feat(sdk-py): resumable streams (#4765)

Remove outdated docs regarding graph construction.

.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

@@ -12,8 +12,9 @@
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
+[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)
 
-Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a powerful low-level orchestration framework for building, managing, and deploying long-running, stateful agents. 
+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
 
@@ -76,8 +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

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

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

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

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

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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/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/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/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/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_data_plane.md

@@ -18,7 +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.
@@ -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.
 
@@ -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/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"
 

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

@@ -1,17 +1,12 @@
-# Adding nodes as dataset examples in Studio
+# Add node to dataset
 
-In LangGraph Studio you can create dataset examples from the thread history in the right-hand pane. This can be especially useful when you want to evaluate intermediate steps of the agent.
+This guide shows how to add examples to [LangSmith datasets](https://docs.smith.langchain.com/evaluation/how_to_guides#dataset-management) from nodes in the thread log. This is useful to evaluate individual steps of the agent.
 
-1. Click on the `Add to Dataset` button to enter the dataset mode.
-1. Select nodes which you want to add to dataset.
-1. Select the target dataset to create the example in.
-
-You can edit the example payload before sending it to the dataset, which is useful if you need to make changes to conform the example to the dataset schema.
-
-Finally, you can customise the target dataset by clicking on the `Settings` button.
+1. Select a thread.
+2. Click on the `Add to Dataset` button.
+3. Select nodes whose input/output you want to add to a dataset.
+4. For each selected node, select the target dataset to create the example in. By default a dataset for the specific assistant and node will be selected. If this dataset does not yet exist, it will be created.
+5. Edit the example's input/output as needed before adding it to the dataset.
+6. Select "Add to dataset" at the bottom of the page to add all selected nodes to their respective datasets.
 
 See [Evaluating intermediate steps](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph#evaluating-intermediate-steps) for more details on how to evaluate intermediate steps.
-
-<video controls allowfullscreen="true" poster="../img/studio_datasets.jpg">
-    <source src="https://langgraph-docs-assets.pages.dev/studio_datasets.mp4" type="video/mp4">
-</video>

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

@@ -335,7 +335,7 @@ const { thread, submit } = useStream({
 });
 ```
 
-Then you can pushing updates to the UI component by calling `ui.push()` / `push_ui_message()` with the same ID as the UI message you wish to update.
+Then you can push updates to the UI component by calling `ui.push()` / `push_ui_message()` with the same ID as the UI message you wish to update.
 
 === "Python"
 

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

@@ -1,24 +1,86 @@
-# How to add static breakpoints
+# Breakpoints
 
-!!! tip "Prerequisites"
+[Breakpoints](../../concepts/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](../../concepts/persistence.md), which saves the graph state after each step.
 
-    This guide assumes familiarity with the following concepts:
+With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
 
-    * [Breakpoints](../../concepts/breakpoints.md)
-    * [LangGraph Glossary](../../concepts/low_level.md)
-    
+## Set breakpoints
 
-Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). [Breakpoints](../../concepts/breakpoints.md) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).
+=== "Compile time"
 
-Breakpoints are built on top of LangGraph [checkpoints](../../concepts/persistence.md#checkpoints), which save the graph's state after each node execution. Checkpoints are saved in [threads](../../concepts/persistence.md#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.
+    ```python
+    # highlight-next-line
+    graph = graph_builder.compile( # (1)!
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"], # (3)!
+    )
+    ```
 
-## Setup
+    1. The breakpoints are set during `compile` time.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
 
-### Code for your graph
+=== "Run time"
 
-In this how-to we use a simple ReAct style hosted graph (you can see the full code for defining it [here](../../how-tos/human_in_the_loop/breakpoints.ipynb)). The important thing is that there are two nodes (one named `agent` that calls the LLM, and one named `action` that calls the tool), and a routing function from `agent` that determines whether to call `action` next or just end the graph run (the `action` node always calls the `agent` node after execution).
+    === "Python"
 
-### SDK Initialization
+        ```python
+        # highlight-next-line
+        await client.runs.wait( # (1)!
+            thread_id,
+            assistant_id,
+            inputs=inputs,
+            # highlight-next-line
+            interrupt_before=["node_a"], # (2)!
+            # highlight-next-line
+            interrupt_after=["node_b", "node_c"] # (3)!
+        )
+        ```
+
+        1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+        2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+        3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+    === "JavaScript"
+
+        ```js
+        // highlight-next-line
+        await client.runs.wait( // (1)!
+          threadID,
+          assistantID,
+          {
+            input: input,
+            // highlight-next-line
+            interruptBefore: ["node_a"], // (2)!
+            // highlight-next-line
+            interruptAfter: ["node_b", "node_c"] // (3)!
+          }
+        )
+        ```
+
+        1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
+        2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
+        3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
+
+    === "cURL"
+
+        ```bash
+        curl --request POST \
+        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+        --header 'Content-Type: application/json' \
+        --data "{
+          \"assistant_id\": \"agent\",
+          \"interrupt_before\": [\"node_a\"],
+          \"interrupt_after\": [\"node_b\", \"node_c\"],
+          \"input\": <INPUT>
+        }"
+        ```
+
+!!! tip
+
+    This example shows how to add **static** breakpoints. See [this guide](../../how-tos/human_in_the_loop/breakpoints.ipynb) for more options for how to add breakpoints.
 
 
 === "Python"
@@ -26,130 +88,97 @@ In this how-to we use a simple ReAct style hosted graph (you can see the full co
     ```python
     from langgraph_sdk import get_client
     client = get_client(url=<DEPLOYMENT_URL>)
+
     # Using the graph deployed with the name "agent"
     assistant_id = "agent"
+
+    # create a thread
     thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph until the breakpoint
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=inputs   # (1)!
+    )
+
+    # Resume the graph
+    await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=None   # (2)!
+    )
     ```
 
-=== "Javascript"
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "JavaScript"
 
     ```js
     import { Client } from "@langchain/langgraph-sdk";
-
     const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
     // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
+    const assistantID = "agent";
+
+    // create a thread
     const thread = await client.threads.create();
-    ```
+    const threadID = thread["thread_id"];
 
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{}'
-    ```
-
-## Adding a breakpoint
-
-We now want to add a breakpoint in our graph run, which we will do before a tool is called.
-We can do this by adding `interrupt_before=["action"]`, which tells us to interrupt before calling the action node.
-We can do this either when compiling the graph or when kicking off a run.
-Here we will do it when kicking of a run, if you would like to to do it at compile time you need to edit the python file where your graph is defined and add the `interrupt_before` parameter when you call `.compile`.
-
-First let's access our hosted LangGraph instance through the SDK:
-
-And, now let's compile it with a breakpoint before the tool node:
-
-=== "Python"
-
-    ```python
-    input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-        interrupt_before=["action"],
-    ):
-        print(f"Receiving new event of type: {chunk.event}...")
-        print(chunk.data)
-        print("\n\n")
-    ```
-=== "Javascript"
-
-    ```js
-    const input = { messages: [{ role: "human", content: "what's the weather in sf" }] };
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-        interruptBefore: ["action"]
-      }
+    // Run the graph until the breakpoint
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: input }   // (1)!
     );
 
-    for await (const chunk of streamResponse) {
-      console.log(`Receiving new event of type: ${chunk.event}...`);
-      console.log(chunk.data);
-      console.log("\n\n");
-    }
+    // Resume the graph
+    await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: null }   // (2)!
+    );
     ```
-    
-=== "CURL"
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "cURL"
+
+    Create a thread:
 
     ```bash
     curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf\"}]},
-       \"interrupt_before\": [\"action\"],
-       \"stream_mode\": [
-         \"messages\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "Receiving event of type: ", $0)
-         printf "%s...\n", $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "") {
-             print data_content "\n"
-         }
-     }
-     '
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
     ```
 
-Output:
+    Run the graph until the breakpoint:
 
-    Receiving new event of type: metadata...
-    {'run_id': '3b77ef83-687a-4840-8858-0371f91a92c3'}
-    
-    
-    
-    Receiving new event of type: data...
-    {'agent': {'messages': [{'content': [{'id': 'toolu_01HwZqM1ptX6E15A5LAmyZTB', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-e5d17791-4d37-4ad2-815f-a0c4cba62585', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in san francisco'}, 'id': 'toolu_01HwZqM1ptX6E15A5LAmyZTB'}], 'invalid_tool_calls': []}]}}
-    
-    
-    
-    Receiving new event of type: end...
-    None
-    
-    
-    
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <INPUT>
+    }"
+    ```
 
+    Resume the graph:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\"
+    }"
+    ```
+
+## Learn more
+
+- [**LangGraph breakpoints guide**](../../how-tos/human_in_the_loop/breakpoints.ipynb): learn more about adding breakpoints in LangGraph.

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

@@ -1,277 +0,0 @@
-# How to Edit State of a Deployed Graph
-
-When creating LangGraph agents, it is often nice to add a human-in-the-loop component. This can be helpful when giving them access to tools. Often in these situations you may want to edit the graph state before continuing (for example, to edit what tool is being called, or how it is being called).
-
-This can be in several ways, but the primary supported way is to add an "interrupt" before a node is executed. This interrupts execution at that node. You can then use update_state to update the state, and then resume from that spot to continue.
-
-## Setup
-
-We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/time-travel.ipynb) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.
-
-### SDK initialization
-
-First, we need to setup our client so that we can communicate with our hosted graph:
-
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{}'
-    ```
-
-## Editing state
-
-### Initial invocation
-
-Now let's invoke our graph, making sure to interrupt before the `action` node.
-
-=== "Python"
-
-    ```python
-    input = { 'messages':[{ "role":"user", "content":"search for weather in SF" }] }
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-        interrupt_before=["action"],
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const input = { messages: [{ role: "human", content: "search for weather in SF" }] };
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-        interruptBefore: ["action"],
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"search for weather in SF\"}]},
-       \"interrupt_before\": [\"action\"],
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
-    ```
-
-Output:
-
-    {'agent': {'messages': [{'content': [{'text': "Certainly! I'll search for the current weather in San Francisco for you using the search function. Here's how I'll do that:", 'type': 'text'}, {'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-6dbb0167-f8f6-4e2a-ab68-229b2d1fbb64', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
-
-### Edit the state
-
-Now, let's assume we actually meant to search for the weather in Sidi Frej (another city with the initials SF). We can edit the state to properly reflect that:
-
-
-=== "Python"
-
-    ```python
-    # First, lets get the current state
-    current_state = await client.threads.get_state(thread['thread_id'])
-
-    # Let's now get the last message in the state
-    # This is the one with the tool calls that we want to update
-    last_message = current_state['values']['messages'][-1]
-
-    # Let's now update the args for that tool call
-    last_message['tool_calls'][0]['args'] = {'query': 'current weather in Sidi Frej'}
-
-    # Let's now call `update_state` to pass in this message in the `messages` key
-    # This will get treated as any other update to the state
-    # It will get passed to the reducer function for the `messages` key
-    # That reducer function will use the ID of the message to update it
-    # It's important that it has the right ID! Otherwise it would get appended
-    # as a new message
-    await client.threads.update_state(thread['thread_id'], {"messages": last_message})
-    ```
-
-=== "Javascript"
-
-    ```js
-    // First, let's get the current state
-    const currentState = await client.threads.getState(thread["thread_id"]);
-
-    // Let's now get the last message in the state
-    // This is the one with the tool calls that we want to update
-    let lastMessage = currentState.values.messages.slice(-1)[0];
-
-    // Let's now update the args for that tool call
-    lastMessage.tool_calls[0].args = { query: "current weather in Sidi Frej" };
-
-    // Let's now call `update_state` to pass in this message in the `messages` key
-    // This will get treated as any other update to the state
-    // It will get passed to the reducer function for the `messages` key
-    // That reducer function will use the ID of the message to update it
-    // It's important that it has the right ID! Otherwise it would get appended
-    // as a new message
-    await client.threads.updateState(thread["thread_id"], { values: { messages: lastMessage } });
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \                                                                                      
-    jq '.values.messages[-1] | (.tool_calls[0].args = {"query": "current weather in Sidi Frej"})' | \
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-      --header 'Content-Type: application/json' \
-      --data @-
-    ```
-
-Output:
-
-    {'configurable': {'thread_id': '9c8f1a43-9dd8-4017-9271-2c53e57cf66a',
-      'checkpoint_ns': '',
-      'checkpoint_id': '1ef58e7e-3641-649f-8002-8b4305a64858'}}
-
-
-
-### Resume invocation
-
-Now we can resume our graph run but with the updated state:
-
-
-=== "Python"
-
-    ```python
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=None,
-        stream_mode="updates",
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-=== "Javascript"
-
-    ```js
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: null,
-        streamMode: "updates",
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \                                                                             
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }"| \ 
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
-    ```
-
-Output:
-
-    {'action': {'messages': [{'content': '["I looked up: current weather in Sidi Frej. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '1161b8d1-bee4-4188-9be8-698aecb69f10', 'tool_call_id': 'toolu_01KEJMBFozSiZoS4mAcPZeqQ'}]}}
-    {'agent': {'messages': [{'content': [{'text': 'I apologize for the confusion in my search query. It seems the search function interpreted "SF" as "Sidi Frej" instead of "San Francisco" as we intended. Let me search again with the full city name to get the correct information:', 'type': 'text'}, {'id': 'toolu_0111rrwgfAcmurHZn55qjqTR', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-b8c25779-cfb4-46fc-a421-48553551242f', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_0111rrwgfAcmurHZn55qjqTR'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-    {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '6bc632ae-5ee6-4d01-9532-79c524a2d443', 'tool_call_id': 'toolu_0111rrwgfAcmurHZn55qjqTR'}]}}
-    {'agent': {'messages': [{'content': "Now, based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. \n\nIt's worth noting that the search result included an unusual comment about Gemini, which doesn't seem directly related to the weather. This might be due to the search engine including some astrological information or a joke in its results. However, for the purpose of weather information, we can focus on the fact that it's sunny in San Francisco right now.\n\nIs there anything else you'd like to know about the weather in San Francisco or any other location?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-227a042b-dd97-476e-af32-76a3703af5d8', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
-
-As you can see it now looks up the current weather in Sidi Frej (although our dummy search node still returns results for SF because we don't actually do a search in this example, we just return the same "It's sunny in San Francisco ..." result every time).

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

@@ -86,7 +86,7 @@ First, we need to setup our client so that we can communicate with our hosted gr
     const thread = await client.threads.create();
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -135,7 +135,7 @@ First, let's run the agent with an input that requires tool calls with approval:
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -194,7 +194,7 @@ To approve the tool call, we need to let `human_review_node` know what value to
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -257,7 +257,7 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -323,7 +323,7 @@ To do this, we will use `Command` with a different resume value of `{"action": "
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -395,7 +395,7 @@ For this example we will just add a single tool call representing the feedback (
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -462,7 +462,7 @@ To do this, we will use `Command` with a different resume value of `{"action": "
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \
@@ -525,7 +525,7 @@ We can see that we now get to another interrupt - because it went back to the mo
     }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
     curl --request POST \

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

@@ -1,386 +1,240 @@
-# How to Replay and Branch from Prior States
+# Time travel
 
-With LangGraph Cloud you have the ability to return to any of your prior states and either re-run the graph to reproduce issues noticed during testing, or branch out in a different way from what was originally done in the prior states. In this guide we will show a quick example of how to rerun past states and how to branch off from previous states as well.
+LangGraph provides [**time travel**](../../concepts/time-travel.md) functionality to **resume execution from a prior checkpoint** — either replaying the same state or modifying it to explore alternatives. In all cases, resuming past execution produces a **new fork** in the history.
 
-## Setup
+## Use time travel
 
-The examples below are executed against a specific deployment on LangGraph Cloud. You will use
-the SDK in a similar way, but you will expect to see different results based on the graph you have deployed.
+To use time-travel in LangGraph:
 
-### SDK initialization
+1. **Run the graph** with initial inputs using [LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)'s [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs.
+2. **Identify a checkpoint in an existing thread**: Use [`client.threads.get_history`][langgraph_sdk.client.ThreadsClient.get_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.
+   Alternatively, set a [breakpoint](./human_in_the_loop_breakpoint.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
+3. **(Optional) modify the graph state**: Use the [`client.threads.update_state`][langgraph_sdk.client.ThreadsClient.update_state] method to modify the graph’s state at the checkpoint and resume execution from alternative state.
+4. **Resume execution from the checkpoint**: Use the [`client.runs.wait`][langgraph_sdk.client.RunsClient.wait] or [`client.runs.stream`][langgraph_sdk.client.RunsClient.stream] APIs with an input of `None` and the appropriate `thread_id` and `checkpoint_id`.
 
-First, we need to setup our client so that we can communicate with our hosted graph:
+## Example
+
+??? example "Example graph"
+
+    ```python
+    from typing_extensions import TypedDict, NotRequired
+    from langgraph.graph import StateGraph, START, END
+    from langchain.chat_models import init_chat_model
+    from langgraph.checkpoint.memory import InMemorySaver
+
+    class State(TypedDict):
+        topic: NotRequired[str]
+        joke: NotRequired[str]
+
+    llm = init_chat_model(
+        "anthropic:claude-3-7-sonnet-latest",
+        temperature=0,
+    )
+
+    def generate_topic(state: State):
+        """LLM call to generate a topic for the joke"""
+        msg = llm.invoke("Give me a funny topic for a joke")
+        return {"topic": msg.content}
+
+    def write_joke(state: State):
+        """LLM call to write a joke based on the topic"""
+        msg = llm.invoke(f"Write a short joke about {state['topic']}")
+        return {"joke": msg.content}
+
+    # Build workflow
+    builder = StateGraph(State)
+
+    # Add nodes
+    builder.add_node("generate_topic", generate_topic)
+    builder.add_node("write_joke", write_joke)
+
+    # Add edges to connect nodes
+    builder.add_edge(START, "generate_topic")
+    builder.add_edge("generate_topic", "write_joke")
+
+    # Compile
+    graph = builder.compile()
+    ```
+
+### 1. Run the 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"
+
+    # create a thread
     thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input={}
+    )
     ```
 
-=== "Javascript"
+=== "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 assistantID = "agent";
+
+    // create a thread
     const thread = await client.threads.create();
-    ```
+    const threadID = thread["thread_id"];
 
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{}'
-    ```
-
-## Replay a state
-
-### Initial invocation
-
-Before replaying a state - we need to create states to replay from! In order to do this, let's invoke our graph with a simple message:
-
-=== "Python"
-
-    ```python
-    input = {"messages": [{"role": "user", "content": "Please search the weather in SF"}]}
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const input = { "messages": [{ "role": "user", "content": "Please search the weather in SF" }] }
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-      }
+    // Run the graph
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: {}}
     );
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
     ```
 
-=== "CURL"
+=== "cURL"
+
+    Create a thread:
 
     ```bash
     curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Please search the weather in SF\"}]},
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
-    ```
-    
-Output:
-
-    {'agent': {'messages': [{'content': [{'text': "Certainly! I'll use the search function to look up the current weather in San Francisco for you. Let me do that now.", 'type': 'text'}, {'id': 'toolu_011vroKUtWU7SBdrngpgpFMn', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ee639877-d97d-40f8-96dc-d0d1ae22d203', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-    {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '7bad0e72-5ebe-4b08-9b8a-b99b0fe22fb7', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. This is great news for outdoor activities and enjoying the city's beautiful sights.\n\nIt's worth noting that the search result included an unusual comment about Geminis, which isn't typically part of a weather report. This might be due to the search engine including some astrological information or a joke in its results. However, for the purpose of answering your question about the weather, we can focus on the fact that it's sunny in San Francisco.\n\nIf you need any more specific information about the weather in San Francisco, such as temperature, wind speed, or forecast for the coming days, please let me know, and I'd be happy to search for that information for you.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-dbac539a-33c8-4f0c-9e20-91f318371e7c', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
-
-Now let's get our list of states, and invoke from the third state (right before the tool get called):
-
-
-=== "Python"
-
-    ```python
-    states = await client.threads.get_history(thread['thread_id'])
-
-    # We can confirm that this state is correct by checking the 'next' attribute and seeing that it is the tool call node
-    state_to_replay = states[2]
-    print(state_to_replay['next'])
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
     ```
 
-=== "Javascript"
-
-    ```js
-    const states = await client.threads.getHistory(thread['thread_id']);
-
-    // We can confirm that this state is correct by checking the 'next' attribute and seeing that it is the tool call node
-    const stateToReplay = states[2];
-    console.log(stateToReplay['next']);
-    ```
-
-=== "CURL"
+    Run the graph:
 
     ```bash
-    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -r '.[2].next'
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": {}
+    }"
     ```
 
-Output:
-
-    ['action']
-
-
-
-To rerun from a state, we need first issue an empty update to the thread state. Then we need to pass in the resulting `checkpoint_id` as follows:
+### 2. Identify a checkpoint
 
 === "Python"
 
     ```python
-    state_to_replay = states[2]
-    updated_config = await client.threads.update_state(
-        thread["thread_id"],
-        {"messages": []},
-        checkpoint_id=state_to_replay["checkpoint_id"]
+    # The states are returned in reverse chronological order.
+    states = await client.threads.get_history(thread_id)
+    selected_state = states[1]
+    print(selected_state)
+    ```
+
+=== "JavaScript"
+
+    ```js
+    // The states are returned in reverse chronological order.
+    const states = await client.threads.getHistory(threadID);
+    const selectedState = states[1];
+    console.log(selectedState);
+    ```
+
+=== "cURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history \
+    --header 'Content-Type: application/json'
+    ```
+
+### 3. Update the state (optional)
+
+`update_state` will create a new checkpoint. The new checkpoint will be associated with the same thread, but a new checkpoint ID.
+
+=== "Python"
+
+    ```python
+    new_config = await client.threads.update_state(
+        thread_id,
+        {"topic": "chickens"},
+        # highlight-next-line
+        checkpoint_id=selected_state["checkpoint_id"]
     )
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id, # graph_id
-        input=None,
-        stream_mode="updates",
-        checkpoint_id=updated_config["checkpoint_id"]
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
+    print(new_config)
     ```
 
-=== "Javascript"
+=== "JavaScript"
 
     ```js
-    const stateToReplay = states[2];
-    const config = await client.threads.updateState(thread["thread_id"], { values: {"messages": [] }, checkpointId: stateToReplay["checkpoint_id"] });
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
+    const newConfig = await client.threads.updateState(
+      threadID,
       {
-        input: null,
-        streamMode: "updates",
-        checkpointId: config["checkpoint_id"]
+        values: { "topic": "chickens" },
+        checkpointId: selectedState["checkpoint_id"]
       }
     );
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
+    console.log(newConfig);
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
-    curl --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | jq -c '
-        .[2] as $state_to_replay |
-        {
-            values: { messages: .[2].values.messages[-1] },
-            checkpoint_id: $state_to_replay.checkpoint_id
-        }' | \
     curl --request POST \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-        --header 'Content-Type: application/json' \
-        --data @- | jq .checkpoint_id | \
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"checkpoint_id\": \"$1\",
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"checkpoint_id\": <CHECKPOINT_ID>,
+      \"values\": {\"topic\": \"chickens\"}
+    }"
     ```
 
-Output:
-
-    {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'eba650e5-400e-4938-8508-f878dcbcc532', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. This is great news if you're planning any outdoor activities or simply want to enjoy a pleasant day in the city.\n\nIt's worth noting that the search result included an unusual comment about Geminis, which doesn't seem directly related to the weather. This appears to be a playful or humorous addition to the weather report, possibly from the source where this information was obtained.\n\nIs there anything else you'd like to know about the weather in San Francisco or any other information you need?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-bc6dca3f-a1e2-4f59-a69b-fe0515a348bb', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
-
-As we can see, the graph restarted from the tool node with the same input as our original graph run.
-
-## Branch off from previous state
-
-Using LangGraph's checkpointing, you can do more than just replay past states. You can branch off previous locations to let the agent explore alternate trajectories or to let a user "version control" changes in a workflow.
-
-Let's show how to do this to edit the state at a particular point in time. Let's update the state to change the input to the tool
+### 4. Resume execution from the checkpoint
 
 === "Python"
 
     ```python
-    # Let's now get the last message in the state
-    # This is the one with the tool calls that we want to update
-    last_message = state_to_replay['values']['messages'][-1]
-
-    # Let's now update the args for that tool call
-    last_message['tool_calls'][0]['args'] = {'query': 'current weather in SF'}
-
-    config = await client.threads.update_state(thread['thread_id'],{"messages":[last_message]},checkpoint_id=state_to_replay['checkpoint_id'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    // Let's now get the last message in the state
-    // This is the one with the tool calls that we want to update
-    let lastMessage = stateToReplay['values']['messages'][-1];
-
-    // Let's now update the args for that tool call
-    lastMessage['tool_calls'][0]['args'] = { 'query': 'current weather in SF' };
-
-    const config = await client.threads.updateState(thread['thread_id'], { values: { "messages": [lastMessage] }, checkpointId: stateToReplay['checkpoint_id'] });
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/history | \
-    jq -c '
-        .[2] as $state_to_replay |
-        .[2].values.messages[-1].tool_calls[0].args.query = "current weather in SF" |
-        {
-            values: { messages: .[2].values.messages[-1] },
-            checkpoint_id: $state_to_replay.checkpoint_id
-        }' | \
-    curl --request POST \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-        --header 'Content-Type: application/json' \
-        --data @-
-    ```
-
-Now we can rerun our graph with this new config, starting from the `new_state`, which is a branch of our `state_to_replay`:
-
-=== "Python"
-
-    ```python
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
+    await client.runs.wait(
+        thread_id,
         assistant_id,
+        # highlight-next-line
         input=None,
-        stream_mode="updates",
-        checkpoint_id=config['checkpoint_id']
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
+        # highlight-next-line
+        checkpoint_id=new_config["checkpoint_id"]
+    )
     ```
 
-=== "Javascript"
+=== "JavaScript"
 
     ```js
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
+    await client.runs.wait(
+      threadID,
+      assistantID,
       {
+        // highlight-next-line
         input: null,
-        streamMode: "updates",
-        checkpointId: config['checkpoint_id'],
+        // highlight-next-line
+        checkpointId: newConfig["checkpoint_id"]
       }
     );
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
     ```
 
-=== "CURL"
+=== "cURL"
 
     ```bash
-    curl -s --request GET --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | \
-    jq -c '.checkpoint_id' | \
     curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"checkpoint_id\": \"$1\",
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"checkpoint_id\": <CHECKPOINT_ID>
+    }"
     ```
 
-Output:
+## Learn more
 
-
-    {'action': {'messages': [{'content': '["I looked up: current weather in SF. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '2baf9941-4fda-4081-9f87-d76795d289f1', 'tool_call_id': 'toolu_011vroKUtWU7SBdrngpgpFMn'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco (SF):\n\nThe weather in San Francisco is currently sunny. This means it's a clear day with plenty of sunshine. \n\nIt's worth noting that the specific temperature wasn't provided in the search result, but sunny weather in San Francisco typically means comfortable temperatures. San Francisco is known for its mild climate, so even on sunny days, it's often not too hot.\n\nThe search result also included a playful reference to astrological signs, mentioning Gemini. However, this is likely just a joke or part of the search engine's presentation and not related to the actual weather conditions.\n\nIs there any specific information about the weather in San Francisco you'd like to know more about? I'd be happy to perform another search if you need details on temperature, wind conditions, or the forecast for the coming days.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-a83de52d-ed18-4402-9384-75c462485743', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
-
-As we can see, the search query changed from San Francisco to SF, just as we had hoped!
+- [**LangGraph time travel guide**](../../how-tos/human_in_the_loop/time-travel.ipynb): learn more about using time travel in LangGraph.

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

@@ -1,191 +0,0 @@
-# How to wait for user input using `interrupt`
-
-!!! tip "Prerequisites"
-
-    This guide assumes familiarity with the following concepts:
-
-    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
-    * [LangGraph Glossary](../../concepts/low_level.md)
-    
-
-**Human-in-the-loop (HIL)** interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding.
-
-We can implement this in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input.
-
-## Setup
-
-We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/wait-user-input.ipynb#agent) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input.
-
-### SDK initialization
-
-First, we need to setup our client so that we can communicate with our hosted graph:
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-    thread = await client.threads.create()
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-    // Using the graph deployed with the name "agent"
-    const assistantId = "agent";
-    const thread = await client.threads.create();
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-      --url <DEPLOYMENT_URL>/threads \
-      --header 'Content-Type: application/json' \
-      --data '{}'
-    ```
-
-## Waiting for user input
-
-### Initial invocation
-
-Now, let's invoke our graph.
-
-=== "Python"
-
-    ```python
-    input = {
-        "messages": [
-            {
-                "role": "user",
-                "content": "Ask the user where they are, then look up the weather there",
-            }
-        ]
-    }
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-=== "Javascript"
-
-    ```js
-    const input = {
-      messages: [
-        {
-          role: "human",
-          content: "Ask the user where they are, then look up the weather there" }
-      ]
-    };
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Ask the user where they are, then look up the weather there\"}]},
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }"
-    ```
-    
-Output:
-
-    {'agent': {'messages': [{'content': [{'text': "I'll help you ask the user about their location and then search for weather information.", 'type': 'text'}, {'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01UBEdS6UvuFMetdokNsykVG', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 438, 'output_tokens': 76}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-1b1210d8-39e0-4607-9f0e-0ea932d28d5c-0', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you located?'}, 'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 438, 'output_tokens': 76, 'total_tokens': 514, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
-    {'__interrupt__': [{'value': 'Where are you located?', 'resumable': True, 'ns': ['ask_human:2d41f894-f297-211e-9bfe-1d162ecba54a'], 'when': 'during'}]}
-
-You can see that our graph got interrupted inside the `ask_human` node, which is now waiting for a `location` to be provided.
-
-### Providing human input
-
-We can provide human input (`location`) by invoking the graph with a `Command(resume="<location>")`:
-
-=== "Python"
-
-    ```python
-    # highlight-next-line
-    from langgraph_sdk.schema import Command
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        # highlight-next-line
-        command=Command(resume="san francisco"),
-        stream_mode="updates",
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-=== "Javascript"
-
-    ```js
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        // highlight-next-line
-        command: { resume: "san francisco" },
-        streamMode: "updates"
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"command\": {
-         \"resume\": \"san francisco\"
-       },
-       \"stream_mode\": [
-         \"updates\"
-       ]
-     }"
-    ```
-
-Output:
-
-    {'ask_human': {'messages': [{'tool_call_id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool', 'content': 'san francisco'}]}}
-    {'agent': {'messages': [{'content': [{'text': 'Let me search for the weather in San Francisco.', 'type': 'text'}, {'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'input': {'query': 'current weather in san francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_0152YFm7DtnzfZQuiMUzaSsw', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 527, 'output_tokens': 67}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f509b5b2-eb30-4200-a8da-fa79ed68812a-0', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in san francisco'}, 'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 527, 'output_tokens': 67, 'total_tokens': 594, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
-    {'action': {'messages': [{'content': "I looked up: current weather in san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'cbd0f623-cc12-48a2-8c18-3cbb943e46e0', 'tool_call_id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'artifact': None, 'status': 'success'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, it's currently sunny in San Francisco. Would you like any specific details about the weather forecast?", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01FhzXj72CehBYkJGX69vsBc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 639, 'output_tokens': 29}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f48e818e-dd88-415e-9a0b-4a958498b553-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 639, 'output_tokens': 29, 'total_tokens': 668, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}

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

@@ -1,19 +1,48 @@
-# How to manage Assistants
+# Run application
 
-!!! info "Prerequisites"
+!!!info  "Prerequisites"
+    - [Running agents](../../agents/run_agents.md#running-agents)
 
-    - [Assistants Overview](../../concepts/assistants.md)
-
-LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
+This guide shows how to submit a [run](../concepts/runs.md) to your application.
 
 ## Graph mode
 
-To view your assistants, click the "Manage Assistants" button in the bottom left corner.
+### Specify input
+First define the input to your graph with in the "Input" section on the left side of the page, below the graph interface.
 
-This opens a modal for you to view all the assistants for the selected graph. Specify the assistant and its version you would like to mark as "Active", and this assistant will be used when submitting runs.
+Studio will attempt to render a form for your input based on the graph's defined [state schema](../../concepts/low_level.md/#schema). To disable this, click the "View Raw" button, which will present you with a JSON editor.
+
+Click the up/down arrows at the top of the "Input" section to toggle through and use previously submitted inputs.
+
+### Run settings
+
+#### Assistant
+
+To specify the [assistant](../../concepts/assistants.md) that is used for the run click the settings button in the bottom left corner. If an assistant is currently selected the button will also list the assistant name. If no assistant is selected it will say "Manage Assistants".
+
+Select the assistant to run and click the "Active" toggle at the top of the modal to activate it. [See here](./studio/manage_assistants.md) for more information on managing assistants.
+
+#### Streaming
+Click the dropdown next to "Submit" and click the toggle to enable/disable streaming.
+
+#### Breakpoints
+To run your graph with breakpoints, click the "Interrupt" button. Select a node and whether to pause before and/or after that node has executed. Click "Continue" in the thread log to resume execution.
+
+
+For more information on breakpoints see [here](../../concepts/breakpoints.md).
+
+### Submit run
+
+To submit the run with the specified input and run settings, click the "Submit" button. This will add a [run](../concepts/runs.md) to the existing selected [thread](../concepts/threads.md). If no thread is currently selected, a new one will be created.
+
+To cancel the ongoing run, click the "Cancel" button.
 
-By default, the "Default configuration" option will be active. This option reflects the default configuration defined in your graph. Edits made to this configuration will be used to update the run-time configuration, but will not update or create a new assistant unless you click "Create new assistant".
 
 ## Chat mode
+Specify the input to your chat application in the bottom of the conversation panel. Click the "Send message" button to submit the input as a Human message and have the response streamed back.
 
-Chat mode enables you to switch through the different assistants in your graph via the dropdown selector at the top of the page. To create, edit, or delete assistants, use Graph mode.
+To cancel the ongoing run, click the "Cancel" button. Click the "Show tool calls" toggle to hide/show tool calls in the conversation.
+
+## Learn more
+
+To run your application from a specific checkpoint in an existing thread, see [this guide](./threads_studio.md#edit-thread-history).

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

@@ -1,23 +1,28 @@
-# Prompt Engineering in LangGraph Studio
+# Iterate on prompts
 
 ## Overview
 
-A central aspect of agent development is prompt engineering. LangGraph Studio makes it easy to iterate on the prompts used within your graph directly within the UI.
+LangGraph Studio supports two methods for modifying prompts in your graph: direct node editing and the LangSmith Playground interface.
 
-## Setup
+## Direct Node Editing
 
-The first step is to define your [configuration](https://langchain-ai.github.io/langgraph/how-tos/configuration/) such that LangGraph Studio is aware of the prompts you want to iterate on and which nodes they are associated with.
+Studio allows you to edit prompts used inside individual nodes, directly from the graph interface.
 
-### Reference
+!!! info "Prerequisites"
 
-When defining your configuration, you can use special metadata keys to instruct LangGraph Studio how to handle different fields. Here's a reference for the available configuration options:
+    - [Assistants overview](../../concepts/assistants.md)
 
-#### `langgraph_nodes`
+### Graph Configuration
 
-- **Description**: Specifies which graph nodes a configuration field is associated with.
+Define your [configuration](https://langchain-ai.github.io/langgraph/how-tos/configuration/) to specify prompt fields and their associated nodes using `langgraph_nodes` and `langgraph_type` keys.
+
+#### Configuration Reference
+
+##### `langgraph_nodes`
+
+- **Description**: Specifies which nodes of the graph a configuration field is associated with.
 - **Value Type**: Array of strings, where each string is the name of a node in your graph.
 - **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
-- **Required**: No, but necessary if you want a field to be editable for specific nodes in the UI.
 - **Example**:
   ```python
   system_prompt: str = Field(
@@ -26,14 +31,13 @@ When defining your configuration, you can use special metadata keys to instruct
   )
   ```
 
-#### `langgraph_type`
+##### `langgraph_type`
 
 - **Description**: Specifies the type of configuration field, which determines how it's handled in the UI.
 - **Value Type**: String
 - **Supported Values**:
   - `"prompt"`: Indicates the field contains prompt text that should be treated specially in the UI.
 - **Usage Context**: Include in the `json_schema_extra` dictionary for Pydantic models or the `metadata["json_schema_extra"]` dictionary for dataclasses.
-- **Required**: No, but helpful for prompt fields to enable special handling.
 - **Example**:
   ```python
   system_prompt: str = Field(
@@ -45,9 +49,7 @@ When defining your configuration, you can use special metadata keys to instruct
   )
   ```
 
-### Example
-
-For example, if you have a node called `call_model` whose system prompt you want to iterate on, you can define a configuration like the following.
+#### Example Configuration
 
 ```python
 ## Using Pydantic
@@ -111,30 +113,22 @@ class Configuration:
 
 ```
 
-## Iterating on prompts
+### Editing prompts in UI
 
-### Node Configuration
+1. Locate the gear icon on nodes with associated configuration fields
+2. Click to open the configuration modal
+3. Edit the values
+4. Save to update the current assistant version or create a new one
 
-With this set up, running your graph and viewing in LangGraph Studio will result in the graph rendering like such.
+## LangSmith Playground
 
-**Note the configuration icon in the top right corner of the `call_model` node**:
+The [LangSmith Playground](https://
+docs.smith.langchain.com/prompt_engineering/how_to_guides#playground) interface allows testing individual LLM calls without running the full graph:
 
-![Graph in Studio](img/studio_graph_with_configuration.png){width=1200}
+1. Select a thread
+2. Click "View LLM Runs" on a node. This lists all the LLM calls (if any) made inside the node.
+3. Select an LLM run to open in Playground
+4. Modify prompts and test different model and tool settings
+5. Copy updated prompts back to your graph
 
-Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the `call_model` node. From here, you can save your changes and apply them to the graph. Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.
-
-![Configuration modal](img/studio_node_configuration.png){width=1200}
-
-### Playground
-
-LangGraph Studio also supports prompt engineering through an integration with the LangSmith Playground. To do so:
-
-1. Open an existing thread or create a new one.
-2. Within the thread log, any nodes that have made an LLM call will have a "View LLM Runs" button. Clicking this will open a popover with the LLM runs for that node.
-3. Select the LLM run you want to edit. This will open the LangSmith Playground with the selected LLM run.
-
-![Playground in Studio](img/studio_playground.png){width=1200}
-
-From here you can edit the prompt, test different model configurations and re-run just this LLM call without having to re-run the entire graph. When you are happy with your changes, you can copy the updated prompt back into your graph.
-
-For more information on how to use the LangSmith Playground, see the [LangSmith Playground documentation](https://docs.smith.langchain.com/prompt_engineering/how_to_guides#playground).
+For advanced Playground features, click the expand button in the top right corner.

docs/docs/cloud/how-tos/same-thread.md

@@ -1,6 +1,6 @@
 # How to run multiple agents on the same thread
 
-In LangGraph Cloud, a thread is not explicitly associated with a particular agent.
+In LangGraph Platform, a thread is not explicitly associated with a particular agent.
 This means that you can run multiple agents on the same thread, which allows a different agent to continue from an initial agent's progress.
 
 In this example, we will create two agents and then call them both on the same thread.

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

@@ -1,6 +1,6 @@
 # Stateless Runs
 
-Most of the time, you provide a `thread_id` to your client when you run your graph in order to keep track of prior runs through the persistent state implemented in LangGraph Cloud. However, if you don't need to persist the runs you don't need to use the built in persistent state and can create stateless runs.
+Most of the time, you provide a `thread_id` to your client when you run your graph in order to keep track of prior runs through the persistent state implemented in LangGraph Platform. However, if you don't need to persist the runs you don't need to use the built in persistent state and can create stateless runs.
 
 ## Setup
 

docs/docs/cloud/how-tos/studio/faqs.md

@@ -1,50 +0,0 @@
-# LangGraph Studio FAQs
-
-## Why is my project failing to start?
-
-A project may fail to start if the configuration file is defined incorrectly, or if required environment variables are missing. See [here](../../reference/cli.md#configuration-file) for how your configuration file should be defined.
-
-## How does interrupt work?
-
-When you select the `Interrupts` dropdown and select a node to interrupt the graph will pause execution before and after (unless the node goes straight to `END`) that node has run. This means that you will be able to both edit the state before the node is ran and the state after the node has ran. This is intended to allow developers more fine-grained control over the behavior of a node and make it easier to observe how the node is behaving. You will not be able to edit the state after the node has ran if the node is the final node in the graph.
-
-For more information on interrupts and human in the loop, see [here](./human_in_the_loop.md).
-
-## Why are extra edges showing up in my graph?
-
-If you don't define your conditional edges carefully, you might notice extra edges appearing in your graph. This is because without proper definition, LangGraph Studio assumes the conditional edge could access all other nodes. In order for this to not be the case, you need to be explicit about how you define the nodes the conditional edge routes to. There are two ways you can do this:
-
-### Solution 1: Include a path map
-
-The first way to solve this is to add path maps to your conditional edges. A path map is just a dictionary or array that maps the possible outputs of your router function with the names of the nodes that each output corresponds to. The path map is passed as the third argument to the `add_conditional_edges` function like so:
-
-=== "Python"
-
-    ```python
-    graph.add_conditional_edges("node_a", routing_function, {True: "node_b", False: "node_c"})
-    ```
-
-=== "Javascript"
-
-    ```ts
-    graph.addConditionalEdges("node_a", routingFunction, { true: "node_b", false: "node_c" });
-    ```
-
-In this case, the routing function returns either True or False, which map to `node_b` and `node_c` respectively.
-
-### Solution 2: Update the typing of the router (Python only)
-
-Instead of passing a path map, you can also be explicit about the typing of your routing function by specifying the nodes it can map to using the `Literal` python definition. Here is an example of how to define a routing function in that way:
-
-```python
-def routing_function(state: GraphState) -> Literal["node_b","node_c"]:
-    if state['some_condition'] == True:
-        return "node_b"
-    else:
-        return "node_c"
-```
-
-
-## Why is my graph taking so long to startup?
-
-The LangGraph Studio interacts with a local LangGraph API server. To stay aligned with ongoing updates, the LangGraph API requires regular rebuilding. As a result, you may occasionally experience slight delays when starting up your project.

docs/docs/cloud/how-tos/studio/manage_assistants.md

@@ -0,0 +1,19 @@
+# Manage assistants
+
+!!! info "Prerequisites"
+
+    - [Assistants Overview](../../concepts/assistants.md)
+
+LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
+
+## Graph mode
+
+To view your assistants, click the "Manage Assistants" button in the bottom left corner.
+
+This opens a modal for you to view all the assistants for the selected graph. Specify the assistant and its version you would like to mark as "Active", and this assistant will be used when submitting runs.
+
+By default, the "Default configuration" option will be active. This option reflects the default configuration defined in your graph. Edits made to this configuration will be used to update the run-time configuration, but will not update or create a new assistant unless you click "Create new assistant".
+
+## Chat mode
+
+Chat mode enables you to switch through the different assistants in your graph via the dropdown selector at the top of the page. To create, edit, or delete assistants, use Graph mode.

docs/docs/cloud/how-tos/studio/quick_start.md

@@ -7,16 +7,21 @@ LangGraph Studio supports connecting to two types of graphs:
 - Graphs deployed on [LangGraph Platform](../../../cloud/quick_start.md)
 - Graphs running locally via the [LangGraph Server](../../../tutorials/langgraph-platform/local-server.md).
 
-## Deployed Application
+LangGraph Studio is accessed from the LangSmith UI, within the LangGraph Platform Deployments tab.
 
-For applications that are deployed on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
+## Deployed application
+
+For applications that are [deployed](../../quick_start.md) on LangGraph Platform, you can access Studio as part of that deployment. To do so, navigate to the deployment in LangGraph Platform within the LangSmith UI and click the "LangGraph Studio" button.
 
 This will load the Studio UI connected to your live deployment, allowing you to create, read, and update the [threads](../../concepts/threads.md), [assistants](../../../concepts/assistants.md), and [memory](../../../concepts//memory.md) in that deployment.
 
-## Local Development Server
+## Local development server
 
 To test your locally running application using LangGraph Studio, ensure your application is set up following [this guide](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/).
 
+!!! info "LangSmith Tracing"
+    For local development, if you do not wish to have data traced to LangSmith, set `LANGSMITH_TRACING=false` in your application's `.env` file. With tracing disabled, no data will leave your local server.
+
 Next, install the [LangGraph CLI](../../../concepts/langgraph_cli.md):
 
 ```
@@ -44,15 +49,14 @@ If successful, you will see the following logs:
 >
 > - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
 
-Once running, you will automatically be directed to LangGraph Studio. 
+Once running, you will automatically be directed to LangGraph Studio.
 
+For an already running server, access Studio by either:
 
-If your server is already running, to access Studio, either:
+1.  Directly navigate to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024`.
+2.  Within LangSmith, navigate to the LangGraph Platform Deployments tab, click the "LangGraph Studio" button, enter `http://127.0.0.1:2024` and click "Connect".
 
- 1. Directly navigate to the following URL: `https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024`. 
- 2. Within LangSmith, navigate to the LangGraph Platform Deployments tab, click the "LangGraph Studio" button, enter `http://127.0.0.1:2024` and click "Connect".
- 
- If running your server at a different host or port, simply update the `baseUrl` to match.
+If running your server at a different host or port, simply update the `baseUrl` to match.
 
 ### (Optional) Attach a debugger
 
@@ -69,8 +73,8 @@ langgraph dev --debug-port 5678
 Then attach your preferred debugger:
 
 === "VS Code"
-    Add this configuration to `launch.json`:
-    ```json
+Add this configuration to `launch.json`:
+`json
       {
         "name": "Attach to LangGraph",
         "type": "debugpy",
@@ -80,23 +84,22 @@ Then attach your preferred debugger:
           "port": 5678
         }
       }
-    ```
-    Specify the port number you chose in the previous step.
+    `
+Specify the port number you chose in the previous step.
 
-=== "PyCharm" 
-    1. Go to Run → Edit Configurations 
-    2. Click + and select "Python Debug Server" 
-    3. Set IDE host name: `localhost` 
-    4. Set port: `5678` (or the port number you chose in the previous step) 
-    5. Click "OK" and start debugging
+=== "PyCharm" 1. Go to Run → Edit Configurations 2. Click + and select "Python Debug Server" 3. Set IDE host name: `localhost` 4. Set port: `5678` (or the port number you chose in the previous step) 5. Click "OK" and start debugging
 
+## Troubleshooting
+
+For issues getting started, please see this [troubleshooting guide](../../../troubleshooting/studio.md).
 
 ## Next steps
 
-See the following how-tos for more information on how to use Studio:
+See the following guides for more information on how to use Studio:
 
-- [How to manage Assistants](../invoke_studio.md)
-- [How to manage Threads](../threads_studio.md)
-- [How to create datasets](../datasets_studio.md)
-- [How to prompt engineer](../iterate_graph_studio.md)
-- [How to locally debug remote traces](../clone_traces_studio.md)
+- [Run application](../invoke_studio.md)
+- [Manage assistants](./manage_assistants.md)
+- [Manage threads](../threads_studio.md)
+- [Iterate on prompts](../iterate_graph_studio.md)
+- [Debug LangSmith traces](../clone_traces_studio.md)
+- [Add node to dataset](../datasets_studio.md)

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

@@ -1,4 +1,4 @@
-# How to manage Threads
+# Manage threads
 
 !!! info "Prerequisites"
 
@@ -6,25 +6,36 @@
 
 Studio allows you to view threads from the server and edit their state.
 
-## View Threads
+## View threads
 
 ### Graph mode
 
-1. In the top of the right-hand pane, select the `New Thread` dropdown menu to view existing threads.
+1. In the top of the right-hand pane, select the dropdown menu to view existing threads.
 1. Select the desired thread, and the thread history will populate in the right-hand side of the page.
-1. To create a new thread, select `+ New Thread`.
+1. To create a new thread, click `+ New Thread` and [submit a run](../how-tos/invoke_studio.md#graph-mode).
+
+To view more granular information in the thread, drag the slider at the top of the page to the right. To view less information, drag the slider to the left. Additionally, collapse or expand individual turns, nodes, and keys of the state.
+
+Switch between `Pretty` and `JSON` mode for different rendering formats.
 
 ### Chat mode
 
 1. View all threads in the right-hand pane of the page.
-2. Click the plus button to create a new thread.
+2. Select the desired thread and the thread history will populate in the center panel.
+3. To create a new thread, click the plus button and [submit a run](../how-tos/invoke_studio.md#chat-mode).
 
-## Edit Thread State
+## Edit thread history
 
 ### Graph mode
 
-To edit the state of the thread, select "edit node state" next to the desired node. This enables you to edit the node's output and create a new fork of the thread history. For more information about time travel, [see here](../../concepts/time-travel.md).
+To edit the state of the thread, select "edit node state" next to the desired node. Edit the node's output as desired and click "fork" to confirm. This will create a new forked run from the checkpoint of the selected node.
+
+If you instead want to re-run the thread from a given checkpoint without editing the state, click the "Re-run from here". This will again create a new forked run from the selected checkpoint. This is useful for re-running with changes that are not specific to the state, such as the selected assistant.
 
 ### Chat mode
 
 To edit a human message in the thread, click the edit button below the human message. Edit the message as desired and submit. This will create a new fork of the conversation history. To re-generate an AI message, click the retry icon below the AI message.
+
+## Learn more
+
+For more information about time travel, [see here](../../concepts/time-travel.md).

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

@@ -158,7 +158,7 @@ export default function HomePage() {
 }
 ```
 
-Under the hood, the `useStream()` hook will use the `streamMode: "messages-tuple"` to receive a stream of messages (i.e. individual LLM tokens) from any LangChain chat model invocations inside your graph nodes. Learn more about messages streaming in the [How to stream messages from your graph](./stream_messages.md) guide.
+Under the hood, the `useStream()` hook will use the `streamMode: "messages-tuple"` to receive a stream of messages (i.e. individual LLM tokens) from any LangChain chat model invocations inside your graph nodes. Learn more about messages streaming in the [streaming](../how-tos/streaming.md#messages) guide.
 
 ### Interrupts
 
@@ -476,7 +476,7 @@ The `useStream()` hook provides several callback options to help you respond to
 - `onError`: Called when an error occurs.
 - `onFinish`: Called when the stream is finished.
 - `onUpdateEvent`: Called when an update event is received.
-- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../how-tos/streaming.ipynb#custom) to learn how to stream custom events.
+- `onCustomEvent`: Called when a custom event is received. See the [streaming](../../how-tos/streaming.md#stream-custom-data) guide to learn how to stream custom events.
 - `onMetadataEvent`: Called when a metadata event is received, which contains the Run ID and Thread ID.
 
 ## Learn More

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

@@ -0,0 +1,491 @@
+# Use threads
+
+!!! info "Prerequisites"
+
+    - [Threads Overview](../concepts/threads.md)
+
+In this guide, we will show how to create, view, and inspect threads.
+
+## Create a thread
+
+To run your graph and the state persisted, you must first create a thread.
+
+### Empty thread
+
+To create a new thread, 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.ThreadsClient.create) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#create_3) SDK reference docs for more information.
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+
+    client = get_client(url=<DEPLOYMENT_URL>)
+    thread = await client.threads.create()
+
+    print(thread)
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+    const thread = await client.threads.create();
+
+    console.log(thread);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{}'
+    ```
+
+Output:
+
+    {
+      "thread_id": "123e4567-e89b-12d3-a456-426614174000",
+      "created_at": "2025-05-12T14:04:08.268Z",
+      "updated_at": "2025-05-12T14:04:08.268Z",
+      "metadata": {},
+      "status": "idle",
+      "values": {}
+    }
+
+### Copy thread
+
+Alternatively, if you already have a thread in your application whose state you wish to copy, you can use the `copy` method. This will create an independent thread whose history is identical to the original thread at the time of the operation. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.copy) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#copy) SDK reference docs for more information.
+
+=== "Python"
+
+    ```python
+    copied_thread = await client.threads.copy(<THREAD_ID>)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const copiedThread = await client.threads.copy(<THREAD_ID>);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/copy \
+    --header 'Content-Type: application/json'
+    ```
+
+### Prepopulated State
+
+Finally, you can create a thread with an arbitrary pre-defined state by providing a list of `supersteps` into the `create` method. The `supersteps` describe a list of a sequence of state updates. For example:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+
+    client = get_client(url=<DEPLOYMENT_URL>)
+    thread = await client.threads.create(
+      graph_id="agent",
+      supersteps=[
+        {
+          updates: [
+            {
+              values: {},
+              as_node: '__input__',
+            },
+          ],
+        },
+        {
+          updates: [
+            {
+              values: {
+                messages: [
+                  {
+                    type: 'human',
+                    content: 'hello',
+                  },
+                ],
+              },
+              as_node: '__start__',
+            },
+          ],
+        },
+        {
+          updates: [
+            {
+              values: {
+                messages: [
+                  {
+                    content: 'Hello! How can I assist you today?',
+                    type: 'ai',
+                  },
+                ],
+              },
+              as_node: 'call_model',
+            },
+          ],
+        },
+      ])
+
+    print(thread)
+    ```
+
+=== "Javascript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+    const thread = await client.threads.create({
+        graphId: 'agent',
+        supersteps: [
+        {
+          updates: [
+            {
+              values: {},
+              asNode: '__input__',
+            },
+          ],
+        },
+        {
+          updates: [
+            {
+              values: {
+                messages: [
+                  {
+                    type: 'human',
+                    content: 'hello',
+                  },
+                ],
+              },
+              asNode: '__start__',
+            },
+          ],
+        },
+        {
+          updates: [
+            {
+              values: {
+                messages: [
+                  {
+                    content: 'Hello! How can I assist you today?',
+                    type: 'ai',
+                  },
+                ],
+              },
+              asNode: 'call_model',
+            },
+          ],
+        },
+      ],
+    });
+
+    console.log(thread);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+        --url <DEPLOYMENT_URL>/threads \
+        --header 'Content-Type: application/json' \
+        --data '{"metadata":{"graph_id":"agent"},"supersteps":[{"updates":[{"values":{},"as_node":"__input__"}]},{"updates":[{"values":{"messages":[{"type":"human","content":"hello"}]},"as_node":"__start__"}]},{"updates":[{"values":{"messages":[{"content":"Hello\u0021 How can I assist you today?","type":"ai"}]},"as_node":"call_model"}]}]}'
+    ```
+
+Output:
+
+    {
+        "thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
+        "created_at": "2025-05-12T15:37:08.935038+00:00",
+        "updated_at": "2025-05-12T15:37:08.935046+00:00",
+        "metadata": {"graph_id": "agent"},
+        "status": "idle",
+        "config": {},
+        "values": {
+            "messages": [
+                {
+                    "content": "hello",
+                    "additional_kwargs": {},
+                    "response_metadata": {},
+                    "type": "human",
+                    "name": null,
+                    "id": "8701f3be-959c-4b7c-852f-c2160699b4ab",
+                    "example": false
+                },
+                {
+                    "content": "Hello! How can I assist you today?",
+                    "additional_kwargs": {},
+                    "response_metadata": {},
+                    "type": "ai",
+                    "name": null,
+                    "id": "4d8ea561-7ca1-409a-99f7-6b67af3e1aa3",
+                    "example": false,
+                    "tool_calls": [],
+                    "invalid_tool_calls": [],
+                    "usage_metadata": null
+                }
+            ]
+        }
+    }
+
+## List threads
+
+### LangGraph SDK
+
+To list threads, use the [LangGraph SDK](../../concepts/sdk.md) `search` method. This will list the threads in the application that match the provided filters. See the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/#langgraph_sdk.client.ThreadsClient.search) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#search_2) SDK reference docs for more information.
+
+#### Filter by thread status
+
+Use the `status` field to filter threads based on their status. Supported values are `idle`, `busy`, `interrupted`, and `error`. See [here](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/?h=thread+status#langgraph_sdk.auth.types.ThreadStatus) for information on each status. For example, to view `idle` threads:
+
+=== "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': {}}
+      }
+    ]
+
+#### Filter by metadata
+
+The `search` method allows you to filter on metadata:
+
+=== "Python"
+
+    ```python
+    print((await client.threads.search(metadata={"graph_id":"agent"},limit=1)))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log((await client.threads.search({ metadata: { "graph_id": "agent" }, limit: 1 })));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/search \
+    --header 'Content-Type: application/json' \
+    --data '{"metadata": {"graph_id":"agent"}, "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': {}}
+      }
+    ]
+
+#### Sorting
+
+The SDK also supports sorting threads by `thread_id`, `status`, `created_at`, and `updated_at` using the `sort_by` and `sort_order` params.
+
+### LangGraph Platform UI
+
+You can also view threads in a deployment via the LangGraph Platform UI.
+
+Inside your deployment, select the "Threads" tab. This will load a table of all of the threads in your deployment.
+
+To filter by thread status, select a status in the top bar. To sort by a supported property, click on the arrow icon for the desired column.
+
+## Inspect threads
+
+### LangGraph SDK
+
+#### Get Thread
+
+To view a specific thread given its `thread_id`, use the `get` method:
+
+=== "Python"
+
+    ```python
+    print((await client.threads.get(<THREAD_ID>)))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log((await client.threads.get(<THREAD_ID>)));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID> \
+    --header 'Content-Type: application/json'
+    ```
+
+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': {}}
+    }
+
+#### Inspect Thread State
+
+To view the current state of a given thread, use the `get_state` method:
+
+=== "Python"
+
+    ```python
+    print((await client.threads.get_state(<THREAD_ID>)))
+    ```
+
+=== "Javascript"
+
+    ```js
+    console.log((await client.threads.getState(<THREAD_ID>)));
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
+    --header 'Content-Type: application/json'
+    ```
+
+Output:
+
+    {
+        "values": {
+            "messages": [
+                {
+                    "content": "hello",
+                    "additional_kwargs": {},
+                    "response_metadata": {},
+                    "type": "human",
+                    "name": null,
+                    "id": "8701f3be-959c-4b7c-852f-c2160699b4ab",
+                    "example": false
+                },
+                {
+                    "content": "Hello! How can I assist you today?",
+                    "additional_kwargs": {},
+                    "response_metadata": {},
+                    "type": "ai",
+                    "name": null,
+                    "id": "4d8ea561-7ca1-409a-99f7-6b67af3e1aa3",
+                    "example": false,
+                    "tool_calls": [],
+                    "invalid_tool_calls": [],
+                    "usage_metadata": null
+                }
+            ]
+        },
+        "next": [],
+        "tasks": [],
+        "metadata": {
+            "thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
+            "checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955",
+            "graph_id": "agent_with_quite_a_long_name",
+            "source": "update",
+            "step": 1,
+            "writes": {
+                "call_model": {
+                    "messages": [
+                        {
+                            "content": "Hello! How can I assist you today?",
+                            "type": "ai"
+                        }
+                    ]
+                }
+            },
+            "parents": {}
+        },
+        "created_at": "2025-05-12T15:37:09.008055+00:00",
+        "checkpoint": {
+            "checkpoint_id": "1f02f46f-733f-6b58-8001-ea90dcabb1bd",
+            "thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
+            "checkpoint_ns": ""
+        },
+        "parent_checkpoint": {
+            "checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955",
+            "thread_id": "f15d70a1-27d4-4793-a897-de5609920b7d",
+            "checkpoint_ns": ""
+        },
+        "checkpoint_id": "1f02f46f-733f-6b58-8001-ea90dcabb1bd",
+        "parent_checkpoint_id": "1f02f46f-7308-616c-8000-1b158a9a6955"
+    }
+
+Optionally, to view the state of a thread at a given checkpoint, simply pass in the checkpoint id (or the entire checkpoint object):
+
+=== "Python"
+
+    ```python
+    thread_state = await client.threads.get_state(
+      thread_id=<THREAD_ID>
+      checkpoint_id=<CHECKPOINT_ID>
+    )
+    ```
+
+=== "Javascript"
+
+    ```js
+    const threadState = await client.threads.getState(<THREAD_ID>, <CHECKPOINT_ID>);
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state/<CHECKPOINT_ID> \
+    --header 'Content-Type: application/json'
+    ```
+
+#### Inspect Full Thread History
+
+To view a thread's history, use the `get_history` method. This returns a list of every state the thread experienced. For more information see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/?h=thread+status#langgraph_sdk.client.ThreadsClient.get_history) and [JS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/#gethistory) reference docs.
+
+### LangGraph Platform UI
+
+You can also view threads in a deployment via the LangGraph Platform UI.
+
+Inside your deployment, select the "Threads" tab. This will load a table of all of the threads in your deployment.
+
+Select a thread to inspect its current state. To view its full history and for further debugging, open the thread in [LangGraph Studio](../../concepts//langgraph_studio.md).

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

@@ -1,10 +1,10 @@
-# Using Webhooks
+# Use webhooks
 
-When working with LangGraph Cloud, you may want to use webhooks to receive updates after an API call completes. Webhooks are useful for triggering actions in your service once a run has finished processing. To implement this, you need to expose an endpoint that can accept `POST` requests and pass this endpoint as a `webhook` parameter in your API request.
+When working with LangGraph Platform, you may want to use webhooks to receive updates after an API call completes. Webhooks are useful for triggering actions in your service once a run has finished processing. To implement this, you need to expose an endpoint that can accept `POST` requests and pass this endpoint as a `webhook` parameter in your API request.
 
 Currently, the SDK does not provide built-in support for defining webhook endpoints, but you can specify them manually using API requests.
 
-## Supported Endpoints
+## Supported endpoints
 
 The following API endpoints accept a `webhook` parameter:
 
@@ -20,7 +20,7 @@ The following API endpoints accept a `webhook` parameter:
 
 In this guide, we’ll show how to trigger a webhook after streaming a run.
 
-## Setting Up Your Assistant and Thread
+## Set up your assistant and thread
 
 Before making API calls, set up your assistant and thread.
 
@@ -56,7 +56,8 @@ curl --request POST \
     --data '{}'
 ```
 
-### Example Response
+Example response:
+
 ```json
 {
     "thread_id": "9dde5490-2b67-47c8-aa14-4bfec88af217",
@@ -69,9 +70,9 @@ curl --request POST \
 }
 ```
 
-## Using a Webhook with a Graph Run
+## Use a webhook with a graph run
 
-To use a webhook, specify the `webhook` parameter in your API request. When the run completes, LangGraph Cloud sends a `POST` request to the specified webhook URL.
+To use a webhook, specify the `webhook` parameter in your API request. When the run completes, LangGraph Platform sends a `POST` request to the specified webhook URL.
 
 For example, if your server listens for webhook events at `https://my-server.app/my-webhook-endpoint`, include this in your request:
 
@@ -119,11 +120,11 @@ curl --request POST \
     }'
 ```
 
-## Webhook Payload
+## Webhook payload
 
-LangGraph Cloud sends webhook notifications in the format of a [Run](../../cloud/concepts/runs.md). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.
+LangGraph Platform sends webhook notifications in the format of a [Run](../../cloud/concepts/runs.md). See the [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for details. The request payload includes run input, configuration, and other metadata in the `kwargs` field.
 
-## Securing Webhooks
+## Secure webhooks
 
 To ensure only authorized requests hit your webhook endpoint, consider adding a security token as a query parameter:
 
@@ -133,15 +134,11 @@ https://my-server.app/my-webhook-endpoint?token=YOUR_SECRET_TOKEN
 
 Your server should extract and validate this token before processing requests.
 
-## Testing Webhooks
+## Test webhooks
 
 You can test your webhook using online services like:
 
 - **[Beeceptor](https://beeceptor.com/)** – Quickly create a test endpoint and inspect incoming webhook payloads.
 - **[Webhook.site](https://webhook.site/)** – View, debug, and log incoming webhook requests in real time.
 
-These tools help you verify that LangGraph Cloud is correctly triggering and sending webhooks to your service.
-
----
-
-By following these steps, you can integrate webhooks into your LangGraph Cloud workflow, automating actions based on completed runs.
+These tools help you verify that LangGraph Platform is correctly triggering and sending webhooks to your service.

docs/docs/cloud/quick_start.md

@@ -9,31 +9,21 @@ Before you begin, ensure you have the following:
 - A [GitHub account](https://github.com/)
 - A [LangSmith account](https://smith.langchain.com/) – free to sign up
 
-This quickstart uses the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent), which requires the following:
-
-- An API key for [Anthropic](https://console.anthropic.com/)
-- An API key for [Tavily](https://app.tavily.com/)
-
 ## 1. Create a repository on GitHub
 
-To deploy a LangGraph application to **LangGraph Cloud**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [pre-built Python ReAct agent template](https://github.com/langchain-ai/react-agent) for your application:
+To deploy an application to **LangGraph Platform**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [`new-langgraph-project` template](https://github.com/langchain-ai/react-agent) for your application:
 
-1. Go to the [ReAct Agent](https://github.com/langchain-ai/react-agent) repository.
+1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project).
 1. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
 1. Click **Create fork**. 
 
 ## 2. Deploy to LangGraph Platform
 
 1. Log in to [LangSmith](https://smith.langchain.com/).
-1. In the left sidebar, select **LangGraph Platform**.
-1. Click the **+ New Deployment** button. A modal will open where you can fill in the required fields.
+1. In the left sidebar, select **Deployments**.
+1. Click the **+ New Deployment** button. A pane will open where you can fill in the required fields.
 1. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
-1. Select your ReAct Agent repository.
-1. In the **Environment Variables** section, set the following secrets:
-    
-    - **ANTHROPIC_API_KEY**: Get an API key from [Anthropic](https://console.anthropic.com/).
-    - **TAVILY_API_KEY**: Get an API key on the [Tavily website](https://app.tavily.com/).
-
+1. Select your New LangGraph Project repository.
 1. Click **Submit** to deploy.
 
     This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.
@@ -48,7 +38,7 @@ Once your application is deployed:
     LangGraph Studio will open to display your graph.
 
     <figure markdown="1">
-    [![image](deployment/img/09_langgraph_studio.png){: style="max-height:400px"}](deployment/img/09_langgraph_studio.png)
+    [![image](deployment/img/langgraph_studio.png){: style="max-height:400px"}](deployment/img/langgraph_studio.png)
     <figcaption>
         Sample graph run in LangGraph Studio.
     </figcaption>
@@ -125,7 +115,7 @@ You can now test the API:
             print("\n\n")
         ```
 
-=== "Javascript SDK"
+=== "JavaScript SDK"
 
     1. Install the LangGraph JS SDK
 
@@ -181,7 +171,7 @@ You can now test the API:
     ```
 
 
-## Next Steps
+## Next steps
 
 Congratulations! You have deployed an application using LangGraph Platform.
 

docs/docs/cloud/reference/api/api_ref.html

@@ -1,7 +1,7 @@
 <!doctype html>
 <html>
   <head>
-    <title>LangGraph Cloud API Reference</title>
+    <title>LangGraph Platform API Reference</title>
     <meta charset="utf-8" />
     <meta
       name="viewport"

docs/docs/cloud/reference/api/api_ref.md

@@ -1,12 +1,12 @@
 # API Reference
 
-The LangGraph Cloud API reference is available with each deployment at the `/docs` URL path (e.g. `http://localhost:8124/docs`).
+The LangGraph Platform API reference is available with each deployment at the `/docs` URL path (e.g. `http://localhost:8124/docs`).
 
 Click <a href="/langgraph/cloud/reference/api/api_ref.html" target="_blank">here</a> to view the API reference.
 
 ## Authentication
 
-For deployments to LangGraph Cloud, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Cloud API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.
+For deployments to LangGraph Platform, authentication is required. Pass the `X-Api-Key` header with each request to the LangGraph Platform API. The value of the header should be set to a valid LangSmith API key for the organization where the API is deployed.
 
 Example `curl` command:
 ```shell

docs/docs/cloud/reference/api/api_ref_control_plane.html

@@ -1,7 +1,7 @@
 <!doctype html>
 <html>
   <head>
-    <title>LangGraph Cloud API Reference</title>
+    <title>LangGraph Platform API Reference</title>
     <meta charset="utf-8" />
     <meta
       name="viewport"