State modifier

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -7,29 +7,35 @@ body:
       value: >
         Thank you for taking the time to file a bug report. 
         
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
+        Use this to report bugs in LangChain. 
+        
+        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
+        to ask for help with your issue.
         
         Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
         if there's another way to solve your problem:
         
-        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
+        [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
         [GitHub search](https://github.com/langchain-ai/langgraph),
+        [LangChain Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
+        [LangChain Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
+      description: Please confirm and check all the following options.
       options:
-        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
+        - label: I added a very descriptive title to this issue.
           required: true
-        - label: I added a clear and detailed title that summarizes the issue.
+        - label: I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
           required: true
-        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
+        - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
-        - label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
+        - label: I am sure that this is a bug in LangGraph/LangChain rather than my code.
+          required: true
+        - label: I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
           required: true
   - type: textarea
     id: reproduction
@@ -39,6 +45,14 @@ body:
       label: Example Code
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        
+        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+        
+        **Important!** 
+        
+        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
+        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -78,8 +92,25 @@ body:
     attributes:
       label: System Info
       description: |
+        Please share your system info with us. 
+        
+        "pip freeze | grep langchain" 
+        platform (windows / linux / mac)
+        python version
+        
+        OR if you're on a recent version of langchain-core you can paste the output of:
+        
         python -m langchain_core.sys_info
       placeholder: |
+        "pip freeze | grep langgraph"
+        platform
+        python version
+        
+        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
+        
         python -m langchain_core.sys_info
+        
+        These will only surface LangChain packages, don't forget to include any other relevant
+        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true

.github/scripts/run_langgraph_cli_test.py

@@ -1,115 +0,0 @@
-import asyncio
-import json
-import os
-import pathlib
-import sys
-import langgraph_cli
-import langgraph_cli.docker
-import langgraph_cli.config
-
-from langgraph_cli.exec import Runner, subp_exec
-from langgraph_cli.progress import Progress
-from langgraph_cli.constants import DEFAULT_PORT
-
-
-def test(
-    config: pathlib.Path,
-    port: int,
-    tag: str,
-    verbose: bool,
-):
-    with Runner() as runner, Progress(message="Pulling...") as set:
-        # check docker available
-        capabilities = langgraph_cli.docker.check_capabilities(runner)
-        # open config
-        config_json = langgraph_cli.config.validate_config_file(config)
-
-        set("Running...")
-        args = [
-            "run",
-            "--rm",
-            "-p",
-            f"{port}:8000",
-        ]
-        if isinstance(config_json["env"], str):
-            args.extend(
-                [
-                    "--env-file",
-                    str(config.parent / config_json["env"]),
-                ]
-            )
-        else:
-            for k, v in config_json["env"].items():
-                args.extend(
-                    [
-                        "-e",
-                        f"{k}={v}",
-                    ]
-                )
-        if capabilities.healthcheck_start_interval:
-            args.extend(
-                [
-                    "--health-interval",
-                    "5s",
-                    "--health-retries",
-                    "1",
-                    "--health-start-period",
-                    "10s",
-                    "--health-start-interval",
-                    "1s",
-                ]
-            )
-        else:
-            args.extend(
-                [
-                    "--health-interval",
-                    "5s",
-                    "--health-retries",
-                    "2",
-                ]
-            )
-
-        _task = None
-
-        def on_stdout(line: str):
-            nonlocal _task
-            if "GET /ok" in line or "Uvicorn running on" in line:
-                set("")
-                sys.stdout.write(
-                    f"""Ready!
-- API: http://localhost:{port}
-"""
-                )
-                sys.stdout.flush()
-                _task.cancel()
-                return True
-            return False
-
-        async def subp_exec_task(*args, **kwargs):
-            nonlocal _task
-            _task = asyncio.create_task(subp_exec(*args, **kwargs))
-            await _task
-
-        try:
-            runner.run(
-                subp_exec_task(
-                    "docker",
-                    *args,
-                    tag,
-                    verbose=verbose,
-                    on_stdout=on_stdout,
-                )
-            )
-        except asyncio.CancelledError:
-            pass
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-t", "--tag", type=str)
-    parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
-    parser.add_argument("-p", "--port", default=DEFAULT_PORT)
-    args = parser.parse_args()
-    test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -39,36 +39,22 @@ jobs:
       - name: Install cli globally
         if: steps.changed-files.outputs.all
         run: pip install -e .
-      - name: Build and test service A
+      - name: Start service A
         if: steps.changed-files.outputs.all
-        working-directory: libs/cli/examples
         run: |
-          # The build-arg isn't used; just testing that we accept other args
-          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
-          cp .env.example .envg
-          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
-      - name: Build and test service B
+          timeout 60 langgraph test -c examples/langgraph.json --verbose || (exit "$(($? == 124 ? 0 : $?))")
+      - name: Start service B
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs
         run: |
-          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
-      - name: Build and test service C
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
+      - name: Start service C
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_a
         run: |
-          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
-      - name: Build and test service D
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
+      - name: Start service D
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_b
         run: |
-          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
-
-      - name: Build JS service
-        if: steps.changed-files.outputs.all
-        working-directory: libs/cli/js-examples
-        run: |
-          langgraph build -t langgraph-test-e
-  
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")

.github/workflows/_lint.yml

@@ -42,6 +42,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: lint-${{ inputs.working-directory }}
 
       - name: Check Poetry File
@@ -54,7 +55,7 @@ jobs:
         if: steps.changed-files.outputs.all
         shell: bash
         working-directory: ${{ inputs.working-directory }}
-        run: poetry check --lock
+        run: poetry lock --check
 
       - name: Install dependencies
         if: steps.changed-files.outputs.all

.github/workflows/_test.yml

@@ -21,7 +21,6 @@ jobs:
           - "3.10"
           - "3.11"
           - "3.12"
-          - "3.13"
 
     name: "test #${{ matrix.python-version }}"
     steps:
@@ -31,19 +30,8 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: test-${{ inputs.working-directory }}
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        if: ${{ !github.event.pull_request.head.repo.fork }}
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
-
-      - name: Check Lock
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          poetry check --lock
 
       - name: Install dependencies
         shell: bash

.github/workflows/_test_langgraph.yml

@@ -16,12 +16,14 @@ jobs:
           - "3.10"
           - "3.11"
           - "3.12"
-          - "3.13"
+        core-version:
+          - ">=0.2.39,<0.3.0"
+          - "latest"
 
     defaults:
       run:
         working-directory: libs/langgraph
-    name: "test #${{ matrix.python-version }}"
+    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }})"
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
@@ -30,22 +32,19 @@ jobs:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           cache-key: test-langgraph
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        if: ${{ !github.event.pull_request.head.repo.fork }}
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
 
       - name: Install dependencies
         shell: bash
         run: |
           poetry install --with dev
+          if [ "${{ matrix.core-version }}" != "latest" ]; then
+            poetry run pip install "langchain-core${{ matrix.core-version }}"
+          fi
 
       - name: Run tests
         shell: bash
         run: |
-          make test_parallel
+          make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_release.yml

@@ -29,6 +29,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
@@ -92,5 +93,3 @@ jobs:
           # This is *only for CI use* and is *extremely dangerous* otherwise!
           # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
           skip-existing: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false

.github/workflows/_test_scheduler_kafka.yml

@@ -27,12 +27,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
           cache-key: test-scheduler-kafka
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        if: ${{ !github.event.pull_request.head.repo.fork }}
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_RO_TOKEN }}
 
       - name: Install dependencies
         shell: bash

.github/workflows/ci.yml

@@ -20,31 +20,7 @@ env:
   POETRY_VERSION: "1.7.1"
 
 jobs:
-  changes:
-    runs-on: ubuntu-latest
-    outputs:
-      python: ${{ steps.filter.outputs.python }}
-      sdk-js: ${{ steps.filter.outputs.sdk-js }}
-    steps:
-      - uses: actions/checkout@v4
-      - uses: dorny/paths-filter@v3
-        id: filter
-        with:
-          filters: |
-            python:
-              - 'libs/langgraph/**'
-              - 'libs/sdk-py/**'
-              - 'libs/cli/**'
-              - 'libs/checkpoint/**'
-              - 'libs/checkpoint-sqlite/**'
-              - 'libs/checkpoint-postgres/**'
-              - 'libs/scheduler-kafka/**'
-              - 'libs/prebuilt/**'
-            sdk-js:
-              - 'libs/sdk-js/**'
-
   lint:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
@@ -57,28 +33,22 @@ jobs:
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
             "libs/scheduler-kafka",
-            "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   test:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
-        working-directory:
-          [
+        working-directory: [
             "libs/cli",
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
-            "libs/checkpoint-postgres",
-            "libs/prebuilt",
+            "libs/checkpoint-postgres"
           ]
-    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -86,23 +56,17 @@ jobs:
 
   # NOTE: we're testing langgraph separately because it requires a different matrix
   test-langgraph:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "cd libs/langgraph"
     uses: ./.github/workflows/_test_langgraph.yml
     secrets: inherit
 
   # NOTE: we're testing scheduler-kafka separately because it requires a different matrix
   test-scheduler-kafka:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "cd libs/scheduler-kafka"
     uses: ./.github/workflows/_test_scheduler_kafka.yml
     secrets: inherit
 
   check-sdk-methods:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "Check SDK methods matching"
     runs-on: ubuntu-latest
     steps:
@@ -110,56 +74,16 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: '3.11'
       - name: Run check_sdk_methods script
         run: python .github/scripts/check_sdk_methods.py
 
-  check-schema:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
-    name: "Check CLI schema hasn't changed #${{ matrix.python-version }}"
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.11"
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: schema-check-cli
-      - name: Install CLI dependencies
-        run: |
-          cd libs/cli
-          poetry install
-      - name: Generate schema and check for changes
-        run: |
-          cd libs/cli
-          # Create a temporary copy of the current schema
-          cp schemas/schema.json schemas/schema.current.json
-          # Generate new schema
-          poetry run python generate_schema.py
-          # Compare the new schema with the original
-          if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
-            echo "Error: Langgraph.json configuration schema has changed. Please run 'poetry run python generate_schema.py' in the libs/cli directory and commit the changes."
-            diff schemas/schema.json schemas/schema.current.json
-            exit 1
-          fi
-          echo "Schema check passed - no changes detected"
-
   integration-test:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: CLI integration test
     uses: ./.github/workflows/_integration_test.yml
     secrets: inherit
 
   lint-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -183,44 +107,9 @@ jobs:
       - name: Build
         run: yarn build
 
-  test-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        working-directory:
-          - "libs/sdk-js"
-    defaults:
-      run:
-        working-directory: ${{ matrix.working-directory }}
-    steps:
-      - uses: actions/checkout@v3
-      - name: Setup Node.js (LTS)
-        uses: actions/setup-node@v3
-        with:
-          node-version: "20"
-          cache: "yarn"
-          cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
-      - name: Install dependencies
-        run: yarn install
-      - name: Run tests
-        run: yarn test
-
   ci_success:
     name: "CI Success"
-    needs:
-      [
-        lint,
-        lint-js,
-        test,
-        test-langgraph,
-        test-scheduler-kafka,
-        check-sdk-methods,
-        check-schema,
-        integration-test,
-        test-js,
-      ]
+    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
     if: |
       always()
     runs-on: ubuntu-latest

.github/workflows/codespell.yml

@@ -9,11 +9,7 @@
     
     permissions:
       contents: read
-
-    defaults:
-      run:
-        working-directory: docs
-
+    
     jobs:
       codespell:
         name: (Check for spelling errors)
@@ -25,18 +21,18 @@
     
           - name: Install Dependencies
             run: |
-              pip install toml codespell==2.3.0 jupytext
+              pip install toml codespell jupytext
     
           - name: Extract Ignore Words List
             run: |
               # Use a Python script to extract the ignore words list from pyproject.toml
-              python ../.github/workflows/extract_ignored_words_list.py
+              python .github/workflows/extract_ignored_words_list.py
             id: extract_ignore_words
     
           - name: Codespell
             uses: codespell-project/actions-codespell@v2
             with:
-              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib'
               ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
           # We do this to avoid spellchecking cell outputs
           - name: Codespell Notebooks

.github/workflows/deploy_docs.yml

@@ -21,15 +21,11 @@ concurrency:
   group: "pages"
   cancel-in-progress: false
 
-defaults:
-  run:
-    working-directory: docs
-
 jobs:
   get-changed-files:
     runs-on: ubuntu-latest
     outputs:
-      changed-files: ${{ steps.changed-files.outputs.added_modified }}
+      changed-files: ${{ steps.changed-files.outputs.all }}
     steps:
       - uses: actions/checkout@v4
       - name: Get changed files
@@ -48,9 +44,6 @@ jobs:
   deploy:
     # needs: run-changed-notebooks
     runs-on: ubuntu-latest
-    timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
-    env:
-      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -63,57 +56,20 @@ jobs:
           poetry-version: ${{ env.POETRY_VERSION }}
           cache-key: docs
 
-      - name: Use Node.js
-        uses: actions/setup-node@v3
-        with:
-          node-version: "22"
-          cache: "yarn"
-          cache-dependency-path: docs/yarn.lock
-
       - name: Install dependencies
         run: |
-          yarn
-          poetry install --with test --with docs --no-root
-          poetry run pip install -U \
-            pytest \
-            pytest-check-links \
-            GitPython \
-            "git+https://github.com/benjamincburns/markdown-exec.git@cc0d39d737e5ffd4b83d23cd8729d7ea16e363c8"
+          poetry install --with docs
+          poetry run pip install -U pytest pytest-check-links langsmith langchain GitPython
 
-          # 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"
-          fi
-
-          poetry run jupyter kernelspec list
-          poetry run python3 -m ipykernel install --user --name=python3
-          npm install -g tslab
-          poetry run tslab install --python=python3
-          poetry run jupyter kernelspec list
-
-      - name: Run unit tests
-        # Run unit tests on the docs build pipeline
-        run: make tests
       - name: Lint Docs
         # This step lints the docs using the existing linting set up.
         # It should be very fast and should not require any external services.
         run: make lint-docs
-      - name: Build llms-text
-        run: make llms-text
       - name: Build site
-        run: |
-          # If this is main branch, then we want to download stats. we do this 
-          # with the env variable DOWNLOAD_STATS=true
-          if [ "${{ github.ref }}" == "refs/heads/main" ]; then
-            DOWNLOAD_STATS=true make build-docs
-          else
-            make build-docs
-          fi
+        run: make build-docs
         env:
           MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
-          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
-          ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
+
       - name: Check links in notebooks
         env:
           LANGCHAIN_API_KEY: test
@@ -122,40 +78,23 @@ jobs:
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
-              --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
-              --check-links-ignore "https://twitter.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
-              --check-links-ignore "http://localhost:8123/.*" \
-              --check-links-ignore "http://localhost:2024.*" \
-              --check-links-ignore "http://127.0.0.1:.*" \
               --check-links-ignore "/.*\.(ipynb|html)$" \
-              --check-links-ignore "https://python\.langchain\.com/.*" \
-              --check-links-ignore "https://openai\.com/.*" \
-              --check-links-ignore "https://www\.uber\.com/.*" \
-              --check-links-ignore "https://pepy\.tech/.*" \
-              --check-links-ignore "docs/docs/static/wordmark_*" \
-              --check-links $(find site -name "index.html" | grep -v 'storm/index.html')
-              
+              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html')
           else
             echo "Fetching changes from origin/main..."
             git fetch origin main
             echo "Checking for changed notebook files..."
-            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
+            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
             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 \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
-                --check-links-ignore "https://academy\.langchain\.com/.*" \
-                --check-links-ignore "http://localhost:8123/.*" \
-                --check-links-ignore "http://localhost:2024.*" \
-                --check-links-ignore "http://127.0.0.1:.*" \
                 --check-links-ignore "https://x.com/.*" \
-                --check-links-ignore "https://twitter.com/.*" \
                 --check-links-ignore "https://github\.com/.*" \
                 --check-links-ignore "/.*\.(ipynb|html)$" \
-                --check-links-ignore "docs/docs/static/wordmark_*" \
                 --check-links ${CHANGED_FILES} \
                 || ([ $? = 5 ] && exit 0 || exit $?)
             else
@@ -168,7 +107,7 @@ jobs:
         uses: actions/configure-pages@v4
 
       - name: Upload Pages Artifact
-        # if: github.ref == 'refs/heads/main'
+        if: github.ref == 'refs/heads/main'
         uses: actions/upload-pages-artifact@v3
         with:
           path: ./docs/site/

.github/workflows/extract_ignored_words_list.py

@@ -1,6 +1,6 @@
 import toml
 
-pyproject_toml = toml.load("pyproject.toml")
+pyproject_toml = toml.load("libs/langgraph/pyproject.toml")
 
 # Extract the ignore words list (adjust the key as per your TOML structure)
 ignore_words_list = (

.github/workflows/release.yml

@@ -31,6 +31,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
@@ -168,6 +169,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
 
       - name: Import published package
         shell: bash
@@ -195,11 +197,7 @@ jobs:
               "$PKG_NAME==$VERSION" \
           )
 
-          if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            poetry run pip install langgraph
-          fi
-
-          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
+          if [[ "$PKG_NAME" == *checkpoint* ]]; then
             # since checkpoint packages are namespace packages, import them with . convention
             # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"
@@ -258,6 +256,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       - uses: actions/download-artifact@v4
@@ -271,8 +270,6 @@ jobs:
           packages-dir: ${{ inputs.working-directory }}/dist/
           verbose: true
           print-hash: true
-          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
-          attestations: false
 
   mark-release:
     needs:
@@ -299,6 +296,7 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       - uses: actions/download-artifact@v4

.github/workflows/run_notebooks.yml

@@ -11,10 +11,6 @@ on:
   schedule:
     - cron: '0 13 * * *'
 
-defaults:
-  run:
-    working-directory: docs
-
 jobs:
   build:
     runs-on: ubuntu-latest
@@ -43,14 +39,14 @@ jobs:
 
       - name: Pre-download tiktoken files
         run: |
-          poetry run python _scripts/download_tiktoken.py
+          poetry run python docs/_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
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
           else
-            poetry run python _scripts/prepare_notebooks_for_ci.py
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py
           fi
 
       - name: Run notebooks
@@ -67,12 +63,12 @@ jobs:
         run: |
           if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
             echo "Running all notebooks"
-            ./_scripts/execute_notebooks.sh
+            ./docs/_scripts/execute_notebooks.sh
           else
-            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
+            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
             if [ -n "$CHANGED_FILES" ]; then
               echo "Running changed notebooks: $CHANGED_FILES"
-              ./_scripts/execute_notebooks.sh $CHANGED_FILES
+              ./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
             else
               echo "No notebook files changed, skipping execution"
             fi

.github/workflows/size.yml

@@ -0,0 +1,29 @@
+name: Check File Size
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  file-size-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+      - name: Filter by size
+        # TODO: roll back the web voyager hack
+        run: |
+          large_added_files=$(find ${{ steps.changed-files.outputs.added_files }} -maxdepth 0 -size +1M | grep -v "web_voyager" || true)
+          if [ -n "$large_added_files" ]; then
+            echo "Large files added: $large_added_files"
+            echo "# Large files added:" >> $GITHUB_STEP_SUMMARY
+            echo "$large_added_files" >> $GITHUB_STEP_SUMMARY
+            exit 1
+          fi

.gitignore

@@ -178,5 +178,4 @@ Untitled*.ipynb
 
 Chinook.db
 
-.vercel
-.turbo
+libs/langgraph/out

CONTRIBUTING.md

@@ -49,7 +49,7 @@ gain understanding of concepts and how they interact by showing one way to achie
 
 They should **avoid** giving
 multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
+be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction.
 
 To quote the Diataxis website:
 

Makefile

@@ -0,0 +1,39 @@
+.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc
+
+build-typedoc:
+	cd libs/sdk-js && yarn install --include-dev && yarn typedoc
+	cd libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
+	#  Add links to the monorepo
+	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md
+
+build-docs: build-typedoc
+	poetry run python -m mkdocs build --clean -f docs/mkdocs.yml --strict
+
+serve-clean-docs: clean-docs
+	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
+
+serve-docs: build-typedoc
+	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
+
+clean-docs:
+	find ./docs/docs -name "*.ipynb" -type f -delete
+	rm -rf docs/site
+
+## Run format against the project documentation.
+format-docs:
+	poetry run ruff format docs/docs
+	poetry run ruff check --fix docs/docs
+
+# Check the docs for linting violations
+lint-docs:
+	poetry run ruff format --check docs/docs
+	poetry run ruff check docs/docs
+
+codespell:
+	./docs/codespell_notebooks.sh .
+
+start-services:
+	docker compose -f docs/test-compose.yml up -V --force-recreate --wait --remove-orphans
+
+stop-services:
+	docker compose -f docs/test-compose.yml down

README.md

@@ -1,87 +1,233 @@
-<picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="docs/docs/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="docs/docs/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="docs/docs/static/wordmark_dark.svg" width="80%">
-</picture>
+# 🦜🕸️LangGraph
 
-<div>
-<br>
-</div>
-
-[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
+![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
+⚡ Building language agents as graphs ⚡
+
 > [!NOTE]
-> Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
+> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
 
-LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+## Overview
 
-```bash
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
+
+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.
+
+To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
+
+### Key Features
+
+- **Cycles and Branching**: Implement loops and conditionals in your apps.
+- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
+- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
+- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
+- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+
+## Installation
+
+```shell
 pip install -U langgraph
 ```
 
-To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
+## Example
+
+One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
+
+Let's take a look at a simple example of an agent that can use a search tool.
+
+```shell
+pip install langchain-anthropic
+```
+
+```shell
+export ANTHROPIC_API_KEY=sk-...
+```
+
+Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
+
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=lsv2_sk_...
+```
 
 ```python
-# This code depends on pip install langchain[anthropic]
-from langgraph.prebuilt import create_react_agent
+from typing import Annotated, Literal, TypedDict
 
+from langchain_core.messages import HumanMessage
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
+
+
+# Define the tools for the agent to use
+@tool
 def search(query: str):
     """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
     if "sf" in query.lower() or "san francisco" in query.lower():
         return "It's 60 degrees and foggy."
     return "It's 90 degrees and sunny."
 
-agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+
+tools = [search]
+
+tool_node = ToolNode(tools)
+
+model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
+
+# Define the function that determines whether to continue or not
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state['messages']
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user)
+    return END
+
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    messages = state['messages']
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
+
+
+# Define a new graph
+workflow = StateGraph(MessagesState)
+
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
+
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.add_edge(START, "agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
 )
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("tools", 'agent')
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable.
+# Note that we're (optionally) passing the memory when compiling the graph
+app = workflow.compile(checkpointer=checkpointer)
+
+# Use the Runnable
+final_state = app.invoke(
+    {"messages": [HumanMessage(content="what is the weather in sf")]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
 ```
 
-## Why use LangGraph?
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
+```
 
-LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
+Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
 
-- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
-- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
-- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
+```python
+final_state = app.invoke(
+    {"messages": [HumanMessage(content="what about ny")]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
 
-LangGraph is trusted in production and powering agents for companies like:
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
 
-- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
-- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
-- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
-- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
-- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
+### Step-by-step Breakdown
 
-## LangGraph’s ecosystem
+1. <details>
+    <summary>Initialize the model and tools.</summary>
 
-While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
+    - we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
+    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
+   </details>
 
-- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+2. <details>
+    <summary>Initialize graph with state.</summary>
 
-## Pairing with LangGraph Platform
+    - we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
+    - `MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain `Message` objects, as well as logic for merging the updates from each node into the state
+   </details>
 
-While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
+3. <details>
+    <summary>Define graph nodes.</summary>
 
-LangGraph Platform can help engineering teams:
+    There are two main nodes we need:
 
-- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
-- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
-- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
+      - The `agent` node: responsible for deciding what (if any) actions to take.
+      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
+   </details>
 
-## Additional resources
+4. <details>
+    <summary>Define entry point and graph edges.</summary>
 
-- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
-- [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.
-- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+      First, we need to set the entry point for graph execution - `agent` node.
 
-## Acknowledgements
+      Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
 
-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.
+      - Conditional edge: after the agent is called, we should either:
+        - a. Run tools if the agent said to take an action, OR
+        - b. Finish (respond to the user) if the agent did not ask to run tools
+      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
+   </details>
+
+5. <details>
+    <summary>Compile the graph.</summary>
+
+    - When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
+    - We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
+    </details>
+
+6. <details>
+   <summary>Execute the graph.</summary>
+
+    1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
+    2. The `"agent"` node executes, invoking the chat model.
+    3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
+    4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
+
+        - If `AIMessage` has `tool_calls`, `"tools"` node executes
+        - The `"agent"` node executes again and returns `AIMessage`
+
+    5. Execution progresses to the special `END` value and outputs the final state.
+    And as a result, we get a list of all our chat messages as output.
+   </details>
+
+
+## Documentation
+
+* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
+* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
+* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
+
+## Contributing
+
+For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

docs/.gitignore

@@ -1,4 +1,2 @@
 site/
 docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-.vercel

docs/Makefile

@@ -1,74 +0,0 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
-
-build-typedoc:
-	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
-	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
-	#  Add links to the monorepo
-	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-build-prebuilt:
-	# Use to create an update to date prebuilt page.
-	# Looks up download stats for each of the prebuilt packages and
-	# generates the final prebuilt page.
-	@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
-		set -x; \
-		poetry 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; \
-		set +x; \
-	fi
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/prebuilt.md --language python
-
-build-docs: build-typedoc build-prebuilt
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
-
-llms-text:
-	poetry 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
-
-tests:
-	# Run unit tests
-	poetry run pytest tests/unit_tests
-
-
-vercel-build-docs: install-vercel-deps
-	make build-docs
-
-
-serve-clean-docs: clean-docs
-	poetry 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
-
-clean-docs:
-	find ./docs -name "*.ipynb" -type f -delete
-	rm -rf site
-
-## Run format against the project documentation.
-format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
-
-# Check the docs for linting violations
-lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
-
-codespell:
-	./codespell_notebooks.sh .
-
-start-services:
-	docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
-
-stop-services:
-	docker compose -f test-compose.yml down

docs/README.md

@@ -19,21 +19,23 @@ make serve-docs
 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
 
 ```bash
-python _scripts/prepare_notebooks_for_ci.py
-./_scripts/execute_notebooks.sh
+python docs/_scripts/prepare_notebooks_for_ci.py
+./docs/_scripts/execute_notebooks.sh
 ```
 
 **Note**: if you want to run the notebooks without `%pip install` cells, you can run:
 
 ```bash
-python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
-./_scripts/execute_notebooks.sh
+python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
+./docs/_scripts/execute_notebooks.sh
 ```
 
 `prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
 * when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
 * when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
 
+**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
+
 ## Adding new notebooks
 
 If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
@@ -46,14 +48,14 @@ Then, run
 jupyter execute <path_to_notebook>
 ```
 
-Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
+Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
 
 ## Updating existing notebooks
 
-If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
+If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
 
 To delete cassettes for a notebook, you can run:
 
 ```bash
-rm cassettes/<notebook_name>*
+rm docs/cassettes/<notebook_name>*
 ```

docs/_scripts/_patch.py

@@ -1,93 +0,0 @@
-import functools
-
-from urllib3 import __version__ as urllib3version  # type: ignore[import-untyped]
-from urllib3 import connection  # type: ignore[import-untyped]
-
-
-def _ensure_str(s, encoding="utf-8", errors="strict") -> str:
-    if isinstance(s, str):
-        return s
-
-    if isinstance(s, bytes):
-        return s.decode(encoding, errors)
-    return str(s)
-
-
-# Copied from https://github.com/urllib3/urllib3/blob/1c994dfc8c5d5ecaee8ed3eb585d4785f5febf6e/src/urllib3/connection.py#L231
-def request(self, method, url, body=None, headers=None):
-    """Make the request.
-
-    This function is based on the urllib3 request method, with modifications
-    to handle potential issues when using vcrpy in concurrent workloads.
-
-    Args:
-        self: The HTTPConnection instance.
-        method (str): The HTTP method (e.g., 'GET', 'POST').
-        url (str): The URL for the request.
-        body (Optional[Any]): The body of the request.
-        headers (Optional[dict]): Headers to send with the request.
-
-    Returns:
-        The result of calling the parent request method.
-    """
-    # Update the inner socket's timeout value to send the request.
-    # This only triggers if the connection is reused.
-    if getattr(self, "sock", None) is not None:
-        self.sock.settimeout(self.timeout)
-
-    if headers is None:
-        headers = {}
-    else:
-        # Avoid modifying the headers passed into .request()
-        headers = headers.copy()
-    if "user-agent" not in (_ensure_str(k.lower()) for k in headers):
-        headers["User-Agent"] = connection._get_default_user_agent()
-    # The above is all the same ^^^
-    # The following is different:
-    return self._parent_request(method, url, body=body, headers=headers)
-
-
-_PATCHED = False
-
-
-def patch_urllib3():
-    """Patch the request method of urllib3 to avoid type errors when using vcrpy.
-
-    In concurrent workloads (such as the tracing background queue), the
-    connection pool can get in a state where an HTTPConnection is created
-    before vcrpy patches the HTTPConnection class. In urllib3 >= 2.0 this isn't
-    a problem since they use the proper super().request(...) syntax, but in older
-    versions, super(HTTPConnection, self).request is used, resulting in a TypeError
-    since self is no longer a subclass of "HTTPConnection" (which at this point
-    is vcr.stubs.VCRConnection).
-
-    This method patches the class to fix the super() syntax to avoid mixed inheritance.
-    In the case of the LangSmith tracing logic, it doesn't really matter since we always
-    exclude cache checks for calls to LangSmith.
-
-    The patch is only applied for urllib3 versions older than 2.0.
-    """
-    global _PATCHED
-    if _PATCHED:
-        return
-    from packaging import version
-
-    if version.parse(urllib3version) >= version.parse("2.0"):
-        _PATCHED = True
-        return
-
-    # Lookup the parent class and its request method
-    parent_class = connection.HTTPConnection.__bases__[0]
-    parent_request = parent_class.request
-
-    def new_request(self, *args, **kwargs):
-        """Handle parent request.
-
-        This method binds the parent's request method to self and then
-        calls our modified request function.
-        """
-        self._parent_request = functools.partial(parent_request, self)
-        return request(self, *args, **kwargs)
-
-    connection.HTTPConnection.request = new_request
-    _PATCHED = True

docs/_scripts/add_translation.py

@@ -1,157 +0,0 @@
-"""Add typescript translation to a given markdown file."""
-
-import argparse
-import re
-
-import requests
-from langchain_anthropic import ChatAnthropic
-
-URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
-response = requests.get(URL)
-response.raise_for_status()
-reference_snippets = response.text
-
-model = ChatAnthropic(model="claude-3-5-sonnet-latest")
-
-
-def _get_tqdm():
-    try:
-        from tqdm import tqdm
-    except ImportError:
-        # If not available return a simple identity function
-        def tqdm(iterable, *args, **kwargs):
-            return iterable
-
-    return tqdm
-
-
-_tqdm = _get_tqdm()
-
-opening_pattern = re.compile(r"^\s*```python(?:\s+.*)?\s*$")
-closing_pattern = re.compile(r"^\s*```\s*$")
-
-
-def extract_python_snippets(markdown: str) -> list[str]:
-    """
-    Extract all python code blocks (including their fence lines) from the markdown content.
-    A python block is defined as any block that starts with a line containing an opening fence
-    with '```python' (optionally with extra parameters) and ends with a closing fence '```'.
-    """
-    snippets = []
-    inside_block = False
-    current_snippet = []
-
-    for line in markdown.splitlines(keepends=True):
-        if not inside_block:
-            if opening_pattern.match(line):
-                inside_block = True
-                current_snippet = [line]
-        else:
-            current_snippet.append(line)
-            if closing_pattern.match(line):
-                inside_block = False
-                snippets.append("".join(current_snippet))
-                current_snippet = []
-    return snippets
-
-
-def translate_snippet(python_snippet: str) -> str:
-    """Translate a python code block into a TypeScript code block using Langchain.
-    The response is expected to be a properly fenced TypeScript code block (i.e.
-    starting with ```typescript and ending with ```).
-    """
-    ai_message = model.invoke(
-        [
-            {
-                "role": "system",
-                "content": (
-                    f"You have access to the following up-to-date example TypeScript code "
-                    f"snippets that show examples of building with langgraph "
-                    f"and langchain:\n\n{reference_snippets}\n\n"
-                    "Use this context to translate the following Python code to equivalent "
-                    "TypeScript. Ensure that your output is a valid fenced TypeScript "
-                    "code block (i.e. starts with ```typescript and ends with ```)."
-                ),
-            },
-            {
-                "role": "user",
-                "content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
-            },
-        ]
-    )
-
-    # Use a regular expression to search for a TypeScript code block in the response.
-    pattern = r"```typescript\s*(.*?)\s*```"
-    match = re.search(pattern, ai_message.content, re.DOTALL)
-    if match:
-        # Reconstruct the code block with proper fences.
-        typescript_code = match.group(1).strip()
-        return f"```typescript\n{typescript_code}\n```"
-    else:
-        raise ValueError("No TypeScript code block found in the model's response.")
-
-
-def insert_translations_into_markdown(
-    markdown: str, typescript_snippets: list[str]
-) -> str:
-    """Walks through the original markdown content and, after each
-    Python snippet block, inserts the corresponding translated TypeScript snippet.
-    It assumes that the ordering of the Python snippets
-    (from extract_python_snippets) matches the order they appear in the markdown.
-    """
-    output_lines = []
-    lines = markdown.splitlines(keepends=True)
-    inside_block = False
-    snippet_index = 0
-
-    for line in lines:
-        output_lines.append(line)
-        if not inside_block and opening_pattern.match(line):
-            # We've encountered the start of a python code block.
-            inside_block = True
-        elif inside_block:
-            if closing_pattern.match(line):
-                # End of a python snippet block.
-                inside_block = False
-                if snippet_index < len(typescript_snippets):
-                    # Insert an extra newline for clarity, then the translated TypeScript snippet.
-                    output_lines.append("\n")
-                    output_lines.append(typescript_snippets[snippet_index])
-                    output_lines.append("\n")
-                    snippet_index += 1
-    return "".join(output_lines)
-
-
-def main(file_path: str) -> None:
-    # Read the markdown file.
-    with open(file_path, "r") as f:
-        markdown_content = f.read()
-
-    # 1. Extract all Python snippets.
-    python_snippets = extract_python_snippets(markdown_content)[:1]
-
-    # 2. Translate each Python snippet to TypeScript.
-    typescript_snippets = []
-    # Replace with .batch() for faster translation
-    for python_snippet in _tqdm(python_snippets):
-        ts_snippet = translate_snippet(python_snippet)
-        typescript_snippets.append(ts_snippet)
-
-    # 3. Insert the TypeScript translations after their respective Python snippets.
-    updated_markdown = insert_translations_into_markdown(
-        markdown_content, typescript_snippets
-    )
-
-    # Overwrite the original markdown file with the updated content.
-    with open(file_path, "w") as f:
-        f.write(updated_markdown)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
-    )
-    parser.add_argument("file_path", type=str, help="Path to the markdown file.")
-    args = parser.parse_args()
-
-    main(args.file_path)

docs/_scripts/execute_notebooks.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Read the list of notebooks to skip from the JSON file
-SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
+SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
 
 # Function to execute a single notebook
 execute_notebook() {
@@ -24,10 +24,10 @@ export -f execute_notebook
 
 # Check if custom notebook paths are provided
 if [ $# -gt 0 ]; then
-    notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+    notebooks="$@"
 else
     # Find all notebooks and filter out those in the skip list
-    notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+    notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
 fi
 
 # Execute notebooks sequentially

docs/_scripts/generate_api_reference_links.py

@@ -1,261 +0,0 @@
-import ast
-import importlib
-import logging
-import re
-from functools import lru_cache
-from typing import List, Optional
-
-from typing_extensions import TypedDict
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-# Base URL for all class documentation
-_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
-_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
-
-
-# (alias/re-exported modules, source module, class, docs namespace)
-MANUAL_API_REFERENCES_LANGGRAPH = [
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.chat_agent_executor",
-        "create_react_agent",
-        "prebuilt",
-    ),
-    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.tool_node",
-        "tools_condition",
-        "prebuilt",
-    ),
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.tool_node",
-        "InjectedState",
-        "prebuilt",
-    ),
-    # Graph
-    (["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
-    (["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
-    (["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
-    (["langgraph.graph"], "langgraph.constants", "START", "constants"),
-    (["langgraph.graph"], "langgraph.constants", "END", "constants"),
-    (["langgraph.constants"], "langgraph.types", "Send", "types"),
-    (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
-    (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
-    (["langgraph.constants"], "langgraph.types", "Command", "types"),
-    (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
-    (["langgraph.func"], "langgraph.func", "task", "func"),
-    (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamMode", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamWriter", "types"),
-    ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
-    ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
-    ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
-    ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
-]
-
-WELL_KNOWN_LANGGRAPH_OBJECTS = {
-    (module_, class_): (source_module, namespace)
-    for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
-    for module_ in modules + [source_module]
-}
-
-
-@lru_cache(maxsize=10_000)
-def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
-    """Get full module name using inspect, with LRU cache to memoize results."""
-    try:
-        module = importlib.import_module(module_path)
-        symbol = getattr(module, class_name)
-        # First check the __module__ attribute on the symbol.
-        mod_name = getattr(symbol, "__module__", None)
-        # If __module__ is not set or comes from typing,
-        # assume the definition is in module_path.
-        if mod_name is None or mod_name.startswith("typing"):
-            return module_path
-        return mod_name
-    except AttributeError as e:
-        logger.warning(f"API Reference: Could not find module for {class_name}, {e}")
-        return None
-    except ImportError as e:
-        logger.warning(f"API Reference: Failed to load for class {class_name}, {e}")
-        return None
-
-
-class ImportInformation(TypedDict):
-    imported: str  # The name of the class that was imported.
-    source: str  # The full module path from which the class was imported.
-    docs: str  # The URL pointing to the class's documentation.
-    path: str  # The path of the file where the markdown content originated.
-
-
-def get_imports(code: str, path: str) -> List[ImportInformation]:
-    """Retrieve all import references from the given code for specified ecosystems.
-
-    Args:
-        code: The source code from which to extract import references.
-        path: The path of the file where the markdown content originated.
-
-    Returns:
-        A list of import information for each import found.
-    """
-    # Parse the code into an AST.
-    try:
-        tree = ast.parse(code)
-    except SyntaxError:
-        return []
-
-    found_imports = []
-
-    # Walk through the AST and process ImportFrom nodes.
-    for node in ast.walk(tree):
-        if isinstance(node, ast.ImportFrom):
-            # node.module is the source module.
-            if node.module is None:
-                continue
-            for alias in node.names:
-                if not (
-                    node.module.startswith("langchain")
-                    or node.module.startswith("langgraph")
-                ):
-                    continue
-
-                found_imports.append(
-                    {
-                        "source": node.module,
-                        # alias.name is the original name even if an alias exists.
-                        "imported": alias.name,
-                    }
-                )
-
-    imports: list[ImportInformation] = []
-
-    for found_import in found_imports:
-        module = found_import["source"]
-
-        if module.startswith("langchain"):
-            # Handles things like `langchain` or `langchain_anthropic`
-            package_ecosystem = "langchain"
-        elif module.startswith("langgraph"):
-            package_ecosystem = "langgraph"
-        else:
-            continue
-
-        class_name = found_import["imported"]
-        module_path = _get_full_module_name(module, class_name)
-        if not module_path:
-            continue
-        if len(module_path.split(".")) < 2:
-            continue
-
-        if package_ecosystem == "langchain":
-            pkg = module_path.split(".")[0].replace("langchain_", "")
-            top_level_mod = module_path.split(".")[1]
-
-            url = (
-                _LANGCHAIN_API_REFERENCE
-                + pkg
-                + "/"
-                + top_level_mod
-                + "/"
-                + module_path
-                + "."
-                + class_name
-                + ".html"
-            )
-        elif package_ecosystem == "langgraph":
-            if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
-                # Likely not documented yet
-                continue
-
-            source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
-                (module, class_name)
-            ]
-            url = (
-                _LANGGRAPH_API_REFERENCE
-                + namespace
-                + "/#"
-                + source_module
-                + "."
-                + class_name
-            )
-        else:
-            raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
-
-        # Add the import information to our list
-        imports.append(
-            {
-                "imported": class_name,
-                "source": module,
-                "docs": url,
-                "path": path,
-            }
-        )
-
-    return imports
-
-
-def update_markdown_with_imports(markdown: str, path: str) -> str:
-    """Update markdown to include API reference links for imports in Python code blocks.
-
-    This function scans the markdown content for Python code blocks, extracts any
-    imports, and appends links to their API documentation.
-
-    Args:
-        markdown: The markdown content to process.
-        path: The path of the file where the markdown content originated.
-
-    Returns:
-        Updated markdown with API reference links appended to Python code blocks.
-
-    Example:
-        Given a markdown with a Python code block:
-
-        ```python
-        from langchain.nlp import TextGenerator
-        ```
-        This function will append an API reference link to the `TextGenerator` class
-        from the `langchain.nlp` module if it's recognized.
-    """
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```",
-        re.DOTALL,
-    )
-
-    def replace_code_block(match: re.Match) -> str:
-        """Replace the matched code block with additional API reference links if imports are found.
-
-        Args:
-            match (re.Match): The regex match object containing the code block.
-
-        Returns:
-            str: The modified code block with API reference links appended if applicable.
-        """
-        indent = match.group("indent")
-        code_block = match.group("code")
-        # Retrieve import information from the code block
-        imports = get_imports(code_block, "__unused__")
-
-        original_code_block = match.group(0)
-        # If no imports are found, return the original code block
-        if not imports:
-            return original_code_block
-
-        # Generate API reference links for each import
-        api_links = " | ".join(
-            f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
-        )
-        # Return the code block with appended API reference links
-        return f"{original_code_block}\n\n{indent}API Reference: {api_links}"
-
-    # Apply the replace_code_block function to all matches in the markdown
-    updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
-    return updated_markdown

docs/_scripts/generate_llms_text.py

@@ -1,90 +0,0 @@
-"""Experimental script to generate consolidated llms text from the docs."""
-
-import glob
-import os
-
-from mkdocs.structure.files import File
-from mkdocs.structure.pages import Page
-
-from _scripts.notebook_hooks import _on_page_markdown_with_config
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-# Get source directory (parent of HERE / docs)
-SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
-
-
-def _make_llms_text(output_file: str) -> str:
-    """Generate a consolidated text file from markdown/notebook files for LLM training.
-
-    Args:
-        output_file: Path to output the consolidated text file
-    """
-    # Collect all markdown and notebook files
-    relative_paths = [
-        # Files relative to docs/docs/
-        "tutorials/introduction.ipynb",
-    ]
-    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
-
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
-    )
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
-    )
-    # Add all concepts
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
-    )
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.ipynb"), recursive=True)
-    )
-
-    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)
-
-        # Create File and Page objects to match mkdocs structure
-        file_obj = File(
-            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
-        )
-        page = Page(
-            title="",
-            file=file_obj,
-            config={},
-        )
-
-        # Read raw content
-        with open(file_path, "r", encoding="utf-8") as f:
-            content = f.read()
-
-        # Convert to markdown without logic to resolve API references
-        processed_content = _on_page_markdown_with_config(
-            content, page, add_api_references=False, remove_base64_images=True
-        )
-        if processed_content:
-            # Add file name
-            all_content.append(f"---\n{rel_path}\n---")
-            # Add content
-            all_content.append(processed_content)
-
-    # Write consolidated output
-    with open(output_file, "w", encoding="utf-8") as f:
-        f.write("\n\n".join(all_content))
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description=(
-            "Generate consolidated text file from markdown/notebook files for LLMs."
-        )
-    )
-    parser.add_argument("output_file", help="Path to output the consolidated text file")
-
-    args = parser.parse_args()
-    _make_llms_text(args.output_file)

docs/_scripts/notebook_convert.py

@@ -1,363 +0,0 @@
-import ast
-import os
-import re
-from pathlib import Path
-from typing import Literal
-
-import nbformat
-from nbconvert.exporters import MarkdownExporter
-from nbconvert.preprocessors import Preprocessor
-
-
-def _uses_input(source: str) -> bool:
-    """Parse the source code to determine if it uses the input() function."""
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        # If there's a syntax error, assume input() might be present to be safe.
-        return False
-
-    for node in ast.walk(tree):
-        if isinstance(node, ast.Call):
-            # Check if the function called is named 'input'
-            if isinstance(node.func, ast.Name) and node.func.id == "input":
-                return True
-    return False
-
-
-def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.:w
-
-    - Lines starting with "%%capture" are ignored.
-    - Lines starting with "%pip" are rewritten by removing the leading "%" character.
-    - Any other non-empty line causes a NotImplementedError.
-
-    Args:
-        code (str): The original code block.
-
-    Returns:
-        str: The transformed code block.
-
-    Raises:
-        NotImplementedError: If a line doesn't start with either "%%capture" or "%pip".
-    """
-    rewritten_lines = []
-
-    for line in code.splitlines():
-        stripped = line.strip()
-        # Skip empty lines
-        if not stripped:
-            continue
-        # Ignore %%capture lines
-        if stripped.startswith("%%capture"):
-            continue
-        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%pip"):
-            # Drop the leading '%' character
-            rewritten_lines.append(stripped[1:])
-        # Anything else is not supported
-        else:
-            raise NotImplementedError(f"Unhandled line: {line}")
-
-    return "\n".join(rewritten_lines)
-
-
-class PrintCallVisitor(ast.NodeVisitor):
-    """
-    This visitor sets self.has_print to True if it encounters a call
-    to a print within the global scope.
-
-    This should catch calls to print(), print_stream(), etc. (Prefixed with "print").
-
-    May have some false positives, but it's not meant to be perfect.
-
-    Temporary code for notebook conversion.
-    """
-
-    def __init__(self):
-        self.has_print = False
-        self.scope_level = 0  # counter to track whether we're inside a def/lambda
-
-    def visit_FunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_AsyncFunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Lambda(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_ClassDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Call(self, node):
-        # Only consider calls when not inside a function definition.
-        if self.scope_level == 0:
-            if isinstance(node.func, ast.Name) and node.func.id.startswith("print"):
-                self.has_print = True
-        self.generic_visit(node)
-
-
-def _has_output(source: str) -> bool:
-    """Determine if the code block is expected to produce output.
-
-    Args:
-        source (str): The source code of the code block.
-
-    Returns:
-        True if the code block is expected to produce output, False otherwise.
-
-        Must meet the following conditions:
-
-        1. There is a call to a printing function (name starts with "print")
-           that is not inside a function definition.
-        2. The last top-level statement is an expression that is valid if:
-         - It is any expression (including calls) AND
-         - It is NOT a call to `display(...)`.
-
-        `display` isn't handled currently by markdown-exec
-    """
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        return False
-
-    # Condition (1): Check for a global print-like call.
-    visitor = PrintCallVisitor()
-    visitor.visit(tree)
-    condition_a = visitor.has_print
-
-    # Condition (2): Check the last top-level statement.
-    condition_b = False
-    if tree.body:
-        last_stmt = tree.body[-1]
-        if isinstance(last_stmt, ast.Expr):
-            # If the expression is a call, ensure it's not a call to "display"
-            if isinstance(last_stmt.value, ast.Call):
-                if (
-                    isinstance(last_stmt.value.func, ast.Name)
-                    and last_stmt.value.func.id == "display"
-                ):
-                    condition_b = False  # exclude display-wrapped expressions
-                else:
-                    condition_b = True
-            else:
-                # Any other expression qualifies.
-                condition_b = True
-
-    return condition_a or condition_b
-
-
-def _convert_links_in_markdown(markdown: str) -> str:
-    """Convert links present in notebook markdown cells to standardized format.
-
-    We want to update markdown links code cells by linking to markdown
-    files rather than assuming that the link is to the finalized HTML.
-
-    This code is needed temporarily since the markdown links that are present
-    in ipython notebooks do not follow the same conventions as regular markdown
-    files in mkdocs (which should link to a .md file).
-    """
-
-    # Define the regex pattern in parts for clarity:
-    pattern = (
-        r"(?<!!)"  # Negative lookbehind: ensure the link is not an image (i.e., doesn't start with "!")
-        r"\["  # Literal '[' indicating the start of the link text.
-        r"(?P<text>[^\]]*)"  # Named group 'text': match any characters except ']', representing the link text.
-        r"\]"  # Literal ']' indicating the end of the link text.
-        r"\("  # Literal '(' indicating the start of the URL.
-        r"(?![^\)]*//)"  # Negative lookahead: ensure that the URL does not contain '//' (skip absolute URLs).
-        r"(?P<url>[^)]*)"  # Named group 'url': match any characters except ')', representing the URL.
-        r"\)"  # Literal ')' indicating the end of the URL.
-    )
-
-    def custom_replacement(match):
-        """logic will correct the link format used in ipython notebooks
-
-        Ipython notebooks were being converted directly into HTML links
-        instead of markdown links that retain the markdown extension.
-
-        It needs to handle the following cases:
-        - optional fragments (e.g., `#section`)
-            e.g., `[text](url/#section)` -> `[text](url.md#section)`
-            e.g., `[text](url#section)` -> `[text](url.md#section)`
-        - relative paths (e.g., `../path/to/file`) need to be denested by 1 level
-        """
-        text = match.group("text")
-        url = match.group("url")
-
-        if url.startswith("../"):
-            # we strip the "../" from the start of the URL
-            # We only need to denest one level.
-            url = url[3:]
-
-        url = url.rstrip("/")  # Strip `/` from the end of the URL
-
-        # if url has a fragment
-        if "#" in url:
-            url, fragment = url.split("#")
-            url = url.rstrip("/")
-            # Strip `/` from the end of the URL
-            return f"[{text}]({url}.md#{fragment})"
-        # Otherwise add the .md extension
-        return f"[{text}]({url}.md)"
-
-    return re.sub(
-        pattern,
-        custom_replacement,
-        markdown,
-    )
-
-
-class EscapePreprocessor(Preprocessor):
-    def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.markdown_exec_migration = markdown_exec_migration
-
-    def preprocess_cell(self, cell, resources, cell_index):
-        if cell.cell_type == "markdown":
-            if not self.markdown_exec_migration:
-                # Old logic is to convert ipynb links to HTML links
-                cell.source = re.sub(
-                    r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
-                    r'<a href="\2">\1</a>',
-                    cell.source,
-                )
-            else:
-                cell.source = _convert_links_in_markdown(cell.source)
-
-            # Fix image paths in <img> tags
-            cell.source = re.sub(
-                r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
-            )
-
-        elif cell.cell_type == "code":
-            # Determine if the cell has bash or cell magic
-            source = cell.source
-            is_exec = not (
-                source.startswith("%") or source.startswith("!") or _uses_input(source)
-            )
-            cell.metadata["exec"] = is_exec
-
-            if self.markdown_exec_migration:
-                # For markdown exec migration we'll re-write cell magic as bash commands
-                if source.startswith("%%"):
-                    cell.source = _rewrite_cell_magic(source)
-                    cell.metadata["language"] = "shell"
-
-                cell.metadata["has_output"] = _has_output(source)
-
-            # Remove noqa comments
-            cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
-            # escape ``` in code
-            # This is needed because the markdown exporter will wrap code blocks in
-            # triple backticks, which will break the markdown output if the code block
-            # contains triple backticks.
-            cell.source = cell.source.replace("```", r"\`\`\`")
-            # escape ``` in output
-            if "outputs" in cell:
-                filter_out = set()
-                for i, output in enumerate(cell["outputs"]):
-                    if "text" in output:
-                        if not output["text"].strip():
-                            filter_out.add(i)
-                            continue
-
-                        value = output["text"].replace("```", r"\`\`\`")
-                        # handle a funky case w/ references in text
-                        value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
-                        output["text"] = value
-                    elif "data" in output:
-                        for key, value in output["data"].items():
-                            if isinstance(value, str):
-                                value = value.replace("```", r"\`\`\`")
-                                # handle a funky case w/ references in text
-                                output["data"][key] = re.sub(
-                                    r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
-                                )
-                cell["outputs"] = [
-                    output
-                    for i, output in enumerate(cell["outputs"])
-                    if i not in filter_out
-                ]
-
-        return cell, resources
-
-
-class ExtractAttachmentsPreprocessor(Preprocessor):
-    """
-    Extracts all of the outputs from the notebook file.  The extracted
-    outputs are returned in the 'resources' dictionary.
-    """
-
-    def preprocess_cell(self, cell, resources, cell_index):
-        """
-        Apply a transformation on each cell,
-        Parameters
-        ----------
-        cell : NotebookNode cell
-            Notebook cell being processed
-        resources : dictionary
-            Additional resources used in the conversion process.  Allows
-            preprocessors to pass variables into the Jinja engine.
-        cell_index : int
-            Index of the cell being processed (see base.py)
-        """
-
-        # Get files directory if it has been specified
-
-        # Make sure outputs key exists
-        if not isinstance(resources["outputs"], dict):
-            resources["outputs"] = {}
-
-        # Loop through all of the attachments in the cell
-        for name, attach in cell.get("attachments", {}).items():
-            for mime, data in attach.items():
-                if mime not in {
-                    "image/png",
-                    "image/jpeg",
-                    "image/svg+xml",
-                    "application/pdf",
-                }:
-                    continue
-
-                # attachments are pre-rendered. Only replace markdown-formatted
-                # images with the following logic
-                attach_str = f"({name})"
-                if attach_str in cell.source:
-                    data = f"(data:{mime};base64,{data})"
-                    cell.source = cell.source.replace(attach_str, data)
-
-        return cell, resources
-
-
-exporter = MarkdownExporter(
-    preprocessors=[
-        EscapePreprocessor,
-        ExtractAttachmentsPreprocessor,
-    ],
-    template_name="mdoutput",
-    extra_template_basedirs=[
-        os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
-    ],
-)
-
-
-def convert_notebook(
-    notebook_path: Path,
-    mode: Literal["markdown", "exec"] = "markdown",
-) -> str:
-    with open(notebook_path) as f:
-        nb = nbformat.read(f, as_version=4)
-
-    nb.metadata.mode = mode
-    body, _ = exporter.from_notebook_node(nb)
-    return body

docs/_scripts/notebook_convert_templates/mdoutput/conf.json

@@ -1,5 +0,0 @@
-{
-  "mimetypes": {
-    "text/markdown": true
-  }
-}

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -1,33 +0,0 @@
-{% extends 'markdown/index.md.j2' %}
-
-{%- block traceback_line -%}
-```output
-{{ line.rstrip() | strip_ansi }}
-```
-{%- endblock traceback_line -%}
-
-{%- block stream -%}
-```output
-{{ output.text.rstrip() }}
-```
-{%- endblock stream -%}
-
-{%- block data_text scoped -%}
-```output
-{{ output.data['text/plain'].rstrip() }}
-```
-{%- endblock data_text -%}
-
-{%- block data_html scoped -%}
-```html
-{{ output.data['text/html'] | safe }} 
-```
-{%- endblock data_html -%}
-
-{%- block data_jpg scoped -%}
-![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
-{%- endblock data_jpg -%}
-
-{%- block data_png scoped -%}
-![](data:image/png;base64,{{ output.data['image/png'] }})
-{%- endblock data_png -%}

docs/_scripts/notebook_hooks.py

@@ -1,255 +0,0 @@
-import logging
-import os
-import posixpath
-import re
-from typing import Any, Dict
-
-from mkdocs.structure.files import Files, File
-from mkdocs.structure.pages import Page
-
-from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
-
-logger = logging.getLogger(__name__)
-logging.basicConfig()
-logger.setLevel(logging.INFO)
-DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
-
-
-REDIRECT_MAP = {
-    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
-    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
-    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
-    # cloud redirects
-    "cloud/index.md": "concepts/index.md#langgraph-platform",
-    "cloud/how-tos/index.md": "how-tos/index.md#langgraph-platform",
-    "cloud/concepts/api.md": "concepts/langgraph_server.md",
-    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
-    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
-}
-
-
-class NotebookFile(File):
-    def is_documentation_page(self):
-        return True
-
-
-def on_files(files: Files, **kwargs: Dict[str, Any]):
-    if DISABLED:
-        return files
-    new_files = Files([])
-    for file in files:
-        if file.src_path.endswith(".ipynb"):
-            new_file = NotebookFile(
-                path=file.src_path,
-                src_dir=file.src_dir,
-                dest_dir=file.dest_dir,
-                use_directory_urls=file.use_directory_urls,
-            )
-            new_files.append(new_file)
-        else:
-            new_files.append(file)
-    return new_files
-
-
-def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
-    """Add the path to the code blocks."""
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
-        r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
-        r"(?P=indent)```"  # Match closing backticks with the same indentation
-    )
-
-    def replace_code_block_header(match: re.Match) -> str:
-        indent = match.group("indent")
-        language = match.group("language")
-        attributes = match.group("attributes").rstrip()
-
-        if 'exec="on"' not in attributes:
-            # Return original code block
-            return match.group(0)
-
-        code = match.group("code")
-        return f'{indent}```{language} {attributes} path="{page.file.src_path}"\n{code}{indent}```'
-
-    return code_block_pattern.sub(replace_code_block_header, markdown)
-
-
-def _highlight_code_blocks(markdown: str) -> str:
-    """Find code blocks with highlight comments and add hl_lines attribute.
-
-    Args:
-        markdown: The markdown content to process.
-
-    Returns:
-        updated Markdown code with code blocks containing highlight comments
-        updated to use the hl_lines attribute.
-    """
-    # Pattern to find code blocks with highlight comments and without
-    # existing hl_lines for Python and JavaScript
-    # Pattern to find code blocks with highlight comments, handling optional indentation
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
-        r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
-        r"(?P=indent)```"  # Match closing backticks with the same indentation
-    )
-
-    def replace_highlight_comments(match: re.Match) -> str:
-        indent = match.group("indent")
-        language = match.group("language")
-        code_block = match.group("code")
-        attributes = match.group("attributes").rstrip()
-
-        # Account for a case where hl_lines is manually specified
-        if "hl_lines" in attributes:
-            # Return original code block
-            return match.group(0)
-
-        lines = code_block.split("\n")
-        highlighted_lines = []
-
-        # Skip initial empty lines
-        while lines and not lines[0].strip():
-            lines.pop(0)
-
-        lines_to_keep = []
-
-        comment_syntax = (
-            "# highlight-next-line"
-            if language in ["py", "python"]
-            else "// highlight-next-line"
-        )
-
-        for line in lines:
-            if comment_syntax in line:
-                count = len(lines_to_keep) + 1
-                highlighted_lines.append(str(count))
-            else:
-                lines_to_keep.append(line)
-
-        # Reconstruct the new code block
-        new_code_block = "\n".join(lines_to_keep)
-
-        # Construct the full code block that also includes
-        # the fenced code block syntax.
-        opening_fence = f"```{language}"
-
-        if attributes:
-            opening_fence += f" {attributes}"
-
-        if highlighted_lines:
-            opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
-
-        return (
-            # The indent and opening fence
-            f"{indent}{opening_fence}\n"
-            # The indent and terminating \n is already included in the code block
-            f"{new_code_block}"
-            f"{indent}```"
-        )
-
-    # Replace all code blocks in the markdown
-    markdown = code_block_pattern.sub(replace_highlight_comments, markdown)
-    return markdown
-
-
-def _on_page_markdown_with_config(
-    markdown: str,
-    page: Page,
-    *,
-    add_api_references: bool = True,
-    remove_base64_images: bool = False,
-    **kwargs: Any,
-) -> str:
-    if DISABLED:
-        return markdown
-
-    if page.file.src_path.endswith(".ipynb"):
-        # logger.info("Processing Jupyter notebook: %s", page.file.src_path)
-        markdown = convert_notebook(page.file.abs_src_path)
-
-    # Append API reference links to code blocks
-    if add_api_references:
-        markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
-    # Apply highlight comments to code blocks
-    markdown = _highlight_code_blocks(markdown)
-
-    # Add file path as an attribute to code blocks that are executable.
-    # This file path is used to associate fixtures with the executable code
-    # which can be used in CI to test the docs without making network requests.
-    markdown = _add_path_to_code_blocks(markdown, page)
-
-    if remove_base64_images:
-        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
-
-    return markdown
-
-
-def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
-    )
-
-
-# redirects
-
-HTML_TEMPLATE = """
-<!doctype html>
-<html lang="en">
-<head>
-    <meta charset="utf-8">
-    <title>Redirecting...</title>
-    <link rel="canonical" href="{url}">
-    <meta name="robots" content="noindex">
-    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
-    <meta http-equiv="refresh" content="0; url={url}">
-</head>
-<body>
-Redirecting...
-</body>
-</html>
-"""
-
-
-def write_html(site_dir, old_path, new_path):
-    """Write an HTML file in the site_dir with a meta redirect to the new page"""
-    # Determine all relevant paths
-    old_path_abs = os.path.join(site_dir, old_path)
-    old_dir_abs = os.path.dirname(old_path_abs)
-
-    # Create parent directories if they don't exist
-    if not os.path.exists(old_dir_abs):
-        os.makedirs(old_dir_abs)
-
-    # Write the HTML redirect file in place of the old file
-    content = HTML_TEMPLATE.format(url=new_path)
-    with open(old_path_abs, "w", encoding="utf-8") as f:
-        f.write(content)
-
-
-# Create HTML files for redirects after site dir has been built
-def on_post_build(config):
-    use_directory_urls = config.get("use_directory_urls")
-    for page_old, page_new in REDIRECT_MAP.items():
-        page_old = page_old.replace(".ipynb", ".md")
-        page_new = page_new.replace(".ipynb", ".md")
-        page_new_before_hash, hash, suffix = page_new.partition("#")
-        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
-            os.sep, "/"
-        )
-        new_html_path = File(page_new_before_hash, "", "", True).url
-        new_html_path = (
-            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-            + hash
-            + suffix
-        )
-        write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -7,7 +7,7 @@ import click
 import nbformat
 
 logger = logging.getLogger(__name__)
-NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
+NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
 DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
 
@@ -19,37 +19,31 @@ BLOCKLIST_COMMANDS = (
 )
 
 NOTEBOOKS_NO_CASSETTES = (
-    "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/many-tools.ipynb"
+    "docs/docs/how-tos/visualization.ipynb",
+    "docs/docs/how-tos/many-tools.ipynb"
 )
 
 NOTEBOOKS_NO_EXECUTION = [
     # this uses a user provided project name for langsmith
-    "docs/tutorials/tnt-llm/tnt-llm.ipynb",
+    "docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
     # this uses langsmith datasets
-    "docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
+    "docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
     # this uses browser APIs
-    "docs/tutorials/web-navigation/web_voyager.ipynb",
+    "docs/docs/tutorials/web-navigation/web_voyager.ipynb",
     # these RAG guides use an ollama model
-    "docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
-    "docs/tutorials/rag/langgraph_crag_local.ipynb",
-    "docs/tutorials/rag/langgraph_self_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
-    "docs/tutorials/usaco/usaco.ipynb",
-    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
-    "docs/how-tos/autogen-integration.ipynb",
-    "docs/how-tos/autogen-integration-functional.ipynb",
+    "docs/docs/tutorials/usaco/usaco.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
-    "docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
-    "docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
-    "docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
-    "docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
-    "docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
-    "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
-    "docs/tutorials/tot/tot.ipynb",
-    "docs/how-tos/visualization.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb",  # taking a very long time to run
+    "docs/docs/tutorials/customer-support/customer-support.ipynb",  # user input - update
+    "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
+    "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR
 ]
 
 
@@ -76,13 +70,10 @@ def is_comment(code: str) -> bool:
     return code.strip().startswith("#")
 
 
-def has_blocklisted_command(code: str, metadata: dict) -> bool:
-    if 'hide_from_vcr' in metadata:
-        return True
-    
+def has_blocklisted_command(code: str) -> bool:
     code = code.strip()
-    for blocklisted_pattern in BLOCKLIST_COMMANDS:
-        if blocklisted_pattern in code:
+    for blocklisted_command in BLOCKLIST_COMMANDS:
+        if blocklisted_command in code:
             return True
     return False
 
@@ -92,7 +83,6 @@ def add_vcr_to_notebook(
 ) -> nbformat.NotebookNode:
     """Inject `with vcr.cassette` into each code cell of the notebook."""
 
-    uses_langsmith = False
     # Inject VCR context manager into each code cell
     for idx, cell in enumerate(notebook.cells):
         if cell.cell_type != "code":
@@ -118,7 +108,7 @@ def add_vcr_to_notebook(
         if all(is_comment(line) or not line.strip() for line in lines):
             continue
 
-        if has_blocklisted_command(cell.source, cell.metadata):
+        if has_blocklisted_command(cell.source):
             continue
 
         cell_id = cell.get("id", idx)
@@ -127,30 +117,14 @@ def add_vcr_to_notebook(
             f"    {line}" for line in lines
         )
 
-        if any("hub.pull" in line or "from langsmith import" in line for line in lines):
-            uses_langsmith = True
-
     # Add import statement
-    vcr_import_lines = []
-    if uses_langsmith:
-        vcr_import_lines.extend([
-            # patch urllib3 to handle vcr errors, see more here:
-            # https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
-            "import sys",
-            f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
-            "import _patch as patch_urllib3",
-            "patch_urllib3.patch_urllib3()",
-        ])
-
-    vcr_import_lines.extend([
+    vcr_import_lines = [
         "import nest_asyncio",
         "nest_asyncio.apply()",
         "import vcr",
         "import msgpack",
         "import base64",
         "import zlib",
-        "import os",
-        "os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
         "custom_vcr = vcr.VCR()",
         "",
         "def compress_data(data, compression_level=9):",
@@ -172,8 +146,7 @@ def add_vcr_to_notebook(
         "",
         "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
         "custom_vcr.serializer = 'advanced_compressed'",
-    ])
-
+    ]
     import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
     import_cell.pop("id", None)
     notebook.cells.insert(0, import_cell)
@@ -217,7 +190,7 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                 except Exception as e:
                     logger.error(f"Error processing {notebook_path}: {e}")
     
-    with open("notebooks_no_execution.json", "w") as f:
+    with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
         json.dump(NOTEBOOKS_NO_EXECUTION, f)
 
 

docs/_scripts/third_party_page/create_third_party_page.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-"""Create the third party page for the documentation."""
-
-import argparse
-from typing import List
-from typing import TypedDict
-
-import yaml
-
-MARKDOWN = """\
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# 🚀 Prebuilt Agents
-
-LangGraph includes a prebuilt React agent. For more information on how to use it, 
-check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
-
-If you’re looking for other prebuilt libraries, explore the community-built options 
-below. These libraries can extend LangGraph's functionality in various ways.
-
-## 📚 Available Libraries
-
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-{library_list}
-
-## ✨ Contributing Your Library
-
-Have you built an awesome open-source library using LangGraph? We'd love to feature 
-your project on the official LangGraph documentation pages! 🏆
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
-
-**Guidelines**
-
-- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
-  for JavaScript/TypeScript, etc.) 📦
-- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
-  the Functional API (exposing an `entrypoint`).
-- The package must include documentation (e.g., a `README.md` or docs site) 
-  explaining how to use it.
-  
-We'll review your contribution and merge it in!
-
-Thanks for contributing! 🚀
-"""
-
-
-class ResolvedPackage(TypedDict):
-    name: str
-    """The name of the package."""
-    repo: str
-    """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    weekly_downloads: int | None
-    """The weekly download count of the package."""
-    description: str
-    """A brief description of what the package does."""
-
-
-def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
-    """Generate the markdown content for the third party page.
-
-    Args:
-        resolved_packages: A list of resolved package information.
-        language: str
-
-    Returns:
-        The markdown content as a string.
-    """
-    # Update the URL to the actual file once the initial version is merged
-    if language == "python":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraph/blob/main/docs"
-            "/_scripts/third_party_page/packages.yml"
-        )
-    elif language == "js":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
-            "/_scripts/third_party/packages.yml"
-        )
-    else:
-        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
-
-    sorted_packages = sorted(
-        resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
-    )
-    rows = [
-        "| Name | GitHub URL | Description | Weekly Downloads | Stars |",
-        "| --- | --- | --- | --- | --- |",
-    ]
-    for package in sorted_packages:
-        name = f"**{package['name']}**"
-        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
-        stars_badge = (
-            f"https://img.shields.io/github/stars/{package['repo']}?style=social"
-        )
-        stars = f"![GitHub stars]({stars_badge})"
-        downloads = package["weekly_downloads"] or "-"
-        row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
-        rows.append(row)
-    markdown_content = MARKDOWN.format(
-        library_list="\n".join(rows), langgraph_url=langgraph_url
-    )
-    return markdown_content
-
-
-def main(input_file: str, output_file: str, language: str) -> None:
-    """Main function to create the third party page.
-
-    Args:
-        input_file: Path to the input YAML file containing resolved package information.
-        output_file: Path to the output file for the third party page.
-        language: The language for which to generate the third party page.
-    """
-    # Parse the input YAML file
-    with open(input_file, "r") as f:
-        resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
-
-    markdown_content = generate_markdown(resolved_packages, language)
-
-    # Write the markdown content to the output file
-    with open(output_file, "w", encoding="utf-8") as f:
-        f.write(markdown_content)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Create the third party page.")
-    parser.add_argument(
-        "input_file",
-        help="Path to the input YAML file containing resolved package information.",
-    )
-    parser.add_argument(
-        "output_file", help="Path to the output file for the third party page."
-    )
-    parser.add_argument(
-        "--language",
-        choices=["python", "js"],
-        default="python",
-        help="The language for which to generate the third party page. Defaults to 'python'.",
-    )
-    args = parser.parse_args()
-
-    main(args.input_file, args.output_file, args.language)

docs/_scripts/third_party_page/get_download_stats.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python
-"""Retrieve download count for a list of Python packages from PyPI."""
-
-import argparse
-from datetime import datetime
-from typing import TypedDict
-import pathlib
-
-import requests
-import yaml
-
-
-class Package(TypedDict):
-    """A TypedDict representing a package"""
-
-    name: str
-    """The name of the package."""
-    repo: str
-    """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    description: str
-    """A brief description of what the package does."""
-
-
-class ResolvedPackage(Package):
-    weekly_downloads: int | None
-
-
-HERE = pathlib.Path(__file__).parent
-PACKAGES_FILE = HERE / "packages.yml"
-PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
-
-
-def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedPackage]:
-    """Retrieve the monthly download count for a list of packages from PyPIStats."""
-    resolved_packages: list[ResolvedPackage] = []
-
-    if fake:
-        # To avoid making network requests during testing, return fake download counts
-        for package in packages:
-            resolved_packages.append(
-                {
-                    "name": package["name"],
-                    "repo": package["repo"],
-                    "weekly_downloads": -12345,
-                    "description": package["description"],
-                }
-            )
-        return resolved_packages
-
-    for package in packages:
-        # First check if package exists on PyPI
-        pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
-        try:
-            pypi_response = requests.get(pypi_url)
-            pypi_response.raise_for_status()
-        except requests.exceptions.HTTPError:
-            raise AssertionError(f"Package {package['name']} does not exist on PyPI")
-
-        # Get first release date
-        pypi_data = pypi_response.json()
-        releases = pypi_data["releases"]
-        first_release_date = None
-        for version_releases in releases.values():
-            if version_releases:  # Some versions may be empty lists
-                upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
-                if first_release_date is None or upload_time < first_release_date:
-                    first_release_date = upload_time
-
-        if first_release_date is None:
-            raise AssertionError(f"Package {package['name']} has no releases yet")
-
-        # If package was published in last 48 hours, skip download stats
-        if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
-            url = f"https://pypistats.org/api/packages/{package['name']}/overall"
-
-            response = requests.get(url)
-            response.raise_for_status()
-            data = response.json()
-
-            sorted_data = sorted(
-                data["data"],
-                key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
-                reverse=True,
-            )
-
-            # Sum the last 7 days of downloads
-            num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
-        else:
-            num_downloads = None
-
-        resolved_packages.append(
-            {
-                "name": package["name"],
-                "repo": package["repo"],
-                "weekly_downloads": num_downloads,
-                "description": package["description"],
-            }
-        )
-
-    return resolved_packages
-
-
-
-def main(output_file: str, fake: bool) -> None:
-    """Main function to generate package download information.
-
-    Args:
-        output_file: Path to the output YAML file.
-    """
-    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES, fake)
-
-    if not output_file.endswith(".yml"):
-        raise ValueError("Output file must have a .yml extension")
-
-    with open(output_file, "w") as f:
-        f.write("# This file is auto-generated. Do not edit.\n")
-        yaml.dump(resolved_packages, f)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Generate package download information."
-    )
-    parser.add_argument(
-        "output_file",
-        help=(
-            "Path to the output YAML file. Example: python generate_downloads.py "
-            "downloads.yml"
-        ),
-    )
-    parser.add_argument(
-        "--fake",
-        default=False,
-        action="store_true",
-        help=(
-            "Generate fake download counts for testing purposes. "
-            "This option will not make any network requests."
-        ),
-    )
-    args = parser.parse_args()
-
-    main(args.output_file, args.fake)

docs/_scripts/third_party_page/packages.yml

@@ -1,35 +0,0 @@
-#A list of third-party packages to surface on the third-party page.
-packages:
-  - name: "trustcall"
-    repo: "hinthornw/trustcall"
-    description: "Tenacious tool calling built on LangGraph."
-  - name: "breeze-agent"
-    repo: "andrestorres123/breeze-agent"
-    description: "A streamlined research system built inspired on STORM and built on LangGraph."
-  - name: "langgraph-supervisor"
-    repo: "langchain-ai/langgraph-supervisor-py"
-    description: "Build supervisor multi-agent systems with LangGraph."
-  - name: "langmem"
-    repo: "langchain-ai/langmem"
-    description: "Build agents that learn and adapt from interactions over time."
-  - name: "langchain-mcp-adapters"
-    repo: "langchain-ai/langchain-mcp-adapters"
-    description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-  - name: "open-deep-research"
-    repo: "langchain-ai/open_deep_research"
-    description: "Open source assistant for iterative web research and report writing."
-  - name: "langgraph-swarm"
-    repo: "langchain-ai/langgraph-swarm-py"
-    description: "Build swarm-style multi-agent systems using LangGraph."
-  - name: "delve-taxonomy-generator"
-    repo: "andrestorres123/delve"
-    description: "A taxonomy generator for unstructured data"
-  - name: "nodeology"
-    repo: "xyin-anl/Nodeology"
-    description: "Enable researcher to build scientific workflows easily with simplified interface."
-  - name: "langgraph-bigtool"
-    repo: "langchain-ai/langgraph-bigtool"
-    description: "Build LangGraph agents with large numbers of tools."
-  - name: "langgraph-reflection"
-    repo: "langchain-ai/langgraph-reflection"
-    description: "LangGraph agent that runs a reflection step."

docs/cassettes/LLMCompiler_15dd9639-691f-4906-9012-83fd6e9ac126.msgpack.zlib

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docs/cassettes/LLMCompiler_31854dfd-b82f-4c24-9b58-6bae66777909.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/LLMCompiler_730490c6-6e3a-4173-82a1-9eb9d5eeff20.msgpack.zlib

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docs/cassettes/agent-handoffs_f39f99b0-95c7-422f-96a6-e612fde186df.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

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docs/cassettes/introduction_11d5b934-6d8b-4f52-a3bc-b3daa7207e00.msgpack.zlib

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docs/cassettes/introduction_4527cf9a-b191-4bde-858a-e33a74a48c55.msgpack.zlib

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docs/cassettes/introduction_6b32914d-4d60-491f-8e11-1e6867e38ffd.msgpack.zlib

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docs/cassettes/introduction_faa345c6-38a2-42e8-9035-9cf56f7bb5b1.msgpack.zlib

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docs/cassettes/langgraph_adaptive_rag_4dec9d98-f3dc-4b7f-abc0-9d01c754f2be.msgpack.zlib

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