chore: typo fix

2026-08-27 01:52:25 +02:00 · 2025-03-02 22:57:50 -05:00
631 changed files with 54131 additions and 59057 deletions
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -71,3 +71,4 @@ jobs:
        working-directory: libs/cli/js-examples
        run: |
          langgraph build -t langgraph-test-e
+  
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

  # This env var allows us to get inline annotations when ruff has complaints.
  RUFF_OUTPUT_FORMAT: github
@@ -50,6 +50,12 @@ jobs:
        working-directory: ${{ inputs.working-directory }}
        run: poetry check

+      - name: Check lock file
+        if: steps.changed-files.outputs.all
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: poetry lock --check
+
      - name: Install dependencies
        if: steps.changed-files.outputs.all
        # Also installs dev/lint/test/typing dependencies, to ensure we have
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -9,7 +9,7 @@ on:
        description: "From which folder this pipeline executes"

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
  PYTHON_VERSION: "3.10"

 jobs:
@@ -4,7 +4,7 @@ on:
  workflow_call:

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -8,7 +8,7 @@ on:
      - "libs/**"

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  benchmark:
@@ -6,7 +6,7 @@ on:
      - "libs/**"

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  benchmark:
@@ -43,7 +43,7 @@ jobs:
        run: |
          {
            echo 'OUTPUT<<EOF'
-            make -s benchmark-fast
+            make -s benchmark
            echo EOF
          } >> "$GITHUB_OUTPUT"
      - name: Compare benchmarks
@@ -17,7 +17,7 @@ concurrency:
  cancel-in-progress: true

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  changes:
@@ -114,42 +114,6 @@ jobs:
      - 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'
@@ -216,8 +180,6 @@ jobs:
        test,
        test-langgraph,
        test-scheduler-kafka,
-        check-sdk-methods,
-        check-schema,
        integration-test,
        test-js,
      ]
@@ -10,7 +10,7 @@ on:
  workflow_dispatch:

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 permissions:
  contents: read
@@ -38,13 +38,12 @@ jobs:
        with:
          filter: "docs/docs/**"

-  # TODO: Uncomment this to run on PRs
-  # run-changed-notebooks:
-  #   needs: get-changed-files
-  #   uses: ./.github/workflows/run_notebooks.yml
-  #   secrets: inherit
-  #   with:
-  #     changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
+  run-changed-notebooks:
+    needs: get-changed-files
+    uses: ./.github/workflows/run_notebooks.yml
+    secrets: inherit
+    with:
+      changed-files: ${{ needs.get-changed-files.outputs.changed-files }}

  deploy:
    # needs: run-changed-notebooks
@@ -64,16 +63,35 @@ 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"
+
          # 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
@@ -84,14 +102,7 @@ jobs:
      - 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: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
@@ -99,9 +110,8 @@ jobs:
      - name: Check links in notebooks
        env:
          LANGCHAIN_API_KEY: test
-        if: github.event_name == 'schedule'
        run: |
-          if [ "${{ github.event_name }}" == "schedule" ]; then
+          if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
            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/.*" \
@@ -117,7 +127,6 @@ jobs:
              --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')
              
          else
@@ -138,7 +147,6 @@ jobs:
                --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
@@ -12,7 +12,7 @@ on:
  workflow_dispatch:

 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  markdown-link-check:
@@ -42,8 +42,8 @@ jobs:

      - name: Check README.md is in sync
        run: |
-          if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
-          echo "README.md is out of sync with libs/langgraph/README.md"
-          diff -C 3 README.md libs/langgraph/README.md
-          exit 1
-          fi
+            if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
+            echo "README.md is out of sync with libs/langgraph/README.md"
+            diff -C 3 README.md libs/langgraph/README.md
+            exit 1
+            fi
@@ -10,7 +10,7 @@ on:

 env:
  PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"

 jobs:
  build:
@@ -9,7 +9,7 @@ on:
        type: string
        description: "JSON string of changed files"
  schedule:
-    - cron: "0 13 * * *"
+    - cron: '0 13 * * *'

 defaults:
  run:
@@ -30,12 +30,12 @@ jobs:
        uses: "./.github/actions/poetry_setup"
        with:
          python-version: 3.11
-          poetry-version: 2.1.2
+          poetry-version: 1.7.1
          cache-key: test-langgraph-notebooks

      - name: Install dependencies
        run: |
-          poetry install --with test --no-root
+          poetry install --with test
          poetry run pip install jupyter

      - name: Start services
@@ -57,13 +57,13 @@ jobs:
        env:
          # these won't actually be used because of the VCR cassettes
          # but need to set them to avoid triggering getpass()
-          OPENAI_API_KEY: "very-secret-key"
-          ANTHROPIC_API_KEY: "very-secret-key"
-          TAVILY_API_KEY: "very-secret-key"
-          LANGSMITH_API_KEY: "very-secret-key"
-          NOMIC_API_KEY: "very-secret-key"
-          COHERE_API_KEY: "very-secret-key"
-          FIREWORKS_API_KEY: "very-secret-key"
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
        run: |
          if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
            echo "Running all notebooks"
@@ -0,0 +1,29 @@
+name: Check File Size
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  file-size-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+      - name: Filter by size
+        # TODO: roll back the web voyager hack
+        run: |
+          large_added_files=$(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
@@ -180,4 +180,3 @@ Chinook.db

 .vercel
 .turbo
-.editorconfig
@@ -1,84 +1,339 @@
-<picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/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/)
-[![GitMCP](https://img.shields.io/endpoint?url=https://gitmcp.io/badge/langchain-ai/langgraph)](https://gitmcp.io/langchain-ai/langgraph)

-Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a powerful low-level orchestration framework for building, managing, and deploying long-running, stateful agents. 
+⚡ Building language agents as graphs ⚡

-## Get started
+> [!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/).

-Install LangGraph:
+## Overview

-```
+[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. 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?
+
+LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your 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,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+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
+
+```shell
 pip install -U langgraph
 ```

-Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/):
+## Example

-```python
-# pip install -qU "langchain[anthropic]" to call the model
+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!

-from langgraph.prebuilt import create_react_agent
-
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
-
-# Run the agent
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
+```shell
+pip install langchain-anthropic
 ```

-For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).
+```shell
+export ANTHROPIC_API_KEY=sk-...
+```

-## Core benefits
+Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.

-LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=lsv2_sk_...
+```

- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:

-## LangGraph’s ecosystem
+<details open>
+  <summary>High-level implementation</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:
+```python
+from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool

- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
- [LangChain](https://python.langchain.com/docs/introduction/) – Provides integrations and composable components to streamline LLM application development.
+# 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."

-> [!NOTE]
-> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).

-## Additional resources
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)

- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()

-## Acknowledgements
+app = create_react_agent(model, tools, checkpointer=checkpointer)

-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.
+# 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
+
+
+# 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]
+
+tool_node = ToolNode(tools)
+
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", 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 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
+```
+
+<b>Step-by-step Breakdown</b>:
+
+<details>
+<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>
+<summary>Initialize graph with state.</summary>
+
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <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>
+<summary>Define graph nodes.</summary>
+
+There are two main nodes we need:
+
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <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>
+</ul>
+</details>
+
+<details>
+<summary>Define entry point and graph edges.</summary>
+
+First, we need to set the entry point for graph execution - <code>agent</code> node.
+
+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.
+
+<ul>
+  <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>
+<summary>Compile the graph.</summary>
+
+<ul>
+  <li>
+    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>
+<summary>Execute the graph.</summary>
+
+<ol>
+  <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>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <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>
+</details>
+
+</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.
+* [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.
+
+## Resources
+
+* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+
+## Contributing
+
+For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
@@ -10,16 +10,8 @@ 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/agents/prebuilt.md --language python
+	poetry run python -m _scripts.third_party_page.get_download_stats stats.yml
+	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
@@ -14,8 +14,6 @@ To run the documentation server locally you can run:
 make serve-docs
 ```

-This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
-
 ## Execute notebooks

 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
@@ -58,4 +56,4 @@ To delete cassettes for a notebook, you can run:

 ```bash
 rm cassettes/<notebook_name>*
-```
+```
@@ -22,12 +22,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
        "create_react_agent",
        "prebuilt",
    ),
-    (
-        [],
-        "langgraph.prebuilt.chat_agent_executor",
-        "AgentState",
-        "prebuilt",
-    ),
    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
    (
        ["langgraph.prebuilt"],
@@ -51,8 +45,6 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
    (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
    (["langgraph.constants"], "langgraph.types", "Command", "types"),
-    (["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
-    (["langgraph.config"], "langgraph.config", "get_store", "config"),
    (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
    (["langgraph.func"], "langgraph.func", "task", "func"),
    (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
@@ -64,23 +56,10 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
    ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
    ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
-    ([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
    ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
-    # other prebuilts
-    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
-    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
-    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
-    (["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
-    ([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
-    ([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
-    ([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
-    ([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
 ]

 WELL_KNOWN_LANGGRAPH_OBJECTS = {
@@ -162,9 +141,7 @@ def get_imports(code: str, path: str) -> List[ImportInformation]:
    for found_import in found_imports:
        module = found_import["source"]

-        if module.startswith("langchain_mcp_adapters"):
-            package_ecosystem = "langgraph"
-        elif module.startswith("langchain"):
+        if module.startswith("langchain"):
            # Handles things like `langchain` or `langchain_anthropic`
            package_ecosystem = "langchain"
        elif module.startswith("langgraph"):
@@ -237,7 +214,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
        path: The path of the file where the markdown content originated.

    Returns:
-        Updated markdown with API reference links prepended to Python code blocks.
+        Updated markdown with API reference links appended to Python code blocks.

    Example:
        Given a markdown with a Python code block:
@@ -260,7 +237,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
            match (re.Match): The regex match object containing the code block.

        Returns:
-            str: The modified code block with API reference links prepended if applicable.
+            str: The modified code block with API reference links appended if applicable.
        """
        indent = match.group("indent")
        code_block = match.group("code")
@@ -276,8 +253,8 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
        api_links = " | ".join(
            f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
        )
-        # Return the code block with prepended API reference links
-        return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
+        # 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)
@@ -20,8 +20,15 @@ def _make_llms_text(output_file: str) -> str:
        output_file: Path to output the consolidated text file
    """
    # Collect all markdown and notebook files
-    all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
+    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)
    )
@@ -1,6 +1,7 @@
 import ast
 import os
 import re
+from pathlib import Path
 from typing import Literal

 import nbformat
@@ -25,7 +26,7 @@ def _uses_input(source: str) -> bool:


 def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.
+    """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.
@@ -51,14 +52,10 @@ def _rewrite_cell_magic(code: str) -> str:
        if stripped.startswith("%%capture"):
            continue
        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%") or stripped.startswith("!"):
-            # Drop the leading '%' character and then drop all leading whitespace
-            stripped = stripped.lstrip("%! \t")
-            # Check if the line starts with "pip"
-            if stripped.startswith("pip"):
-                rewritten_lines.append(stripped)
-            else:
-                raise NotImplementedError(f"Unhandled line: {line}")
+        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}")

@@ -220,24 +217,6 @@ def _convert_links_in_markdown(markdown: str) -> str:
    )


-class HideCellTagPreprocessor(Preprocessor):
-    """
-    Removes cells that have '# hide-cell' at the beginning of the cell content.
-    This allows authors to include cells in the notebook that should not
-    appear in the generated markdown output.
-    """
-
-    def preprocess(self, nb, resources):
-        # Filter out cells with the '# hide-cell' comment at the beginning
-        nb.cells = [
-            cell
-            for cell in nb.cells
-            if not (cell.source.strip().startswith("# hide-cell"))
-        ]
-
-        return nb, resources
-
-
 class EscapePreprocessor(Preprocessor):
    def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
@@ -268,10 +247,13 @@ class EscapePreprocessor(Preprocessor):
            )
            cell.metadata["exec"] = is_exec

-            # 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"
+            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)
@@ -359,7 +341,6 @@ class ExtractAttachmentsPreprocessor(Preprocessor):

 exporter = MarkdownExporter(
    preprocessors=[
-        HideCellTagPreprocessor,
        EscapePreprocessor,
        ExtractAttachmentsPreprocessor,
    ],
@@ -371,7 +352,7 @@ exporter = MarkdownExporter(


 def convert_notebook(
-    notebook_path: str,
+    notebook_path: Path,
    mode: Literal["markdown", "exec"] = "markdown",
 ) -> str:
    with open(notebook_path) as f:
@@ -1,18 +1,5 @@
 {% extends 'markdown/index.md.j2' %}

-{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
-```{%- if 'language' in cell.metadata -%}
-{{ cell.metadata.language }}
-{%- elif 'magics_language' in cell.metadata -%}
-{{ cell.metadata.magics_language }}
-{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
-{{ nb.metadata.language_info.name }}
-{%- endif %}
-{{ cell.source }}
-```
-{% endblock input %}
-
-
 {%- block traceback_line -%}
 ```output
 {{ line.rstrip() | strip_ansi }}
@@ -21,13 +8,13 @@

 {%- block stream -%}
 ```output
-{{ output.text.rstrip() | strip_ansi }}
+{{ output.text.rstrip() }}
 ```
 {%- endblock stream -%}

 {%- block data_text scoped -%}
 ```output
-{{ output.data['text/plain'].rstrip() | strip_ansi }}
+{{ output.data['text/plain'].rstrip() }}
 ```
 {%- endblock data_text -%}

@@ -38,13 +25,9 @@
 {%- endblock data_html -%}

 {%- block data_jpg scoped -%}
-<p>
-<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
-</p>
+![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
 {%- endblock data_jpg -%}

 {%- block data_png scoped -%}
-<p>
-<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
-</p>
+![](data:image/png;base64,{{ output.data['image/png'] }})
 {%- endblock data_png -%}
@@ -18,89 +18,19 @@ DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")

 REDIRECT_MAP = {
    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.md",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
+    "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",
-    # graph-api
-    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
-    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
-    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
-    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
-    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
-    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
-    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
-    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
-    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
-    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
-    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
-    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
-    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
-    "how-tos/async.ipynb": "how-tos/graph-api/#async",
-    # memory how-tos
-    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
-    "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
-    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
-    # subgraph how-tos
-    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
-    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
-    # persistence how-tos
-    "how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
-    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
-    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
-    # tool calling how-tos
-    "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
-    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
-    "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
-    "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
-    # multi-agent how-tos
-    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
-    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
-    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
    # cloud redirects
-    "cloud/index.md": "index.md",
-    "cloud/how-tos/index.md": "concepts/langgraph_platform",
+    "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",
-    "cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
-    "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
-    # cloud streaming redirects
-    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
-    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
-    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
-    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
-    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
-    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
-    # prebuit redirects
-    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
-    "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
-    "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
-    "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
-    "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
-    # Time-travel
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.ipynb",
-    # breakpoints
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
-    # misc
-    "prebuilt.md": "agents/prebuilt.md",
-    "reference/prebuilt.md": "reference/agents.md",
-    "concepts/high_level.md": "index.md",
-    "concepts/index.md": "index.md",
-    "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
-    "how-tos/index.md": "index.md",
-    "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
-    # deployment redirects
-    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
-    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
-    # assistant redirects
-    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md"
-
 }


@@ -256,7 +186,7 @@ def _on_page_markdown_with_config(

    if remove_base64_images:
        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
+        markdown = re.sub(r"!\[.*?\]\(data:image/+;base64,[^\)]+\)", "", markdown)

    return markdown

@@ -5,7 +5,6 @@ import os
 import json
 import click
 import nbformat
-import re

 logger = logging.getLogger(__name__)
 NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
@@ -21,6 +20,7 @@ BLOCKLIST_COMMANDS = (

 NOTEBOOKS_NO_CASSETTES = (
    "docs/how-tos/visualization.ipynb",
+    "docs/how-tos/many-tools.ipynb"
 )

 NOTEBOOKS_NO_EXECUTION = [
@@ -49,10 +49,7 @@ NOTEBOOKS_NO_EXECUTION = [
    "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/how-tos/streaming-specific-nodes.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb",
-    "docs/tutorials/customer-support/customer-support.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
-    "docs/how-tos/many-tools.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
+    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
 ]


@@ -89,11 +86,6 @@ def has_blocklisted_command(code: str, metadata: dict) -> bool:
            return True
    return False

-MERMAID_PATTERN = re.compile(r'display\(Image\((\w+)\.get_graph\(\)\.draw_mermaid_png\(\)\)\)')
-
-def remove_mermaid(code: str) -> str:
-    return MERMAID_PATTERN.sub('print()', code)
-

 def add_vcr_to_notebook(
    notebook: nbformat.NotebookNode, cassette_prefix: str
@@ -107,8 +99,6 @@ def add_vcr_to_notebook(
            continue

        lines = cell.source.splitlines()
-        # remove the special tag for hidden cells
-        lines = [line for line in lines if not line.strip().startswith("# hide-cell")]
        # skip if empty cell
        if not lines:
            continue
@@ -190,20 +180,6 @@ def add_vcr_to_notebook(
    return notebook


-def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
-    for cell in notebook.cells:
-        if cell.cell_type != "code":
-            continue
-
-        cell.source = remove_mermaid(cell.source)
-
-        # skip the cell entirely if it contains PYPPETEER
-        if "PYPPETEER" in cell.source:
-            cell.source = ""
-
-    return notebook
-
-
 def process_notebooks(should_comment_install_cells: bool) -> None:
    for directory in NOTEBOOK_DIRS:
        for root, _, files in os.walk(directory):
@@ -225,8 +201,6 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                            notebook, cassette_prefix=cassette_prefix
                        )

-                    notebook = remove_mermaid_from_notebook(notebook)
-
                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
                        # Add a cell at the beginning to indicate that this notebook should not be executed
                        warning_cell = nbformat.v4.new_markdown_cell(
@@ -9,7 +9,10 @@ import yaml

 MARKDOWN = """\
 [//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# Community Agents
+# 🚀 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.
@@ -30,23 +30,10 @@ 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]:
+def _get_weekly_downloads(packages: list[Package]) -> 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"
@@ -101,13 +88,13 @@ def _get_weekly_downloads(packages: list[Package], fake: bool) -> list[ResolvedP



-def main(output_file: str, fake: bool) -> None:
+def main(output_file: str) -> 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)
+    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES)

    if not output_file.endswith(".yml"):
        raise ValueError("Output file must have a .yml extension")
@@ -128,15 +115,6 @@ if __name__ == "__main__":
            "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)
+    main(args.output_file)
@@ -23,19 +23,4 @@ packages:
    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: "ai-data-science-team"
-    repo: "business-science/ai-data-science-team"
-    description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
-  - name: "langgraph-reflection"
-    repo: "langchain-ai/langgraph-reflection"
-    description: "LangGraph agent that runs a reflection step."
-  - name: "langgraph-codeact"
-    repo: "langchain-ai/langgraph-codeact"
-    description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."
+    description: "A taxonomy generator for unstructured data"
@@ -0,0 +1 @@
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
@@ -0,0 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +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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				`@@ -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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				`@@ -0,0 +1 @@`
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				`@@ -0,0 +1 @@`
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