",
+        help="Only include link references in the output",
+    )
+    parser.add_argument(
+        "--replace-links",
+        action="store_true",
+        help="Replace markdown links with full URLs in the output",
+    )
 
     args = parser.parse_args()
-    _make_llms_text(args.output_file)
+    if args.link_only:
+        coro = generate_nav_links_text(
+            args.output_file, replace_links=args.replace_links
+        )
+    else:
+        coro = generate_full_llms_text(args.output_file)
+
+    asyncio.run(coro)

docs/_scripts/link_map.py

@@ -0,0 +1,5 @@
+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",
+}

docs/_scripts/notebook_hooks.py

@@ -1,14 +1,22 @@
+"""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()
@@ -26,47 +34,58 @@ REDIRECT_MAP = {
     "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
     "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
     # graph-api
-    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
-    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
-    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
-    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
-    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
-    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
-    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
-    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
-    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
-    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
-    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
-    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
-    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    "how-tos/state-reducers.ipynb": "how-tos/graph-api.md#define-and-update-state",
+    "how-tos/sequence.ipynb": "how-tos/graph-api.md#create-a-sequence-of-steps",
+    "how-tos/branching.ipynb": "how-tos/graph-api.md#create-branches",
+    "how-tos/recursion-limit.ipynb": "how-tos/graph-api.md#create-and-control-loops",
+    "how-tos/visualization.ipynb": "how-tos/graph-api.md#visualize-your-graph",
+    "how-tos/input_output_schema.ipynb": "how-tos/graph-api.md#define-input-and-output-schemas",
+    "how-tos/pass_private_state.ipynb": "how-tos/graph-api.md#pass-private-state-between-nodes",
+    "how-tos/state-model.ipynb": "how-tos/graph-api.md#use-pydantic-models-for-graph-state",
+    "how-tos/map-reduce.ipynb": "how-tos/graph-api.md#map-reduce-and-the-send-api",
+    "how-tos/command.ipynb": "how-tos/graph-api.md#combine-control-flow-and-state-updates-with-command",
+    "how-tos/configuration.ipynb": "how-tos/graph-api.md#add-runtime-configuration",
+    "how-tos/node-retries.ipynb": "how-tos/graph-api.md#add-retry-policies",
+    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api.md#impose-a-recursion-limit",
+    "how-tos/async.ipynb": "how-tos/graph-api.md#async",
     # memory how-tos
-    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
-    "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
-    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
+    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory/add-memory.md",
+    "how-tos/memory/delete-messages.ipynb": "how-tos/memory/add-memory.md#delete-messages",
+    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory/add-memory.md#summarize-messages",
+    "how-tos/memory.ipynb": "how-tos/memory/add-memory.md",
+    "agents/memory.ipynb": "how-tos/memory/add-memory.md",
     # subgraph how-tos
-    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
-    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
+    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.md#different-state-schemas",
+    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.md#add-persistence",
     # persistence how-tos
-    "how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
-    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
+    "how-tos/persistence_postgres.ipynb": "how-tos/memory/add-memory.md#use-in-production",
+    "how-tos/persistence_mongodb.ipynb": "how-tos/memory/add-memory.md#use-in-production",
+    "how-tos/persistence_redis.ipynb": "how-tos/memory/add-memory.md#use-in-production",
+    "how-tos/subgraph-persistence.ipynb": "how-tos/memory/add-memory.md#use-with-subgraphs",
+    "how-tos/cross-thread-persistence.ipynb": "how-tos/memory/add-memory.md#add-long-term-memory",
+    "cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
+    "cloud/how-tos/check-thread-status": "cloud/how-tos/use_threads",
+    "cloud/concepts/threads.md": "concepts/persistence.md#threads",
+    "how-tos/persistence.ipynb": "how-tos/memory/add-memory.md",
     # tool calling how-tos
     "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
     "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
     "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
     "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
+    "agents/tools.md": "how-tos/tool-calling.md",
     # multi-agent how-tos
-    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
-    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
-    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
+    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.md#handoffs",
+    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.md#use-in-a-multi-agent-system",
+    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.md#multi-turn-conversation",
     # cloud redirects
     "cloud/index.md": "index.md",
     "cloud/how-tos/index.md": "concepts/langgraph_platform",
     "cloud/concepts/api.md": "concepts/langgraph_server.md",
     "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
     "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+    "cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
+    "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
+    "concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
     # cloud streaming redirects
     "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
     "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
@@ -74,16 +93,17 @@ REDIRECT_MAP = {
     "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
     "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
     "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
-    # prebuit redirects
+    "cloud/concepts/streaming.md": "concepts/streaming.md",
+    "agents/streaming.md": "how-tos/streaming.md",
+    # prebuilt redirects
     "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
-    "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
     "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
     # Time-travel
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.ipynb",
+    "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.ipynb",
+    "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",
@@ -92,10 +112,19 @@ REDIRECT_MAP = {
     "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
     "how-tos/index.md": "index.md",
     "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
+    "agents/deployment.md": "tutorials/langgraph-platform/local-server.md",
     # deployment redirects
     "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
-    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md"
-
+    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
+    "tutorials/deployment.md": "concepts/deployment_options.md",
+    # assistant redirects
+    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
+    "cloud/concepts/runs.md": "concepts/assistants.md#execution",
+    # hitl redirects
+    "how-tos/wait-user-input-functional.ipynb": "how-tos/use-functional-api.md",
+    "how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
+    "how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
+    "agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
 }
 
 
@@ -145,6 +174,62 @@ 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.
 
@@ -244,6 +329,20 @@ 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.
@@ -285,7 +384,7 @@ Redirecting...
 """
 
 
-def write_html(site_dir, old_path, new_path):
+def _write_html(site_dir, old_path, new_path):
     """Write an HTML file in the site_dir with a meta redirect to the new page"""
     # Determine all relevant paths
     old_path_abs = os.path.join(site_dir, old_path)
@@ -301,6 +400,52 @@ 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")
@@ -317,4 +462,4 @@ 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)

docs/_scripts/prepare_notebooks_for_ci.py

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

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

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

@@ -0,0 +1 @@
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docs/cassettes/persistence-functional_08ae8246-11d5-40e1-8567-361e5bef8917.msgpack.zlib

@@ -1 +1 @@
-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docs/cassettes/persistence-functional_273d56a8-f40f-4a51-a27f-7c6bb2bda0ba.msgpack.zlib

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docs/docs/additional-resources/index.md

@@ -0,0 +1,11 @@
+# Additional resources
+
+This section contains additional resources for LangGraph.
+
+- [Community agents](../agents/prebuilt.md): A collection of prebuilt libraries that you can use in your LangGraph applications.
+- [LangGraph Academy](https://academy.langchain.com/courses/intro-to-langgraph): A collection of courses that teach you how to use LangGraph.
+- [Case studies](../adopters.md): A collection of case studies that show how LangGraph is used in production.
+- [FAQ](../concepts/faq.md): A collection of frequently asked questions about LangGraph.
+- [llms.txt](../llms-txt-overview.md): A list of documentation files in the `llms.txt` format that allow LLMs and agents to access our documentation.
+- [LangChain Forum](https://forum.langchain.com/): A place to ask questions and get help from other LangGraph users.
+- [Troubleshooting](../troubleshooting/errors/index.md.md): A collection of troubleshooting guides for common issues.

docs/docs/adopters.md

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

docs/docs/agents/agents.md

@@ -15,7 +15,7 @@ This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable*
 
 Before you start this tutorial, ensure you have the following:
 
-- An [Anthropic](https://console.anthropic.com/settings/admin-keys) API key 
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key 
 
 ## 1. Install dependencies
 
@@ -31,7 +31,7 @@ pip install -U langgraph "langchain[anthropic]"
 
 ## 2. Create an agent
 
-To create an agent, use [`create_react_agent`](langgraph.prebuilt.chat_agent_executor.create_react_agent):
+To create an agent, use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -52,7 +52,7 @@ agent.invoke(
 )
 ```
 
-1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
+1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](../how-tos/tool-calling.md) page.
 2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
 3. Provide a list of tools for the model to use.
 4. Provide a system prompt (instructions) to the language model used by the agent.
@@ -180,14 +180,14 @@ ny_response = agent.invoke(
 )
 ```
 
-1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities.
+1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](../how-tos/memory/add-memory.md#add-short-term-memory) and [human-in-the-loop](../concepts/human_in_the_loop.md) capabilities.
 2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
 
 When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
 
 Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.
 
-For more information, see [Memory](./memory.md).
+For more information, see [Memory](../how-tos/memory/add-memory.md).
 
 ## 6. Configure structured output
 

docs/docs/agents/context.md

@@ -1,17 +1,8 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
 # Context
 
-Agents often require more than a list of messages to function effectively. They need **context**.
+**Context engineering** is the practice of building dynamic systems that provide the right information and tools, in the right format, so that a language model can plausibly accomplish a task.
 
-Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
+Context includes *any* data outside the message list that can shape behavior. This can be:
 
 - Information passed at runtime, like a `user_id` or API credentials.
 - Internal state updated during a multi-step reasoning process.
@@ -22,18 +13,10 @@ LangGraph provides **three** primary ways to supply context:
 | Type                                                                         | Description                                   | Mutable? | Lifetime                |
 |------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
 | [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
-| [**State**](#state-mutable-context)                                          | dynamic data that can change during execution | ✅        | per run or conversation |
-| [**Long-term Memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
+| [**Short-term memory (State)**](#short-term-memory-mutable-context)          | dynamic data that can change during execution | ✅        | per run or conversation |
+| [**Long-term memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
 
-You can use context to:
-
-- Adjust the system prompt the model sees
-- Feed tools with necessary inputs
-- Track facts during an ongoing conversation
-
-## Providing Runtime Context
-
-Use this when you need to inject data into an agent at runtime.
+## Provide runtime context
 
 ### Config (static context)
 
@@ -44,88 +27,83 @@ Specify configuration using a key called **"configurable"** which is reserved
 for this purpose:
 
 ```python
-agent.invoke(
-    {"messages": [{"role": "user", "content": "hi!"}]},
+graph.invoke( # (1)!
+    {"messages": [{"role": "user", "content": "hi!"}]}, # (2)!
     # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
+    config={"configurable": {"user_id": "user_123"}} # (3)!
 )
 ```
 
-### State (mutable context)
+1. This is the invocation of the agent or graph. The `invoke` method runs the underlying graph with the provided input.
+2. This example uses messages as an input, which is common, but your application may use different input structures.
+3. This is where you pass the configuration data. The `config` parameter allows you to provide additional context that the agent can use during its execution.
 
-State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
-
-```python
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-agent = create_react_agent(
-    # Other agent parameters...
-    # highlight-next-line
-    state_schema=CustomState,
-)
-
-agent.invoke({
-    "messages": "hi!",
-    "user_name": "Jane"
-})
-```
-
-!!! tip "Turning on memory"
-
-    Please see the [memory guide](./memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations.
-    Otherwise, the state is scoped only to a single agent run.
-
-
-
-### Long-Term Memory (cross-conversation context)
-
-For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](./memory.md).
-
-## Customizing Prompts with Context { #prompts }
-
-Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
-
-Common use cases:
-
-- Personalization
-- Role or goal customization
-- Conditional behavior (e.g., user is admin)
-
-=== "Using config"
+=== "Agent prompt"
 
     ```python
     from langchain_core.messages import AnyMessage
     from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent
 
-    def prompt(
-        state: AgentState,
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> list[AnyMessage]:
-        # highlight-next-line
+    # highlight-next-line
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:
         user_name = config["configurable"].get("user_name")
-        system_msg = f"You are a helpful assistant. User's name is {user_name}"
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
         return [{"role": "system", "content": system_msg}] + state["messages"]
 
     agent = create_react_agent(
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[get_weather],
-        # highlight-next-line
         prompt=prompt
     )
 
     agent.invoke(
-        ...,
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
         # highlight-next-line
         config={"configurable": {"user_name": "John Smith"}}
     )
     ```
 
-=== "Using state"
+    * See [Agents](../agents/agents.md) for details.
+
+=== "Workflow node"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    # highlight-next-line
+    def node(state: State, config: RunnableConfig):
+        user_name = config["configurable"].get("user_name")
+        ...
+    ```
+
+    * See [the Graph API](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#add-runtime-configuration) for details.
+
+=== "In a tool"
+
+    ```python
+    from langchain_core.runnables import RunnableConfig
+
+    @tool
+    # highlight-next-line
+    def get_user_info(config: RunnableConfig) -> str:
+        """Retrieve user information based on user ID."""
+        user_id = config["configurable"].get("user_id")
+        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    ```
+
+    See the [tool calling guide](../how-tos/tool-calling.md#configuration) for details.
+
+### Short-term memory (mutable context)
+
+State acts as [short-term memory](../concepts/memory.md) during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
+
+=== "In an agent"
+
+    Example shows how to incorporate state into an agent **prompt**.
+
+    State can also be accessed by the agent's **tools**, which can read or update the state as needed. See [tool calling guide](../how-tos/tool-calling.md#short-term-memory) for details.
 
     ```python
     from langchain_core.messages import AnyMessage
@@ -133,15 +111,14 @@ Common use cases:
     from langgraph.prebuilt import create_react_agent
     from langgraph.prebuilt.chat_agent_executor import AgentState
 
-    class CustomState(AgentState):
-        # highlight-next-line
+    # highlight-next-line
+    class CustomState(AgentState): # (1)!
         user_name: str
 
     def prompt(
         # highlight-next-line
         state: CustomState
     ) -> list[AnyMessage]:
-        # highlight-next-line
         user_name = state["user_name"]
         system_msg = f"You are a helpful assistant. User's name is {user_name}"
         return [{"role": "system", "content": system_msg}] + state["messages"]
@@ -150,87 +127,58 @@ Common use cases:
         model="anthropic:claude-3-7-sonnet-latest",
         tools=[...],
         # highlight-next-line
-        state_schema=CustomState,
-        # highlight-next-line
+        state_schema=CustomState, # (2)!
         prompt=prompt
     )
 
     agent.invoke({
         "messages": "hi!",
-        # highlight-next-line
         "user_name": "John Smith"
     })
     ```
 
-## Accessing Context in Tools { #tools }
-
-Tools can access context through special parameter **annotations**.
-
-* Use `RunnableConfig` for config access
-* Use `Annotated[StateSchema, InjectedState]` for agent state
+    1. Define a custom state schema that extends `AgentState` or `MessagesState`.
+    2. Pass the custom state schema to the agent. This allows the agent to access and modify the state during execution.
 
 
-!!! tip 
-
-    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
-
-=== "Using config"
+=== "In a workflow"
 
     ```python
-    def get_user_info(
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = config["configurable"].get("user_id")
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+    from typing_extensions import TypedDict
+    from langchain_core.messages import AnyMessage
+    from langgraph.graph import StateGraph
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-    )
+    # highlight-next-line
+    class CustomState(TypedDict): # (1)!
+        messages: list[AnyMessage]
+        extra_field: int
 
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "look up user information"}]},
-        # highlight-next-line
-        config={"configurable": {"user_id": "user_123"}}
-    )
+    # highlight-next-line
+    def node(state: CustomState): # (2)!
+        messages = state["messages"]
+        ...
+        return { # (3)!
+            # highlight-next-line
+            "extra_field": state["extra_field"] + 1
+        }
+
+    builder = StateGraph(State)
+    builder.add_node(node)
+    builder.set_entry_point("node")
+    graph = builder.compile()
     ```
+    
+    1. Define a custom state
+    2. Access the state in any node or tool
+    3. The Graph API is designed to work as easily as possible with state. The return value of a node represents a requested update to the state.
 
-=== "Using State"
 
-    ```python
-    from typing import Annotated
-    from langgraph.prebuilt import InjectedState
+!!! tip "Turning on memory"
 
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_id: str
+    Please see the [memory guide](../how-tos/memory/add-memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations. Otherwise, the state is scoped only to a single run.
 
-    def get_user_info(
-        # highlight-next-line
-        state: Annotated[CustomState, InjectedState]
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = state["user_id"]
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
+### Long-term memory (cross-conversation context)
 
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-        # highlight-next-line
-        state_schema=CustomState,
-    )
+For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). 
 
-    agent.invoke({
-        "messages": "look up user information",
-        # highlight-next-line
-        "user_id": "user_123"
-    })
-    ```
-
-### Update Context from Tools
-
-Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.
+For more information, see the [Memory guide](../how-tos/memory/add-memory.md).

docs/docs/agents/deployment.md

@@ -1,92 +0,0 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
-# Deployment
-
-To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.
-
-Features: 
-
-* 🖥️ Local server for development
-* 🧩 Studio Web UI for visual debugging
-* ☁️ Cloud and 🔧 self-hosted deployment options
-* 📊 LangSmith integration for tracing and observability
-
-!!! info "Requirements" 
-
-    - ✅ You **must** have a [LangSmith account](https://www.langchain.com/langsmith). You can sign up for **free** and get started with the free tier.
-
-## Create a LangGraph app
-
-```bash
-pip install -U "langgraph-cli[inmem]"
-langgraph new path/to/your/app --template new-langgraph-project-python
-```
-
-This will create an empty LangGraph project. You can modify it by replacing the code in `src/agent/graph.py` with your agent code. For example:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-graph = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
-```
-
-### Install dependencies
-
-In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
-
-```shell
-pip install -e .
-```
-
-### Create an `.env` file
-
-You will find a `.env.example` in the root of your new LangGraph app. Create
-a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
-
-```bash
-LANGSMITH_API_KEY=lsv2...
-ANTHROPIC_API_KEY=sk-
-```
-
-## Launch LangGraph server locally
-
-```shell
-langgraph dev
-```
-
-This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
-
->    Ready!
-> 
->    - API: [http://localhost:2024](http://localhost:2024/)
->     
->    - Docs: http://localhost:2024/docs
->     
->    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-
-See this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) to learn more about running LangGraph app locally.
-
-## LangGraph Studio Web UI
-
-LangGraph Studio Web is a specialized UI that you can connect to LangGraph API server to enable visualization, interaction, and debugging of your application locally. Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph dev` command.
-
->    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-
-## Deployment
-
-Once your LangGraph app is running locally, you can deploy it using LangGraph Cloud or self-hosted options. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

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

@@ -1,238 +0,0 @@
----
-search:
-  boost: 2
-tags:
-  - human-in-the-loop
-  - hil
-  - agent
-hide:
-  - tags
----
-
-# Human-in-the-loop
-
-To review, edit and approve tool calls in an agent you can use LangGraph's built-in [Human-In-the-Loop (HIL)](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
-
-LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.
-
-This is possible because the agent state is **checkpointed into a database**, which allows the system to persist execution context and later resume the workflow, continuing from where it left off.
-
-For a deeper dive into the **human-in-the-loop** concept, see the [concept guide](../concepts/human_in_the_loop.md).
-
-<figure markdown="1">
-![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
-<figcaption>
-A human can review and edit the output from the agent before proceeding. This is particularly critical in applications where the tool calls requested may be sensitive or require human oversight.
-</figcaption>
-</figure>
-
-
-## Review tool calls
-
-To add a human approval step to a tool:
-
-1. Use `interrupt()` in the tool to pause execution.
-2. Resume with a `Command(resume=...)` to continue based on human input.
-
-```python
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.types import interrupt
-from langgraph.prebuilt import create_react_agent
-
-# An example of a sensitive tool that requires human review / approval
-def book_hotel(hotel_name: str):
-    """Book a hotel"""
-    # highlight-next-line
-    response = interrupt(  # (1)!
-        f"Trying to call `book_hotel` with args {{'hotel_name': {hotel_name}}}. "
-        "Please approve or suggest edits."
-    )
-    if response["type"] == "accept":
-        pass
-    elif response["type"] == "edit":
-        hotel_name = response["args"]["hotel_name"]
-    else:
-        raise ValueError(f"Unknown response type: {response['type']}")
-    return f"Successfully booked a stay at {hotel_name}."
-
-# highlight-next-line
-checkpointer = InMemorySaver() # (2)!
-
-agent = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    tools=[book_hotel],
-    # highlight-next-line
-    checkpointer=checkpointer, # (3)!
-)
-```
-
-1. The [`interrupt` function][langgraph.types.interrupt] pauses the agent graph at a specific node. In this case, we call `interrupt()` at the beginning of the tool function, which pauses the graph at the node that executes the tool. The information inside `interrupt()` (e.g., tool calls) can be presented to a human, and the graph can be resumed with the user input (tool call approval, edit or feedback).
-2. The `InMemorySaver` is used to store the agent state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities. In this example, we use `InMemorySaver` to store the agent state in memory. In a production application, the agent state will be stored in a database.
-3. Initialize the agent with the `checkpointer`.
-
-Run the agent with the `stream()` method, passing the `config` object to specify the thread ID. This allows the agent to resume the same conversation on future invocations.
-
-```python
-config = {
-   "configurable": {
-      # highlight-next-line
-      "thread_id": "1"
-   }
-}
-
-for chunk in agent.stream(
-    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
-    # highlight-next-line
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-> You should see that the agent runs until it reaches the `interrupt()` call, at which point it pauses and waits for human input.
-
-Resume the agent with a `Command(resume=...)` to continue based on human input.
-
-```python
-from langgraph.types import Command
-
-for chunk in agent.stream(
-    # highlight-next-line
-    Command(resume={"type": "accept"}),  # (1)!
-    # Command(resume={"type": "edit", "args": {"hotel_name": "McKittrick Hotel"}}),
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`](../reference/types.md#langgraph.types.Command) object to resume the graph with a value provided by the human.
-
-## Using with Agent Inbox
-
-You can create a wrapper to add interrupts to *any* tool. 
-
-The example below provides a reference implementation compatible with [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox) and [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui).
-
-```python title="Wrapper that adds human-in-the-loop to any tool"
-from typing import Callable
-from langchain_core.tools import BaseTool, tool as create_tool
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import interrupt 
-from langgraph.prebuilt.interrupt import HumanInterruptConfig, HumanInterrupt
-
-def add_human_in_the_loop(
-    tool: Callable | BaseTool,
-    *,
-    interrupt_config: HumanInterruptConfig = None,
-) -> BaseTool:
-    """Wrap a tool to support human-in-the-loop review.""" 
-    if not isinstance(tool, BaseTool):
-        tool = create_tool(tool)
-
-    if interrupt_config is None:
-        interrupt_config = {
-            "allow_accept": True,
-            "allow_edit": True,
-            "allow_respond": True,
-        }
-
-    @create_tool(  # (1)!
-        tool.name,
-        description=tool.description,
-        args_schema=tool.args_schema
-    )
-    def call_tool_with_interrupt(config: RunnableConfig, **tool_input):
-        request: HumanInterrupt = {
-            "action_request": {
-                "action": tool.name,
-                "args": tool_input
-            },
-            "config": interrupt_config,
-            "description": "Please review the tool call"
-        }
-        # highlight-next-line
-        response = interrupt([request])[0]  # (2)!
-        # approve the tool call
-        if response["type"] == "accept":
-            tool_response = tool.invoke(tool_input, config)
-        # update tool call args
-        elif response["type"] == "edit":
-            tool_input = response["args"]["args"]
-            tool_response = tool.invoke(tool_input, config)
-        # respond to the LLM with user feedback
-        elif response["type"] == "response":
-            user_feedback = response["args"]
-            tool_response = user_feedback
-        else:
-            raise ValueError(f"Unsupported interrupt response type: {response['type']}")
-
-        return tool_response
-
-    return call_tool_with_interrupt
-```
-
-1. This wrapper creates a new tool that calls `interrupt()` **before** executing the wrapped tool.
-2. `interrupt()` is using special input and output format that's expected by [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox):
-    - a list of [`HumanInterrupt`][langgraph.prebuilt.interrupt.HumanInterrupt] objects is sent to `AgentInbox` render interrupt information to the end user
-    - resume value is provided by `AgentInbox` as a list (i.e., `Command(resume=[...])`)
-
-You can use the `add_human_in_the_loop` wrapper to add `interrupt()` to any tool without having to add it *inside* the tool:
-
-```python
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.prebuilt import create_react_agent
-
-# highlight-next-line
-checkpointer = InMemorySaver()
-
-def book_hotel(hotel_name: str):
-   """Book a hotel"""
-   return f"Successfully booked a stay at {hotel_name}."
-
-
-agent = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    tools=[
-        # highlight-next-line
-        add_human_in_the_loop(book_hotel), # (1)!
-    ],
-    # highlight-next-line
-    checkpointer=checkpointer,
-)
-
-config = {"configurable": {"thread_id": "1"}}
-
-# Run the agent
-for chunk in agent.stream(
-    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
-    # highlight-next-line
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-1. The `add_human_in_the_loop` wrapper is used to add `interrupt()` to the tool. This allows the agent to pause execution and wait for human input before proceeding with the tool call.
-
-> You should see that the agent runs until it reaches the `interrupt()` call, 
->  at which point it pauses and waits for human input.
-
-Resume the agent with a `Command(resume=...)`  to continue based on human input.
-
-```python
-from langgraph.types import Command 
-
-for chunk in agent.stream(
-    # highlight-next-line
-    Command(resume=[{"type": "accept"}]),
-    # Command(resume=[{"type": "edit", "args": {"args": {"hotel_name": "McKittrick Hotel"}}}]),
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-## Additional resources
-
-* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)

docs/docs/agents/mcp.md

@@ -7,47 +7,43 @@ hide:
   - tags
 ---
 
-# MCP Integration
+# Use MCP
 
-[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
-
-![MCP](./assets/mcp.png)
-
-Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
-
-```bash
-pip install langchain-mcp-adapters
-```
+The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
 
 ## Use MCP tools
 
 The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
 
-```python title="Agent using tools defined on MCP servers"
-# highlight-next-line
-from langchain_mcp_adapters.client import MultiServerMCPClient
-from langgraph.prebuilt import create_react_agent
+=== "In an agent"
 
-# highlight-next-line
-async with MultiServerMCPClient(
-    {
-        "math": {
-            "command": "python",
-            # Replace with absolute path to your math_server.py file
-            "args": ["/path/to/math_server.py"],
-            "transport": "stdio",
-        },
-        "weather": {
-            # Ensure your start your weather server on port 8000
-            "url": "http://localhost:8000/sse",
-            "transport": "sse",
+    ```python title="Agent using tools defined on MCP servers"
+    # highlight-next-line
+    from langchain_mcp_adapters.client import MultiServerMCPClient
+    from langgraph.prebuilt import create_react_agent
+
+    # highlight-next-line
+    client = MultiServerMCPClient(
+        {
+            "math": {
+                "command": "python",
+                # Replace with absolute path to your math_server.py file
+                "args": ["/path/to/math_server.py"],
+                "transport": "stdio",
+            },
+            "weather": {
+                # Ensure you start your weather server on port 8000
+                "url": "http://localhost:8000/mcp",
+                "transport": "streamable_http",
+            }
         }
-    }
-) as client:
+    )
+    # highlight-next-line
+    tools = await client.get_tools()
     agent = create_react_agent(
         "anthropic:claude-3-7-sonnet-latest",
         # highlight-next-line
-        client.get_tools()
+        tools
     )
     math_response = await agent.ainvoke(
         {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
@@ -55,7 +51,54 @@ async with MultiServerMCPClient(
     weather_response = await agent.ainvoke(
         {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
     )
-```
+    ```
+
+=== "In a workflow"
+
+    ```python
+    from langchain_mcp_adapters.client import MultiServerMCPClient
+    from langgraph.graph import StateGraph, MessagesState, START
+    from langgraph.prebuilt import ToolNode, tools_condition
+
+    from langchain.chat_models import init_chat_model
+    model = init_chat_model("openai:gpt-4.1")
+
+    client = MultiServerMCPClient(
+        {
+            "math": {
+                "command": "python",
+                # Make sure to update to the full absolute path to your math_server.py file
+                "args": ["./examples/math_server.py"],
+                "transport": "stdio",
+            },
+            "weather": {
+                # make sure you start your weather server on port 8000
+                "url": "http://localhost:8000/mcp/",
+                "transport": "streamable_http",
+            }
+        }
+    )
+    tools = await client.get_tools()
+
+    def call_model(state: MessagesState):
+        response = model.bind_tools(tools).invoke(state["messages"])
+        return {"messages": response}
+
+    builder = StateGraph(MessagesState)
+    builder.add_node(call_model)
+    builder.add_node(ToolNode(tools))
+    builder.add_edge(START, "call_model")
+    builder.add_conditional_edges(
+        "call_model",
+        tools_condition,
+    )
+    builder.add_edge("tools", "call_model")
+    graph = builder.compile()
+    math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
+    weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
+    ```
+
+
 
 ## Custom MCP servers
 
@@ -87,7 +130,7 @@ if __name__ == "__main__":
     mcp.run(transport="stdio")
 ```
 
-```python title="Example Weather Server (SSE transport)"
+```python title="Example Weather Server (Streamable HTTP transport)"
 from mcp.server.fastmcp import FastMCP
 
 mcp = FastMCP("Weather")
@@ -98,10 +141,11 @@ async def get_weather(location: str) -> str:
     return "It's always sunny in New York"
 
 if __name__ == "__main__":
-    mcp.run(transport="sse")
+    mcp.run(transport="streamable-http")
 ```
 
 ## Additional resources
 
 - [MCP documentation](https://modelcontextprotocol.io/introduction)
-- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)
+- [langchain_mcp_adapters](https://github.com/langchain-ai/langchain-mcp-adapters)

docs/docs/agents/memory.md

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

docs/docs/agents/models.md

@@ -1,80 +1,78 @@
----
-search:
-  boost: 2
-tags:
-  - anthropic
-  - openai
-  - agent
-hide:
-  - tags
----
-
 # Models
 
-This page describes how to configure the chat model used by an agent.
+LangGraph provides built-in support for [LLMs (language models)](https://python.langchain.com/docs/concepts/chat_models/) via the LangChain library. This makes it easy to integrate various LLMs into your agents and workflows.
 
-## Tool calling support
 
-To enable tool-calling agents, the underlying LLM must support [tool calling](https://python.langchain.com/docs/concepts/tool_calling/).
+## Initialize a model
 
-Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
+Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
 
-## Specifying a model by name
+{!snippets/chat_model_tabs.md!}
 
-You can configure an agent with a model name string:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-agent = create_react_agent(
-    # highlight-next-line
-    model="anthropic:claude-3-7-sonnet-latest",
-    # other parameters
-)
-```
-
-## Using `init_chat_model`
-
-The [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) utility simplifies model initialization with configurable parameters:
-
-```python
-from langchain.chat_models import init_chat_model
-
-model = init_chat_model(
-    "anthropic:claude-3-7-sonnet-latest",
-    temperature=0,
-    max_tokens=2048
-)
-```
-
-Refer to the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for advanced options.
-
-## Using provider-specific LLMs 
+### Instantiate a model directly
 
 If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:
 
+
 ```python
+# Anthropic is already supported by `init_chat_model`,
+# but you can also instantiate it directly.
 from langchain_anthropic import ChatAnthropic
-from langgraph.prebuilt import create_react_agent
 
 model = ChatAnthropic(
-    model="claude-3-7-sonnet-latest",
-    temperature=0,
-    max_tokens=2048
-)
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model,
-    # other parameters
+  model="claude-3-7-sonnet-latest",
+  temperature=0,
+  max_tokens=2048
 )
 ```
 
-!!! note "Illustrative example" 
+!!! important "Tool calling support"
 
-    The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.
+    If you are building an agent or workflow that requires the model to call external tools, ensure that the underlying
+    language model supports [tool calling](../concepts/tools.md). Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
 
-## Disable streaming
+
+## Use in an agent
+
+When using `create_react_agent` you can specify the model by its name string, which is a shorthand for initializing the model using `init_chat_model`. This allows you to use the model without needing to import or instantiate it directly.
+
+=== "model name"
+
+
+      ```python
+      from langgraph.prebuilt import create_react_agent
+
+      create_react_agent(
+         # highlight-next-line
+         model="anthropic:claude-3-7-sonnet-latest",
+         # other parameters
+      )
+      ```
+
+=== "model instance"
+
+      ```python
+      from langchain_anthropic import ChatAnthropic
+      from langgraph.prebuilt import create_react_agent
+
+      model = ChatAnthropic(
+          model="claude-3-7-sonnet-latest",
+          temperature=0,
+          max_tokens=2048
+      )
+      # Alternatively
+      # model = init_chat_model("anthropic:claude-3-7-sonnet-latest")
+
+      agent = create_react_agent(
+        # highlight-next-line
+        model=model,
+        # other parameters
+      )
+      ```
+
+## Advanced model configuration
+
+### Disable streaming
 
 To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
 
@@ -104,7 +102,7 @@ To disable streaming of the individual LLM tokens, set `disable_streaming=True`
 
 Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
 
-## Adding model fallbacks
+### Add model fallbacks
 
 You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
 
@@ -139,7 +137,43 @@ You can add a fallback to a different model or a different LLM provider using `m
 
 See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
 
+### Use the built-in rate limiter
+
+Langchain includes a built-in in-memory rate limiter. This rate limiter is thread safe and can be shared by multiple threads in the same process.
+
+```python
+from langchain_core.rate_limiters import InMemoryRateLimiter
+from langchain_anthropic import ChatAnthropic
+
+rate_limiter = InMemoryRateLimiter(
+    requests_per_second=0.1,  # <-- Super slow! We can only make a request once every 10 seconds!!
+    check_every_n_seconds=0.1,  # Wake up every 100 ms to check whether allowed to make a request,
+    max_bucket_size=10,  # Controls the maximum burst size.
+)
+
+model = ChatAnthropic(
+   model_name="claude-3-opus-20240229", 
+   rate_limiter=rate_limiter
+)
+```
+
+See the LangChain docs for more information on how to [handle rate limiting](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/).
+
+## Bring your own model
+
+If your desired LLM isn't officially supported by LangChain, consider these options:
+
+1. **Implement a custom LangChain chat model**: Create a model conforming to the [LangChain chat model interface](https://python.langchain.com/docs/how_to/custom_chat_model/). This enables full compatibility with LangGraph's agents and workflows but requires understanding of the LangChain framework.
+
+2. **Direct invocation with custom streaming**: Use your model directly by [adding custom streaming logic](../how-tos/streaming.md#use-with-any-llm) with `StreamWriter`.
+   Refer to the [custom streaming documentation](../how-tos/streaming.md#use-with-any-llm) for guidance. This approach suits custom workflows where prebuilt agent integration is not necessary.
+
+ 
 ## Additional resources
 
+- [Multimodal inputs](https://python.langchain.com/docs/how_to/multimodal_inputs/)
+- [Structured outputs](https://python.langchain.com/docs/how_to/structured_output/)
 - [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
-- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
+- [Force model to call a specific tool](https://python.langchain.com/docs/how_to/tool_choice/)
+- [All chat model how-to guides](https://python.langchain.com/docs/how_to/#chat-models)
+- [Chat model integrations](https://python.langchain.com/docs/integrations/chat/)

docs/docs/agents/multi-agent.md

@@ -9,7 +9,7 @@ hide:
 
 # Multi-agent
 
-A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).
+A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and compose them into a [multi-agent system](../concepts/multi_agent.md).
 
 In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.
 

docs/docs/agents/overview.md

@@ -8,9 +8,9 @@ hide:
   - tags
 ---
 
-# Agent development with LangGraph
+# Agent development using prebuilt components
 
-**LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
+LangGraph provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the prebuilt, ready-to-use components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
 
 ## What is an agent?
 
@@ -27,12 +27,12 @@ The LLM operates in a loop. In each iteration, it selects a tool to invoke, prov
 
 LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
 
-- [**Memory integration**](./memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
-- [**Human-in-the-loop control**](./human-in-the-loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
-- [**Streaming support**](./streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
-- [**Deployment tooling**](./deployment.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
+- [**Memory integration**](../how-tos/memory/add-memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
+- [**Human-in-the-loop control**](../concepts/human_in_the_loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
+- [**Streaming support**](../how-tos/streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
+- [**Deployment tooling**](../tutorials/langgraph-platform/local-server.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
     - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
-    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for production.
+    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/concepts/deployment_options.md) for production.
 
 ## High-level building blocks
 
@@ -50,6 +50,142 @@ The high-level components are organized into several packages, each with a speci
 | `langgraph-supervisor`                     | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `pip install -U langgraph-supervisor`   |
 | `langgraph-swarm`                          | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm`        |
 | `langchain-mcp-adapters`                   | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
-| `langmem`                                  | Agent memory management: [**short-term and long-term**](./memory.md)        | `pip install -U langmem`                |
+| `langmem`                                  | Agent memory management: [**short-term and long-term**](../how-tos/memory/add-memory.md)        | `pip install -U langmem`                |
 | `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
 
+## Visualize an agent graph
+
+Use the following tool to visualize the graph generated by
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
+and to view an outline of the corresponding code.
+It allows you to explore the infrastructure of the agent as defined by the presence of:
+
+* [`tools`](../how-tos/tool-calling.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
+* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
+* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
+
+<div class="agent-layout">
+  <div class="agent-graph-features-container">
+    <div class="agent-graph-features">
+      <h3 class="agent-section-title">Features</h3>
+      <label><input type="checkbox" id="tools" checked> <code>tools</code></label>
+      <label><input type="checkbox" id="pre_model_hook"> <code>pre_model_hook</code></label>
+      <label><input type="checkbox" id="post_model_hook"> <code>post_model_hook</code></label>
+      <label><input type="checkbox" id="response_format"> <code>response_format</code></label>
+    </div>
+  </div>
+
+  <div class="agent-graph-container">
+    <h3 class="agent-section-title">Graph</h3>
+    <img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
+  </div>
+</div>
+
+
+The following code snippet shows how to create the above agent (and underlying graph) with
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
+
+<div class="language-python">
+  <pre><code id="agent-code" class="language-python"></code></pre>
+</div>
+
+
+<script>
+function getCheckedValue(id) {
+  return document.getElementById(id).checked ? "1" : "0";
+}
+
+function getKey() {
+  return [
+    getCheckedValue("response_format"),
+    getCheckedValue("post_model_hook"),
+    getCheckedValue("pre_model_hook"),
+    getCheckedValue("tools")
+  ].join("");
+}
+
+function generateCodeSnippet({ tools, pre, post, response }) {
+  const lines = [
+    "from langgraph.prebuilt import create_react_agent",
+    "from langchain_openai import ChatOpenAI"
+  ];
+
+  if (response) lines.push("from pydantic import BaseModel");
+
+  lines.push("", 'model = ChatOpenAI("o4-mini")', "");
+
+  if (tools) {
+    lines.push(
+      "def tool() -> None:",
+      '    """Testing tool."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (pre) {
+    lines.push(
+      "def pre_model_hook() -> None:",
+      '    """Pre-model hook."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (post) {
+    lines.push(
+      "def post_model_hook() -> None:",
+      '    """Post-model hook."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (response) {
+    lines.push(
+      "class ResponseFormat(BaseModel):",
+      '    """Response format for the agent."""',
+      "    result: str",
+      ""
+    );
+  }
+
+  lines.push("agent = create_react_agent(");
+  lines.push("    model,");
+
+  if (tools) lines.push("    tools=[tool],");
+  if (pre) lines.push("    pre_model_hook=pre_model_hook,");
+  if (post) lines.push("    post_model_hook=post_model_hook,");
+  if (response) lines.push("    response_format=ResponseFormat,");
+
+  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");
+
+  return lines.join("\n");
+}
+
+async function render() {
+  const key = getKey();
+  document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
+
+  const state = {
+    tools: document.getElementById("tools").checked,
+    pre: document.getElementById("pre_model_hook").checked,
+    post: document.getElementById("post_model_hook").checked,
+    response: document.getElementById("response_format").checked
+  };
+
+  document.getElementById("agent-code").textContent = generateCodeSnippet(state);
+}
+
+function initializeWidget() {
+  render(); // no need for `await` here
+  document.querySelectorAll(".agent-graph-features input").forEach((input) => {
+    input.addEventListener("change", render);
+  });
+}
+
+// Init for both full reload and SPA nav (used by MkDocs Material)
+window.addEventListener("DOMContentLoaded", initializeWidget);
+document$.subscribe(initializeWidget);
+</script>

docs/docs/agents/run_agents.md

@@ -10,7 +10,7 @@ hide:
 # Running agents
 
 
-Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
+Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .ainvoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](../how-tos/streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
 
 
 ## Basic usage
@@ -18,7 +18,7 @@ Agents support both synchronous and asynchronous execution using either `.invoke
 Agents can be executed in two primary modes:
 
 - **Synchronous** using `.invoke()` or `.stream()`
-- **Asynchronous** using `await .invoke()` or `async for` with `.astream()`
+- **Asynchronous** using `await .ainvoke()` or `async for` with `.astream()`
 
 === "Sync invocation"
     ```python
@@ -109,7 +109,7 @@ Streaming is available in both sync and async modes:
 
 !!! tip
 
-    For full details, see the [streaming guide](./streaming.md).
+    For full details, see the [streaming guide](../how-tos/streaming.md).
 
 ## Max iterations
 

docs/docs/agents/streaming.md

@@ -1,223 +0,0 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
-# Streaming
-
-Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
-
-1. [**Agent progress**](#agent-progress) — get updates after each node in the agent graph is executed.
-2. [**LLM tokens**](#llm-tokens) — stream tokens as they are generated by the language model.
-3. [**Custom updates**](#tool-updates) — emit custom data from tools during execution (e.g., "Fetched 10/100 records")
-
-You can stream [more than one type of data](#stream-multiple-modes) at a time. 
-
-
-<figure markdown="1">
-![image](./assets/fast_parrot.png){: style="max-height:300px"}
-<figcaption>
-Waiting is for pigeons.
-</figcaption>
-</figure>
-
-## Agent progress
-
-To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with [`stream_mode="updates"`](https://langchain-ai.github.io/langgraph/how-tos/streaming/#updates). This emits an event after every agent step.
-
-For example, if you have an agent that calls a tool once, you should see the following updates:
-
-* **LLM node**: AI message with tool call requests
-* **Tool node**: Tool message with execution result
-* **LLM node**: Final AI response
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## LLM tokens
-
-To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for token, metadata in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for token, metadata in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-## Tool updates
-
-To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
-
-=== "Sync"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-!!! Note
-    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
-
-## Stream multiple modes
-
-You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for stream_mode, chunk in agent.stream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for stream_mode, chunk in agent.astream(
-        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## Disable streaming
-
-In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
-
-See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
-
-## Additional resources
-
-* [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)

docs/docs/agents/tools.md

@@ -1,296 +0,0 @@
----
-search:
-  boost: 2
-tags:
-  - agent
-hide:
-  - tags
----
-
-# Tools
-
-[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
-
-You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
-
-## Define simple tools
-
-You can pass a vanilla function to `create_react_agent` to use as a tool:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-create_react_agent(
-    model="anthropic:claude-3-7-sonnet",
-    tools=[multiply]
-)
-```
-
-`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
-
-## Customize tools
-
-For more control over tool behavior, use the `@tool` decorator:
-
-```python
-# highlight-next-line
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool("multiply_tool", parse_docstring=True)
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-
-    Args:
-        a: First operand
-        b: Second operand
-    """
-    return a * b
-```
-
-You can also define a custom input schema using Pydantic:
-
-```python
-from pydantic import BaseModel, Field
-
-class MultiplyInputSchema(BaseModel):
-    """Multiply two numbers"""
-    a: int = Field(description="First operand")
-    b: int = Field(description="Second operand")
-
-# highlight-next-line
-@tool("multiply_tool", args_schema=MultiplyInputSchema)
-def multiply(a: int, b: int) -> int:
-    return a * b
-```
-
-For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
-
-## Hide arguments from the model
-
-Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
-
-You can put these arguments in the `state` or `config` of the agent, and access
-this information inside the tool:
-
-```python
-from langgraph.prebuilt import InjectedState
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langchain_core.runnables import RunnableConfig
-
-def my_tool(
-    # This will be populated by an LLM
-    tool_arg: str,
-    # access information that's dynamically updated inside the agent
-    # highlight-next-line
-    state: Annotated[AgentState, InjectedState],
-    # access static data that is passed at agent invocation
-    # highlight-next-line
-    config: RunnableConfig,
-) -> str:
-    """My tool."""
-    do_something_with_state(state["messages"])
-    do_something_with_config(config)
-    ...
-```
-
-## Disable parallel tool calling
-
-Some model providers support executing multiple tools in parallel, but
-allow users to disable this feature.
-
-For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
-
-```python
-from langchain.chat_models import init_chat_model
-
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
-tools = [add, multiply]
-agent = create_react_agent(
-    # disable parallel tool calls
-    # highlight-next-line
-    model=model.bind_tools(tools, parallel_tool_calls=False),
-    tools=tools
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
-)
-```
-
-## Return tool results directly
-
-Use `return_direct=True` to return tool results immediately and stop the agent loop:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[add]
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
-)
-```
-
-## Force tool use
-
-To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def greet(user_name: str) -> int:
-    """Greet user."""
-    return f"Hello {user_name}!"
-
-tools = [greet]
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
-    tools=tools
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
-)
-```
-
-!!! Warning "Avoid infinite loops"
-
-    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
-
-    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
-    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
-
-## Handle tool errors
-
-By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
-
-=== "Enable error handling (default)"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # Run with error handling (default)
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[multiply]
-    )
-    agent.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-=== "Disable error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=False  # (1)!
-    )
-    agent_no_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_no_error_handling.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-=== "Custom error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=(
-            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
-        )
-    )
-    agent_custom_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_custom_error_handling.invoke(
-        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
-    )
-    ```
-
-    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
-
-## Working with memory
-
-LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
-
-* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
-* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
-
-## Prebuilt tools
-
-LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
-
-You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
-
-Some commonly used tool categories include:
-
-- **Search**: Bing, SerpAPI, Tavily
-- **Code interpreters**: Python REPL, Node.js REPL
-- **Databases**: SQL, MongoDB, Redis
-- **Web data**: Web scraping and browsing
-- **APIs**: OpenWeatherMap, NewsAPI, and others
-
-These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
-

docs/docs/agents/ui.md

@@ -13,7 +13,7 @@ You can use a prebuilt chat UI for interacting with any LangGraph agent through
 
 ## Run agent in UI
 
-First, set up LangGraph API server [locally](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
+First, set up LangGraph API server [locally](../tutorials/langgraph-platform/local-server.md) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
 
 Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):
 
@@ -25,7 +25,7 @@ Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the re
 
 ## Add human-in-the-loop
 
-Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](./deployment.md) guide) with this [agent implementation](./human-in-the-loop.md#using-with-agent-inbox):
+Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](../tutorials/langgraph-platform/local-server.md) guide) with this [agent implementation](../how-tos/human_in_the_loop/add-human-in-the-loop.md#add-interrupts-to-any-tool):
 
 <video controls src="../assets/interrupt-chat-ui.mp4" type="video/mp4"></video>
 

docs/docs/assets/react-agent-graphs.json

@@ -0,0 +1,19 @@
+{
+    "0000": "graph TD;\n\t__start__ --> agent;\n\tagent --> __end__;",
+    "0001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\ttools --> agent;",
+    "0010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tpre_model_hook --> agent;\n\tagent --> __end__;",
+    "0011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
+    "0100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> __end__;",
+    "0101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;",
+    "0110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpre_model_hook --> agent;\n\tpost_model_hook --> __end__;",
+    "0111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
+    "1000": "graph TD;\n\t__start__ --> agent;\n\tagent --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
+    "1001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;",
+    "1100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
+    "1101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;"
+}
+  

docs/docs/cloud/concepts/cron_jobs.md

@@ -5,11 +5,11 @@ There are many situations in which it is useful to run an assistant on a schedul
 For example, say that you're building an assistant that runs daily and sends an email summary
 of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
 
-LangGraph Cloud supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server will:
+LangGraph Platform supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server will:
 
 - Create a new thread with the specified assistant
 - Send the specified input to that thread
 
 Note that this sends the same input to the thread every time. See the [how-to guide](../../cloud/how-tos/cron_jobs.md) for creating cron jobs.
 
-The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
+The LangGraph Platform API provides several endpoints for creating and managing cron jobs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.

docs/docs/cloud/concepts/data_storage_and_privacy.md

@@ -0,0 +1,54 @@
+# Data Storage and Privacy
+
+This document describes how data is processed in the LangGraph CLI and the LangGraph Server for both the in-memory server (`langgraph dev`) and the local Docker server (`langgraph up`). It also describes what data is tracked when interacting with the hosted LangGraph Studio frontend.
+
+## CLI
+
+LangGraph **CLI** is the command-line interface for building and running LangGraph applications; see the [CLI guide](../../concepts/langgraph_cli.md) to learn more.
+
+By default, calls to most CLI commands log a single analytics event upon invocation. This helps us better prioritize improvements to the CLI experience. Each telemetry event contains the calling process's OS, OS version, Python version, the CLI version, the command name (`dev`, `up`, `run`, etc.), and booleans representing whether a flag was passed to the command. You can see the full analytics logic [here](https://github.com/langchain-ai/langgraph/blob/main/libs/cli/langgraph_cli/analytics.py). 
+
+You can disable all CLI telemetry by setting `LANGGRAPH_CLI_NO_ANALYTICS=1`.
+
+## LangGraph Server (in-memory & docker)
+
+The [LangGraph Server](../../concepts/langgraph_server.md) provides a durable execution runtime that relies on persisting checkpoints of your application state, long-term memories, thread metadata, assistants, and similar resources to the local file system or a database. Unless you have deliberately customized the storage location, this information is either written to local disk (for `langgraph dev`) or a PostgreSQL database (for `langgraph up` and in all deployments).
+
+### LangSmith Tracing
+
+When running the LangGraph server (either in-memory or in Docker), LangSmith tracing may be enabled to facilitate faster debugging and offer observability of graph state and LLM prompts in production. You can always disable tracing by setting `LANGSMITH_TRACING=false` in your server's runtime environment.
+
+### In-memory development server (`langgraph dev`)
+
+`langgraph dev` runs an [in-memory development server](../../tutorials/langgraph-platform/local-server.md) as a single Python process, designed for quick development and testing. It saves all checkpointing and memory data to disk within a `.langgraph_api` directory in the current working directory. Apart from the telemetry data described in the [CLI](#cli) section, no data leaves the machine unless you have enabled tracing or your graph code explicitly contacts an external service.
+
+### Standalone Container (`langgraph up`)
+
+`langgraph up` builds your local package into a Docker image and runs the server as a [standalone container](../../concepts/deployment_options.md#standalone-container) consisting of three containers: the API server, a PostgreSQL container, and a Redis container. All persistent data (checkpoints, assistants, etc.) are stored in the PostgreSQL database. Redis is used as a pubsub connection for real-time streaming of events. You can encrypt all checkpoints before saving to the database by setting a valid `LANGGRAPH_AES_KEY` environment variable. You can also specify [TTLs](../../how-tos/ttl/configure_ttl.md) for checkpoints and cross-thread memories in `langgraph.json` to control how long data is stored. All persisted threads, memories, and other data can be deleted via the relevant API endpoints.
+
+Additional API calls are made to confirm that the server has a valid license and to track the number of executed runs and tasks. Periodically, the API server validates the provided license key (or API key).
+
+If you've disabled [tracing](#langsmith-tracing), no user data is persisted externally unless your graph code explicitly contacts an external service.
+
+## Studio
+
+[LangGraph Studio](../../concepts/langgraph_studio.md) is a graphical interface for interacting with your LangGraph server. It does not persist any private data (the data you send to your server is not sent to LangSmith). Though the studio interface is served at [smith.langchain.com](https://smith.langchain.com), it is run in your browser and connects directly to your local LangGraph server so that no data needs to be sent to LangSmith.
+
+If you are logged in, LangSmith does collect some usage analytics to help improve studio's user experience. This includes:
+
+- Page visits and navigation patterns
+- User actions (button clicks)
+- Browser type and version
+- Screen resolution and viewport size
+
+Importantly, no application data or code (or other sensitive configuration details) are collected. All of that is stored in the persistence layer of your LangGraph server. When using Studio anonymously, no account creation is required and usage analytics are not collected.
+
+## Quick reference
+
+In summary, you can opt-out of server-side telemetry by turning off CLI analytics and disabling tracing.
+
+| Variable                       | Purpose                   | Default                          |
+| ------------------------------ | ------------------------- | -------------------------------- |
+| `LANGGRAPH_CLI_NO_ANALYTICS=1` | Disable CLI analytics     | Analytics enabled                |
+| `LANGSMITH_API_KEY`            | Enable LangSmith tracing  | Tracing disabled                 |
+| `LANGSMITH_TRACING=false`      | Disable LangSmith tracing | Depends on environment           |

docs/docs/cloud/concepts/runs.md

@@ -1,5 +0,0 @@
-# Runs
-
-A run is an invocation of an [assistant](../../concepts/assistants.md). Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a [thread](./threads.md).
-
-The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/cloud/concepts/streaming.md

@@ -1,138 +0,0 @@
-# Streaming
-
-Streaming is critical for making LLM applications feel responsive to end users.  
-When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
-
-## Supported streaming modes
-
-LangGraph Platform supports the following streaming modes:
-
-| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
-|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
-| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
-| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
-| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
-| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
-| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
-| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
-
-✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
-
-## Stateless runs
-
-If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    async for chunk in client.runs.stream(
-        # highlight-next-line
-        None,  # (1)!
-        assistant_id,
-        input=inputs,
-        stream_mode="updates"
-    ):
-        print(chunk.data)
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // create a streaming run
-    // highlight-next-line
-    const streamResponse = client.runs.stream(
-      // highlight-next-line
-      null,  // (1)!
-      assistantID,
-      {
-        input,
-        streamMode: "updates"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk.data);
-    }
-    ```
-
-    1. We are passing `None` instead of a `thread_id` UUID.
-
-=== "cURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <inputs>,
-      \"stream_mode\": \"updates\"
-    }"
-    ```
-
-## Join and stream
-
-LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
-
-    # highlight-next-line
-    async for chunk in client.runs.join_stream(
-        thread_id,
-        # highlight-next-line
-        run_id,  # (1)!
-    ):
-        print(chunk)
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
-
-    // highlight-next-line
-    const streamResponse = client.runs.joinStream(
-      threadID,
-      // highlight-next-line
-      runId  // (1)!
-    );
-    for await (const chunk of streamResponse) {
-      console.log(chunk);
-    }
-    ```
-
-    1. This is the `run_id` of an existing run you want to join.
-
-=== "cURL"
-
-    ```bash
-    curl --request GET \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
-    --header 'Content-Type: application/json' \
-    --header 'x-api-key: <API_KEY>'
-    ```
-
-!!! warning "Outputs not buffered"
-
-    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
-
-## API Reference
-
-For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
-

docs/docs/cloud/concepts/threads.md

@@ -1,11 +0,0 @@
-# Threads
-
-A thread contains the accumulated state of a sequence of [runs](./runs.md). If a run is executed on a thread, then the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
-
-A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
-
-The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
-
-For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
-
-The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.

docs/docs/cloud/concepts/webhooks.md

@@ -1,7 +1,7 @@
 # Webhooks
 
-Webhooks enable event-driven communication from your LangGraph Cloud application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Cloud has finished running.
+Webhooks enable event-driven communication from your LangGraph Platform application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Platform has finished running.
 
-Many LangGraph Cloud endpoints accept a `webhook` parameter. If this parameter is specified by a an endpoint that can accept POST requests, LangGraph Cloud will send a request at the completion of a run.
+Many LangGraph Platform endpoints accept a `webhook` parameter. If this parameter is specified by an endpoint that can accept POST requests, LangGraph Platform will send a request at the completion of a run.
 
 See the corresponding [how-to guide](../../cloud/how-tos/webhooks.md) for more detail.

docs/docs/cloud/deployment/cloud.md

@@ -4,14 +4,14 @@ Before deploying, review the [conceptual guide for the Cloud SaaS](../../concept
 
 ## Prerequisites
 
-1. LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
-1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
+1. LangGraph Platform applications are deployed from GitHub repositories. Configure and upload a LangGraph Platform application to a GitHub repository in order to deploy it to LangGraph Platform.
+1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Platform will fail as well.
 
 ## Create New Deployment
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
 1. In the top-right corner, select `+ New Deployment` to create a new deployment.
 1. In the `Create New Deployment` panel, fill out the required fields.
     1. `Deployment details`
@@ -38,7 +38,7 @@ When [creating a new deployment](#create-new-deployment), a new revision is crea
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
 1. Select an existing deployment to create a new revision for.
 1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
 1. In the `New Revision` modal, fill out the required fields.
@@ -62,6 +62,15 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
 
+## View Deployment Metrics
+
+Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
+
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
+1. Select an existing deployment to monitor.
+1. Select the `Monitoring` tab to view the deployment metrics. See a list of [all available metrics](../../concepts/langgraph_control_plane.md#monitoring).
+1. Within the `Monitoring` tab, use the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
+
 ## Interrupt Revision
 
 Interrupting a revision will stop deployment of the revision.
@@ -79,7 +88,7 @@ Starting from the `LangGraph Platform` view...
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
 1. Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select `Delete`.
 1. A `Confirmation` modal will appear. Select `Delete`.
 

docs/docs/cloud/deployment/custom_docker.md

@@ -16,4 +16,4 @@ Users can add an array of additional lines to add to the Dockerfile following th
 }
 ```
 
-This would install the system packages required to use Pillow if we were working with `jpeq` or `png` image formats. 
+This would install the system packages required to use Pillow if we were working with `jpeg` or `png` image formats.