POC streaming hints

chore: Update CLI schema (#6858 )
fix: bump js-yaml to 3.14.2 to resolve CVE-2025-64718 (#6879 )
2026-08-18 05:35:43 +02:00 · 2026-02-23 09:09:13 -05:00 · 2026-02-20 01:11:21 +00:00 · 2026-02-19 19:41:51 +00:00 · 2026-02-19 11:10:19 -08:00 · 2026-02-19 11:10:07 -08:00
182 changed files with 22038 additions and 11084 deletions
@@ -1,6 +0,0 @@
-# Contributing to LangGraph
-
-Hi there! Thank you for even being interested in contributing to LangGraph.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
-
-To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).
@@ -1,43 +1,60 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
-labels: [pending, bug]
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
+labels: ["bug"]
+type: bug
 body:
  - type: markdown
    attributes:
      value: |
-        Thank you for taking the time to file a bug report. 
+        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 the [LangChain Forum](https://forum.langchain.com/).
+        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).

-        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:
+        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:

-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/),
+        * [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
+        * [LangChain ChatBot](https://chat.langchain.com/)
        * [GitHub search](https://github.com/langchain-ai/langgraph),
+        * [LangChain Forum](https://forum.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 the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question.
          required: true
-        - label: I added a clear and detailed title that summarizes the issue.
+        - label: I added a clear and descriptive title that summarizes this issue.
          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 rather than my code.
+          required: true
+        - label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
+          required: true
+        - label: This is not related to the langchain-community package.
+          required: true
+        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
          required: true
  - type: textarea
    id: reproduction
    validations:
      required: true
    attributes:
-      label: Example Code
+      label: Reproduction Steps / Example Code (Python)
      description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
+        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!**
+
+        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        * Reduce your code to the minimum required to reproduce the issue if possible.
+
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: python
      placeholder: |
        from langgraph.graph import StateGraph

@@ -46,17 +63,13 @@ body:
          
        chain = StateGraph(list)
        chain.invoke('Hello!')
-      render: python
  - type: textarea
-    id: error
-    validations:
-      required: false
    attributes:
      label: Error Message and Stack Trace (if applicable)
      description: |
-        If you are reporting an error, please include the full error message and stack trace.
-      placeholder: |
-        Exception + full stack trace
+        If you are reporting an error, please copy and paste the full error message and
+        stack trace.
+        (This will be automatically formatted into code, so no need for backticks.)
      render: shell
  - type: textarea
    id: description
@@ -77,7 +90,18 @@ body:
    attributes:
      label: System Info
      description: |
-        Run on your machine: `python -m langchain_core.sys_info`
+        Please share your system info with us.
+
+        Run the following command in your terminal and paste the output here:
+
+        `python -m langchain_core.sys_info`
+
+        or if you have an existing python interpreter running:
+
+        ```python
+        from langchain_core import sys_info
+        sys_info.print_sys_info()
+        ```
      placeholder: |
        python -m langchain_core.sys_info
    validations:
@@ -1,9 +1,15 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: Documentation
-    url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
-    about: Report an issue related to the LangGraph documentation
-  - name: LangChain Forum
+  - name: 💬 LangChain Forum
    url:  https://forum.langchain.com/
    about: General community discussions and support
+  - name: 📚 LangGraph Documentation
+    url: https://docs.langchain.com/oss/python/langgraph/overview
+    about: View the official LangGraph documentation
+  - name: 📚 API Reference Documentation
+    url: https://reference.langchain.com/python/
+    about: View the official LangGraph API reference documentation
+  - name: 📚 Documentation issue
+    url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
+    about: Report an issue related to the LangGraph documentation
@@ -21,7 +21,7 @@ Thank you for contributing to LangGraph! Follow these steps to mark your pull re
  1. A test for the integration, preferably unit tests that do not rely on network access,
  2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.

- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.

 Additional guidelines:

@@ -4,15 +4,108 @@ updates:
    directory: "/"
    schedule:
      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"

-  - package-ecosystem: "pip"
-    directories:
-      - "libs/checkpoint"
-      - "libs/checkpoint-postgres"
-      - "libs/checkpoint-sqlite"
-      - "libs/cli"
-      - "libs/langgraph"
-      - "libs/prebuilt"
-      - "libs/sdk-py"
+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint"
    schedule:
      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint-conformance"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+
+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint-postgres"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint-sqlite"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/cli"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/langgraph"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/prebuilt"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/sdk-py"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "npm"
+    directory: "/libs/cli/js-examples"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "npm"
+    directory: "/libs/cli/js-monorepo-example"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
@@ -63,7 +63,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
            try:
                sys.stderr.write("\n== docker compose ps ==\n")
                runner.run(
-                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
+                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
                )
            except Exception:
                pass
@@ -76,7 +76,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
                        "logs",
                        "langgraph-api",
                        input=stdin,
-                        verbose=False,
+                        verbose=True,
                    )
                )
            except Exception:
@@ -2,6 +2,9 @@ name: CLI integration test

 on:
  workflow_call:
+    secrets:
+      LANGSMITH_API_KEY:
+        required: false

 permissions:
  contents: read
@@ -28,6 +31,8 @@ jobs:
            workdir: libs/cli/examples/graphs_reqs_b
            tag: langgraph-test-d
    name: "CLI integration test"
+    env:
+      HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
    defaults:
      run:
        working-directory: libs/cli
@@ -49,19 +54,22 @@ jobs:
      - name: Install cli globally
        if: steps.changed-files.outputs.all
        run: pip install -e .
-      - name: Build and test service ${{ matrix.example.name }}
+      - name: Build service ${{ matrix.example.name }}
        if: steps.changed-files.outputs.all
        working-directory: ${{ matrix.example.workdir }}
+        run: |
+          langgraph build -t ${{ matrix.example.tag }}
+      - name: Test service ${{ matrix.example.name }}
+        if: ${{ steps.changed-files.outputs.all && env.HAS_LANGSMITH_API_KEY == 'true' }}
+        working-directory: ${{ matrix.example.workdir }}
        env:
          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
        run: |
-          # Build the image for this example
-          langgraph build -t ${{ matrix.example.tag }}
          # Prepare environment file from local or parent example directory
          if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
+          if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
          # Run the integration test using the built tag
-          # Compute repo root to reference the shared script robustly
          REPO_ROOT=$(git rev-parse --show-toplevel)
          timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}

@@ -82,22 +90,34 @@ jobs:
        working-directory: libs/cli/python-monorepo-example
        run: |
          langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
+      - name: Test Python monorepo service
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
+        working-directory: libs/cli/python-monorepo-example
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
          cp apps/agent/.env.example apps/agent/.env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json

-      - name: Build and test prerelease reqs service
+      - name: Build prerelease reqs service
        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
        working-directory: libs/cli/examples/graph_prerelease_reqs
        run: |
          langgraph build -t langgraph-test-h
+      - name: Test prerelease reqs service
+        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
+        working-directory: libs/cli/examples/graph_prerelease_reqs
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
          cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
          echo "Finished starting up langgraph-test-h"
          LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
-            echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
+          if [ "$LANGGRAPH_VERSION" != "1.0.8" ]; then
+            echo "LANGGRAPH_VERSION != 1.0.8; $LANGGRAPH_VERSION"
            exit 1
          fi
          LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
@@ -39,6 +39,7 @@ jobs:
              - 'libs/checkpoint/**'
              - 'libs/checkpoint-sqlite/**'
              - 'libs/checkpoint-postgres/**'
+              - 'libs/checkpoint-conformance/**'
              - 'libs/prebuilt/**'
            deps:
              - '**/pyproject.toml'
@@ -57,7 +58,7 @@ jobs:
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
-
+            "libs/checkpoint-conformance",
            "libs/prebuilt",
          ]
    if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
@@ -77,6 +78,7 @@ jobs:
            "libs/checkpoint",
            "libs/checkpoint-sqlite",
            "libs/checkpoint-postgres",
+            "libs/checkpoint-conformance",
            "libs/prebuilt",
            "libs/sdk-py",
          ]
@@ -0,0 +1,49 @@
+name: Deploy Redirects to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'docs/**'
+      - '.github/workflows/deploy-redirects.yml'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Generate redirect files
+        run: python docs/generate_redirects.py
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: 'docs/_site'
+
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4
@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```

 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```

 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.
@@ -0,0 +1 @@
+_site/
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Generate HTML redirect files from redirects.json.
+
+Usage:
+    python generate_redirects.py
+
+This script reads redirects.json and generates individual HTML files
+for each redirect path. Each HTML file uses meta refresh (0 delay)
+which is SEO-friendly and treated similarly to 301 redirects by Google.
+
+To add new redirects, simply edit redirects.json and re-run this script.
+"""
+
+import json
+import os
+from pathlib import Path
+
+# Default fallback URL for any path not in the redirect map
+DEFAULT_REDIRECT = "https://docs.langchain.com/oss/python/langgraph/overview"
+
+HTML_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting...</title>
+    <link rel="canonical" href="{url}">
+    <meta name="robots" content="noindex">
+    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
+    <meta http-equiv="refresh" content="0; url={url}">
+</head>
+<body>
+Redirecting...
+</body>
+</html>
+"""
+
+ROOT_HTML_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting to LangGraph Documentation</title>
+    <link rel="canonical" href="{url}">
+    <meta name="robots" content="noindex">
+    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
+    <meta http-equiv="refresh" content="0; url={url}">
+</head>
+<body>
+<h1>Documentation has moved</h1>
+<p>The LangGraph documentation has moved to <a href="{url}">docs.langchain.com</a>.</p>
+<p>Redirecting you now...</p>
+</body>
+</html>
+"""
+
+CATCHALL_404_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting to LangGraph Documentation</title>
+    <link rel="canonical" href="{default_url}">
+    <meta name="robots" content="noindex">
+    <script>
+        // Catchall redirect for any unmapped paths
+        window.location.replace("{default_url}");
+    </script>
+    <meta http-equiv="refresh" content="0; url={default_url}">
+</head>
+<body>
+<h1>Documentation has moved</h1>
+<p>The LangGraph documentation has moved to <a href="{default_url}">docs.langchain.com</a>.</p>
+<p>Redirecting you now...</p>
+</body>
+</html>
+"""
+
+
+def generate_redirects():
+    script_dir = Path(__file__).parent
+    output_dir = script_dir / "_site"
+
+    # Load redirects
+    with open(script_dir / "redirects.json") as f:
+        redirects = json.load(f)
+
+    # Clean output directory
+    if output_dir.exists():
+        import shutil
+        shutil.rmtree(output_dir)
+    output_dir.mkdir(parents=True)
+
+    # Generate individual HTML files for each redirect
+    for old_path, new_url in redirects.items():
+        # Remove leading slash and create directory structure
+        path = old_path.lstrip("/")
+
+        # Check if path has a file extension (e.g., .txt, .xml)
+        # If so, create the file directly instead of a directory with index.html
+        path_obj = Path(path)
+        has_extension = path_obj.suffix and len(path_obj.suffix) <= 5
+
+        if not path:
+            html_path = output_dir / "index.html"
+        elif has_extension:
+            # For files with extensions, create the file directly
+            html_path = output_dir / path
+        else:
+            # For directory-style URLs, create index.html inside
+            html_path = output_dir / path / "index.html"
+
+        # Create parent directories
+        html_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Write the redirect HTML
+        html_path.write_text(HTML_TEMPLATE.format(url=new_url))
+        print(f"Created: {html_path}")
+
+    # Create root index.html
+    root_index = output_dir / "index.html"
+    if not root_index.exists():
+        root_index.write_text(ROOT_HTML_TEMPLATE.format(url=DEFAULT_REDIRECT))
+        print(f"Created: {root_index}")
+
+    # Create 404.html for catchall
+    catchall_404 = output_dir / "404.html"
+    catchall_404.write_text(CATCHALL_404_TEMPLATE.format(default_url=DEFAULT_REDIRECT))
+    print(f"Created: {catchall_404}")
+
+    # Copy static files (like llms.txt) that can't be redirected via HTML
+    static_files = ["llms.txt"]
+    for static_file in static_files:
+        src = script_dir / static_file
+        if src.exists():
+            dst = output_dir / static_file
+            dst.write_text(src.read_text())
+            print(f"Copied: {dst}")
+
+    print(f"\nGenerated {len(redirects)} redirect files in {output_dir}")
+
+
+if __name__ == "__main__":
+    generate_redirects()
@@ -0,0 +1,35 @@
+# LangGraph
+
+LangGraph documentation has moved to docs.langchain.com.
+
+## Overview
+
+- [LangGraph Overview](https://docs.langchain.com/oss/python/langgraph/overview): Introduction to LangGraph, a library for building stateful, multi-actor applications with LLMs.
+- [Why LangGraph?](https://docs.langchain.com/oss/python/langgraph/why-langgraph): Motivation for LangGraph and its key features.
+
+## Core Concepts
+
+- [Graph API](https://docs.langchain.com/oss/python/langgraph/graph-api): Learn how to define state, create nodes, and connect them with edges.
+- [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming): Stream outputs from your graph for better UX.
+- [Persistence](https://docs.langchain.com/oss/python/langgraph/persistence): Add memory and checkpointing to your graphs.
+- [Add Memory](https://docs.langchain.com/oss/python/langgraph/add-memory): Implement short-term and long-term memory.
+- [Workflows & Agents](https://docs.langchain.com/oss/python/langgraph/workflows-agents): Build agents and workflows with LangGraph.
+
+## How-To Guides
+
+- [Use Subgraphs](https://docs.langchain.com/oss/python/langgraph/use-subgraphs): Compose graphs using subgraphs.
+- [Observability](https://docs.langchain.com/oss/python/langgraph/observability): Add tracing and debugging to your graphs.
+- [Common Errors](https://docs.langchain.com/oss/python/langgraph/common-errors): Troubleshoot common LangGraph errors.
+
+## Tutorials
+
+- [Agentic RAG](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Build an agentic RAG system with LangGraph.
+- [SQL Agent](https://docs.langchain.com/oss/python/langgraph/sql-agent): Create a SQL agent with LangGraph.
+
+## Reference
+
+- [API Reference](https://reference.langchain.com/python/langgraph/): Complete API documentation for LangGraph.
+
+## LangGraph Platform
+
+For deploying LangGraph applications in production, see the [LangSmith documentation](https://docs.langchain.com/langsmith/agent-server).
@@ -0,0 +1,296 @@
+{
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+  "/how-tos/stream-updates": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-content": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/stream-multiple": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-tokens-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-from-final-node": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-events-from-within-tools-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/state-reducers": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-and-update-state",
+  "/how-tos/sequence": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-a-sequence-of-steps",
+  "/how-tos/branching": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-branches",
+  "/how-tos/recursion-limit": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-and-control-loops",
+  "/how-tos/visualization": "https://docs.langchain.com/oss/python/langgraph/graph-api#visualize-your-graph",
+  "/how-tos/input_output_schema": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-input-and-output-schemas",
+  "/how-tos/pass_private_state": "https://docs.langchain.com/oss/python/langgraph/graph-api#pass-private-state-between-nodes",
+  "/how-tos/state-model": "https://docs.langchain.com/oss/python/langgraph/graph-api#use-pydantic-models-for-graph-state",
+  "/how-tos/map-reduce": "https://docs.langchain.com/oss/python/langgraph/graph-api#map-reduce-and-the-send-api",
+  "/how-tos/command": "https://docs.langchain.com/oss/python/langgraph/graph-api#combine-control-flow-and-state-updates-with-command",
+  "/how-tos/configuration": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-runtime-configuration",
+  "/how-tos/node-retries": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-retry-policies",
+  "/how-tos/return-when-recursion-limit-hits": "https://docs.langchain.com/oss/python/langgraph/graph-api#impose-a-recursion-limit",
+  "/how-tos/async": "https://docs.langchain.com/oss/python/langgraph/graph-api#async",
+  "/how-tos/memory/manage-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/memory/delete-messages": "https://docs.langchain.com/oss/python/langgraph/add-memory#delete-messages",
+  "/how-tos/memory/add-summary-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory#summarize-messages",
+  "/how-tos/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/agents/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/subgraph-transform-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#different-state-schemas",
+  "/how-tos/subgraphs-manage-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#add-persistence",
+  "/how-tos/persistence_postgres": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/persistence_mongodb": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/persistence_redis": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/subgraph-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-with-subgraphs",
+  "/how-tos/cross-thread-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
+  "/cloud/how-tos/copy_threads": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/how-tos/check-thread-status": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/concepts/threads": "https://docs.langchain.com/oss/python/langgraph/persistence#threads",
+  "/how-tos/persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/tool-calling-errors": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/pass-config-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/pass-run-time-values-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/update-state-from-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/agents/tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/agent-handoffs": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-network": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-multi-turn-convo": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/cloud/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/cloud/how-tos/index": "https://docs.langchain.com/langsmith/home",
+  "/cloud/concepts/api": "https://docs.langchain.com/langsmith/agent-server",
+  "/cloud/concepts/cloud": "https://docs.langchain.com/langsmith/cloud",
+  "/cloud/faq/studio": "https://docs.langchain.com/langsmith/studio",
+  "/cloud/how-tos/human_in_the_loop_edit_state": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/cloud/how-tos/human_in_the_loop_user_input": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/concepts/platform_architecture": "https://docs.langchain.com/langsmith/cloud#architecture",
+  "/cloud/how-tos/stream_values": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_updates": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_messages": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_events": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_debug": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_multiple": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/concepts/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/agents/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/create-react-agent": "https://docs.langchain.com/oss/python/langchain/agents#basic-configuration",
+  "/how-tos/create-react-agent-memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/create-react-agent-system-prompt": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/create-react-agent-structured-output": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+  "/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/reference/prebuilt": "https://reference.langchain.com/python/langgraph/agents/",
+  "/concepts/high_level": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/v0-human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/introduction": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/deployment": "https://docs.langchain.com/oss/python/langgraph/local-server",
+  "/how-tos/deploy-self-hosted": "https://docs.langchain.com/langsmith/platform-setup",
+  "/concepts/self_hosted": "https://docs.langchain.com/langsmith/platform-setup",
+  "/tutorials/deployment": "https://docs.langchain.com/langsmith/deployments",
+  "/cloud/how-tos/assistant_versioning": "https://docs.langchain.com/langsmith/configuration-cloud",
+  "/cloud/concepts/runs": "https://docs.langchain.com/langsmith/assistants#execution",
+  "/how-tos/wait-user-input-functional": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/how-tos/review-tool-calls-functional": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/how-tos/create-react-agent-hitl": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/agents/human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/human_in_the_loop/dynamic_breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/concepts/breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/human_in_the_loop/breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/cloud/how-tos/human_in_the_loop_breakpoint": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/how-tos/human_in_the_loop/edit-graph-state": "https://docs.langchain.com/oss/python/langgraph/use-time-travel",
+  "/examples/index": "https://docs.langchain.com/oss/python/langgraph/case-studies",
+  "/guides/index": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/tutorials/index": "https://docs.langchain.com/oss/python/learn",
+  "/llms-txt-overview": "https://docs.langchain.com/llms.txt",
+  "/tutorials/rag/langgraph_adaptive_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/multi-agent-collaboration": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+  "/how-tos/create-react-agent-manage-message-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/many-tools": "https://docs.langchain.com/oss/python/langchain/tools",
+  "/tutorials/customer-support/customer-support": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/how-tos/react-agent-structured-output": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+  "/tutorials/code_assistant/langgraph_code_assistant": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/hierarchical_agent_teams": "https://docs.langchain.com/oss/python/langchain/supervisor",
+  "/tutorials/auth/getting_started": "https://docs.langchain.com/langsmith/auth",
+  "/tutorials/auth/resource_auth": "https://docs.langchain.com/langsmith/resource-auth",
+  "/tutorials/auth/add_auth_server": "https://docs.langchain.com/langsmith/add-auth-server",
+  "/how-tos/use-remote-graph": "https://docs.langchain.com/langsmith/use-remote-graph",
+  "/how-tos/autogen-integration": "https://docs.langchain.com/langsmith/autogen-integration",
+  "/how-tos/human_in_the_loop/wait-user-input": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/cloud/how-tos/use_stream_react": "https://docs.langchain.com/langsmith/use-stream-react",
+  "/cloud/how-tos/generative_ui_react": "https://docs.langchain.com/langsmith/generative-ui-react",
+  "/concepts/langgraph_platform": "https://docs.langchain.com/langsmith/home",
+  "/concepts/langgraph_components": "https://docs.langchain.com/langsmith/components",
+  "/concepts/langgraph_server": "https://docs.langchain.com/langsmith/agent-server",
+  "/concepts/langgraph_data_plane": "https://docs.langchain.com/langsmith/data-plane",
+  "/concepts/langgraph_control_plane": "https://docs.langchain.com/langsmith/control-plane",
+  "/concepts/langgraph_cli": "https://docs.langchain.com/langsmith/cli",
+  "/concepts/langgraph_studio": "https://docs.langchain.com/langsmith/studio",
+  "/cloud/how-tos/studio/quick_start": "https://docs.langchain.com/langsmith/quick-start-studio",
+  "/cloud/how-tos/invoke_studio": "https://docs.langchain.com/langsmith/use-studio",
+  "/cloud/how-tos/studio/manage_assistants": "https://docs.langchain.com/langsmith/use-studio",
+  "/cloud/how-tos/threads_studio": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/how-tos/iterate_graph_studio": "https://docs.langchain.com/langsmith/use-studio",
+  "/cloud/how-tos/studio/run_evals": "https://docs.langchain.com/langsmith/observability",
+  "/cloud/how-tos/clone_traces_studio": "https://docs.langchain.com/langsmith/observability",
+  "/cloud/how-tos/datasets_studio": "https://docs.langchain.com/langsmith/use-studio",
+  "/concepts/sdk": "https://docs.langchain.com/langsmith/sdk",
+  "/concepts/plans": "https://docs.langchain.com/langsmith/home",
+  "/concepts/application_structure": "https://docs.langchain.com/langsmith/application-structure",
+  "/concepts/scalability_and_resilience": "https://docs.langchain.com/langsmith/scalability-and-resilience",
+  "/concepts/auth": "https://docs.langchain.com/langsmith/auth",
+  "/how-tos/auth/custom_auth": "https://docs.langchain.com/langsmith/custom-auth",
+  "/how-tos/auth/openapi_security": "https://docs.langchain.com/langsmith/openapi-security",
+  "/concepts/assistants": "https://docs.langchain.com/langsmith/assistants",
+  "/cloud/how-tos/configuration_cloud": "https://docs.langchain.com/langsmith/configuration-cloud",
+  "/cloud/how-tos/use_threads": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/how-tos/background_run": "https://docs.langchain.com/langsmith/background-run",
+  "/cloud/how-tos/same-thread": "https://docs.langchain.com/langsmith/same-thread",
+  "/cloud/how-tos/stateless_runs": "https://docs.langchain.com/langsmith/stateless-runs",
+  "/cloud/how-tos/configurable_headers": "https://docs.langchain.com/langsmith/configurable-headers",
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+  "/cloud/how-tos/rollback_concurrent": "https://docs.langchain.com/langsmith/rollback-concurrent",
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+  "/cloud/deployment/setup_javascript": "https://docs.langchain.com/langsmith/setup-javascript",
+  "/cloud/deployment/custom_docker": "https://docs.langchain.com/langsmith/custom-docker",
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+  "/concepts/langgraph_cloud": "https://docs.langchain.com/langsmith/cloud",
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+  "/concepts/langgraph_self_hosted_control_plane": "https://docs.langchain.com/langsmith/platform-setup",
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+  "/cloud/deployment/egress": "https://docs.langchain.com/langsmith/env-var",
+  "/cloud/how-tos/streaming": "https://docs.langchain.com/langsmith/streaming",
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+  "/cloud/reference/langgraph_server_changelog": "https://docs.langchain.com/langsmith/agent-server-changelog",
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+  "/cloud/reference/cli": "https://docs.langchain.com/langsmith/cli",
+  "/cloud/reference/env_var": "https://docs.langchain.com/langsmith/env-var",
+  "/troubleshooting/studio": "https://docs.langchain.com/langsmith/troubleshooting-studio",
+  "/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/agents": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/concepts/why-langgraph": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/get-started/1-build-basic-chatbot": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/2-add-tools": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/3-add-memory": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/4-human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/5-customize-state": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/6-time-travel": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/langsmith/local-server": "https://docs.langchain.com/oss/python/langgraph/local-server",
+  "/tutorials/workflows": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/tutorials/plan-and-execute/plan-and-execute": "https://docs.langchain.com/oss/python/langchain/middleware/built-in#to-do-list",
+  "/tutorials/langgraph-platform/local-server/local-server": "https://docs.langchain.com/langsmith/local-server",
+  "/concepts/agentic_concepts": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/agents/overview": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/agents/run_agents": "https://docs.langchain.com/oss/python/langgraph/quickstart",
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+  "/how-tos/graph-api": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/react-agent-from-scratch": "https://docs.langchain.com/oss/python/langchain/quickstart",
+  "/concepts/functional_api": "https://docs.langchain.com/oss/python/langgraph/functional-api",
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+  "/concepts/pregel": "https://docs.langchain.com/oss/python/langgraph/pregel",
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+  "/concepts/subgraphs": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
+  "/how-tos/subgraph": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
+  "/concepts/multi_agent": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/agents/multi-agent": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+  "/how-tos/multi_agent": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/concepts/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/how-tos/enable-tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/agents/evals": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/template_applications": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rag/langgraph_agentic_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/agent_supervisor": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/tutorials/sql/sql-agent": "https://docs.langchain.com/oss/python/langgraph/sql-agent",
+  "/agents/ui": "https://docs.langchain.com/oss/python/langgraph/ui",
+  "/how-tos/run-id-langsmith": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/troubleshooting/errors/index": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/troubleshooting/errors/INVALID_CHAT_HISTORY": "https://docs.langchain.com/oss/python/langgraph/INVALID_CHAT_HISTORY",
+  "/troubleshooting/errors/INVALID_LICENSE": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/adopters": "https://docs.langchain.com/oss/python/langgraph/case-studies",
+  "/concepts/faq": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/reference/index": "https://reference.langchain.com/python/langgraph/",
+  "/reference/graphs": "https://reference.langchain.com/python/langgraph/graphs/",
+  "/reference/func": "https://reference.langchain.com/python/langgraph/func/",
+  "/reference/pregel": "https://reference.langchain.com/python/langgraph/pregel/",
+  "/reference/checkpoints": "https://reference.langchain.com/python/langgraph/checkpoints/",
+  "/reference/store": "https://reference.langchain.com/python/langgraph/store/",
+  "/reference/cache": "https://reference.langchain.com/python/langgraph/cache/",
+  "/reference/types": "https://reference.langchain.com/python/langgraph/types/",
+  "/reference/runtime": "https://reference.langchain.com/python/langgraph/runtime/",
+  "/reference/config": "https://reference.langchain.com/python/langgraph/config/",
+  "/reference/errors": "https://reference.langchain.com/python/langgraph/errors/",
+  "/reference/constants": "https://reference.langchain.com/python/langgraph/constants/",
+  "/reference/channels": "https://reference.langchain.com/python/langgraph/channels/",
+  "/reference/agents": "https://reference.langchain.com/python/langgraph/agents/",
+  "/reference/supervisor": "https://reference.langchain.com/python/langgraph/supervisor/",
+  "/reference/swarm": "https://reference.langchain.com/python/langgraph/swarm/",
+  "/reference/mcp": "https://reference.langchain.com/python/langgraph/mcp/",
+  "/cloud/reference/sdk/python_sdk_ref": "https://reference.langchain.com/python/langsmith/deployment/sdk/",
+  "/reference/remote_graph": "https://reference.langchain.com/python/langsmith/deployment/remote_graph/",
+  "/additional-resources/index": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/cloud/reference/sdk/js_ts_sdk_ref": "https://reference.langchain.com/javascript/modules/langsmith.html",
+  "/snippets/chat_model_tabs": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/troubleshooting/errors/GRAPH_RECURSION_LIMIT": "https://docs.langchain.com/oss/python/langgraph/GRAPH_RECURSION_LIMIT",
+  "/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE": "https://docs.langchain.com/oss/python/langgraph/INVALID_CONCURRENT_GRAPH_UPDATE",
+  "/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE": "https://docs.langchain.com/oss/python/langgraph/INVALID_GRAPH_NODE_RETURN_VALUE",
+  "/troubleshooting/errors/MULTIPLE_SUBGRAPHS": "https://docs.langchain.com/oss/python/langgraph/MULTIPLE_SUBGRAPHS",
+  "/tutorials/rag/langgraph_self_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/additional-resources": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/examples": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/guides": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/how-tos/autogen-integration-functional": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/how-tos/cross-thread-persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
+  "/how-tos/disable-streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/memory/semantic-search": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/multi-agent-multi-turn-convo-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-network-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/react-agent-from-scratch-functional": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/reference": "https://reference.langchain.com/python/langgraph/",
+  "/troubleshooting/errors": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/chatbots/information-gather-prompting": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/extraction/retries": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/langgraph-platform/local-server": "https://docs.langchain.com/langsmith/agent-server",
+  "/tutorials/lats/lats": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/llm-compiler/LLMCompiler": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rag/langgraph_adaptive_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_crag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_crag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_self_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/reflection/reflection": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/reflexion/reflexion": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rewoo/rewoo": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/self-discover/self-discover": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/tnt-llm/tnt-llm": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/tot/tot": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/usaco/usaco": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/web-navigation/web_voyager": "https://docs.langchain.com/oss/python/langgraph/overview"
+}
@@ -1,3 +1,3 @@
 # LangGraph examples

-This directory should NOT be used for documentation. All new documentation must be added to `docs/docs/` directory.
+This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23544406",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/async.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "14f7ca50",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/branching.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "10251c1c",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fc63df",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "a4351a24",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cc9af1e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "a9014f94",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f47ce992",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2b789e16",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/cloud/how-tos/langgraph_to_langgraph_cloud.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "1f2f13ca",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4c9bfe",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d38cbab",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
@@ -33,7 +41,9 @@
   "id": "e501686f-323f-4b87-8f9c-8ba89133078b",
   "metadata": {},
   "outputs": [],
-   "source": ["! pip install -U langchain_community langchain-mistralai langchain langgraph"]
+   "source": [
+    "! pip install -U langchain_community langchain-mistralai langchain langgraph"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -51,7 +61,12 @@
   "id": "982e4609-86e4-4934-828f-e03d89c20393",
   "metadata": {},
   "outputs": [],
-   "source": ["import os\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\nmistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"]
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
+    "mistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -69,7 +84,12 @@
   "id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
   "metadata": {},
   "outputs": [],
-   "source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\nos.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""]
+   "source": [
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
+   ]
  },
  {
   "cell_type": "markdown",
@@ -87,7 +107,42 @@
   "id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
   "metadata": {},
   "outputs": [],
-   "source": ["# Select LLM\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.pydantic_v1 import BaseModel, Field\nfrom langchain_mistralai import ChatMistralAI\n\nmistral_model = \"mistral-large-latest\"\nllm = ChatMistralAI(model=mistral_model, temperature=0)\n\n# Prompt\ncode_gen_prompt_claude = ChatPromptTemplate.from_messages(\n    [\n        (\n            \"system\",\n            \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n            defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n            \\n Here is the user question:\"\"\",\n        ),\n        (\"placeholder\", \"{messages}\"),\n    ]\n)\n\n\n# Data model\nclass code(BaseModel):\n    \"\"\"Code output\"\"\"\n\n    prefix: str = Field(description=\"Description of the problem and approach\")\n    imports: str = Field(description=\"Code block import statements\")\n    code: str = Field(description=\"Code block not including import statements\")\n    description = \"Schema for code solutions to questions about LCEL.\"\n\n\n# LLM\ncode_gen_chain = llm.with_structured_output(code, include_raw=False)"]
+   "source": [
+    "# Select LLM\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_mistralai import ChatMistralAI\n",
+    "\n",
+    "mistral_model = \"mistral-large-latest\"\n",
+    "llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
+    "            defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
+    "            \\n Here is the user question:\"\"\",\n",
+    "        ),\n",
+    "        (\"placeholder\", \"{messages}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class code(BaseModel):\n",
+    "    \"\"\"Code output\"\"\"\n",
+    "\n",
+    "    prefix: str = Field(description=\"Description of the problem and approach\")\n",
+    "    imports: str = Field(description=\"Code block import statements\")\n",
+    "    code: str = Field(description=\"Code block not including import statements\")\n",
+    "    description = \"Schema for code solutions to questions about LCEL.\"\n",
+    "\n",
+    "\n",
+    "# LLM\n",
+    "code_gen_chain = llm.with_structured_output(code, include_raw=False)"
+   ]
  },
  {
   "cell_type": "code",
@@ -95,7 +150,10 @@
   "id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
   "metadata": {},
   "outputs": [],
-   "source": ["question = \"Write a function for fibonacci.\"\nmessages = [(\"user\", question)]"]
+   "source": [
+    "question = \"Write a function for fibonacci.\"\n",
+    "messages = [(\"user\", question)]"
+   ]
  },
  {
   "cell_type": "code",
@@ -114,7 +172,11 @@
     "output_type": "execute_result"
    }
   ],
-   "source": ["# Test\nresult = code_gen_chain.invoke(messages)\nresult"]
+   "source": [
+    "# Test\n",
+    "result = code_gen_chain.invoke(messages)\n",
+    "result"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -130,7 +192,28 @@
   "id": "183d77b8-f180-4815-b39f-8ef507ec0534",
   "metadata": {},
   "outputs": [],
-   "source": ["from typing import Annotated, TypedDict\n\nfrom langgraph.graph.message import AnyMessage, add_messages\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        error : Binary flag for control flow to indicate whether test error was tripped\n        messages : With user question, error messages, reasoning\n        generation : Code solution\n        iterations : Number of tries\n    \"\"\"\n\n    error: str\n    messages: Annotated[list[AnyMessage], add_messages]\n    generation: str\n    iterations: int"]
+   "source": [
+    "from typing import Annotated, TypedDict\n",
+    "\n",
+    "from langgraph.graph.message import AnyMessage, add_messages\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        error : Binary flag for control flow to indicate whether test error was tripped\n",
+    "        messages : With user question, error messages, reasoning\n",
+    "        generation : Code solution\n",
+    "        iterations : Number of tries\n",
+    "    \"\"\"\n",
+    "\n",
+    "    error: str\n",
+    "    messages: Annotated[list[AnyMessage], add_messages]\n",
+    "    generation: str\n",
+    "    iterations: int"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -146,7 +229,163 @@
   "id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
   "metadata": {},
   "outputs": [],
-   "source": ["import uuid\n\nfrom langchain_core.pydantic_v1 import BaseModel, Field\n\n### Parameters\nmax_iterations = 3\n\n\n### Nodes\ndef generate(state: GraphState):\n    \"\"\"\n    Generate a code solution\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation\n    \"\"\"\n\n    print(\"---GENERATING CODE SOLUTION---\")\n\n    # State\n    messages = state[\"messages\"]\n    iterations = state[\"iterations\"]\n\n    # Solution\n    code_solution = code_gen_chain.invoke(messages)\n    messages += [\n        (\n            \"assistant\",\n            f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n        )\n    ]\n\n    # Increment\n    iterations = iterations + 1\n    return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n\n\ndef code_check(state: GraphState):\n    \"\"\"\n    Check code\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, error\n    \"\"\"\n\n    print(\"---CHECKING CODE---\")\n\n    # State\n    messages = state[\"messages\"]\n    code_solution = state[\"generation\"]\n    iterations = state[\"iterations\"]\n\n    # Get solution components\n    imports = code_solution.imports\n    code = code_solution.code\n\n    # Check imports\n    try:\n        exec(imports)\n    except Exception as e:\n        print(\"---CODE IMPORT CHECK: FAILED---\")\n        error_message = [\n            (\n                \"user\",\n                f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n            )\n        ]\n        messages += error_message\n        return {\n            \"generation\": code_solution,\n            \"messages\": messages,\n            \"iterations\": iterations,\n            \"error\": \"yes\",\n        }\n\n    # Check execution\n    try:\n        combined_code = f\"{imports}\\n{code}\"\n        print(f\"CODE TO TEST: {combined_code}\")\n        # Use a shared scope for exec\n        global_scope = {}\n        exec(combined_code, global_scope)\n    except Exception as e:\n        print(\"---CODE BLOCK CHECK: FAILED---\")\n        error_message = [\n            (\n                \"user\",\n                f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n            )\n        ]\n        messages += error_message\n        return {\n            \"generation\": code_solution,\n            \"messages\": messages,\n            \"iterations\": iterations,\n            \"error\": \"yes\",\n        }\n\n    # No errors\n    print(\"---NO CODE TEST FAILURES---\")\n    return {\n        \"generation\": code_solution,\n        \"messages\": messages,\n        \"iterations\": iterations,\n        \"error\": \"no\",\n    }\n\n\n### Conditional edges\n\n\ndef decide_to_finish(state: GraphState):\n    \"\"\"\n    Determines whether to finish.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Next node to call\n    \"\"\"\n    error = state[\"error\"]\n    iterations = state[\"iterations\"]\n\n    if error == \"no\" or iterations == max_iterations:\n        print(\"---DECISION: FINISH---\")\n        return \"end\"\n    else:\n        print(\"---DECISION: RE-TRY SOLUTION---\")\n        return \"generate\"\n\n\n### Utilities\n\n\ndef _print_event(event: dict, _printed: set, max_length=1500):\n    current_state = event.get(\"dialog_state\")\n    if current_state:\n        print(\"Currently in: \", current_state[-1])\n    message = event.get(\"messages\")\n    if message:\n        if isinstance(message, list):\n            message = message[-1]\n        if message.id not in _printed:\n            msg_repr = message.pretty_repr(html=True)\n            if len(msg_repr) > max_length:\n                msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n            print(msg_repr)\n            _printed.add(message.id)"]
+   "source": [
+    "import uuid\n",
+    "\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "### Parameters\n",
+    "max_iterations = 3\n",
+    "\n",
+    "\n",
+    "### Nodes\n",
+    "def generate(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Generate a code solution\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---GENERATING CODE SOLUTION---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Solution\n",
+    "    code_solution = code_gen_chain.invoke(messages)\n",
+    "    messages += [\n",
+    "        (\n",
+    "            \"assistant\",\n",
+    "            f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    # Increment\n",
+    "    iterations = iterations + 1\n",
+    "    return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
+    "\n",
+    "\n",
+    "def code_check(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Check code\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, error\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECKING CODE---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    code_solution = state[\"generation\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Get solution components\n",
+    "    imports = code_solution.imports\n",
+    "    code = code_solution.code\n",
+    "\n",
+    "    # Check imports\n",
+    "    try:\n",
+    "        exec(imports)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE IMPORT CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # Check execution\n",
+    "    try:\n",
+    "        combined_code = f\"{imports}\\n{code}\"\n",
+    "        print(f\"CODE TO TEST: {combined_code}\")\n",
+    "        # Use a shared scope for exec\n",
+    "        global_scope = {}\n",
+    "        exec(combined_code, global_scope)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE BLOCK CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # No errors\n",
+    "    print(\"---NO CODE TEST FAILURES---\")\n",
+    "    return {\n",
+    "        \"generation\": code_solution,\n",
+    "        \"messages\": messages,\n",
+    "        \"iterations\": iterations,\n",
+    "        \"error\": \"no\",\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "### Conditional edges\n",
+    "\n",
+    "\n",
+    "def decide_to_finish(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Determines whether to finish.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "    error = state[\"error\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    if error == \"no\" or iterations == max_iterations:\n",
+    "        print(\"---DECISION: FINISH---\")\n",
+    "        return \"end\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: RE-TRY SOLUTION---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "### Utilities\n",
+    "\n",
+    "\n",
+    "def _print_event(event: dict, _printed: set, max_length=1500):\n",
+    "    current_state = event.get(\"dialog_state\")\n",
+    "    if current_state:\n",
+    "        print(\"Currently in: \", current_state[-1])\n",
+    "    message = event.get(\"messages\")\n",
+    "    if message:\n",
+    "        if isinstance(message, list):\n",
+    "            message = message[-1]\n",
+    "        if message.id not in _printed:\n",
+    "            msg_repr = message.pretty_repr(html=True)\n",
+    "            if len(msg_repr) > max_length:\n",
+    "                msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
+    "            print(msg_repr)\n",
+    "            _printed.add(message.id)"
+   ]
  },
  {
   "cell_type": "code",
@@ -154,7 +393,31 @@
   "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
   "metadata": {},
   "outputs": [],
-   "source": ["from langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\nbuilder = StateGraph(GraphState)\n\n# Define the nodes\nbuilder.add_node(\"generate\", generate)  # generation solution\nbuilder.add_node(\"check_code\", code_check)  # check code\n\n# Build graph\nbuilder.add_edge(START, \"generate\")\nbuilder.add_edge(\"generate\", \"check_code\")\nbuilder.add_conditional_edges(\n    \"check_code\",\n    decide_to_finish,\n    {\n        \"end\": END,\n        \"generate\": \"generate\",\n    },\n)\n\nmemory = InMemorySaver()\ngraph = builder.compile(checkpointer=memory)"]
+   "source": [
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "builder = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "builder.add_node(\"generate\", generate)  # generation solution\n",
+    "builder.add_node(\"check_code\", code_check)  # check code\n",
+    "\n",
+    "# Build graph\n",
+    "builder.add_edge(START, \"generate\")\n",
+    "builder.add_edge(\"generate\", \"check_code\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"check_code\",\n",
+    "    decide_to_finish,\n",
+    "    {\n",
+    "        \"end\": END,\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "memory = InMemorySaver()\n",
+    "graph = builder.compile(checkpointer=memory)"
+   ]
  },
  {
   "cell_type": "code",
@@ -173,7 +436,15 @@
     "output_type": "display_data"
    }
   ],
-   "source": ["from IPython.display import Image, display\n\ntry:\n    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n    # This requires some extra dependencies and is optional\n    pass"]
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "try:\n",
+    "    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
+    "except Exception:\n",
+    "    # This requires some extra dependencies and is optional\n",
+    "    pass"
+   ]
  },
  {
   "cell_type": "code",
@@ -181,7 +452,23 @@
   "id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
   "metadata": {},
   "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"Write a Python program that prints 'Hello, World!' to the console.\"\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -199,7 +486,31 @@
   "id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
   "metadata": {},
   "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n\nRequirements:\nThe program should define a function is_palindrome(s) that takes a string s as input.\nThe function should return True if the string is a palindrome and False otherwise.\nIgnore spaces, punctuation, and case differences when checking for palindromes.\n\nGive an example of it working on an example input word.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
+    "\n",
+    "Requirements:\n",
+    "The program should define a function is_palindrome(s) that takes a string s as input.\n",
+    "The function should return True if the string is a palindrome and False otherwise.\n",
+    "Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
+    "\n",
+    "Give an example of it working on an example input word.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -217,7 +528,26 @@
   "id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
   "metadata": {},
   "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Write a program that prints the numbers from 1 to 100. \nBut for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \nFor numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
+    "But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
+    "For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -235,7 +565,37 @@
   "id": "2bb883df-540b-46ab-9415-fe27db68456f",
   "metadata": {},
   "outputs": [],
-   "source": ["import uuid\n\n_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"I want to vectorize a function\n\n        frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n        for i, val1 in enumerate(rows):\n            for j, val2 in enumerate(cols):\n                for j, val3 in enumerate(ch):\n                    # Assuming you want to store the pair as tuples in the matrix\n                    frame[i, j, k] = image[val1, val2, val3]\n\n        out.write(np.array(frame))\n\nwith a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "import uuid\n",
+    "\n",
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"I want to vectorize a function\n",
+    "\n",
+    "        frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
+    "        for i, val1 in enumerate(rows):\n",
+    "            for j, val2 in enumerate(cols):\n",
+    "                for j, val3 in enumerate(ch):\n",
+    "                    # Assuming you want to store the pair as tuples in the matrix\n",
+    "                    frame[i, j, k] = image[val1, val2, val3]\n",
+    "\n",
+    "        out.write(np.array(frame))\n",
+    "\n",
+    "with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -253,7 +613,34 @@
   "id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
   "metadata": {},
   "outputs": [],
-   "source": ["_printed = set()\nthread_id = str(uuid.uuid4())\nconfig = {\n    \"configurable\": {\n        # Checkpoints are accessed by thread_id\n        \"thread_id\": thread_id,\n    }\n}\n\nquestion = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n\n- Allow players to take turns to input their moves.\n- Check for invalid moves (e.g., placing a marker on an already occupied space).\n- Determine and announce the winner or if the game ends in a draw.\n\nRequirements:\n- Use a 2D list to represent the Tic-Tac-Toe board.\n- Use functions to modularize the code.\n- Validate player input.\n- Check for win conditions and draw conditions after each move.\"\"\"\n\nevents = graph.stream(\n    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n)\nfor event in events:\n    _print_event(event, _printed)"]
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
+    "\n",
+    "- Allow players to take turns to input their moves.\n",
+    "- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
+    "- Determine and announce the winner or if the game ends in a draw.\n",
+    "\n",
+    "Requirements:\n",
+    "- Use a 2D list to represent the Tic-Tac-Toe board.\n",
+    "- Use functions to modularize the code.\n",
+    "- Validate player input.\n",
+    "- Check for win conditions and draw conditions after each move.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
  },
  {
   "cell_type": "markdown",
@@ -271,7 +658,7 @@
   "id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
   "metadata": {},
   "outputs": [],
-   "source": [""]
+   "source": []
  }
 ],
 "metadata": {
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e9a58c69",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/configuration.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a1e6efeb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-hitl.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1ef41a89",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-memory.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9e2f7902",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent-system-prompt.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "eb07372e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/create-react-agent.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "a8232bc9",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/customer-support/customer-support.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63da8671",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "8dbdba5b",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/extraction/retries.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d444b7f",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "3ecab357",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f2866bd",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fc0793cb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/input_output_schema.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "09038b53",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/lats/lats.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/lats/lats.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1669748",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "85205e97",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fdab366",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "42abb708",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/map-reduce.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "298784f6",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-summary-conversation-history.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3f4370fd",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/delete-messages.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6ec7cb13",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/manage-conversation-history.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "5cc8a2ad",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9f3508a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "d2b507b9",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41a8f10a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "017a01f4",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/node-retries.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "05f6ad0a",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-config-to-tools.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8f38bec5",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass-run-time-values-to-tools.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4da17088",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/pass_private_state.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d16e8b9c",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "78217098",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_mongodb.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "18526f23",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/memory/add-memory.md"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "eee6ecdd",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/persistence_redis.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "9138f92e",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "093678ba",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fedd6d23",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "36fa621a-9d3d-4860-a17c-5d20e6987481.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39b26b09",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "3755396d-c4a8-45bd-87d4-00cb56339fe5.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "47e3b43b",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "425fb020-e864-40ce-a31f-8da40c73d14b",
@@ -200,11 +208,11 @@
     "output_type": "stream",
     "text": [
      "********************Prompt[rlm/rag-prompt]********************\n",
-      "================================\u001B[1m Human Message \u001B[0m=================================\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
-      "Question: \u001B[33;1m\u001B[1;3m{question}\u001B[0m \n",
-      "Context: \u001B[33;1m\u001B[1;3m{context}\u001B[0m \n",
+      "Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
+      "Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
      "Answer:\n"
     ]
    }
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c71da2ea",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "683fae34-980f-43f0-a9c2-9894bebd9157.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ac7db067",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "b77a7d3b-b28a-4dcf-9f1a-861f2f2c5f6c.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3d959ff",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "15cba0ab-a549-4909-8373-fb761e384eff.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "345488d8",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {
@@ -1,5 +1,13 @@
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "403aeb6e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
  {
   "attachments": {
    "15cba0ab-a549-4909-8373-fb761e384eff.png": {
@@ -54,7 +62,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "import os\n\nos.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\nos.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
   ]
  },
  {
@@ -64,7 +76,9 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "import os\n\nos.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
   ]
  },
  {
@@ -84,7 +98,18 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langchain_openai import OpenAIEmbeddings\nfrom langchain_pinecone import PineconeVectorStore\n\n# use pinecone movies database\n\n# Add to vectorDB\nvectorstore = PineconeVectorStore(\n    embedding=OpenAIEmbeddings(),\n    index_name=\"sample-movies\",\n    text_key=\"summary\",\n)\nretriever = vectorstore.as_retriever()"
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_pinecone import PineconeVectorStore\n",
+    "\n",
+    "# use pinecone movies database\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = PineconeVectorStore(\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    "    index_name=\"sample-movies\",\n",
+    "    text_key=\"summary\",\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
@@ -113,7 +138,11 @@
    }
   ],
   "source": [
-    "docs = retriever.invoke(\"James Cameron\")\nfor doc in docs:\n    print(\"# \" + doc.metadata[\"title\"])\n    print(doc.page_content)\n    print()"
+    "docs = retriever.invoke(\"James Cameron\")\n",
+    "for doc in docs:\n",
+    "    print(\"# \" + doc.metadata[\"title\"])\n",
+    "    print(doc.page_content)\n",
+    "    print()"
   ]
  },
  {
@@ -173,7 +202,12 @@
    }
   ],
   "source": [
-    "# Test the retrieval grader\nquestion = \"movies starring jason momoa\"\ndocs = retriever.invoke(question)\ndoc_txt = docs[0].page_content\nprint(doc_txt)\nprint(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+    "# Test the retrieval grader\n",
+    "question = \"movies starring jason momoa\"\n",
+    "docs = retriever.invoke(question)\n",
+    "doc_txt = docs[0].page_content\n",
+    "print(doc_txt)\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
   ]
  },
  {
@@ -201,7 +235,23 @@
    }
   ],
   "source": [
-    "### Generate\n\nfrom langchain import hub\nfrom langchain_core.output_parsers import StrOutputParser\n\n# Prompt\nprompt = hub.pull(\"rlm/rag-prompt\")\n\n# LLM\nllm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n\n# Chain\nrag_chain = prompt | llm | StrOutputParser()\n\n# Run\ngeneration = rag_chain.invoke({\"context\": docs, \"question\": question})\nprint(generation)"
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
   ]
  },
  {
@@ -329,7 +379,17 @@
    }
   ],
   "source": [
-    "### Question Re-writer\n\n# LLM\nllm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n\n# Prompt\nre_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n\nquestion_rewriter = re_write_prompt | llm | StrOutputParser()\nprint(question)\nquestion_rewriter.invoke({\"question\": question})"
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "print(question)\n",
+    "question_rewriter.invoke({\"question\": question})"
   ]
  },
  {
@@ -351,7 +411,24 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from typing import List\n\nfrom typing_extensions import TypedDict\n\n\nclass GraphState(TypedDict):\n    \"\"\"\n    Represents the state of our graph.\n\n    Attributes:\n        question: question\n        generation: LLM generation\n        documents: list of documents\n    \"\"\"\n\n    question: str\n    generation: str\n    documents: List[str]"
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
   ]
  },
  {
@@ -361,7 +438,95 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "### Nodes\n\n\ndef retrieve(state):\n    \"\"\"\n    Retrieve documents\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, documents, that contains retrieved documents\n    \"\"\"\n    print(\"---RETRIEVE---\")\n    question = state[\"question\"]\n\n    # Retrieval\n    documents = retriever.invoke(question)\n    return {\"documents\": documents, \"question\": question}\n\n\ndef generate(state):\n    \"\"\"\n    Generate answer\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): New key added to state, generation, that contains LLM generation\n    \"\"\"\n    print(\"---GENERATE---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # RAG generation\n    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n\n\ndef grade_documents(state):\n    \"\"\"\n    Determines whether the retrieved documents are relevant to the question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates documents key with only filtered relevant documents\n    \"\"\"\n\n    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Score each doc\n    filtered_docs = []\n    for d in documents:\n        score = retrieval_grader.invoke(\n            {\"question\": question, \"document\": d.page_content}\n        )\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---GRADE: DOCUMENT RELEVANT---\")\n            filtered_docs.append(d)\n        else:\n            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n            continue\n    return {\"documents\": filtered_docs, \"question\": question}\n\n\ndef transform_query(state):\n    \"\"\"\n    Transform the query to produce a better question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        state (dict): Updates question key with a re-phrased question\n    \"\"\"\n\n    print(\"---TRANSFORM QUERY---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n\n    # Re-write question\n    better_question = question_rewriter.invoke({\"question\": question})\n    return {\"documents\": documents, \"question\": better_question}"
+    "### Nodes\n",
+    "\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.invoke(question)\n",
+    "    return {\"documents\": documents, \"question\": question}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question}\n",
+    "\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}"
   ]
  },
  {
@@ -371,7 +536,74 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "### Edges\n\n\ndef decide_to_generate(state):\n    \"\"\"\n    Determines whether to generate an answer, or re-generate a question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Binary decision for next node to call\n    \"\"\"\n\n    print(\"---ASSESS GRADED DOCUMENTS---\")\n    state[\"question\"]\n    filtered_documents = state[\"documents\"]\n\n    if not filtered_documents:\n        # All documents have been filtered check_relevance\n        # We will re-generate a new query\n        print(\n            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n        )\n        return \"transform_query\"\n    else:\n        # We have relevant documents, so generate answer\n        print(\"---DECISION: GENERATE---\")\n        return \"generate\"\n\n\ndef grade_generation_v_documents_and_question(state):\n    \"\"\"\n    Determines whether the generation is grounded in the document and answers question.\n\n    Args:\n        state (dict): The current graph state\n\n    Returns:\n        str: Decision for next node to call\n    \"\"\"\n\n    print(\"---CHECK HALLUCINATIONS---\")\n    question = state[\"question\"]\n    documents = state[\"documents\"]\n    generation = state[\"generation\"]\n\n    score = hallucination_grader.invoke(\n        {\"documents\": documents, \"generation\": generation}\n    )\n    grade = score.binary_score\n\n    # Check hallucination\n    if grade == \"yes\":\n        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n        # Check question-answering\n        print(\"---GRADE GENERATION vs QUESTION---\")\n        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n        grade = score.binary_score\n        if grade == \"yes\":\n            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n            return \"useful\"\n        else:\n            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n            return \"not useful\"\n    else:\n        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n        return \"not supported\""
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"documents\"]\n",
+    "\n",
+    "    if not filtered_documents:\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "def grade_generation_v_documents_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score.binary_score\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
   ]
  },
  {
@@ -390,7 +622,42 @@
   "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
   "metadata": {},
   "outputs": [],
-   "source": ["from langgraph.graph import END, StateGraph, START\n\nworkflow = StateGraph(GraphState)\n\n# Define the nodes\nworkflow.add_node(\"retrieve\", retrieve)  # retrieve\nworkflow.add_node(\"grade_documents\", grade_documents)  # grade documents\nworkflow.add_node(\"generate\", generate)  # generate\nworkflow.add_node(\"transform_query\", transform_query)  # transform_query\n\n# Build graph\nworkflow.add_edge(START, \"retrieve\")\nworkflow.add_edge(\"retrieve\", \"grade_documents\")\nworkflow.add_conditional_edges(\n    \"grade_documents\",\n    decide_to_generate,\n    {\n        \"transform_query\": \"transform_query\",\n        \"generate\": \"generate\",\n    },\n)\nworkflow.add_edge(\"transform_query\", \"retrieve\")\nworkflow.add_conditional_edges(\n    \"generate\",\n    grade_generation_v_documents_and_question,\n    {\n        \"not supported\": \"generate\",\n        \"useful\": END,\n        \"not useful\": \"transform_query\",\n    },\n)\n\n# Compile\napp = workflow.compile()"]
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"generate\",\n",
+    "    grade_generation_v_documents_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
  },
  {
   "cell_type": "code",
@@ -426,7 +693,18 @@
    }
   ],
   "source": [
-    "from pprint import pprint\n\n# Run\ninputs = {\"question\": \"Movies that star Daniel Craig\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
   ]
  },
  {
@@ -436,7 +714,15 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "inputs = {\"question\": \"Which movies are about aliens?\"}\nfor output in app.stream(inputs):\n    for key, value in output.items():\n        # Node\n        pprint(f\"Node '{key}':\")\n    pprint(\"\\n---\\n\")\n\n# Final generation\npprint(value[\"generation\"])"
+    "inputs = {\"question\": \"Which movies are about aliens?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
   ]
  },
  {
@@ -445,9 +731,7 @@
   "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
   "metadata": {},
   "outputs": [],
-   "source": [
-    ""
-   ]
+   "source": []
  }
 ],
 "metadata": {
@@ -5,7 +5,14 @@
   "id": "294995c4",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-from-scratch.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-from-scratch.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,14 @@
   "id": "40f0d107",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/react-agent-structured-output.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-structured-output.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fa3f7c50",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/recursion-limit.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -5,7 +5,15 @@
   "id": "658773a2",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflection/reflection.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflection/reflection.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb60657",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "caf07859",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflexion/reflexion.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd1df0e0",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "961f43ec",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rewoo/rewoo.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/rewoo/rewoo.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f00c427",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,14 @@
   "id": "bbd6e9b8",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/run-id-langsmith.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/run-id-langsmith.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "f6db1873",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/self-discover/self-discover.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/self-discover/self-discover.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "219a78f9",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4149ffcc",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/state-model.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3e05d7f9",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/storm/storm.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e663f597",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-multiple.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e6829c80",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-updates.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5ec11895",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/stream-values.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6619387c",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-content.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "57b7e303",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8e71a0c8",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-events-from-within-tools.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "756e4554",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-from-final-node.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "47164a72",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-subgraphs.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "218dfbcb",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens-without-langchain.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "99eb887e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/streaming-tokens.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0de7689f",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph-transform-state.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
@@ -5,7 +5,14 @@
   "id": "f49876e1",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraph.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/subgraph.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5106959e",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/subgraphs-manage-state.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dc21501d",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling-errors.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
@@ -5,7 +5,14 @@
   "id": "7fd8bd65",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/tool-calling.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/tool-calling.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "83c2223f",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/sql-agent.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/sql/sql-agent.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f924b1",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "11140167",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2ba3e6",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -5,7 +5,15 @@
   "id": "9dffdb54",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/usaco/usaco.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/usaco/usaco.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579c9959",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9c9cb15a",
-   "metadata": {},
-   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/how-tos/visualization.ipynb"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -5,7 +5,15 @@
   "id": "007ea2e9",
   "metadata": {},
   "source": [
-    "This file has been moved to https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/web-navigation/web_voyager.ipynb"
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/web-navigation/web_voyager.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0d7b895",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
   ]
  }
 ],
@@ -0,0 +1,12 @@
+.PHONY: format lint test
+
+format:
+	uv run ruff format .
+	uv run ruff check --fix .
+
+lint:
+	uv run ruff check .
+	uv run ty check
+
+test:
+	uv run pytest $(TEST)
@@ -0,0 +1,111 @@
+# langgraph-checkpoint-conformance
+
+Conformance test suite for [LangGraph](https://github.com/langchain-ai/langgraph) checkpointer implementations.
+
+Validates that a `BaseCheckpointSaver` subclass correctly implements the checkpoint storage contract — blob round-trips, metadata preservation, namespace isolation, incremental channel updates, and more.
+
+## Installation
+
+```bash
+pip install langgraph-checkpoint-conformance
+```
+
+## Quick start
+
+Register your checkpointer with `@checkpointer_test` and run `validate()`:
+
+```python
+import asyncio
+from langgraph.checkpoint.conformance import checkpointer_test, validate
+
+@checkpointer_test(name="MyCheckpointer")
+async def my_checkpointer():
+    saver = MyCheckpointer(...)
+    yield saver
+    # cleanup runs after yield
+
+async def main():
+    report = await validate(my_checkpointer)
+    report.print_report()
+    assert report.passed_all_base()
+
+asyncio.run(main())
+```
+
+Or in a pytest test:
+
+```python
+import pytest
+from langgraph.checkpoint.conformance import checkpointer_test, validate
+
+@checkpointer_test(name="MyCheckpointer")
+async def my_checkpointer():
+    yield MyCheckpointer(...)
+
+@pytest.mark.asyncio
+async def test_conformance():
+    report = await validate(my_checkpointer)
+    report.print_report()
+    assert report.passed_all_base()
+```
+
+## Capabilities
+
+The suite tests **base** capabilities (required) and **extended** capabilities (optional, auto-detected):
+
+| Capability | Required | Method |
+|---|---|---|
+| `put` | yes | `aput` |
+| `put_writes` | yes | `aput_writes` |
+| `get_tuple` | yes | `aget_tuple` |
+| `list` | yes | `alist` |
+| `delete_thread` | yes | `adelete_thread` |
+| `delete_for_runs` | no | `adelete_for_runs` |
+| `copy_thread` | no | `acopy_thread` |
+| `prune` | no | `aprune` |
+
+Extended capabilities are detected by checking whether the method is overridden from `BaseCheckpointSaver`. If not overridden, those tests are skipped.
+
+## Options
+
+### Progress output
+
+```python
+from langgraph.checkpoint.conformance.report import ProgressCallbacks
+
+# Dot-style progress (. per pass, F per fail)
+report = await validate(my_checkpointer, progress=ProgressCallbacks.default())
+
+# Verbose (per-test names + stacktraces on failure)
+report = await validate(my_checkpointer, progress=ProgressCallbacks.verbose())
+```
+
+### Skip capabilities
+
+```python
+@checkpointer_test(name="MyCheckpointer", skip_capabilities={"prune"})
+async def my_checkpointer():
+    yield MyCheckpointer(...)
+```
+
+### Run specific capabilities
+
+```python
+report = await validate(my_checkpointer, capabilities={"put", "list"})
+```
+
+### Lifespan (one-time setup/teardown)
+
+For expensive setup like database creation:
+
+```python
+async def db_lifespan():
+    await create_database()
+    yield
+    await drop_database()
+
+@checkpointer_test(name="PostgresSaver", lifespan=db_lifespan)
+async def pg_checkpointer():
+    async with PostgresSaver.from_conn_string(CONN_STRING) as saver:
+        yield saver
+```
@@ -0,0 +1,9 @@
+"""langgraph-checkpoint-conformance: conformance test suite for checkpointer implementations."""
+
+from langgraph.checkpoint.conformance.initializer import checkpointer_test
+from langgraph.checkpoint.conformance.validate import validate
+
+__all__ = [
+    "checkpointer_test",
+    "validate",
+]
@@ -0,0 +1,93 @@
+"""Capability detection for checkpointer implementations."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import TYPE_CHECKING
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+if TYPE_CHECKING:
+    pass
+
+
+class Capability(str, Enum):
+    """Capabilities that a checkpointer may support."""
+
+    PUT = "put"
+    PUT_WRITES = "put_writes"
+    GET_TUPLE = "get_tuple"
+    LIST = "list"
+    DELETE_THREAD = "delete_thread"
+    DELETE_FOR_RUNS = "delete_for_runs"
+    COPY_THREAD = "copy_thread"
+    PRUNE = "prune"
+
+
+# Capabilities that every checkpointer must support.
+BASE_CAPABILITIES = frozenset(
+    {
+        Capability.PUT,
+        Capability.PUT_WRITES,
+        Capability.GET_TUPLE,
+        Capability.LIST,
+        Capability.DELETE_THREAD,
+    }
+)
+
+# Capabilities that are optional extensions.
+EXTENDED_CAPABILITIES = frozenset(
+    {
+        Capability.DELETE_FOR_RUNS,
+        Capability.COPY_THREAD,
+        Capability.PRUNE,
+    }
+)
+
+ALL_CAPABILITIES = BASE_CAPABILITIES | EXTENDED_CAPABILITIES
+
+# Maps capability to the async method name on BaseCheckpointSaver (or subclass).
+_CAPABILITY_METHOD_MAP: dict[Capability, str] = {
+    Capability.PUT: "aput",
+    Capability.PUT_WRITES: "aput_writes",
+    Capability.GET_TUPLE: "aget_tuple",
+    Capability.LIST: "alist",
+    Capability.DELETE_THREAD: "adelete_thread",
+    Capability.DELETE_FOR_RUNS: "adelete_for_runs",
+    Capability.COPY_THREAD: "acopy_thread",
+    Capability.PRUNE: "aprune",
+}
+
+
+@dataclass(frozen=True)
+class DetectedCapabilities:
+    """Result of capability detection for a checkpointer type."""
+
+    detected: frozenset[Capability]
+    missing: frozenset[Capability]
+
+    @classmethod
+    def from_instance(cls, saver: BaseCheckpointSaver) -> DetectedCapabilities:
+        """Detect capabilities from a checkpointer instance."""
+        inner_type = type(saver)
+        detected: set[Capability] = set()
+
+        for cap, method_name in _CAPABILITY_METHOD_MAP.items():
+            if _is_overridden(inner_type, method_name):
+                detected.add(cap)
+
+        detected_fs = frozenset(detected)
+        return cls(
+            detected=detected_fs,
+            missing=ALL_CAPABILITIES - detected_fs,
+        )
+
+
+def _is_overridden(inner_type: type, method: str) -> bool:
+    """Check if *method* on *inner_type* differs from the base class default."""
+    base = getattr(BaseCheckpointSaver, method, None)
+    impl = getattr(inner_type, method, None)
+    if base is None or impl is None:
+        return impl is not None
+    return impl is not base
@@ -0,0 +1,100 @@
+"""Checkpointer test registration and factory management."""
+
+from __future__ import annotations
+
+from collections.abc import AsyncGenerator, Callable
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import Any
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+# Type for the lifespan async context manager factory.
+LifespanFactory = Callable[[], AsyncGenerator[None, None]]
+
+# Module-level registry of decorated checkpointer factories.
+_REGISTRY: dict[str, RegisteredCheckpointer] = {}
+
+
+async def _noop_lifespan() -> AsyncGenerator[None, None]:
+    yield
+
+
+@dataclass
+class RegisteredCheckpointer:
+    """A registered checkpointer test factory."""
+
+    name: str
+    factory: Callable[[], AsyncGenerator[BaseCheckpointSaver, None]]
+    skip_capabilities: set[str] = field(default_factory=set)
+    lifespan: LifespanFactory = _noop_lifespan
+
+    @asynccontextmanager
+    async def create(self) -> AsyncGenerator[BaseCheckpointSaver, None]:
+        """Create a fresh checkpointer instance via the async generator."""
+        gen = self.factory()
+        try:
+            saver = await gen.__anext__()
+            yield saver
+        finally:
+            try:
+                await gen.__anext__()
+            except StopAsyncIteration:
+                pass
+
+    @asynccontextmanager
+    async def enter_lifespan(self) -> AsyncGenerator[None, None]:
+        """Enter the lifespan context (once per validation run)."""
+        gen = self.lifespan()
+        try:
+            await gen.__anext__()
+            yield
+        finally:
+            try:
+                await gen.__anext__()
+            except StopAsyncIteration:
+                pass
+
+
+def checkpointer_test(
+    name: str,
+    *,
+    skip_capabilities: set[str] | None = None,
+    lifespan: LifespanFactory | None = None,
+) -> Callable[[Any], RegisteredCheckpointer]:
+    """Register an async generator as a checkpointer test factory.
+
+    The factory is called once per capability suite to create a fresh
+    checkpointer.  The optional `lifespan` is an async generator that
+    runs once for the entire validation run (e.g. to create/destroy a
+    database).
+
+    Example::
+
+        @checkpointer_test(name="InMemorySaver")
+        async def memory_checkpointer():
+            yield InMemorySaver()
+
+    With lifespan::
+
+        async def pg_lifespan():
+            await create_database()
+            yield
+            await drop_database()
+
+        @checkpointer_test(name="PostgresSaver", lifespan=pg_lifespan)
+        async def pg_checkpointer():
+            yield PostgresSaver(conn_string="...")
+    """
+
+    def decorator(fn: Any) -> RegisteredCheckpointer:
+        registered = RegisteredCheckpointer(
+            name=name,
+            factory=fn,
+            skip_capabilities=skip_capabilities or set(),
+            lifespan=lifespan or _noop_lifespan,
+        )
+        _REGISTRY[name] = registered
+        return registered
+
+    return decorator
@@ -0,0 +1,198 @@
+"""Capability report: results, progress callbacks, and pretty-printing."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+from langgraph.checkpoint.conformance.capabilities import (
+    BASE_CAPABILITIES,
+    EXTENDED_CAPABILITIES,
+    Capability,
+)
+
+# Callback type for per-test progress reporting.
+# (capability_name, test_name, passed, error_msg_or_None) -> None
+OnTestResult = Callable[[str, str, bool, str | None], None]
+
+# Callback type for capability-level events.
+# (capability_name, detected) -> None
+OnCapabilityStart = Callable[[str, bool], None]
+
+
+class ProgressCallbacks:
+    """Grouped callbacks for progress reporting during validation."""
+
+    def __init__(
+        self,
+        *,
+        on_capability_start: Callable[[str, bool], None] | None = None,
+        on_test_result: OnTestResult | None = None,
+        on_capability_end: Callable[[str], None] | None = None,
+    ) -> None:
+        self.on_capability_start = on_capability_start
+        self.on_test_result = on_test_result
+        self.on_capability_end = on_capability_end
+
+    @classmethod
+    def default(cls) -> ProgressCallbacks:
+        """Dot-style progress: ``.`` per pass, ``F`` per fail."""
+
+        def _cap_start(capability: str, detected: bool) -> None:
+            if detected:
+                print(f"  {capability}: ", end="", flush=True)
+            else:
+                print(f"  ⊘ {capability} (not implemented)")
+
+        def _test_result(
+            capability: str, test_name: str, passed: bool, error: str | None
+        ) -> None:
+            print("." if passed else "F", end="", flush=True)
+
+        def _cap_end(capability: str) -> None:
+            print()  # newline after dots
+
+        return cls(
+            on_capability_start=_cap_start,
+            on_test_result=_test_result,
+            on_capability_end=_cap_end,
+        )
+
+    @classmethod
+    def verbose(cls) -> ProgressCallbacks:
+        """Per-test output with names and errors."""
+
+        def _cap_start(capability: str, detected: bool) -> None:
+            if detected:
+                print(f"  {capability}:")
+            else:
+                print(f"  ⊘ {capability} (not implemented)")
+
+        def _test_result(
+            capability: str, test_name: str, passed: bool, error: str | None
+        ) -> None:
+            icon = "✓" if passed else "✗"
+            print(f"    {icon} {test_name}")
+            if error:
+                for line in error.rstrip().splitlines():
+                    print(f"      {line}")
+
+        return cls(
+            on_capability_start=_cap_start,
+            on_test_result=_test_result,
+        )
+
+    @classmethod
+    def quiet(cls) -> ProgressCallbacks:
+        """No progress output."""
+        return cls()
+
+
+@dataclass
+class CapabilityResult:
+    """Result of running a single capability's test suite."""
+
+    detected: bool = False
+    passed: bool | None = None  # None = skipped
+    tests_passed: int = 0
+    tests_failed: int = 0
+    tests_skipped: int = 0
+    failures: list[str] = field(default_factory=list)
+
+
+@dataclass
+class CapabilityReport:
+    """Aggregate report across all capabilities."""
+
+    checkpointer_name: str
+    results: dict[str, CapabilityResult] = field(default_factory=dict)
+
+    def passed_all_base(self) -> bool:
+        """Whether all base capability tests passed."""
+        for cap in BASE_CAPABILITIES:
+            result = self.results.get(cap.value)
+            if result is None or result.passed is not True:
+                return False
+        return True
+
+    def passed_all(self) -> bool:
+        """Whether every detected capability's tests passed."""
+        for result in self.results.values():
+            if result.detected and result.passed is not True:
+                return False
+        return True
+
+    def conformance_level(self) -> str:
+        """Return a human-readable conformance level string."""
+        if self.passed_all():
+            return "FULL"
+        if self.passed_all_base():
+            return "BASE+PARTIAL"
+        return "BASE" if self._any_base_passed() else "NONE"
+
+    def _any_base_passed(self) -> bool:
+        for cap in BASE_CAPABILITIES:
+            result = self.results.get(cap.value)
+            if result and result.passed is True:
+                return True
+        return False
+
+    def print_report(self) -> None:
+        """Pretty-print the report to stdout."""
+        width = 52
+        border = "=" * width
+        print(f"\n{'':>2}{border}")
+        print(f"{'':>2}  Checkpointer Validation: {self.checkpointer_name}")
+        print(f"{'':>2}{border}")
+
+        def _section(title: str, caps: frozenset[Capability]) -> None:
+            print(f"{'':>2}  {title}")
+            for cap in sorted(caps, key=lambda c: c.value):
+                result = self.results.get(cap.value)
+                if result is None:
+                    icon = "  "
+                    suffix = "(no tests)"
+                elif not result.detected:
+                    icon = "⊘ "
+                    suffix = "(not implemented)"
+                elif result.passed is True:
+                    icon = "✅"
+                    suffix = ""
+                elif result.passed is False:
+                    icon = "❌"
+                    suffix = f"({result.tests_failed} failed)"
+                else:
+                    icon = "⏭ "
+                    suffix = "(skipped)"
+                print(f"{'':>2}    {icon} {cap.value:20s} {suffix}")
+            print()
+
+        _section("BASE CAPABILITIES", BASE_CAPABILITIES)
+        _section("EXTENDED CAPABILITIES", EXTENDED_CAPABILITIES)
+
+        total = sum(1 for r in self.results.values() if r.detected)
+        passed = sum(
+            1 for r in self.results.values() if r.detected and r.passed is True
+        )
+        level = self.conformance_level()
+        print(f"{'':>2}  Result: {level} ({passed}/{total})")
+        print(f"{'':>2}{border}\n")
+
+    def to_dict(self) -> dict[str, Any]:
+        """Return a JSON-serializable dict."""
+        return {
+            "checkpointer_name": self.checkpointer_name,
+            "conformance_level": self.conformance_level(),
+            "results": {
+                name: {
+                    "detected": r.detected,
+                    "passed": r.passed,
+                    "tests_passed": r.tests_passed,
+                    "tests_failed": r.tests_failed,
+                    "tests_skipped": r.tests_skipped,
+                    "failures": r.failures,
+                }
+                for name, r in self.results.items()
+            },
+        }
@@ -0,0 +1,27 @@
+"""Test spec modules for each checkpointer capability."""
+
+from langgraph.checkpoint.conformance.spec.test_copy_thread import (
+    run_copy_thread_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_delete_for_runs import (
+    run_delete_for_runs_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_delete_thread import (
+    run_delete_thread_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_get_tuple import run_get_tuple_tests
+from langgraph.checkpoint.conformance.spec.test_list import run_list_tests
+from langgraph.checkpoint.conformance.spec.test_prune import run_prune_tests
+from langgraph.checkpoint.conformance.spec.test_put import run_put_tests
+from langgraph.checkpoint.conformance.spec.test_put_writes import run_put_writes_tests
+
+__all__ = [
+    "run_put_tests",
+    "run_put_writes_tests",
+    "run_get_tuple_tests",
+    "run_list_tests",
+    "run_delete_thread_tests",
+    "run_delete_for_runs_tests",
+    "run_copy_thread_tests",
+    "run_prune_tests",
+]
@@ -0,0 +1,250 @@
+"""COPY_THREAD capability tests — acopy_thread."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _setup_source_thread(
+    saver: BaseCheckpointSaver,
+    tid: str,
+    *,
+    n: int = 3,
+    namespaces: list[str] | None = None,
+) -> list[dict]:
+    """Create n checkpoints on tid (optionally across namespaces). Returns stored configs."""
+    nss = namespaces or [""]
+    stored = []
+    for ns in nss:
+        parent_cfg = None
+        for i in range(n):
+            config = generate_config(tid, checkpoint_ns=ns)
+            if parent_cfg:
+                config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                    "checkpoint_id"
+                ]
+            cp = generate_checkpoint(channel_values={"step": i})
+            cp["channel_versions"] = {"step": 1}
+            parent_cfg = await saver.aput(
+                config, cp, generate_metadata(step=i), {"step": 1}
+            )
+            stored.append(parent_cfg)
+    return stored
+
+
+async def test_copy_thread_basic(saver: BaseCheckpointSaver) -> None:
+    """Checkpoints appear on target thread."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src)
+
+    await saver.acopy_thread(src, dst)
+    results = []
+    async for tup in saver.alist(generate_config(dst)):
+        results.append(tup)
+    assert len(results) == 3, f"Expected 3 copied checkpoints, got {len(results)}"
+
+
+async def test_copy_thread_all_checkpoints(saver: BaseCheckpointSaver) -> None:
+    """All checkpoints copied, not just latest."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=3)
+
+    await saver.acopy_thread(src, dst)
+    src_results = []
+    async for tup in saver.alist(generate_config(src)):
+        src_results.append(tup)
+
+    dst_results = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_results.append(tup)
+
+    assert len(dst_results) == len(src_results)
+    # Verify content matches
+    for s, d in zip(
+        sorted(src_results, key=lambda t: t.checkpoint["id"]),
+        sorted(dst_results, key=lambda t: t.checkpoint["id"]),
+        strict=True,
+    ):
+        assert s.checkpoint["channel_values"] == d.checkpoint["channel_values"], (
+            f"channel_values mismatch for checkpoint {s.checkpoint['id']}"
+        )
+
+
+async def test_copy_thread_preserves_metadata(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Metadata intact on copied checkpoints."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=2)
+
+    await saver.acopy_thread(src, dst)
+    src_tuples = []
+    async for tup in saver.alist(generate_config(src)):
+        src_tuples.append(tup)
+
+    dst_tuples = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_tuples.append(tup)
+
+    for s, d in zip(
+        sorted(src_tuples, key=lambda t: t.metadata.get("step", 0)),
+        sorted(dst_tuples, key=lambda t: t.metadata.get("step", 0)),
+        strict=True,
+    ):
+        for key in s.metadata:
+            assert s.metadata.get(key) == d.metadata.get(key), (
+                f"metadata[{key!r}] mismatch: {s.metadata.get(key)!r} != {d.metadata.get(key)!r}"
+            )
+
+
+async def test_copy_thread_preserves_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Root + child namespaces copied."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=1, namespaces=["", "child:1"])
+
+    await saver.acopy_thread(src, dst)
+    for ns in ["", "child:1"]:
+        results = []
+        async for tup in saver.alist(generate_config(dst, checkpoint_ns=ns)):
+            results.append(tup)
+        assert len(results) == 1, (
+            f"Expected 1 checkpoint in namespace '{ns}', got {len(results)}"
+        )
+
+
+async def test_copy_thread_preserves_writes(saver: BaseCheckpointSaver) -> None:
+    """Pending writes copied."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    configs = await _setup_source_thread(saver, src, n=1)
+
+    # Add a write to the source
+    await saver.aput_writes(configs[-1], [("ch", "write_val")], str(uuid4()))
+
+    await saver.acopy_thread(src, dst)
+    tup = await saver.aget_tuple(generate_config(dst))
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected 1 write, got {len(tup.pending_writes)}"
+    )
+    assert tup.pending_writes[0][1] == "ch", (
+        f"channel mismatch: {tup.pending_writes[0][1]!r}"
+    )
+    assert tup.pending_writes[0][2] == "write_val", (
+        f"value mismatch: {tup.pending_writes[0][2]!r}"
+    )
+
+
+async def test_copy_thread_preserves_ordering(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Checkpoint order maintained."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=4)
+
+    await saver.acopy_thread(src, dst)
+    src_ids = []
+    async for tup in saver.alist(generate_config(src)):
+        src_ids.append(tup.checkpoint["id"])
+
+    dst_ids = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_ids.append(tup.checkpoint["id"])
+
+    # Order should match (both newest-first)
+    assert src_ids == dst_ids
+
+
+async def test_copy_thread_source_unchanged(saver: BaseCheckpointSaver) -> None:
+    """Source thread still intact after copy."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=2)
+
+    # Snapshot source before copy
+    src_before = []
+    async for tup in saver.alist(generate_config(src)):
+        src_before.append(tup.checkpoint["id"])
+
+    await saver.acopy_thread(src, dst)
+    # Source should be unchanged
+    src_after = []
+    async for tup in saver.alist(generate_config(src)):
+        src_after.append(tup.checkpoint["id"])
+
+    assert src_before == src_after
+
+
+async def test_copy_thread_nonexistent_source(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Graceful handling of non-existent source thread."""
+    src = str(uuid4())
+    dst = str(uuid4())
+
+    # Should not raise (or raise a known error)
+    try:
+        await saver.acopy_thread(src, dst)
+    except Exception:
+        pass  # Some implementations may raise; that's acceptable
+
+    # Destination should be empty
+    results = []
+    async for tup in saver.alist(generate_config(dst)):
+        results.append(tup)
+    assert len(results) == 0
+
+
+ALL_COPY_THREAD_TESTS = [
+    test_copy_thread_basic,
+    test_copy_thread_all_checkpoints,
+    test_copy_thread_preserves_metadata,
+    test_copy_thread_preserves_namespaces,
+    test_copy_thread_preserves_writes,
+    test_copy_thread_preserves_ordering,
+    test_copy_thread_source_unchanged,
+    test_copy_thread_nonexistent_source,
+]
+
+
+async def run_copy_thread_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all copy_thread tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_COPY_THREAD_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("copy_thread", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "copy_thread", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures
@@ -0,0 +1,218 @@
+"""DELETE_FOR_RUNS capability tests — adelete_for_runs."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _put_with_run_id(
+    saver: BaseCheckpointSaver,
+    tid: str,
+    run_id: str,
+    *,
+    checkpoint_ns: str = "",
+    parent_config: dict | None = None,
+) -> dict:
+    """Put a checkpoint with a run_id in metadata, return stored config."""
+    config = generate_config(tid, checkpoint_ns=checkpoint_ns)
+    if parent_config:
+        config["configurable"]["checkpoint_id"] = parent_config["configurable"][
+            "checkpoint_id"
+        ]
+    cp = generate_checkpoint()
+    md = generate_metadata(run_id=run_id)
+    return await saver.aput(config, cp, md, {})
+
+
+async def test_delete_for_runs_single(saver: BaseCheckpointSaver) -> None:
+    """One run_id removed."""
+    tid = str(uuid4())
+    run1, run2 = str(uuid4()), str(uuid4())
+
+    stored1 = await _put_with_run_id(saver, tid, run1)
+    await _put_with_run_id(saver, tid, run2, parent_config=stored1)
+
+    # Pre-delete: verify both runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run1 in pre_run_ids, "Pre-delete: run1 should exist"
+    assert run2 in pre_run_ids, "Pre-delete: run2 should exist"
+
+    await saver.adelete_for_runs([run1])
+    # run1's checkpoint should be gone; run2 should remain
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run1 not in run_ids
+    assert run2 in run_ids
+
+
+async def test_delete_for_runs_multiple(saver: BaseCheckpointSaver) -> None:
+    """List of run_ids removed."""
+    tid = str(uuid4())
+    run1, run2, run3 = str(uuid4()), str(uuid4()), str(uuid4())
+
+    s1 = await _put_with_run_id(saver, tid, run1)
+    s2 = await _put_with_run_id(saver, tid, run2, parent_config=s1)
+    await _put_with_run_id(saver, tid, run3, parent_config=s2)
+
+    # Pre-delete: verify all 3 runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run1 in pre_run_ids, "Pre-delete: run1 should exist"
+    assert run2 in pre_run_ids, "Pre-delete: run2 should exist"
+    assert run3 in pre_run_ids, "Pre-delete: run3 should exist"
+
+    await saver.adelete_for_runs([run1, run2])
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run1 not in run_ids
+    assert run2 not in run_ids
+    assert run3 in run_ids
+
+
+async def test_delete_for_runs_preserves_other_runs(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Unrelated runs untouched."""
+    tid = str(uuid4())
+    run_keep = str(uuid4())
+    run_delete = str(uuid4())
+
+    await _put_with_run_id(saver, tid, run_keep)
+    await _put_with_run_id(saver, tid, run_delete)
+
+    # Pre-delete: verify both runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run_keep in pre_run_ids, "Pre-delete: run_keep should exist"
+    assert run_delete in pre_run_ids, "Pre-delete: run_delete should exist"
+
+    await saver.adelete_for_runs([run_delete])
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run_keep in run_ids
+
+
+async def test_delete_for_runs_removes_writes(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Associated writes cleaned up."""
+    tid = str(uuid4())
+    run1 = str(uuid4())
+
+    stored = await _put_with_run_id(saver, tid, run1)
+    await saver.aput_writes(stored, [("ch", "val")], str(uuid4()))
+
+    # Pre-delete: verify writes exist
+    pre_tup = await saver.aget_tuple(stored)
+    assert pre_tup is not None, "Pre-delete: checkpoint should exist"
+    assert pre_tup.pending_writes is not None and len(pre_tup.pending_writes) == 1, (
+        f"Pre-delete: expected 1 write, got {len(pre_tup.pending_writes) if pre_tup.pending_writes else 0}"
+    )
+
+    await saver.adelete_for_runs([run1])
+    # The checkpoint (and its writes) should be gone
+    tup = await saver.aget_tuple(stored)
+    assert tup is None
+
+
+async def test_delete_for_runs_empty_list_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Empty list no error."""
+    await saver.adelete_for_runs([])
+
+
+async def test_delete_for_runs_nonexistent_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Missing run_ids no error."""
+    await saver.adelete_for_runs([str(uuid4())])
+
+
+async def test_delete_for_runs_across_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """All namespaces cleaned."""
+    tid = str(uuid4())
+    run1 = str(uuid4())
+
+    await _put_with_run_id(saver, tid, run1, checkpoint_ns="")
+    await _put_with_run_id(saver, tid, run1, checkpoint_ns="child:1")
+
+    # Pre-delete: verify run1 present in both namespaces
+    for ns in ["", "child:1"]:
+        pre_results = []
+        async for tup in saver.alist(generate_config(tid, checkpoint_ns=ns)):
+            pre_results.append(tup)
+        pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+        assert run1 in pre_run_ids, f"Pre-delete: run1 should exist in ns='{ns}'"
+
+    await saver.adelete_for_runs([run1])
+    for ns in ["", "child:1"]:
+        results = []
+        async for tup in saver.alist(generate_config(tid, checkpoint_ns=ns)):
+            results.append(tup)
+        run_ids = {t.metadata.get("run_id") for t in results}
+        assert run1 not in run_ids
+
+
+ALL_DELETE_FOR_RUNS_TESTS = [
+    test_delete_for_runs_single,
+    test_delete_for_runs_multiple,
+    test_delete_for_runs_preserves_other_runs,
+    test_delete_for_runs_removes_writes,
+    test_delete_for_runs_empty_list_noop,
+    test_delete_for_runs_nonexistent_noop,
+    test_delete_for_runs_across_namespaces,
+]
+
+
+async def run_delete_for_runs_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all delete_for_runs tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_DELETE_FOR_RUNS_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("delete_for_runs", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "delete_for_runs", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures
@@ -0,0 +1,149 @@
+"""DELETE_THREAD capability tests — adelete_thread."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_delete_thread_removes_checkpoints(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """All checkpoints gone after delete."""
+    tid = str(uuid4())
+    parent_cfg = None
+    for i in range(3):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent_cfg = await saver.aput(config, cp, generate_metadata(step=i), {})
+
+    # Pre-delete: verify data exists
+    assert await saver.aget_tuple(generate_config(tid)) is not None, (
+        "Pre-delete: checkpoint should exist"
+    )
+
+    await saver.adelete_thread(tid)
+
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is None
+
+    results = []
+    async for t in saver.alist(generate_config(tid)):
+        results.append(t)
+    assert len(results) == 0
+
+
+async def test_delete_thread_removes_writes(saver: BaseCheckpointSaver) -> None:
+    """Pending writes gone after delete."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+    await saver.aput_writes(stored, [("ch", "val")], str(uuid4()))
+
+    # Pre-delete: verify writes exist
+    pre_tup = await saver.aget_tuple(generate_config(tid))
+    assert pre_tup is not None, "Pre-delete: checkpoint should exist"
+    assert pre_tup.pending_writes is not None and len(pre_tup.pending_writes) == 1, (
+        f"Pre-delete: expected 1 write, got {len(pre_tup.pending_writes) if pre_tup.pending_writes else 0}"
+    )
+
+    await saver.adelete_thread(tid)
+
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is None
+
+
+async def test_delete_thread_removes_all_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Root + child namespaces both removed."""
+    tid = str(uuid4())
+
+    for ns in ["", "child:1"]:
+        cfg = generate_config(tid, checkpoint_ns=ns)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(), {})
+
+    # Pre-delete: verify each namespace has data
+    for ns in ["", "child:1"]:
+        pre = await saver.aget_tuple(generate_config(tid, checkpoint_ns=ns))
+        assert pre is not None, f"Pre-delete: namespace '{ns}' should have data"
+
+    await saver.adelete_thread(tid)
+
+    for ns in ["", "child:1"]:
+        tup = await saver.aget_tuple(generate_config(tid, checkpoint_ns=ns))
+        assert tup is None
+
+
+async def test_delete_thread_preserves_other_threads(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Other threads untouched."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+
+    for tid in (tid1, tid2):
+        cfg = generate_config(tid)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(), {})
+
+    await saver.adelete_thread(tid1)
+
+    assert await saver.aget_tuple(generate_config(tid1)) is None
+    assert await saver.aget_tuple(generate_config(tid2)) is not None
+
+
+async def test_delete_thread_nonexistent_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """No error for missing thread."""
+    # Should not raise
+    await saver.adelete_thread(str(uuid4()))
+
+
+ALL_DELETE_THREAD_TESTS = [
+    test_delete_thread_removes_checkpoints,
+    test_delete_thread_removes_writes,
+    test_delete_thread_removes_all_namespaces,
+    test_delete_thread_preserves_other_threads,
+    test_delete_thread_nonexistent_noop,
+]
+
+
+async def run_delete_thread_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all delete_thread tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_DELETE_THREAD_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("delete_thread", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "delete_thread", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures
@@ -0,0 +1,253 @@
+"""GET_TUPLE capability tests — aget_tuple retrieval."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_get_tuple_nonexistent_returns_none(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Missing thread returns None."""
+    config = generate_config(str(uuid4()))
+    tup = await saver.aget_tuple(config)
+    assert tup is None
+
+
+async def test_get_tuple_latest_when_no_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Returns newest checkpoint when no checkpoint_id in config."""
+    tid = str(uuid4())
+    ids = []
+    parent_cfg = None
+    for i in range(3):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent_cfg = await saver.aput(config, cp, generate_metadata(step=i), {})
+        ids.append(cp["id"])
+
+    # Get without checkpoint_id — should return the latest
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is not None
+    assert tup.checkpoint["id"] == ids[-1]
+    assert tup.metadata["step"] == 2, (
+        f"Expected latest step=2, got {tup.metadata['step']}"
+    )
+
+
+async def test_get_tuple_specific_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Returns exact match when checkpoint_id specified."""
+    tid = str(uuid4())
+
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {})
+
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    # Fetch the first one specifically
+    tup = await saver.aget_tuple(stored1)
+    assert tup is not None
+    assert tup.checkpoint["id"] == cp1["id"]
+
+
+async def test_get_tuple_config_structure(saver: BaseCheckpointSaver) -> None:
+    """tuple.config has thread_id, checkpoint_ns, checkpoint_id."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    conf = tup.config["configurable"]
+    assert conf["thread_id"] == tid
+    assert conf.get("checkpoint_ns", "") == "", (
+        f"Expected checkpoint_ns='', got {conf.get('checkpoint_ns')!r}"
+    )
+    assert conf["checkpoint_id"] == cp["id"]
+
+
+async def test_get_tuple_checkpoint_fields(saver: BaseCheckpointSaver) -> None:
+    """All Checkpoint fields present."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint(channel_values={"k": "v"})
+    cp["channel_versions"] = {"k": 1}
+    stored = await saver.aput(config, cp, generate_metadata(), {"k": 1})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    c = tup.checkpoint
+    assert c["id"] == cp["id"], f"id mismatch: {c['id']!r} != {cp['id']!r}"
+    assert c["v"] == 1, f"Expected v=1, got {c['v']!r}"
+    assert "ts" in c and c["ts"], "ts should be non-empty"
+    assert c["channel_values"] == {"k": "v"}, f"channel_values: {c['channel_values']!r}"
+    assert "channel_versions" in c
+    assert "versions_seen" in c
+
+
+async def test_get_tuple_metadata(saver: BaseCheckpointSaver) -> None:
+    """metadata populated correctly."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    md = generate_metadata(source="input", step=-1)
+    stored = await saver.aput(config, cp, md, {})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.metadata["source"] == "input"
+    assert tup.metadata["step"] == -1
+
+
+async def test_get_tuple_parent_config(saver: BaseCheckpointSaver) -> None:
+    """parent_config when parent exists, None otherwise."""
+    tid = str(uuid4())
+
+    # First checkpoint — no parent
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {})
+
+    tup1 = await saver.aget_tuple(stored1)
+    assert tup1 is not None
+    assert tup1.parent_config is None
+
+    # Second checkpoint — has parent
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    tup2 = await saver.aget_tuple(stored2)
+    assert tup2 is not None
+    assert tup2.parent_config is not None
+    assert (
+        tup2.parent_config["configurable"]["checkpoint_id"]
+        == stored1["configurable"]["checkpoint_id"]
+    )
+
+
+async def test_get_tuple_pending_writes(saver: BaseCheckpointSaver) -> None:
+    """pending_writes from put_writes visible."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("ch", "val")], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected 1 write, got {len(tup.pending_writes)}"
+    )
+    assert tup.pending_writes[0][0] == task_id, (
+        f"task_id mismatch: {tup.pending_writes[0][0]!r}"
+    )
+    assert tup.pending_writes[0][1] == "ch", (
+        f"channel mismatch: {tup.pending_writes[0][1]!r}"
+    )
+    assert tup.pending_writes[0][2] == "val", (
+        f"value mismatch: {tup.pending_writes[0][2]!r}"
+    )
+
+
+async def test_get_tuple_respects_namespace(saver: BaseCheckpointSaver) -> None:
+    """checkpoint_ns filtering."""
+    tid = str(uuid4())
+
+    cfg_root = generate_config(tid, checkpoint_ns="")
+    cp_root = generate_checkpoint()
+    stored_root = await saver.aput(cfg_root, cp_root, generate_metadata(), {})
+
+    cfg_child = generate_config(tid, checkpoint_ns="child:1")
+    cp_child = generate_checkpoint()
+    stored_child = await saver.aput(cfg_child, cp_child, generate_metadata(), {})
+
+    tup_root = await saver.aget_tuple(stored_root)
+    assert tup_root is not None
+    assert tup_root.checkpoint["id"] == cp_root["id"]
+
+    tup_child = await saver.aget_tuple(stored_child)
+    assert tup_child is not None
+    assert tup_child.checkpoint["id"] == cp_child["id"]
+
+
+async def test_get_tuple_nonexistent_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Specific but missing checkpoint_id returns None."""
+    tid = str(uuid4())
+    nonexistent_id = str(uuid4())
+    # Put one checkpoint so the thread exists
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    await saver.aput(config, cp, generate_metadata(), {})
+
+    # Ask for a non-existent checkpoint_id
+    bad_cfg = generate_config(tid, checkpoint_id=nonexistent_id)
+    tup = await saver.aget_tuple(bad_cfg)
+    assert tup is None
+
+
+ALL_GET_TUPLE_TESTS = [
+    test_get_tuple_nonexistent_returns_none,
+    test_get_tuple_latest_when_no_checkpoint_id,
+    test_get_tuple_specific_checkpoint_id,
+    test_get_tuple_config_structure,
+    test_get_tuple_checkpoint_fields,
+    test_get_tuple_metadata,
+    test_get_tuple_parent_config,
+    test_get_tuple_pending_writes,
+    test_get_tuple_respects_namespace,
+    test_get_tuple_nonexistent_checkpoint_id,
+]
+
+
+async def run_get_tuple_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all get_tuple tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_GET_TUPLE_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("get_tuple", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "get_tuple", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures
--- a/Show More
+++ b/Show More