InMemorySaver

2026-10-21 15:31:43 +02:00 · 2024-10-06 20:49:11 -07:00
622 changed files with 34432 additions and 85639 deletions
@@ -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 documentation](https://langchain-ai.github.io/langgraph/).
        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
        [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
@@ -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)
@@ -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
+          timeout 60 langgraph test -c examples/langgraph.json --verbose || (exit "$(($? == 124 ? 0 : $?))")
-          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
+      - name: Start service B
          cp .env.example .envg
          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
      - name: Build and test service B
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples/graphs
        run: |
-          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
+      - name: Start service C
      - name: Build and test service C
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples/graphs_reqs_a
        run: |
-          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
+      - name: Start service D
      - name: Build and test service D
        if: steps.changed-files.outputs.all
        working-directory: libs/cli/examples/graphs_reqs_b
        run: |
-          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
          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
@@ -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
@@ -21,7 +21,6 @@ jobs:
          - "3.10"
          - "3.11"
          - "3.12"
          - "3.13"
    name: "test #${{ matrix.python-version }}"
    steps:
@@ -31,13 +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: ${{ secrets.DOCKERHUB_RO_TOKEN }}
      - name: Install dependencies
        shell: bash
@@ -16,12 +16,14 @@ jobs:
          - "3.10"
          - "3.11"
          - "3.12"
-          - "3.13"
+        core-version:
          - ">=0.2.39,<0.3.0"
          - "latest"
    defaults:
      run:
        working-directory: libs/langgraph
-    name: "test #${{ 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
@@ -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
@@ -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
@@ -43,12 +43,11 @@ jobs:
    name: cd ${{ matrix.working-directory }}
    strategy:
      matrix:
-        working-directory:
+        working-directory: [
          [
            "libs/cli",
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
-            "libs/checkpoint-postgres",
+            "libs/checkpoint-postgres"
          ]
    uses: ./.github/workflows/_test.yml
    with:
@@ -75,7 +74,7 @@ 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
@@ -108,40 +107,9 @@ jobs:
      - name: Build
        run: yarn build
  test-js:
    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:
+    needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
      [
        lint,
        lint-js,
        test,
        test-langgraph,
        test-scheduler-kafka,
        integration-test,
        test-js,
      ]
    if: |
      always()
    runs-on: ubuntu-latest
@@ -21,7 +21,7 @@
          - name: Install Dependencies
            run: |
-              pip install toml codespell==2.3.0 jupytext
+              pip install toml codespell jupytext
          - name: Extract Ignore Words List
            run: |
@@ -44,8 +44,6 @@ jobs:
  deploy:
    # needs: run-changed-notebooks
    runs-on: ubuntu-latest
    env:
      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
    steps:
      - uses: actions/checkout@v4
        with:
@@ -60,14 +58,8 @@ jobs:
      - name: Install dependencies
        run: |
-          poetry install --with test --no-root
+          poetry install --with docs
-          poetry run pip install -U \
+          poetry run pip install -U pytest pytest-check-links langsmith langchain GitPython
            pytest \
            pytest-check-links \
            langsmith \
            langchain \
            GitPython \
            "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
      - name: Lint Docs
        # This step lints the docs using the existing linting set up.
@@ -86,18 +78,10 @@ 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://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 $(find docs/site -name "index.html" | grep -v 'storm/index.html')
              --check-links-ignore "https://openai\.com/.*" \
              --check-links-ignore "https://pepy\.tech/.*" \
              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
          else
            echo "Fetching changes from origin/main..."
            git fetch origin main
@@ -108,10 +92,6 @@ jobs:
              echo "Running link check on HTML files matching changed notebook files..."
              poetry run pytest -v \
                --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
                --check-links-ignore "https://academy\.langchain\.com/.*" \
                --check-links-ignore "http://localhost:8123/.*" \
                --check-links-ignore "http://localhost:2024.*" \
                --check-links-ignore "http://127.0.0.1:.*" \
                --check-links-ignore "https://x.com/.*" \
                --check-links-ignore "https://github\.com/.*" \
                --check-links-ignore "/.*\.(ipynb|html)$" \
@@ -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
@@ -254,6 +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
@@ -267,8 +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:
@@ -295,6 +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
@@ -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:
@@ -13,7 +13,7 @@ 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  -w ./libs/checkpoint -w ./libs/sdk-py --dirty
+	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
 clean-docs:
 	find ./docs/docs -name "*.ipynb" -type f -delete
@@ -8,59 +8,24 @@
 ⚡ 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/)).
 ## Overview
-[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+[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.
 stateful, multi-actor applications with LLMs, used to create agent and multi-agent
 workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
-### Why use LangGraph?
+To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
-LangGraph provides fine-grained control over both the flow and state of your
+### Key Features
 agent applications. It implements a central
 [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
 enabling features that are common to most agent architectures:
- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+- **Cycles and Branching**: Implement loops and conditionals in your apps.
-supporting memory of conversations and other updates within and across user
+- **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.
-interactions;
+- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
-and resumed, allowing for decisions, validation, and corrections at key stages via
+- **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).
 human input.
 Standardizing these components allows individuals and teams to focus on the behavior
 of their agent, instead of its supporting infrastructure.
 Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
 the development, deployment, debugging, and monitoring of your applications.
 LangGraph integrates seamlessly with
 [LangChain](https://python.langchain.com/docs/introduction/) and
 [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
 To learn more about LangGraph, check out our first LangChain Academy
 course, *Introduction to LangGraph*, available for free
 [here](https://academy.langchain.com/courses/intro-to-langgraph).
 ### LangGraph Platform
 [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
 See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
 (includes a free tier).
 Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
 - **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
 - **Background runs**: Runs agents asynchronously in the background
 - **Support for long running agents**: Infrastructure that can handle long running processes
 - **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
 - **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
 ## Installation
@@ -70,7 +35,9 @@ pip install -U langgraph
 ## Example
-Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
+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
@@ -87,75 +54,13 @@ export LANGSMITH_TRACING=true
 export LANGSMITH_API_KEY=lsv2_sk_...
 ```
 The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
 <details open>
  <summary>High-level implementation</summary>
 ```python
-from langgraph.prebuilt import create_react_agent
+from typing import Annotated, Literal, TypedDict
-from langgraph.checkpoint.memory import MemorySaver
+
 from langchain_core.messages import HumanMessage
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
-
+from langgraph.checkpoint.memory import InMemorySaver
 # Define the tools for the agent to use
@tool
 def search(query: str):
    """Call to surf the web."""
    # This is a placeholder, but don't tell the LLM that...
    if "sf" in query.lower() or "san francisco" in query.lower():
        return "It's 60 degrees and foggy."
    return "It's 90 degrees and sunny."
 tools = [search]
 model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
 # Initialize memory to persist state between graph runs
 checkpointer = MemorySaver()
 app = create_react_agent(model, tools, checkpointer=checkpointer)
 # Use the agent
 final_state = app.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 ```
 "Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
 ```
 Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
 ```python
 final_state = app.invoke(
    {"messages": [{"role": "user", "content": "what about ny"}]},
    config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
 ```
 "Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
 ```
 </details>
 > [!TIP]
 > LangGraph is a **low-level** framework that allows you to implement any custom agent
 architectures. Click on the low-level implementation below to see how to implement a
 tool-calling agent from scratch.
 <details>
 <summary>Low-level implementation</summary>
 ```python
 from typing import Literal
 from langchain_anthropic import ChatAnthropic
 from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, START, StateGraph, MessagesState
 from langgraph.prebuilt import ToolNode
@@ -174,7 +79,7 @@ tools = [search]
 tool_node = ToolNode(tools)
-model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(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]:
@@ -220,7 +125,7 @@ workflow.add_conditional_edges(
 workflow.add_edge("tools", 'agent')
 # Initialize memory to persist state between graph runs
-checkpointer = MemorySaver()
+checkpointer = InMemorySaver()
 # Finally, we compile it!
 # This compiles it into a LangChain Runnable,
@@ -228,102 +133,92 @@ checkpointer = MemorySaver()
 # Note that we're (optionally) passing the memory when compiling the graph
 app = workflow.compile(checkpointer=checkpointer)
-# Use the agent
+# Use the Runnable
 final_state = app.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    {"messages": [HumanMessage(content="what is the weather in sf")]},
    config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
-<b>Step-by-step Breakdown</b>:
+```
 "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?"
 ```
-<details>
+Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
 <summary>Initialize the model and tools.</summary>
 <ul>
  <li>
    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
  </li>
  <li>
    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
  </li>
 </ul>
 </details>
-<details>
+```python
-<summary>Initialize graph with state.</summary>
+final_state = app.invoke(
    {"messages": [HumanMessage(content="what about ny")]},
    config={"configurable": {"thread_id": 42}}
 )
 final_state["messages"][-1].content
 ```
-<ul>
+```
-    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+"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?"
-    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+```
 </ul>
 </details>
-<details>
+### Step-by-step Breakdown
 <summary>Define graph nodes.</summary>
-There are two main nodes we need:
+1. <details>
    <summary>Initialize the model and tools.</summary>
-<ul>
+    - 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.
-    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    - we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
-    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+   </details>
 </ul>
 </details>
-<details>
+2. <details>
-<summary>Define entry point and graph edges.</summary>
+    <summary>Initialize graph with state.</summary>
-First, we need to set the entry point for graph execution - <code>agent</code> node.
+    - 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>
-Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
+3. <details>
    <summary>Define graph nodes.</summary>
-<ul>
+    There are two main nodes we need:
  <li>Conditional edge: after the agent is called, we should either:
    <ul>
      <li>a. Run tools if the agent said to take an action, OR</li>
      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
    </ul>
  </li>
  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
 </ul>
 </details>
-<details>
+      - The `agent` node: responsible for deciding what (if any) actions to take.
-<summary>Compile the graph.</summary>
+      - The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
   </details>
-<ul>
+4. <details>
-  <li>
+    <summary>Define entry point and graph edges.</summary>
    When we compile the graph, we turn it into a LangChain 
    <a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>, 
    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
    with your inputs
  </li>
  <li>
    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
    a simple in-memory checkpointer
  </li>
 </ul>
 </details>
-<details>
+      First, we need to set the entry point for graph execution - `agent` node.
 <summary>Execute the graph.</summary>
-<ol>
+      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.
-  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+
-  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+      - Conditional edge: after the agent is called, we should either:
-  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+        - a. Run tools if the agent said to take an action, OR
-  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+        - b. Finish (respond to the user) if the agent did not ask to run tools
-    <ul>
+      - Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
-      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+   </details>
-      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+
-    </ul>
+5. <details>
-  </li>
+    <summary>Compile the graph.</summary>
-  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+
-</ol>
+    - 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
-</details>
+    - 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 `InMemorySaver` - 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>
 </details>
 ## Documentation
@@ -331,7 +226,7 @@ Then we define one normal and one conditional edge. Conditional edge means that
 * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
 * [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
-* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
+* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
 ## Contributing
@@ -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
@@ -24,7 +24,7 @@ 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/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
@@ -1,285 +0,0 @@
 import importlib
 import inspect
 import logging
 import re
 from functools import lru_cache
 from typing import List, Literal, 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.types", "StreamMode", "types"),
    (["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", "RetryPolicy", "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]
 }
 def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
    if not pkg_prefix.isidentifier():
        raise ValueError(f"Invalid package prefix: {pkg_prefix}")
    return re.compile(
        r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
        r"((?:\w+(?:,\s*)?)*"  # Match zero or more words separated by a comma+optional ws
        r"(?:\s*\(.*?\))?)",  # Match optional parentheses block
        re.DOTALL,  # Match newlines as well
    )
 # Regular expression to match langchain import lines
 _IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
 _IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
@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)
        class_ = getattr(module, class_name)
        module = inspect.getmodule(class_)
        if module is None:
            # For constants, inspect.getmodule() might return None
            # In this case, we'll return the original module_path
            return module_path
        return module.__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
 def _get_doc_title(data: str, file_name: str) -> str:
    try:
        return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
    except IndexError:
        pass
    # Parse the rst-style titles
    try:
        return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
    except IndexError:
        return file_name
 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.
    title: str  # The title of the document where the import is used.
 def _get_imports(
    code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
 ) -> List[ImportInformation]:
    """Get imports from the given code block.
    Args:
        code: Python code block from which to extract imports
        doc_title: Title of the document
        package_ecosystem: "langchain" or "langgraph". The two live in different
            repositories and have separate documentation sites.
    Returns:
        List of import information for the given code block
    """
    imports = []
    if package_ecosystem == "langchain":
        pattern = _IMPORT_LANGCHAIN_RE
    elif package_ecosystem == "langgraph":
        pattern = _IMPORT_LANGGRAPH_RE
    else:
        raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
    for import_match in pattern.finditer(code):
        module = import_match.group(1)
        if "pydantic_v1" in module:
            continue
        imports_str = (
            import_match.group(2).replace("(\n", "").replace("\n)", "")
        )  # Handle newlines within parentheses
        # remove any newline and spaces, then split by comma
        imported_classes = [
            imp.strip()
            for imp in re.split(r",\s*", imports_str.replace("\n", ""))
            if imp.strip()
        ]
        for class_name in imported_classes:
            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,
                    "title": doc_title,
                }
            )
    return imports
 def get_imports(code: str, doc_title: 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.
        doc_title: The documentation title associated with the code.
    Returns:
        A list of import information for each import found.
    """
    ecosystems = ["langchain", "langgraph"]
    all_imports = []
    for package_ecosystem in ecosystems:
        all_imports.extend(_get_imports(code, doc_title, package_ecosystem))
    return all_imports
 def update_markdown_with_imports(markdown: 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.
    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')
        language = match.group('language')  # Preserve the language from the regex match
        # 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
@@ -1,123 +0,0 @@
 import os
 import re
 from pathlib import Path
 import nbformat
 from nbconvert.exporters import MarkdownExporter
 from nbconvert.preprocessors import Preprocessor
 class EscapePreprocessor(Preprocessor):
    def preprocess_cell(self, cell, resources, cell_index):
        if cell.cell_type == "markdown":
            # rewrite markdown links to html links (excluding image links)
            cell.source = re.sub(
                r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
                r'<a href="\2">\1</a>',
                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":
            # escape ``` in code
            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,
 ) -> Path:
    with open(notebook_path) as f:
        nb = nbformat.read(f, as_version=4)
    body, _ = exporter.from_notebook_node(nb)
    return body
@@ -1,5 +0,0 @@
 {
  "mimetypes": {
    "text/markdown": true
  }
 }
@@ -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 -%}
@@ -1,120 +0,0 @@
 import logging
 import os
 import re
 from typing import Any, Dict
 from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 from notebook_convert import convert_notebook
 from generate_api_reference_links import update_markdown_with_imports
 logger = logging.getLogger(__name__)
 logging.basicConfig()
 logger.setLevel(logging.INFO)
 DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
 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 _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>py|python|js|javascript)(?!\s+hl_lines=)\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")
        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)
        if highlighted_lines:
            return (
                f'{indent}```{language} hl_lines="{" ".join(highlighted_lines)}"\n'
                # The indent and terminating \n is already included in the code block
                f'{new_code_block}'
                f'{indent}```'
            )
        else:
            return (
                f"{indent}```{language}\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(markdown: str, page: Page, **kwargs: Dict[str, Any]):
    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
    markdown = update_markdown_with_imports(markdown)
    # Apply highlight comments to code blocks
    markdown = _highlight_code_blocks(markdown)
    return markdown
@@ -36,19 +36,14 @@ NOTEBOOKS_NO_EXECUTION = [
    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
    # this loads a massive dataset from gcp
    "docs/docs/tutorials/usaco/usaco.ipynb",
    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
    "docs/docs/how-tos/autogen-integration.ipynb",
    # TODO: need to update these notebooks to make sure they are runnable in CI
    "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",  # flakiness only when running in GHA 
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
-    "docs/docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
+    "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR
    "docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
    "docs/docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
    "docs/docs/tutorials/tot/tot.ipynb",
    "docs/docs/how-tos/visualization.ipynb",
    "docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
 ]
@@ -75,13 +70,10 @@ def is_comment(code: str) -> bool:
    return code.strip().startswith("#")
-def has_blocklisted_command(code: str, metadata: dict) -> bool:
+def has_blocklisted_command(code: str) -> bool:
    if 'hide_from_vcr' in metadata:
        return True
    code = code.strip()
-    for blocklisted_pattern in BLOCKLIST_COMMANDS:
+    for blocklisted_command in BLOCKLIST_COMMANDS:
-        if blocklisted_pattern in code:
+        if blocklisted_command in code:
            return True
    return False
@@ -91,7 +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":
@@ -117,7 +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)
@@ -126,30 +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 = []
+    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([
        "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):",
@@ -171,8 +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)
@@ -0,0 +1 @@
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@@ -0,0 +1 @@
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@@ -1 +0,0 @@
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--- a/Show More
+++ b/Show More
		`@@ -0,0 +1 @@`
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		`@@ -0,0 +1 @@`
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		`@@ -0,0 +1 @@`
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		`@@ -0,0 +1 @@`
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		`@@ -1 +0,0 @@`
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		`@@ -1 +0,0 @@`
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		`@@ -1 +0,0 @@`
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		`@@ -1 +0,0 @@`
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		`@@ -0,0 +1 @@`
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		`@@ -1 +1 @@`
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