0.4.7

- The default applied for subgraphs should be applied only when stream mode arg not passed in

.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/workflows/_integration_test.yml

@@ -3,9 +3,6 @@ name: CLI integration test
 on:
   workflow_call:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -25,13 +22,14 @@ 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

@@ -9,8 +9,6 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
-
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github
 
@@ -36,32 +34,18 @@ 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
@@ -71,7 +55,7 @@ jobs:
         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
@@ -86,17 +70,8 @@ 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
@@ -106,7 +81,7 @@ jobs:
         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

@@ -8,9 +8,6 @@ on:
         type: string
         description: "From which folder this pipeline executes"
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -26,12 +23,12 @@ 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 +39,12 @@ 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

@@ -3,9 +3,6 @@ name: test
 on:
   workflow_call:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -24,12 +21,12 @@ 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 +36,11 @@ 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

@@ -9,7 +9,6 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
   PYTHON_VERSION: "3.10"
 
 jobs:
@@ -24,12 +23,12 @@ 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 +42,7 @@ 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 +56,8 @@ 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

@@ -3,9 +3,6 @@ name: test
 on:
   workflow_call:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -21,12 +18,12 @@ 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-scheduler-kafka
+          enable-cache: true
+          cache-suffix: "test-scheduler-kafka"
       - name: Login to Docker Hub
         uses: docker/login-action@v3
         if: ${{ !github.event.pull_request.head.repo.fork }}
@@ -36,13 +33,11 @@ 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
+        run: make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/baseline.yml

@@ -7,9 +7,6 @@ on:
     paths:
       - "libs/**"
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   benchmark:
     runs-on: ubuntu-latest
@@ -19,14 +16,14 @@ 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

@@ -5,9 +5,6 @@ on:
     paths:
       - "libs/**"
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   benchmark:
     runs-on: ubuntu-latest
@@ -21,14 +18,14 @@ 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: ${{ env.POETRY_VERSION }}
-          cache-key: bench
+          enable-cache: true
+          cache-suffix: "bench"
       - name: Install dependencies
-        run: poetry install --with dev
+        run: uv sync --group dev
       - name: Download baseline
         uses: actions/cache/restore@v4
         with:
@@ -53,7 +50,7 @@ jobs:
             echo 'OUTPUT<<EOF'
             mv out/benchmark-baseline.json out/main.json
             mv out/benchmark.json out/changes.json
-            poetry run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
+            uv run pyperf compare_to out/main.json out/changes.json --table --group-by-speed
             echo EOF
           } >> "$GITHUB_OUTPUT"
       - name: Annotation

.github/workflows/ci.yml

@@ -16,9 +16,6 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   changes:
     runs-on: ubuntu-latest
@@ -125,26 +122,26 @@ 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

.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/.*" \

.github/workflows/link_check.yml

@@ -11,9 +11,6 @@ on:
     - cron: "0 5 * * *"
   workflow_dispatch:
 
-env:
-  POETRY_VERSION: "2.1.2"
-
 jobs:
   markdown-link-check:
     runs-on: ubuntu-latest

.github/workflows/release.yml

@@ -10,7 +10,6 @@ on:
 
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "2.1.2"
 
 jobs:
   build:
@@ -26,12 +25,12 @@ 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 +44,7 @@ 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 +58,8 @@ 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 +162,11 @@ 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 +184,18 @@ 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 +208,10 @@ 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 +222,7 @@ 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 +252,12 @@ 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 +293,12 @@ 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/run_notebooks.yml

@@ -27,30 +27,30 @@ 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

CONTRIBUTING.md

@@ -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
 ```

README.md

@@ -12,45 +12,52 @@
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)
 
-> [!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
 
@@ -58,6 +65,10 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+- [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
+
+> [!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
 
@@ -66,8 +77,8 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 - [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
-- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship 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/Makefile

@@ -12,32 +12,30 @@ 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
+	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 +43,10 @@ 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
+	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 +54,13 @@ 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/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 +1,19 @@
 """Experimental script to generate consolidated llms text from the docs."""
 
+import asyncio
 import glob
 import os
+from typing import TypedDict, List, Optional
+import pydantic
+import re
+from pydantic import BaseModel, Field
+from langchain_core.rate_limiters import InMemoryRateLimiter
 
+import yaml
+from langchain.chat_models import init_chat_model
 from mkdocs.structure.files import File
 from mkdocs.structure.pages import Page
+from yaml import SafeLoader
 
 from _scripts.notebook_hooks import _on_page_markdown_with_config
 
@@ -13,7 +22,49 @@ 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 +72,9 @@ 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 +84,14 @@ 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 +102,168 @@ 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 +273,23 @@ 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/notebook_hooks.py

@@ -39,6 +39,7 @@ REDIRECT_MAP = {
     "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
     "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
     "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    "how-tos/async.ipynb": "how-tos/graph-api/#async",
     # memory how-tos
     "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
     "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
@@ -52,6 +53,7 @@ REDIRECT_MAP = {
     "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
     "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
     "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
+    "cloud/how-tos/copy_threads": "cloud/how-tos/use_threads",
     # 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",
@@ -67,6 +69,9 @@ REDIRECT_MAP = {
     "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",
@@ -94,7 +99,9 @@ REDIRECT_MAP = {
     "tutorials/introduction.ipynb": "concepts/why-langgraph.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",
+    # assistant redirects
+    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md"
 
 }
 

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

@@ -0,0 +1 @@
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

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

@@ -0,0 +1 @@
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

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

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docs/cassettes/time-travel_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/time-travel_e986f94f-706f-4b6f-b3c4-f95483b9e9b8.msgpack.zlib

@@ -1 +0,0 @@
-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

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

@@ -0,0 +1 @@
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

docs/docs/agents/agents.md

@@ -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

docs/docs/agents/deployment.md

@@ -89,4 +89,4 @@ LangGraph Studio Web is a specialized UI that you can connect to LangGraph API s
 
 ## 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.
+Once your LangGraph app is running locally, you can deploy it using LangGraph Platform. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

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

@@ -106,7 +106,7 @@ for chunk in agent.stream(
     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.
+1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`][langgraph.types.Command] object to resume the graph with a value provided by the human.
 
 ## Using with Agent Inbox
 

docs/docs/agents/mcp.md

@@ -29,7 +29,7 @@ from langchain_mcp_adapters.client import MultiServerMCPClient
 from langgraph.prebuilt import create_react_agent
 
 # highlight-next-line
-async with MultiServerMCPClient(
+client = MultiServerMCPClient(
     {
         "math": {
             "command": "python",
@@ -39,22 +39,24 @@ async with MultiServerMCPClient(
         },
         "weather": {
             # Ensure your start your weather server on port 8000
-            "url": "http://localhost:8000/sse",
-            "transport": "sse",
+            "url": "http://localhost:8000/mcp",
+            "transport": "streamable_http",
         }
     }
-) as client:
-    agent = create_react_agent(
-        "anthropic:claude-3-7-sonnet-latest",
-        # highlight-next-line
-        client.get_tools()
-    )
-    math_response = await agent.ainvoke(
-        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
-    )
-    weather_response = await agent.ainvoke(
-        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
-    )
+)
+# highlight-next-line
+tools = await client.get_tools()
+agent = create_react_agent(
+    "anthropic:claude-3-7-sonnet-latest",
+    # highlight-next-line
+    tools
+)
+math_response = await agent.ainvoke(
+    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+)
+weather_response = await agent.ainvoke(
+    {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+)
 ```
 
 ## Custom MCP servers
@@ -87,7 +89,7 @@ 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")
@@ -98,7 +100,7 @@ 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

docs/docs/agents/memory.md

@@ -294,7 +294,7 @@ agent.invoke(
 )
 ```
 
-For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
+For more details, see [how to update state from tools](../how-tos/tool-calling.ipynb#update).
 
 ## Long-term memory
 
@@ -302,7 +302,7 @@ Use long-term memory to store user-specific or application-specific data across
 
 To use long-term memory, you need to:
 
-1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
+1. [Configure a store](../how-tos/persistence.ipynb#add-long-term-memory) to persist data across invocations.
 2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
 
 ### Read { #read-long-term }

docs/docs/agents/models.md

@@ -23,29 +23,182 @@ Compatible models can be found in the [LangChain integrations directory](https:/
 
 You can configure an agent with a model name string:
 
-```python
-from langgraph.prebuilt import create_react_agent
+=== "OpenAI"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="openai:gpt-4.1",
+        # other parameters
+    )
+    ```
+
+=== "Anthropic"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="anthropic:claude-3-7-sonnet-latest",
+        # other parameters
+    )
+    ```
+
+=== "Azure"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="azure_openai:gpt-4.1",
+        # other parameters
+    )
+    ```
+
+=== "Google Gemini"
+
+    ```python
+    import os
+    from langgraph.prebuilt import create_react_agent
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="google_genai:gemini-2.0-flash",
+        # other parameters
+    )
+    ```
+
+=== "AWS Bedrock"
+
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    agent = create_react_agent(
+        # highlight-next-line
+        model="bedrock_converse:anthropic.claude-3-5-sonnet-20240620-v1:0",
+        # other parameters
+    )
+    ```
 
-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
+=== "OpenAI"
+
+    ```
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    model = init_chat_model(
+        "openai:gpt-4.1",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Anthropic"
+
+    ```
+    pip install -U "langchain[anthropic]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    model = init_chat_model(
+        "anthropic:claude-3-5-sonnet-latest",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Azure"
+
+    ```
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    model = init_chat_model(
+        "azure_openai:gpt-4.1",
+        azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "Google Gemini"
+
+    ```
+    pip install -U "langchain[google-genai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    model = init_chat_model(
+        "google_genai:gemini-2.0-flash",
+        temperature=0,
+        # other parameters
+    )
+    ```
+
+=== "AWS Bedrock"
+
+    ```
+    pip install -U "langchain[aws]"
+    ```
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    model = init_chat_model(
+        "anthropic.claude-3-5-sonnet-20240620-v1:0",
+        model_provider="bedrock_converse",
+        temperature=0,
+        # other parameters
+    )
+    ```
 
-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.
 

docs/docs/agents/overview.md

@@ -53,3 +53,139 @@ The high-level components are organized into several packages, each with a speci
 | `langmem`                                  | Agent memory management: [**short-term and long-term**](./memory.md)        | `pip install -U langmem`                |
 | `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
 
+## Visualize an agent graph
+
+Use the following tool to visualize the graph generated by
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
+and to view an outline of the corresponding code.
+It allows you to explore the infrastructure of the agent as defined by the presence of:
+
+* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
+* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
+* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
+
+<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/prebuilt.md

@@ -1,10 +1,10 @@
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# Community Agents
+# Community agents
 
 If you’re looking for other prebuilt libraries, explore the community-built options 
 below. These libraries can extend LangGraph's functionality in various ways.
 
-## 📚 Available Libraries
+## 📚 Available libraries
 
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
 | Name | GitHub URL | Description | Weekly Downloads | Stars |
@@ -23,7 +23,7 @@ below. These libraries can extend LangGraph's functionality in various ways.
 | **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
 | **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)
 
-## ✨ Contributing Your Library
+## ✨ Contributing your library
 
 Have you built an awesome open-source library using LangGraph? We'd love to feature 
 your project on the official LangGraph documentation pages! 🏆

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](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

docs/docs/agents/tools.md

@@ -280,7 +280,21 @@ LangGraph allows access to short-term and long-term memory from tools. See [Memo
 
 ## 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 use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
 
 You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
 

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](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
 
 Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):
 

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/runs.md

@@ -2,4 +2,4 @@
 
 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.
+The LangGraph Platform API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/cloud/concepts/threads.md

@@ -1,11 +1,12 @@
 # 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 contains the accumulated state of a sequence of [runs](./runs.md). When a run is executed, the [state](../../concepts/low_level.md#state) of the underlying graph of the assistant will be persisted to the thread.
 
 A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
 
-The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
+The state of a thread at a particular point in time is called a [checkpoint](../../concepts/persistence.md#checkpoints). Checkpoints are persisted and can be used to restore the state of a thread at a later time.
 
-For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+## Learn more
 
-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.
+* For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+* The LangGraph Platform API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.

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.
@@ -79,7 +79,7 @@ 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/self_hosted_control_plane.md

@@ -2,6 +2,9 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
 
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
+
 ## Prerequisites
 
 1. You are using Kubernetes.

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -2,6 +2,9 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
 
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
+
 ## Prerequisites
 
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
@@ -15,7 +18,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
         helm repo add kedacore https://kedacore.github.io/charts 
         helm install keda kedacore/keda --namespace keda --create-namespace
 
-1. A valid `Ingress` controller is install on your cluster.
+1. A valid `Ingress` controller is installed on your cluster.
 1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
 
 ### Setup

docs/docs/cloud/deployment/setup.md

@@ -1,11 +1,11 @@
 # How to Set Up a LangGraph Application with requirements.txt
 
-A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.
 
 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
 
 !!! tip "Setup with pyproject.toml"
-    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.
+    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Platform.
 
 !!! tip "Setup with a Monorepo"
     If you are interested in deploying a graph located inside a monorepo, take a look at [this repository](https://github.com/langchain-ai/langgraph-example-monorepo) for an example of how to do so.
@@ -31,7 +31,7 @@ After each step, an example file directory is provided to demonstrate how code c
 
 ## Specify Dependencies
 
-Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).
+Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
 
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
@@ -129,9 +129,6 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```
 
-!!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
-
 Example file directory:
 
 ```bash

docs/docs/cloud/deployment/setup_javascript.md

@@ -1,6 +1,6 @@
 # How to Set Up a LangGraph.js Application
 
-A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using `package.json` to specify project dependencies.
+A [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph.js application for deployment using `package.json` to specify project dependencies.
 
 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraphjs-studio-starter), which you can play around with to learn more about how to setup your LangGraph application for deployment.
 
@@ -155,10 +155,6 @@ const workflow = new StateGraph(MessagesAnnotation)
 export const graph = workflow.compile();
 ```
 
-!!! info "Assign `CompiledGraph` to Variable"
-
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
-
 Example file directory:
 
 ```bash

docs/docs/cloud/deployment/setup_pyproject.md

@@ -1,6 +1,6 @@
 # How to Set Up a LangGraph Application with pyproject.toml
 
-A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies.
+A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `pyproject.toml` to define your package's dependencies.
 
 This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example-pyproject), which you can play around with to learn more about how to setup your LangGraph application for deployment.
 
@@ -31,7 +31,7 @@ After each step, an example file directory is provided to demonstrate how code c
 
 ## Specify Dependencies
 
-Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-api-config).
+Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
 
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
@@ -56,22 +56,27 @@ cloudpickle>=3.0.0
 Example `pyproject.toml` file:
 
 ```toml
-[tool.poetry]
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
 name = "my-agent"
 version = "0.0.1"
-description = "An excellent agent build for LangGraph cloud."
-authors = ["Polly the parrot <1223+polly@users.noreply.github.com>"]
-license = "MIT"
+description = "An excellent agent build for LangGraph Platform."
+authors = [
+    {name = "Polly the parrot", email = "1223+polly@users.noreply.github.com"}
+]
+license = {text = "MIT"}
 readme = "README.md"
+requires-python = ">=3.9"
+dependencies = [
+    "langgraph>=0.2.0",
+    "langchain-fireworks>=0.1.3"
+]
 
-[tool.poetry.dependencies]
-python = ">=3.9"
-langgraph = "^0.2.0"
-langchain-fireworks = "^0.1.3"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[tool.hatch.build.targets.wheel]
+packages = ["my_agent"]
 ```
 
 Example file directory:
@@ -137,9 +142,6 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```
 
-!!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
-
 Example file directory:
 
 ```bash

docs/docs/cloud/deployment/standalone_container.md

@@ -22,7 +22,7 @@ Before deploying, review the [conceptual guide for the Standalone Container](../
             `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
 
     1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_server.md#server-versions)) LangSmith API key. This will be used to authenticate ONCE at server start up.
-    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
+    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#licensing)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
     1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
 
 ## Kubernetes (Helm)

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -0,0 +1,342 @@
+# Human-in-the-loop
+
+LangGraph supports robust **human-in-the-loop (HIL)** workflows, enabling human intervention at any point in an automated process. This is especially useful in large language model (LLM)-driven applications where model output may require validation, correction, or additional context.
+
+Please see [the overview of LangGraph human-in-the-loop](../../concepts/human_in_the_loop.md) features for more information.
+
+## `interrupt`
+
+The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+
+The graph is resumed using a [`Command`][langgraph.types.Command] object that provides the human's response.
+
+**Graph node with `interrupt`:**
+
+```python
+# highlight-next-line
+from langgraph.types import interrupt, Command
+
+def human_node(state: State):
+    # highlight-next-line
+    value = interrupt( # (1)!
+        {
+            "text_to_revise": state["some_text"] # (2)!
+        }
+    )
+    return {
+        "some_text": value # (3)!
+    }
+```
+
+1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
+2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
+3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
+
+**LangGraph API invoke & resume:**
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+    client = get_client(url=<DEPLOYMENT_URL>)
+
+    # Using the graph deployed with the name "agent"
+    assistant_id = "agent"
+
+    # create a thread
+    thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph until the interrupt is hit.
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input={"some_text": "original text"}   # (1)!
+    )
+
+    print(result['__interrupt__']) # (2)!
+    # > [
+    # >     {
+    # >         'value': {'text_to_revise': 'original text'},
+    # >         'resumable': True,
+    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+    # >         'when': 'during'
+    # >     }
+    # > ]
+
+
+    # Resume the graph
+    print(await client.runs.wait(
+        thread_id,
+        assistant_id,
+        # highlight-next-line
+        command=Command(resume="Edited text")   # (3)!
+    ))
+    # > {'some_text': 'Edited text'}
+    ```
+
+    1. The graph is invoked with some initial state.
+    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
+    3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
+    // Using the graph deployed with the name "agent"
+    const assistantID = "agent";
+
+    // create a thread
+    const thread = await client.threads.create();
+    const threadID = thread["thread_id"];
+
+    // Run the graph until the interrupt is hit.
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: { "some_text": "original text" } }   // (1)!
+    );
+
+    console.log(result['__interrupt__']); // (2)!
+    // > [
+    // >     {
+    // >         'value': {'text_to_revise': 'original text'},
+    // >         'resumable': True,
+    // >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+    // >         'when': 'during'
+    // >     }
+    // > ]
+
+    // Resume the graph
+    console.log(await client.runs.wait(
+        threadID,
+        assistantID,
+        // highlight-next-line
+        { command: { resume: "Edited text" }}   // (3)!
+    ));
+    // > {'some_text': 'Edited text'}
+    ```
+
+    1. The graph is invoked with some initial state.
+    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
+    3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
+
+=== "cURL"
+
+    Create a thread:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+    ```
+
+    Run the graph until the interrupt is hit.:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": {\"some_text\": \"original text\"}
+    }"
+    ```
+
+    Resume the graph:
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": \"Edited text\"
+       }
+     }"
+    ```
+
+??? example "Extended example: using `interrupt`"
+
+    This is an example graph you can run in the LangGraph API server.
+    See [LangGraph Platform quickstart](../quick_start.md) for more details.
+
+    ```python
+    from typing import TypedDict
+    import uuid
+
+    from langgraph.checkpoint.memory import InMemorySaver
+    from langgraph.constants import START
+    from langgraph.graph import StateGraph
+    # highlight-next-line
+    from langgraph.types import interrupt, Command
+
+    class State(TypedDict):
+        some_text: str
+
+    def human_node(state: State):
+        # highlight-next-line
+        value = interrupt( # (1)!
+            {
+                "text_to_revise": state["some_text"] # (2)!
+            }
+        )
+        return {
+            "some_text": value # (3)!
+        }
+
+
+    # Build the graph
+    graph_builder = StateGraph(State)
+    graph_builder.add_node("human_node", human_node)
+    graph_builder.add_edge(START, "human_node")
+
+    graph = graph_builder.compile()
+    ```
+
+    1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
+    2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
+    3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
+
+    Once you have a running LangGraph API server, you can interact with it using
+    [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)
+
+    === "Python"
+
+        ```python
+        from langgraph_sdk import get_client
+        # highlight-next-line
+        from langgraph_sdk.schema import Command
+        client = get_client(url=<DEPLOYMENT_URL>)
+
+        # Using the graph deployed with the name "agent"
+        assistant_id = "agent"
+
+        # create a thread
+        thread = await client.threads.create()
+        thread_id = thread["thread_id"]
+
+        # Run the graph until the interrupt is hit.
+        result = await client.runs.wait(
+            thread_id,
+            assistant_id,
+            input={"some_text": "original text"}   # (1)!
+        )
+
+        print(result['__interrupt__']) # (2)!
+        # > [
+        # >     {
+        # >         'value': {'text_to_revise': 'original text'},
+        # >         'resumable': True,
+        # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+        # >         'when': 'during'
+        # >     }
+        # > ]
+
+
+        # Resume the graph
+        print(await client.runs.wait(
+            thread_id,
+            assistant_id,
+            # highlight-next-line
+            command=Command(resume="Edited text")   # (3)!
+        ))
+        # > {'some_text': 'Edited text'}
+        ```
+
+        1. The graph is invoked with some initial state.
+        2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
+        3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
+
+    === "JavaScript"
+
+        ```js
+        import { Client } from "@langchain/langgraph-sdk";
+        const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
+        // Using the graph deployed with the name "agent"
+        const assistantID = "agent";
+
+        // create a thread
+        const thread = await client.threads.create();
+        const threadID = thread["thread_id"];
+
+        // Run the graph until the interrupt is hit.
+        const result = await client.runs.wait(
+          threadID,
+          assistantID,
+          { input: { "some_text": "original text" } }   // (1)!
+        );
+
+        console.log(result['__interrupt__']); // (2)!
+        // > [
+        // >     {
+        // >         'value': {'text_to_revise': 'original text'},
+        // >         'resumable': True,
+        // >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
+        // >         'when': 'during'
+        // >     }
+        // > ]
+
+        // Resume the graph
+        console.log(await client.runs.wait(
+            threadID,
+            assistantID,
+            // highlight-next-line
+            { command: { resume: "Edited text" }}   // (3)!
+        ));
+        // > {'some_text': 'Edited text'}
+        ```
+
+        1. The graph is invoked with some initial state.
+        2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
+        3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
+
+    === "cURL"
+
+        Create a thread:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{}'
+        ```
+
+        Run the graph until the interrupt is hit:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"input\": {\"some_text\": \"original text\"}
+        }"
+        ```
+
+        Resume the graph:
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"command\": {
+            \"resume\": \"Edited text\"
+          }
+        }"
+        ```
+
+## Learn more
+
+- [**LangGraph human-in-the-loop overview**](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features. 
+- [**Design patterns**](../../how-tos/human_in_the_loop/add-human-in-the-loop.md#design-patterns): learn how to implement patterns like approving/rejecting actions, requesting user input, and more.
+- [**How to review tool calls**](./human_in_the_loop_review_tool_calls.md): detailed examples of how to review and approve/edit tool calls or provide feedback to the tool-calling LLM.

docs/docs/cloud/how-tos/assistant_versioning.md

@@ -1,152 +0,0 @@
-# How to version Assistants
-
-!!! info "Prerequisites"
-
-    - [Assistants Overview](../../concepts/assistants.md)
-    - [How to create an Assistant](./configuration_cloud.md)
-
-In this guide we will show you how to create, manage and use multiple versions of an assistant. If you have not already, please first see [this](./configuration_cloud.md) guide on creating an assistant. For this example, assume you have a graph with the following configuration schema:
-
-=== "Python"
-
-    ```python
-    class Config(BaseModel):
-        model_name: Literal["anthropic", "openai"] = "anthropic"
-        system_prompt: str
-
-    builder = StateGraph(State, config_schema=Config)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const ConfigAnnotation = Annotation.Root({
-        modelName: Annotation<z.enum(["openai", "anthropic"])>({
-            default: () => "anthropic",
-        }),
-        systemPrompt: Annotation<String>
-    });
-
-    // the rest of your code
-
-    const builder = new StateGraph(StateAnnotation, ConfigAnnotation);
-    ```
-
-And that you have the following assistant already created:
-
-    {
-        "assistant_id": "62e209ca-9154-432a-b9e9-2d75c7a9219b",
-        "graph_id": "agent",
-        "name": "Open AI Assistant"
-        "config": {
-            "configurable": {
-                "model_name": "openai",
-                "system_prompt": "You are a helpful assistant."
-            }
-        },
-        "metadata": {}
-        "created_at": "2024-08-31T03:09:10.230718+00:00",
-        "updated_at": "2024-08-31T03:09:10.230718+00:00",
-    }
-
-## Create a new version for your assistant
-
-### LangGraph SDK
-
-To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](../reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient.update) and [JS](../reference/sdk/js_ts_sdk_ref.md#update) SDK reference docs for more information.
-
-!!! note "Note"
-    You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previously versions.
-
-For example, to update your assistant's system prompt:
-=== "Python"
-
-    ```python
-    openai_assistant_v2 = await client.assistants.update(
-        openai_assistant["assistant_id"],
-        config={
-            "configurable": {
-                "model_name": "openai",
-                "system_prompt": "You are an unhelpful assistant!",
-            }
-        },
-    )
-    ```
-
-=== "Javascript"
-
-    ```js
-    const openaiAssistantV2 = await client.assistants.update(
-        openai_assistant["assistant_id"],
-        {
-            config: {
-                configurable: {
-                    model_name: 'openai',
-                    system_prompt: 'You are an unhelpful assistant!',
-                },
-        },
-    });
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request PATCH \
-    --url <DEPOLYMENT_URL>/assistants/<ASSISTANT_ID> \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "config": {"model_name": "openai", "system_prompt": "You are an unhelpful assistant!"}
-    }'
-    ```
-
-This will create a new version of the assistant with the updated parameters and set this as the active version of your assistant. If you now run your graph and pass in this assistant id, it will use this latest version.
-
-### LangGraph Platform UI
-
-You can also edit assistants from the LangGraph Platform UI.
-
-Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.
-
-To edit an existing assistant, select the "Edit" button for the specified assistant. This will open a form where you can edit the assistant's name, description, and configuration.
-
-Additionally, if using LangGraph Studio, you can edit the assistants and create new versions via the "Manage Assistants" button.
-
-## Use a previous assistant version
-
-### LangGraph SDK
-
-You can also change the active version of your assistant. To do so, use the `setLatest` method.
-
-In the example above, to rollback to the first version of the assistant:
-
-=== "Python"
-
-    ```python
-    await client.assistants.set_latest(openai_assistant['assistant_id'], 1)
-    ```
-
-=== "Javascript"
-
-    ```js
-    await client.assistants.setLatest(openaiAssistant['assistant_id'], 1);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/latest \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "version": 1
-    }'
-    ```
-
-If you now run your graph and pass in this assistant id, it will use the first version of the assistant.
-
-### LangGraph Platform UI
-
-If using LangGraph Studio, to set the active version of your asssistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.
-
-!!! warning "Deleting Assistants"
-    Deleting as assistant will delete ALL of it's versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.

docs/docs/cloud/how-tos/check_thread_status.md

@@ -1,203 +0,0 @@
-# Check the Status of your Threads
-
-## Setup
-
-To start, we can setup our client with whatever URL you are hosting your graph from:
-
-### SDK initialization
-
-First, we need to setup our client so that we can communicate with our hosted graph:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{}'
-    ```
-
-## Find idle threads
-
-We can use the following commands to find threads that are idle, which means that all runs executed on the thread have finished running:
-
-=== "Python"
-
-    ```python
-    print(await client.threads.search(status="idle",limit=1))
-    ```
-
-=== "Javascript"
-
-    ```js
-    console.log(await client.threads.search({ status: "idle", limit: 1 }));
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \  
-    --url <DEPLOYMENT_URL>/threads/search \
-    --header 'Content-Type: application/json' \
-    --data '{"status": "idle", "limit": 1}'
-    ```
-
-Output:
-
-    [{'thread_id': 'cacf79bb-4248-4d01-aabc-938dbd60ed2c',
-    'created_at': '2024-08-14T17:36:38.921660+00:00',
-    'updated_at': '2024-08-14T17:36:38.921660+00:00',
-    'metadata': {'graph_id': 'agent'},
-    'status': 'idle',
-    'config': {'configurable': {}}}]
-
-
-## Find interrupted threads
-
-We can use the following commands to find threads that have been interrupted in the middle of a run, which could either mean an error occurred before the run finished or a human-in-the-loop breakpoint was reached and the run is waiting to continue: 
-
-=== "Python"
-
-    ```python
-    print(await client.threads.search(status="interrupted",limit=1))
-    ```
-
-=== "Javascript"
-
-    ```js
-    console.log(await client.threads.search({ status: "interrupted", limit: 1 }));
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \  
-    --url <DEPLOYMENT_URL>/threads/search \
-    --header 'Content-Type: application/json' \
-    --data '{"status": "interrupted", "limit": 1}'
-    ```
-
-Output:
-
-    [{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
-    'created_at': '2024-08-14T17:41:50.235455+00:00',
-    'updated_at': '2024-08-14T17:41:50.235455+00:00',
-    'metadata': {'graph_id': 'agent'},
-    'status': 'interrupted',
-    'config': {'configurable': {}}}]
-    
-## Find busy threads
-
-We can use the following commands to find threads that are busy, meaning they are currently handling the execution of a run:
-
-=== "Python"
-
-    ```python
-    print(await client.threads.search(status="busy",limit=1))
-    ```
-
-=== "Javascript"
-
-    ```js
-    console.log(await client.threads.search({ status: "busy", limit: 1 }));
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \  
-    --url <DEPLOYMENT_URL>/threads/search \
-    --header 'Content-Type: application/json' \
-    --data '{"status": "busy", "limit": 1}'
-    ```
-
-Output:
-
-    [{'thread_id': '0d282b22-bbd5-4d95-9c61-04dcc2e302a5',
-    'created_at': '2024-08-14T17:41:50.235455+00:00',
-    'updated_at': '2024-08-14T17:41:50.235455+00:00',
-    'metadata': {'graph_id': 'agent'},
-    'status': 'busy',
-    'config': {'configurable': {}}}]
-
-## Find specific threads
-
-You may also want to check the status of specific threads, which you can do in a few ways:
-
-### Find by ID
-
-You can use the `get` function to find the status of a specific thread, as long as you have the ID saved
-
-=== "Python"
-
-    ```python
-    print((await client.threads.get(<THREAD_ID>))['status'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    console.log((await client.threads.get(<THREAD_ID>)).status);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \ 
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID> \
-    --header 'Content-Type: application/json' | jq -r '.status'
-    ```
-
-Output:
-
-    'idle'
-
-### Find by metadata
-
-The search endpoint for threads also allows you to filter on metadata, which can be helpful if you use metadata to tag threads in order to keep them organized:
-
-=== "Python"
-
-    ```python
-    print((await client.threads.search(metadata={"foo":"bar"},limit=1))[0]['status'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    console.log((await client.threads.search({ metadata: { "foo": "bar" }, limit: 1 }))[0].status);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \  
-    --url <DEPLOYMENT_URL>/threads/search \
-    --header 'Content-Type: application/json' \
-    --data '{"metadata": {"foo":"bar"}, "limit": 1}' | jq -r '.[0].status'
-    ```
-
-Output:
-
-    'idle'

docs/docs/cloud/how-tos/clone_traces_studio.md

@@ -1,31 +1,36 @@
-# Testing local agents with remote traces
+# Debug LangSmith traces
 
-## Overview
+This guide explains how to open LangSmith traces in LangGraph Studio for interactive investigation and debugging.
 
-A common workflow when debugging production-deployed agents is to test the same thread against a local version of the same agent, which may have modifications.
+## Open deployed threads
 
-To support this, LangGraph Studio, in combination with LangSmith, allows you to clone remote threads traced in LangSmith into your locally running agent. This cloned thread can then be used to re-run specific nodes within Studio.
+1. Open the LangSmith trace, selecting the root run.
+2. Click "Run in Studio".
 
-## Requirements
+This will open LangGraph Studio connected to the associated LangGraph Platform deployment with the trace's parent thread selected.
 
-!!! info "Prerequisites"
+## Testing local agents with remote traces
+
+This section explains how to test a local agent against remote traces from LangSmith. This enables you to use production traces as input for local testing, allowing you to debug and verify agent modifications in your development environment.
+
+### Requirements
+
+- A LangSmith traced thread
+- A locally running agent. See [here](../how-tos/studio/quick_start.md#local-development-server) for setup
+  instructions.
+
+!!! info "Local agent requirements"
 
     - langgraph>=0.3.18
     - langgraph-api>=0.0.32
+    - Contains the same set of nodes present in the remote trace
 
-- A thread traced in LangSmith.
-- A locally running agent. See [here](../../how-tos/local-studio.md) for setup instructions.
-  - Note that your local agent must be using the above specified `langgraph` and `langgraph-api` versions.
-  - The nodes present in the remote trace must exist in at least one of the graphs in your local agent.
+### Cloning Thread
 
-## Cloning Thread
+1. Open the LangSmith trace, selecting the root run.
+2. Click the dropdown next to "Run in Studio".
+3. Enter your local agent's URL.
+4. Select "Clone thread locally".
+5. If multiple graphs exist, select the target graph.
 
-First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".
-
-![Run in Studio](img/run_in_studio.png){width=1200}
-
-This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.
-
-Once selected, a will a new thread in your local agent will be created and the thread history will be reconstruced to reflect the original trace.
-
-Alternatively, if your trace originates from an agent deployed on LangGraph Platform, you can "View original thread" to open Studio with the actual deployed thread.
+A new thread will be created in your local agent with the thread history inferred and copied from the remote thread, and you will be navigated to LangGraph Studio for your locally running application.

docs/docs/cloud/how-tos/configuration_cloud.md

@@ -1,11 +1,6 @@
-# How to create Assistants
+# Manage assistants
 
-!!! info "Prerequisites"
-
-    - [Assistants Overview](../../concepts/assistants.md)
-    - [Configuration](../../concepts/low_level.md#configuration)
-
-In this guide we will show how to create and configure an assistant.
+In this guide we will show how to create, configure, and manage an [assistant](../../concepts/assistants.md).
 
 First, as a brief refresher on the concept of configurations, consider the following simple `call_model` node and configuration schema. Observe that this node tries to read and use the `model_name` as defined by the `config` object's `configurable`.
 
@@ -51,11 +46,11 @@ First, as a brief refresher on the concept of configurations, consider the follo
 
 For more information on configurations, [see here](../../concepts/low_level.md#configuration).
 
-## Creating an Assistant
+## Create an assistant
 
 ### LangGraph SDK
 
-To create an assistant, use the [LangGraph SDK](../../concepts/sdk.md) `create` method. See the [Python](../reference/sdk/python_sdk_ref.md#langgraph_sdk.client.AssistantsClient.create) and [JS](../reference/sdk/js_ts_sdk_ref.md#create) SDK reference docs for more information.
+To create an assistant, use the [LangGraph SDK](../../concepts/sdk.md) `create` method. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.AssistantsClient.create) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#create) SDK reference docs for more information.
 
 This example uses the same configuration schema as above, and creates an assistant with `model_name` set to `openai`.
 
@@ -79,7 +74,7 @@ This example uses the same configuration schema as above, and creates an assista
     import { Client } from "@langchain/langgraph-sdk";
 
     const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    let openAIAssistant = await client.assistants.create({
+    const openAIAssistant = await client.assistants.create({
         graphId: 'agent',
         name: "Open AI Assistant",
         config: { "configurable": { "model_name": "openai" } },
@@ -123,7 +118,7 @@ To create a new assistant, select the "+ New assistant" button. This will open a
 
 To confirm, click "Create assistant". This will take you to [LangGraph Studio](../../concepts/langgraph_studio.md) where you can test the assistant. If you go back to the "Assistants" tab in the deployment, you will see the newly created assistant in the table.
 
-## Using an Assistant
+## Use an assistant
 
 ### LangGraph SDK
 
@@ -150,7 +145,7 @@ We have now created an assistant called "Open AI Assistant" that has `model_name
 
     ```js
     const thread = await client.threads.create();
-    let input = { "messages": [{ "role": "user", "content": "who made you?" }] };
+    const input = { "messages": [{ "role": "user", "content": "who made you?" }] };
 
     const streamResponse = client.runs.stream(
       thread["thread_id"],
@@ -228,3 +223,105 @@ Output:
 ### LangGraph Platform UI
 
 Inside your deployment, select the "Assistants" tab. For the assistant you would like to use, click the "Studio" button. This will open LangGraph Studio with the selected assistant. When you submit an input (either in Graph or Chat mode), the selected assistant and its configuration will be used.
+
+## Create a new version for your assistant
+
+### LangGraph SDK
+
+To edit the assistant, use the `update` method. This will create a new version of the assistant with the provided edits. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.AssistantsClient.update) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#update) SDK reference docs for more information.
+
+!!! note "Note"
+You must pass in the ENTIRE config (and metadata if you are using it). The update endpoint creates new versions completely from scratch and does not rely on previous versions.
+
+For example, to update your assistant's system prompt:
+=== "Python"
+
+    ```python
+    openai_assistant_v2 = await client.assistants.update(
+        openai_assistant["assistant_id"],
+        config={
+            "configurable": {
+                "model_name": "openai",
+                "system_prompt": "You are an unhelpful assistant!",
+            }
+        },
+    )
+    ```
+
+=== "Javascript"
+
+    ```js
+    const openaiAssistantV2 = await client.assistants.update(
+        openai_assistant["assistant_id"],
+        {
+            config: {
+                configurable: {
+                    model_name: 'openai',
+                    system_prompt: 'You are an unhelpful assistant!',
+                },
+        },
+    });
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request PATCH \
+    --url <DEPOLYMENT_URL>/assistants/<ASSISTANT_ID> \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "config": {"model_name": "openai", "system_prompt": "You are an unhelpful assistant!"}
+    }'
+    ```
+
+This will create a new version of the assistant with the updated parameters and set this as the active version of your assistant. If you now run your graph and pass in this assistant id, it will use this latest version.
+
+### LangGraph Platform UI
+
+You can also edit assistants from the LangGraph Platform UI.
+
+Inside your deployment, select the "Assistants" tab. This will load a table of all of the assistants in your deployment, across all graphs.
+
+To edit an existing assistant, select the "Edit" button for the specified assistant. This will open a form where you can edit the assistant's name, description, and configuration.
+
+Additionally, if using LangGraph Studio, you can edit the assistants and create new versions via the "Manage Assistants" button.
+
+## Use a previous assistant version
+
+### LangGraph SDK
+
+You can also change the active version of your assistant. To do so, use the `setLatest` method.
+
+In the example above, to rollback to the first version of the assistant:
+
+=== "Python"
+
+    ```python
+    await client.assistants.set_latest(openai_assistant['assistant_id'], 1)
+    ```
+
+=== "Javascript"
+
+    ```js
+    await client.assistants.setLatest(openaiAssistant['assistant_id'], 1);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/assistants/<ASSISTANT_ID>/latest \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "version": 1
+    }'
+    ```
+
+If you now run your graph and pass in this assistant id, it will use the first version of the assistant.
+
+### LangGraph Platform UI
+
+If using LangGraph Studio, to set the active version of your assistant, click the "Manage Assistants" button and locate the assistant you would like to use. Select the assistant and the version, and then click the "Active" toggle. This will update the assistant to make the selected version active.
+
+!!! warning "Deleting Assistants"
+    Deleting as assistant will delete ALL of its versions. There is currently no way to delete a single version, but by pointing your assistant to the correct version you can skip any versions that you don't wish to use.

docs/docs/cloud/how-tos/copy_threads.md

@@ -1,134 +0,0 @@
-# Copying Threads
-
-You may wish to copy (i.e. "fork") an existing thread in order to keep the existing thread's history and create independent runs that do not affect the original thread. This guide shows how you can do that.
-
-## Setup
-
-This code assumes you already have a thread to copy. 
-
-For more information, see these guides on [Threads](../../cloud/concepts/threads.md) and [Streaming](../../concepts/streaming.md).
-
-### SDK initialization
-
-First, we need to setup our client so that we can communicate with our hosted graph:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url="<DEPLOYMENT_URL>")
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: "<DEPLOYMENT_URL>" });
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{
-        "metadata": {}
-      }'
-    ```
-
-## Copying a thread
-
-The code below assumes that a thread you'd like to copy already exists.
-
-Copying a thread will create a new thread with the same history as the existing thread, and then allow you to continue executing runs.
-
-### Create copy
-
-=== "Python"
-
-    ```python
-    copied_thread = await client.threads.copy(<THREAD_ID>)
-    ```
-
-=== "Javascript"
-
-    ```js
-    let copiedThread = await client.threads.copy(<THREAD_ID>);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/copy \
-    --header 'Content-Type: application/json'
-    ```
-
-### Verify copy
-
-We can verify that the history from the prior thread did indeed copy over correctly:
-
-=== "Python"
-
-    ```python
-    def remove_thread_id(d):
-      if 'metadata' in d and 'thread_id' in d['metadata']:
-          del d['metadata']['thread_id']
-      return d
-
-    original_thread_history = list(map(remove_thread_id,await client.threads.get_history(<THREAD_ID>)))
-    copied_thread_history = list(map(remove_thread_id,await client.threads.get_history(copied_thread['thread_id'])))
-
-    # Compare the two histories
-    assert original_thread_history == copied_thread_history
-    # if we made it here the assertion passed!
-    print("The histories are the same.")
-    ```
-
-=== "Javascript"
-
-    ```js
-    function removeThreadId(d) {
-      if (d.metadata && d.metadata.thread_id) {
-        delete d.metadata.thread_id;
-      }
-      return d;
-    }
-
-    // Assuming `client.threads.getHistory(threadId)` is an async function that returns a list of dicts
-    async function compareThreadHistories(threadId, copiedThreadId) {
-      const originalThreadHistory = (await client.threads.getHistory(threadId)).map(removeThreadId);
-      const copiedThreadHistory = (await client.threads.getHistory(copiedThreadId)).map(removeThreadId);
-
-      // Compare the two histories
-      console.assert(JSON.stringify(originalThreadHistory) === JSON.stringify(copiedThreadHistory));
-      // if we made it here the assertion passed!
-      console.log("The histories are the same.");
-    }
-
-    // Example usage
-    compareThreadHistories(<THREAD_ID>, copiedThread.thread_id);
-    ```
-
-=== "CURL"
-
-    ```bash
-    if diff <(
-        curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
-    ) <(
-        curl --request GET --url <DEPLOYMENT_URL>/threads/<COPIED_THREAD_ID>/history | jq -S 'map(del(.metadata.thread_id))'
-    ) >/dev/null; then
-        echo "The histories are the same."
-    else
-        echo "The histories are different."
-    fi
-    ```
-
-Output:
-
-    The histories are the same.

docs/docs/cloud/how-tos/cron_jobs.md

@@ -1,10 +1,10 @@
-# Cron Jobs
+# Use cron jobs
 
-Sometimes you don't want to run your graph based on user interaction, but rather you would like to schedule your graph to run on a schedule - for example if you wish for your graph to compose and send out a weekly email of to-dos for your team. LangGraph Cloud allows you to do this without having to write your own script by using the `Crons` client. To schedule a graph job, you need to pass a [cron expression](https://crontab.cronhub.io/) to inform the client when you want to run the graph. `Cron` jobs are run in the background and do not interfere with normal invocations of the graph.
+Sometimes you don't want to run your graph based on user interaction, but rather you would like to schedule your graph to run on a schedule - for example if you wish for your graph to compose and send out a weekly email of to-dos for your team. LangGraph Platform allows you to do this without having to write your own script by using the `Crons` client. To schedule a graph job, you need to pass a [cron expression](https://crontab.cronhub.io/) to inform the client when you want to run the graph. `Cron` jobs are run in the background and do not interfere with normal invocations of the graph.
 
 ## Setup
 
-First, let's setup our SDK client, assistant, and thread:
+First, let's set up our SDK client, assistant, and thread:
 
 === "Python"