Updates

.github/workflows/_integration_test.yml

@@ -4,7 +4,7 @@ on:
   workflow_call:
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
@@ -71,3 +71,4 @@ jobs:
         working-directory: libs/cli/js-examples
         run: |
           langgraph build -t langgraph-test-e
+  

.github/workflows/_lint.yml

@@ -9,7 +9,7 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
   # This env var allows us to get inline annotations when ruff has complaints.
   RUFF_OUTPUT_FORMAT: github
@@ -50,6 +50,12 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
         run: poetry check
 
+      - name: Check lock file
+        if: steps.changed-files.outputs.all
+        shell: bash
+        working-directory: ${{ inputs.working-directory }}
+        run: poetry lock --check
+
       - name: Install dependencies
         if: steps.changed-files.outputs.all
         # Also installs dev/lint/test/typing dependencies, to ensure we have

.github/workflows/_test.yml

@@ -9,7 +9,7 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:

.github/workflows/_test_langgraph.yml

@@ -4,7 +4,7 @@ on:
   workflow_call:
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:

.github/workflows/_test_release.yml

@@ -9,7 +9,7 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
   PYTHON_VERSION: "3.10"
 
 jobs:

.github/workflows/_test_scheduler_kafka.yml

@@ -4,7 +4,7 @@ on:
   workflow_call:
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:

.github/workflows/baseline.yml

@@ -8,7 +8,7 @@ on:
       - "libs/**"
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   benchmark:

.github/workflows/bench.yml

@@ -6,7 +6,7 @@ on:
       - "libs/**"
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   benchmark:
@@ -43,7 +43,7 @@ jobs:
         run: |
           {
             echo 'OUTPUT<<EOF'
-            make -s benchmark-fast
+            make -s benchmark
             echo EOF
           } >> "$GITHUB_OUTPUT"
       - name: Compare benchmarks

.github/workflows/ci.yml

@@ -17,34 +17,10 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
-  changes:
-    runs-on: ubuntu-latest
-    outputs:
-      python: ${{ steps.filter.outputs.python }}
-      sdk-js: ${{ steps.filter.outputs.sdk-js }}
-    steps:
-      - uses: actions/checkout@v4
-      - uses: dorny/paths-filter@v3
-        id: filter
-        with:
-          filters: |
-            python:
-              - 'libs/langgraph/**'
-              - 'libs/sdk-py/**'
-              - 'libs/cli/**'
-              - 'libs/checkpoint/**'
-              - 'libs/checkpoint-sqlite/**'
-              - 'libs/checkpoint-postgres/**'
-              - 'libs/scheduler-kafka/**'
-              - 'libs/prebuilt/**'
-            sdk-js:
-              - 'libs/sdk-js/**'
-
   lint:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
@@ -57,16 +33,13 @@ jobs:
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
             "libs/scheduler-kafka",
-            "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_lint.yml
     with:
       working-directory: ${{ matrix.working-directory }}
     secrets: inherit
 
   test:
-    needs: changes
     name: cd ${{ matrix.working-directory }}
     strategy:
       matrix:
@@ -76,9 +49,7 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
-            "libs/prebuilt",
           ]
-    if: needs.changes.outputs.python == 'true'
     uses: ./.github/workflows/_test.yml
     with:
       working-directory: ${{ matrix.working-directory }}
@@ -86,23 +57,17 @@ jobs:
 
   # NOTE: we're testing langgraph separately because it requires a different matrix
   test-langgraph:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "cd libs/langgraph"
     uses: ./.github/workflows/_test_langgraph.yml
     secrets: inherit
 
   # NOTE: we're testing scheduler-kafka separately because it requires a different matrix
   test-scheduler-kafka:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "cd libs/scheduler-kafka"
     uses: ./.github/workflows/_test_scheduler_kafka.yml
     secrets: inherit
 
   check-sdk-methods:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: "Check SDK methods matching"
     runs-on: ubuntu-latest
     steps:
@@ -114,52 +79,12 @@ jobs:
       - name: Run check_sdk_methods script
         run: python .github/scripts/check_sdk_methods.py
 
-  check-schema:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
-    name: "Check CLI schema hasn't changed #${{ matrix.python-version }}"
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.11"
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: "3.11"
-          poetry-version: ${{ env.POETRY_VERSION }}
-          cache-key: schema-check-cli
-      - name: Install CLI dependencies
-        run: |
-          cd libs/cli
-          poetry install
-      - name: Generate schema and check for changes
-        run: |
-          cd libs/cli
-          # Create a temporary copy of the current schema
-          cp schemas/schema.json schemas/schema.current.json
-          # Generate new schema
-          poetry run python generate_schema.py
-          # Compare the new schema with the original
-          if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
-            echo "Error: Langgraph.json configuration schema has changed. Please run 'poetry run python generate_schema.py' in the libs/cli directory and commit the changes."
-            diff schemas/schema.json schemas/schema.current.json
-            exit 1
-          fi
-          echo "Schema check passed - no changes detected"
-
   integration-test:
-    needs: changes
-    if: needs.changes.outputs.python == 'true'
     name: CLI integration test
     uses: ./.github/workflows/_integration_test.yml
     secrets: inherit
 
   lint-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -184,8 +109,6 @@ jobs:
         run: yarn build
 
   test-js:
-    needs: changes
-    if: needs.changes.outputs.sdk-js == 'true'
     runs-on: ubuntu-latest
     strategy:
       matrix:
@@ -216,8 +139,6 @@ jobs:
         test,
         test-langgraph,
         test-scheduler-kafka,
-        check-sdk-methods,
-        check-schema,
         integration-test,
         test-js,
       ]

.github/workflows/codespell.yml

@@ -9,11 +9,7 @@
     
     permissions:
       contents: read
-
-    defaults:
-      run:
-        working-directory: docs
-
+    
     jobs:
       codespell:
         name: (Check for spelling errors)
@@ -30,13 +26,13 @@
           - name: Extract Ignore Words List
             run: |
               # Use a Python script to extract the ignore words list from pyproject.toml
-              python ../.github/workflows/extract_ignored_words_list.py
+              python .github/workflows/extract_ignored_words_list.py
             id: extract_ignore_words
     
           - name: Codespell
             uses: codespell-project/actions-codespell@v2
             with:
-              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib,*.md'
+              skip: '*.ambr,*.lock,*.ipynb,*.yaml,*.zlib'
               ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
           # We do this to avoid spellchecking cell outputs
           - name: Codespell Notebooks

.github/workflows/deploy_docs.yml

@@ -10,7 +10,7 @@ on:
   workflow_dispatch:
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 permissions:
   contents: read
@@ -21,10 +21,6 @@ concurrency:
   group: "pages"
   cancel-in-progress: false
 
-defaults:
-  run:
-    working-directory: docs
-
 jobs:
   get-changed-files:
     runs-on: ubuntu-latest
@@ -48,7 +44,6 @@ jobs:
   deploy:
     # needs: run-changed-notebooks
     runs-on: ubuntu-latest
-    timeout-minutes: 10 # Job will be cancelled if it runs for more than 10 minutes
     env:
       GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
     steps:
@@ -65,47 +60,34 @@ jobs:
 
       - name: Install dependencies
         run: |
-          yarn
-          poetry install --with test --with docs --no-root
-          # we run this installation only for internal PRs
-          # as GITHUB_TOKEN is not available for PRs from outside contributors
-          if [ -n "${GITHUB_TOKEN}" ]; then
-            poetry run pip install "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
-          fi
+          poetry install --with test --no-root
+          poetry run pip install -U \
+            pytest \
+            pytest-check-links \
+            langsmith \
+            langchain \
+            GitPython \
+            "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
 
-      - name: Run unit tests
-        # Run unit tests on the docs build pipeline
-        run: make tests
       - name: Lint Docs
         # This step lints the docs using the existing linting set up.
         # It should be very fast and should not require any external services.
         run: make lint-docs
-      - name: Build llms-text
-        run: make llms-text
       - name: Build site
-        run: |
-          # If this is main branch, then we want to download stats. we do this 
-          # with the env variable DOWNLOAD_STATS=true
-          if [ "${{ github.ref }}" == "refs/heads/main" ]; then
-            DOWNLOAD_STATS=true make build-docs
-          else
-            make build-docs
-          fi
+        run: make build-docs
         env:
           MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
-          OPENAI_API_KEY: sf-proj-1234567890 # fake placeholder, shouldn't actually be used
-          ANTHROPIC_API_KEY: sk-ant-api03-1234567890 # fake placeholder, shouldn't actually be used
+
       - name: Check links in notebooks
         env:
           LANGCHAIN_API_KEY: test
         run: |
-          if [ "${{ github.event_name }}" == "schedule" ]; then
+          if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ github.event_name }}" == "workflow_dispatch" ] || ([ "${{ github.event_name }}" == "push" ] && [ "${{ github.ref }}" == "refs/heads/main" ]); then
             echo "Running link check on all HTML files matching notebooks in docs directory..."
             poetry run pytest -v \
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://academy\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
-              --check-links-ignore "https://twitter.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
               --check-links-ignore "http://localhost:8123/.*" \
               --check-links-ignore "http://localhost:2024.*" \
@@ -113,16 +95,14 @@ jobs:
               --check-links-ignore "/.*\.(ipynb|html)$" \
               --check-links-ignore "https://python\.langchain\.com/.*" \
               --check-links-ignore "https://openai\.com/.*" \
-              --check-links-ignore "https://www\.uber\.com/.*" \
               --check-links-ignore "https://pepy\.tech/.*" \
-              --check-links-ignore "docs/docs/static/wordmark_*" \
-              --check-links $(find site -name "index.html" | grep -v 'storm/index.html')
+              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
               
           else
             echo "Fetching changes from origin/main..."
             git fetch origin main
             echo "Checking for changed notebook files..."
-            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|site/|; s/\.ipynb$/\/index.html/' || true)
+            CHANGED_FILES=$(git diff --name-only --diff-filter=d origin/main | grep 'docs/docs/.*\.ipynb$' | grep -v 'storm.ipynb' | sed -E 's|^docs/docs/|docs/site/|; s/\.ipynb$/\/index.html/' || true)
             echo "Changed files: ${CHANGED_FILES}"
             if [ -n "${CHANGED_FILES}" ]; then
               echo "Running link check on HTML files matching changed notebook files..."
@@ -133,10 +113,8 @@ jobs:
                 --check-links-ignore "http://localhost:2024.*" \
                 --check-links-ignore "http://127.0.0.1:.*" \
                 --check-links-ignore "https://x.com/.*" \
-                --check-links-ignore "https://twitter.com/.*" \
                 --check-links-ignore "https://github\.com/.*" \
                 --check-links-ignore "/.*\.(ipynb|html)$" \
-                --check-links-ignore "docs/docs/static/wordmark_*" \
                 --check-links ${CHANGED_FILES} \
                 || ([ $? = 5 ] && exit 0 || exit $?)
             else
@@ -149,7 +127,7 @@ jobs:
         uses: actions/configure-pages@v4
 
       - name: Upload Pages Artifact
-        # if: github.ref == 'refs/heads/main'
+        if: github.ref == 'refs/heads/main'
         uses: actions/upload-pages-artifact@v3
         with:
           path: ./docs/site/

.github/workflows/extract_ignored_words_list.py

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

.github/workflows/link_check.yml

@@ -12,7 +12,7 @@ on:
   workflow_dispatch:
 
 env:
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   markdown-link-check:
@@ -42,8 +42,8 @@ jobs:
 
       - name: Check README.md is in sync
         run: |
-          if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
-          echo "README.md is out of sync with libs/langgraph/README.md"
-          diff -C 3 README.md libs/langgraph/README.md
-          exit 1
-          fi
+            if ! diff -q README.md libs/langgraph/README.md >/dev/null; then
+            echo "README.md is out of sync with libs/langgraph/README.md"
+            diff -C 3 README.md libs/langgraph/README.md
+            exit 1
+            fi

.github/workflows/release.yml

@@ -10,7 +10,7 @@ on:
 
 env:
   PYTHON_VERSION: "3.11"
-  POETRY_VERSION: "2.1.2"
+  POETRY_VERSION: "1.7.1"
 
 jobs:
   build:
@@ -195,11 +195,7 @@ jobs:
               "$PKG_NAME==$VERSION" \
           )
 
-          if [[ "$PKG_NAME" == *prebuilt* ]]; then
-            poetry run pip install langgraph
-          fi
-
-          if [[ "$PKG_NAME" == *checkpoint* || "$PKG_NAME" == *prebuilt* ]]; then
+          if [[ "$PKG_NAME" == *checkpoint* ]]; then
             # since checkpoint packages are namespace packages, import them with . convention
             # i.e. import langgraph.checkpoint or langgraph.checkpoint.sqlite
             IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/./g)"

.github/workflows/run_notebooks.yml

@@ -9,11 +9,7 @@ on:
         type: string
         description: "JSON string of changed files"
   schedule:
-    - cron: "0 13 * * *"
-
-defaults:
-  run:
-    working-directory: docs
+    - cron: '0 13 * * *'
 
 jobs:
   build:
@@ -30,12 +26,12 @@ jobs:
         uses: "./.github/actions/poetry_setup"
         with:
           python-version: 3.11
-          poetry-version: 2.1.2
+          poetry-version: 1.7.1
           cache-key: test-langgraph-notebooks
 
       - name: Install dependencies
         run: |
-          poetry install --with test --no-root
+          poetry install --with test
           poetry run pip install jupyter
 
       - name: Start services
@@ -43,36 +39,36 @@ jobs:
 
       - name: Pre-download tiktoken files
         run: |
-          poetry run python _scripts/download_tiktoken.py
+          poetry run python docs/_scripts/download_tiktoken.py
 
       - name: Prepare notebooks
         run: |
           if [ "${{ matrix.lib-version }}" = "development" ]; then
-            poetry run python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
           else
-            poetry run python _scripts/prepare_notebooks_for_ci.py
+            poetry run python docs/_scripts/prepare_notebooks_for_ci.py
           fi
 
       - name: Run notebooks
         env:
           # these won't actually be used because of the VCR cassettes
           # but need to set them to avoid triggering getpass()
-          OPENAI_API_KEY: "very-secret-key"
-          ANTHROPIC_API_KEY: "very-secret-key"
-          TAVILY_API_KEY: "very-secret-key"
-          LANGSMITH_API_KEY: "very-secret-key"
-          NOMIC_API_KEY: "very-secret-key"
-          COHERE_API_KEY: "very-secret-key"
-          FIREWORKS_API_KEY: "very-secret-key"
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+          TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+          NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
         run: |
           if [ "${{ github.event_name }}" = "workflow_dispatch" ] || [ "${{ github.event_name }}" = "schedule" ]; then
             echo "Running all notebooks"
-            ./_scripts/execute_notebooks.sh
+            ./docs/_scripts/execute_notebooks.sh
           else
-            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | sed 's|^docs/docs/|docs/|' | grep '\.ipynb$' || true)
+            CHANGED_FILES=$(echo '${{ inputs.changed-files }}' | tr ' ' '\n' | grep '\.ipynb$' || true)
             if [ -n "$CHANGED_FILES" ]; then
               echo "Running changed notebooks: $CHANGED_FILES"
-              ./_scripts/execute_notebooks.sh $CHANGED_FILES
+              ./docs/_scripts/execute_notebooks.sh $CHANGED_FILES
             else
               echo "No notebook files changed, skipping execution"
             fi

.github/workflows/size.yml

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

.gitignore

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

Makefile

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

README.md

@@ -1,90 +1,338 @@
-<picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
-</picture>
+# 🦜🕸️LangGraph
 
-<div>
-<br>
-</div>
-
-[![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/)
+![Version](https://img.shields.io/pypi/v/langgraph)
 [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph)
 [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues)
 [![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/)
 
+⚡ Building language agents as graphs ⚡
+
 > [!NOTE]
 > Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
-LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+## Overview
 
-```bash
+[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building
+stateful, multi-actor applications with LLMs, used to create agent and multi-agent
+workflows. Check out an introductory tutorial [here](https://langchain-ai.github.io/langgraph/tutorials/introduction/).
+
+
+LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+
+### Why use LangGraph?
+
+LangGraph provides fine-grained control over both the flow and state of your
+agent applications. It implements a central
+[persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/),
+enabling features that are common to most agent architectures:
+
+- **Memory**: LangGraph persists arbitrary aspects of your application's state,
+supporting memory of conversations and other updates within and across user
+interactions;
+- **Human-in-the-loop**: Because state is checkpointed, execution can be interrupted
+and resumed, allowing for decisions, validation, and corrections at key stages via
+human input.
+
+Standardizing these components allows individuals and teams to focus on the behavior
+of their agent, instead of its supporting infrastructure.
+
+Through [LangGraph Platform](#langgraph-platform), LangGraph also provides tooling for
+the development, deployment, debugging, and monitoring of your applications.
+
+LangGraph integrates seamlessly with
+[LangChain](https://python.langchain.com/docs/introduction/) and
+[LangSmith](https://docs.smith.langchain.com/) (but does not require them).
+
+To learn more about LangGraph, check out our first LangChain Academy
+course, *Introduction to LangGraph*, available for free
+[here](https://academy.langchain.com/courses/intro-to-langgraph).
+
+### LangGraph Platform
+
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger).
+
+See deployment options [here](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
+(includes a free tier).
+
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
+- **Background runs**: Runs agents asynchronously in the background
+- **Support for long running agents**: Infrastructure that can handle long running processes
+- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
+- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
+
+## Installation
+
+```shell
 pip install -U langgraph
 ```
 
-To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
+## Example
+
+Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
+
+```shell
+pip install langchain-anthropic
+```
+
+```shell
+export ANTHROPIC_API_KEY=sk-...
+```
+
+Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
+
+```shell
+export LANGSMITH_TRACING=true
+export LANGSMITH_API_KEY=lsv2_sk_...
+```
+
+The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
+
+<details open>
+  <summary>High-level implementation</summary>
 
 ```python
-# This code depends on pip install langchain[anthropic]
 from langgraph.prebuilt import create_react_agent
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
 
+# Define the tools for the agent to use
+@tool
 def search(query: str):
     """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
     if "sf" in query.lower() or "san francisco" in query.lower():
         return "It's 60 degrees and foggy."
     return "It's 90 degrees and sunny."
 
-agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+
+tools = [search]
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+app = create_react_agent(model, tools, checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
 )
+final_state["messages"][-1].content
+```
+```
+"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
 ```
 
+Now when we pass the same <code>"thread_id"</code>, the conversation context is retained via the saved state (i.e. stored list of messages)
+
+```python
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what about ny"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+```
+"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
+```
+</details>
+
 > [!TIP]
-> Check out [this guide](https://langchain-ai.github.io/langgraph/tutorials/workflows/) that walks through implementing common patterns (workflows and agents) in LangGraph. 
+> LangGraph is a **low-level** framework that allows you to implement any custom agent
+architectures. Click on the low-level implementation below to see how to implement a
+tool-calling agent from scratch.
 
-## Why use LangGraph?
+<details>
+<summary>Low-level implementation</summary>
 
-LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
+```python
+from typing import Literal
 
-- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
-- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
-- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import END, START, StateGraph, MessagesState
+from langgraph.prebuilt import ToolNode
 
-LangGraph is trusted in production and powering agents for companies like:
 
-- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
-- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
-- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
-- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
-- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
+# Define the tools for the agent to use
+@tool
+def search(query: str):
+    """Call to surf the web."""
+    # This is a placeholder, but don't tell the LLM that...
+    if "sf" in query.lower() or "san francisco" in query.lower():
+        return "It's 60 degrees and foggy."
+    return "It's 90 degrees and sunny."
 
-## LangGraph’s ecosystem
 
-While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
+tools = [search]
 
-- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
-- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
+tool_node = ToolNode(tools)
 
-## Pairing with LangGraph Platform
+model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
 
-While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
+# Define the function that determines whether to continue or not
+def should_continue(state: MessagesState) -> Literal["tools", END]:
+    messages = state['messages']
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then we route to the "tools" node
+    if last_message.tool_calls:
+        return "tools"
+    # Otherwise, we stop (reply to the user)
+    return END
 
-LangGraph Platform can help engineering teams:
 
-- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
-- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
-- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    messages = state['messages']
+    response = model.invoke(messages)
+    # We return a list, because this will get added to the existing list
+    return {"messages": [response]}
 
-## Additional resources
 
-- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
-- [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
-- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+# Define a new graph
+workflow = StateGraph(MessagesState)
 
-## Acknowledgements
+# Define the two nodes we will cycle between
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", tool_node)
 
-LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
+# Set the entrypoint as `agent`
+# This means that this node is the first one called
+workflow.add_edge(START, "agent")
+
+# We now add a conditional edge
+workflow.add_conditional_edges(
+    # First, we define the start node. We use `agent`.
+    # This means these are the edges taken after the `agent` node is called.
+    "agent",
+    # Next, we pass in the function that will determine which node is called next.
+    should_continue,
+)
+
+# We now add a normal edge from `tools` to `agent`.
+# This means that after `tools` is called, `agent` node is called next.
+workflow.add_edge("tools", 'agent')
+
+# Initialize memory to persist state between graph runs
+checkpointer = MemorySaver()
+
+# Finally, we compile it!
+# This compiles it into a LangChain Runnable,
+# meaning you can use it as you would any other runnable.
+# Note that we're (optionally) passing the memory when compiling the graph
+app = workflow.compile(checkpointer=checkpointer)
+
+# Use the agent
+final_state = app.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+    config={"configurable": {"thread_id": 42}}
+)
+final_state["messages"][-1].content
+```
+
+<b>Step-by-step Breakdown</b>:
+
+<details>
+<summary>Initialize the model and tools.</summary>
+<ul>
+  <li>
+    We use <code>ChatAnthropic</code> as our LLM. <strong>NOTE:</strong> we need to make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the <code>.bind_tools()</code> method.
+  </li>
+  <li>
+    We define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that <a href="https://python.langchain.com/docs/how_to/custom_tools/">here</a>.
+  </li>
+</ul>
+</details>
+
+<details>
+<summary>Initialize graph with state.</summary>
+
+<ul>
+    <li>We initialize graph (<code>StateGraph</code>) by passing state schema (in our case <code>MessagesState</code>)</li>
+    <li><code>MessagesState</code> is a prebuilt state schema that has one attribute -- a list of LangChain <code>Message</code> objects, as well as logic for merging the updates from each node into the state.</li>
+</ul>
+</details>
+
+<details>
+<summary>Define graph nodes.</summary>
+
+There are two main nodes we need:
+
+<ul>
+    <li>The <code>agent</code> node: responsible for deciding what (if any) actions to take.</li>
+    <li>The <code>tools</code> node that invokes tools: if the agent decides to take an action, this node will then execute that action.</li>
+</ul>
+</details>
+
+<details>
+<summary>Define entry point and graph edges.</summary>
+
+First, we need to set the entry point for graph execution - <code>agent</code> node.
+
+Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (<code>MessagesState</code>). In our case, the destination is not known until the agent (LLM) decides.
+
+<ul>
+  <li>Conditional edge: after the agent is called, we should either:
+    <ul>
+      <li>a. Run tools if the agent said to take an action, OR</li>
+      <li>b. Finish (respond to the user) if the agent did not ask to run tools</li>
+    </ul>
+  </li>
+  <li>Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next</li>
+</ul>
+</details>
+
+<details>
+<summary>Compile the graph.</summary>
+
+<ul>
+  <li>
+    When we compile the graph, we turn it into a LangChain 
+    <a href="https://python.langchain.com/docs/concepts/runnables/">Runnable</a>, 
+    which automatically enables calling <code>.invoke()</code>, <code>.stream()</code> and <code>.batch()</code> 
+    with your inputs
+  </li>
+  <li>
+    We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, 
+    human-in-the-loop workflows, time travel and more. In our case we use <code>MemorySaver</code> - 
+    a simple in-memory checkpointer
+  </li>
+</ul>
+</details>
+
+<details>
+<summary>Execute the graph.</summary>
+
+<ol>
+  <li>LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, <code>"agent"</code>.</li>
+  <li>The <code>"agent"</code> node executes, invoking the chat model.</li>
+  <li>The chat model returns an <code>AIMessage</code>. LangGraph adds this to the state.</li>
+  <li>Graph cycles the following steps until there are no more <code>tool_calls</code> on <code>AIMessage</code>:
+    <ul>
+      <li>If <code>AIMessage</code> has <code>tool_calls</code>, <code>"tools"</code> node executes</li>
+      <li>The <code>"agent"</code> node executes again and returns <code>AIMessage</code></li>
+    </ul>
+  </li>
+  <li>Execution progresses to the special <code>END</code> value and outputs the final state. And as a result, we get a list of all our chat messages as output.</li>
+</ol>
+</details>
+
+</details>
+
+## Documentation
+
+* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
+* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
+* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
+* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
+* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
+
+## Contributing
+
+For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

docs/.gitignore

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

docs/Makefile

@@ -1,74 +0,0 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
-
-build-typedoc:
-	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
-	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
-	#  Add links to the monorepo
-	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-build-prebuilt:
-	# Use to create an update to date prebuilt page.
-	# Looks up download stats for each of the prebuilt packages and
-	# generates the final prebuilt page.
-	@if [ "$(DOWNLOAD_STATS)" = "true" ]; then \
-		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats stats.yml; \
-		set +x; \
-	else \
-		set -x; \
-		poetry run python -m _scripts.third_party_page.get_download_stats --fake stats.yml; \
-		set +x; \
-	fi
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/agents/prebuilt.md --language python
-
-build-docs: build-typedoc build-prebuilt
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
-
-llms-text:
-	poetry run python -m _scripts.generate_llms_text docs/llms-full.txt
-
-install-vercel-deps:
-	dnf install -y python3.11
-	curl -sSL https://install.python-poetry.org | python3 -
-	poetry self update 1.8.5
-	# don't use vercel's python - it wasn't compiled with sqlite support, and it fails when installing ipython's kernel
-	poetry env use /usr/bin/python3.11
-	poetry install --with docs --with test --no-root
-
-tests:
-	# Run unit tests
-	poetry run pytest tests/unit_tests
-
-
-vercel-build-docs: install-vercel-deps
-	make build-docs
-
-
-serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
-
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
-
-clean-docs:
-	find ./docs -name "*.ipynb" -type f -delete
-	rm -rf site
-
-## Run format against the project documentation.
-format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
-
-# Check the docs for linting violations
-lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
-
-codespell:
-	./codespell_notebooks.sh .
-
-start-services:
-	docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
-
-stop-services:
-	docker compose -f test-compose.yml down

docs/README.md

@@ -14,28 +14,28 @@ To run the documentation server locally you can run:
 make serve-docs
 ```
 
-This will start the documentation server on [http://127.0.0.1:8000/langgraph/](http://127.0.0.1:8000/langgraph/).
-
 ## Execute notebooks
 
 If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
 
 ```bash
-python _scripts/prepare_notebooks_for_ci.py
-./_scripts/execute_notebooks.sh
+python docs/_scripts/prepare_notebooks_for_ci.py
+./docs/_scripts/execute_notebooks.sh
 ```
 
 **Note**: if you want to run the notebooks without `%pip install` cells, you can run:
 
 ```bash
-python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
-./_scripts/execute_notebooks.sh
+python docs/_scripts/prepare_notebooks_for_ci.py --comment-install-cells
+./docs/_scripts/execute_notebooks.sh
 ```
 
 `prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
 * when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
 * when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
 
+**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
+
 ## Adding new notebooks
 
 If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
@@ -48,14 +48,14 @@ Then, run
 jupyter execute <path_to_notebook>
 ```
 
-Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
+Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
 
 ## Updating existing notebooks
 
-If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
+If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
 
 To delete cassettes for a notebook, you can run:
 
 ```bash
-rm cassettes/<notebook_name>*
+rm docs/cassettes/<notebook_name>*
 ```

docs/_scripts/add_translation.py

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

docs/_scripts/execute_notebooks.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 # Read the list of notebooks to skip from the JSON file
-SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
+SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('docs/notebooks_no_execution.json'))))")
 
 # Function to execute a single notebook
 execute_notebook() {
@@ -27,7 +27,7 @@ if [ $# -gt 0 ]; then
     notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
 else
     # Find all notebooks and filter out those in the skip list
-    notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
+    notebooks=$(find docs/docs/tutorials docs/docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
 fi
 
 # Execute notebooks sequentially

docs/_scripts/generate_api_reference_links.py

@@ -1,9 +1,9 @@
-import ast
 import importlib
+import inspect
 import logging
 import re
 from functools import lru_cache
-from typing import List, Optional
+from typing import List, Literal, Optional
 
 from typing_extensions import TypedDict
 
@@ -39,26 +39,22 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
     (["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
     (["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
     (["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
+    ([], "langgraph.types", "StreamMode", "types"),
     (["langgraph.graph"], "langgraph.constants", "START", "constants"),
     (["langgraph.graph"], "langgraph.constants", "END", "constants"),
     (["langgraph.constants"], "langgraph.types", "Send", "types"),
     (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
     (["langgraph.constants"], "langgraph.types", "Command", "types"),
-    (["langgraph.config"], "langgraph.config", "get_stream_writer", "config"),
-    (["langgraph.config"], "langgraph.config", "get_store", "config"),
     (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
     (["langgraph.func"], "langgraph.func", "task", "func"),
-    (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamMode", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamWriter", "types"),
+    ([], "langgraph.types", "RetryPolicy", "types"),
     ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
     ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
     ([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
     ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
     ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
     ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
-    ([], "langgraph.checkpoint.memory", "InMemorySaver", "checkpoints"),
     ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
     ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
     ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
@@ -72,19 +68,34 @@ WELL_KNOWN_LANGGRAPH_OBJECTS = {
 }
 
 
+def _make_regular_expression(pkg_prefix: str) -> re.Pattern:
+    if not pkg_prefix.isidentifier():
+        raise ValueError(f"Invalid package prefix: {pkg_prefix}")
+    return re.compile(
+        r"from\s+(" + pkg_prefix + "(?:_\w+)?(?:\.\w+)*?)\s+import\s+"
+        r"((?:\w+(?:,\s*)?)*"  # Match zero or more words separated by a comma+optional ws
+        r"(?:\s*\(.*?\))?)",  # Match optional parentheses block
+        re.DOTALL,  # Match newlines as well
+    )
+
+
+# Regular expression to match langchain import lines
+_IMPORT_LANGCHAIN_RE = _make_regular_expression("langchain")
+_IMPORT_LANGGRAPH_RE = _make_regular_expression("langgraph")
+
+
 @lru_cache(maxsize=10_000)
 def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
     """Get full module name using inspect, with LRU cache to memoize results."""
     try:
         module = importlib.import_module(module_path)
-        symbol = getattr(module, class_name)
-        # First check the __module__ attribute on the symbol.
-        mod_name = getattr(symbol, "__module__", None)
-        # If __module__ is not set or comes from typing,
-        # assume the definition is in module_path.
-        if mod_name is None or mod_name.startswith("typing"):
+        class_ = getattr(module, class_name)
+        module = inspect.getmodule(class_)
+        if module is None:
+            # For constants, inspect.getmodule() might return None
+            # In this case, we'll return the original module_path
             return module_path
-        return mod_name
+        return module.__name__
     except AttributeError as e:
         logger.warning(f"API Reference: Could not find module for {class_name}, {e}")
         return None
@@ -93,131 +104,142 @@ def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
         return None
 
 
+def _get_doc_title(data: str, file_name: str) -> str:
+    try:
+        return re.findall(r"^#\s*(.*)", data, re.MULTILINE)[0]
+    except IndexError:
+        pass
+    # Parse the rst-style titles
+    try:
+        return re.findall(r"^(.*)\n=+\n", data, re.MULTILINE)[0]
+    except IndexError:
+        return file_name
+
+
 class ImportInformation(TypedDict):
     imported: str  # The name of the class that was imported.
     source: str  # The full module path from which the class was imported.
     docs: str  # The URL pointing to the class's documentation.
-    path: str  # The path of the file where the markdown content originated.
+    title: str  # The title of the document where the import is used.
 
 
-def get_imports(code: str, path: str) -> List[ImportInformation]:
-    """Retrieve all import references from the given code for specified ecosystems.
+def _get_imports(
+    code: str, doc_title: str, package_ecosystem: Literal["langchain", "langgraph"]
+) -> List[ImportInformation]:
+    """Get imports from the given code block.
 
     Args:
-        code: The source code from which to extract import references.
-        path: The path of the file where the markdown content originated.
+        code: Python code block from which to extract imports
+        doc_title: Title of the document
+        package_ecosystem: "langchain" or "langgraph". The two live in different
+            repositories and have separate documentation sites.
 
     Returns:
-        A list of import information for each import found.
+        List of import information for the given code block
     """
-    # Parse the code into an AST.
-    try:
-        tree = ast.parse(code)
-    except SyntaxError:
-        return []
+    imports = []
 
-    found_imports = []
+    if package_ecosystem == "langchain":
+        pattern = _IMPORT_LANGCHAIN_RE
+    elif package_ecosystem == "langgraph":
+        pattern = _IMPORT_LANGGRAPH_RE
+    else:
+        raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
 
-    # Walk through the AST and process ImportFrom nodes.
-    for node in ast.walk(tree):
-        if isinstance(node, ast.ImportFrom):
-            # node.module is the source module.
-            if node.module is None:
+    for import_match in pattern.finditer(code):
+        module = import_match.group(1)
+        if "pydantic_v1" in module:
+            continue
+        imports_str = (
+            import_match.group(2).replace("(\n", "").replace("\n)", "")
+        )  # Handle newlines within parentheses
+        # remove any newline and spaces, then split by comma
+        imported_classes = [
+            imp.strip()
+            for imp in re.split(r",\s*", imports_str.replace("\n", ""))
+            if imp.strip()
+        ]
+        for class_name in imported_classes:
+            module_path = _get_full_module_name(module, class_name)
+            if not module_path:
                 continue
-            for alias in node.names:
-                if not (
-                    node.module.startswith("langchain")
-                    or node.module.startswith("langgraph")
-                ):
+            if len(module_path.split(".")) < 2:
+                continue
+
+            if package_ecosystem == "langchain":
+                pkg = module_path.split(".")[0].replace("langchain_", "")
+                top_level_mod = module_path.split(".")[1]
+
+                url = (
+                    _LANGCHAIN_API_REFERENCE
+                    + pkg
+                    + "/"
+                    + top_level_mod
+                    + "/"
+                    + module_path
+                    + "."
+                    + class_name
+                    + ".html"
+                )
+            elif package_ecosystem == "langgraph":
+                if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
+                    # Likely not documented yet
                     continue
 
-                found_imports.append(
-                    {
-                        "source": node.module,
-                        # alias.name is the original name even if an alias exists.
-                        "imported": alias.name,
-                    }
+                source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
+                    (module, class_name)
+                ]
+                url = (
+                    _LANGGRAPH_API_REFERENCE
+                    + namespace
+                    + "/#"
+                    + source_module
+                    + "."
+                    + class_name
                 )
+            else:
+                raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
 
-    imports: list[ImportInformation] = []
-
-    for found_import in found_imports:
-        module = found_import["source"]
-
-        if module.startswith("langchain"):
-            # Handles things like `langchain` or `langchain_anthropic`
-            package_ecosystem = "langchain"
-        elif module.startswith("langgraph"):
-            package_ecosystem = "langgraph"
-        else:
-            continue
-
-        class_name = found_import["imported"]
-        module_path = _get_full_module_name(module, class_name)
-        if not module_path:
-            continue
-        if len(module_path.split(".")) < 2:
-            continue
-
-        if package_ecosystem == "langchain":
-            pkg = module_path.split(".")[0].replace("langchain_", "")
-            top_level_mod = module_path.split(".")[1]
-
-            url = (
-                _LANGCHAIN_API_REFERENCE
-                + pkg
-                + "/"
-                + top_level_mod
-                + "/"
-                + module_path
-                + "."
-                + class_name
-                + ".html"
+            # Add the import information to our list
+            imports.append(
+                {
+                    "imported": class_name,
+                    "source": module,
+                    "docs": url,
+                    "title": doc_title,
+                }
             )
-        elif package_ecosystem == "langgraph":
-            if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
-                # Likely not documented yet
-                continue
-
-            source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
-                (module, class_name)
-            ]
-            url = (
-                _LANGGRAPH_API_REFERENCE
-                + namespace
-                + "/#"
-                + source_module
-                + "."
-                + class_name
-            )
-        else:
-            raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
-
-        # Add the import information to our list
-        imports.append(
-            {
-                "imported": class_name,
-                "source": module,
-                "docs": url,
-                "path": path,
-            }
-        )
 
     return imports
 
 
-def update_markdown_with_imports(markdown: str, path: str) -> str:
+def get_imports(code: str, doc_title: str) -> List[ImportInformation]:
+    """Retrieve all import references from the given code for specified ecosystems.
+
+    Args:
+        code: The source code from which to extract import references.
+        doc_title: The documentation title associated with the code.
+
+    Returns:
+        A list of import information for each import found.
+    """
+    ecosystems = ["langchain", "langgraph"]
+    all_imports = []
+    for package_ecosystem in ecosystems:
+        all_imports.extend(_get_imports(code, doc_title, package_ecosystem))
+    return all_imports
+
+
+def update_markdown_with_imports(markdown: str) -> str:
     """Update markdown to include API reference links for imports in Python code blocks.
 
-    This function scans the markdown content for Python code blocks, extracts any
-    imports, and appends links to their API documentation.
+    This function scans the markdown content for Python code blocks, extracts any imports, and appends links to their API documentation.
 
     Args:
         markdown: The markdown content to process.
-        path: The path of the file where the markdown content originated.
 
     Returns:
-        Updated markdown with API reference links prepended to Python code blocks.
+        Updated markdown with API reference links appended to Python code blocks.
 
     Example:
         Given a markdown with a Python code block:
@@ -225,12 +247,10 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
         ```python
         from langchain.nlp import TextGenerator
         ```
-        This function will append an API reference link to the `TextGenerator` class
-        from the `langchain.nlp` module if it's recognized.
+        This function will append an API reference link to the `TextGenerator` class from the `langchain.nlp` module if it's recognized.
     """
     code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```",
-        re.DOTALL,
+        r'(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```', re.DOTALL
     )
 
     def replace_code_block(match: re.Match) -> str:
@@ -240,10 +260,11 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
             match (re.Match): The regex match object containing the code block.
 
         Returns:
-            str: The modified code block with API reference links prepended if applicable.
+            str: The modified code block with API reference links appended if applicable.
         """
-        indent = match.group("indent")
-        code_block = match.group("code")
+        indent = match.group('indent')
+        code_block = match.group('code')
+        language = match.group('language')  # Preserve the language from the regex match
         # Retrieve import information from the code block
         imports = get_imports(code_block, "__unused__")
 
@@ -253,11 +274,11 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
             return original_code_block
 
         # Generate API reference links for each import
-        api_links = " | ".join(
+        api_links = ' | '.join(
             f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
         )
-        # Return the code block with prepended API reference links
-        return f"{indent}API Reference: {api_links}\n\n{original_code_block}"
+        # Return the code block with appended API reference links
+        return f'{original_code_block}\n\n{indent}API Reference: {api_links}'
 
     # Apply the replace_code_block function to all matches in the markdown
     updated_markdown = code_block_pattern.sub(replace_code_block, markdown)

docs/_scripts/generate_llms_text.py

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

docs/_scripts/notebook_convert.py

@@ -1,266 +1,28 @@
-import ast
 import os
 import re
 from pathlib import Path
-from typing import Literal
 
 import nbformat
 from nbconvert.exporters import MarkdownExporter
 from nbconvert.preprocessors import Preprocessor
 
 
-def _uses_input(source: str) -> bool:
-    """Parse the source code to determine if it uses the input() function."""
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        # If there's a syntax error, assume input() might be present to be safe.
-        return False
-
-    for node in ast.walk(tree):
-        if isinstance(node, ast.Call):
-            # Check if the function called is named 'input'
-            if isinstance(node.func, ast.Name) and node.func.id == "input":
-                return True
-    return False
-
-
-def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.:w
-
-    - Lines starting with "%%capture" are ignored.
-    - Lines starting with "%pip" are rewritten by removing the leading "%" character.
-    - Any other non-empty line causes a NotImplementedError.
-
-    Args:
-        code (str): The original code block.
-
-    Returns:
-        str: The transformed code block.
-
-    Raises:
-        NotImplementedError: If a line doesn't start with either "%%capture" or "%pip".
-    """
-    rewritten_lines = []
-
-    for line in code.splitlines():
-        stripped = line.strip()
-        # Skip empty lines
-        if not stripped:
-            continue
-        # Ignore %%capture lines
-        if stripped.startswith("%%capture"):
-            continue
-        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%pip"):
-            # Drop the leading '%' character
-            rewritten_lines.append(stripped[1:])
-        # Anything else is not supported
-        else:
-            raise NotImplementedError(f"Unhandled line: {line}")
-
-    return "\n".join(rewritten_lines)
-
-
-class PrintCallVisitor(ast.NodeVisitor):
-    """
-    This visitor sets self.has_print to True if it encounters a call
-    to a print within the global scope.
-
-    This should catch calls to print(), print_stream(), etc. (Prefixed with "print").
-
-    May have some false positives, but it's not meant to be perfect.
-
-    Temporary code for notebook conversion.
-    """
-
-    def __init__(self):
-        self.has_print = False
-        self.scope_level = 0  # counter to track whether we're inside a def/lambda
-
-    def visit_FunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_AsyncFunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Lambda(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_ClassDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Call(self, node):
-        # Only consider calls when not inside a function definition.
-        if self.scope_level == 0:
-            if isinstance(node.func, ast.Name) and node.func.id.startswith("print"):
-                self.has_print = True
-        self.generic_visit(node)
-
-
-def _has_output(source: str) -> bool:
-    """Determine if the code block is expected to produce output.
-
-    Args:
-        source (str): The source code of the code block.
-
-    Returns:
-        True if the code block is expected to produce output, False otherwise.
-
-        Must meet the following conditions:
-
-        1. There is a call to a printing function (name starts with "print")
-           that is not inside a function definition.
-        2. The last top-level statement is an expression that is valid if:
-         - It is any expression (including calls) AND
-         - It is NOT a call to `display(...)`.
-
-        `display` isn't handled currently by markdown-exec
-    """
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        return False
-
-    # Condition (1): Check for a global print-like call.
-    visitor = PrintCallVisitor()
-    visitor.visit(tree)
-    condition_a = visitor.has_print
-
-    # Condition (2): Check the last top-level statement.
-    condition_b = False
-    if tree.body:
-        last_stmt = tree.body[-1]
-        if isinstance(last_stmt, ast.Expr):
-            # If the expression is a call, ensure it's not a call to "display"
-            if isinstance(last_stmt.value, ast.Call):
-                if (
-                    isinstance(last_stmt.value.func, ast.Name)
-                    and last_stmt.value.func.id == "display"
-                ):
-                    condition_b = False  # exclude display-wrapped expressions
-                else:
-                    condition_b = True
-            else:
-                # Any other expression qualifies.
-                condition_b = True
-
-    return condition_a or condition_b
-
-
-def _convert_links_in_markdown(markdown: str) -> str:
-    """Convert links present in notebook markdown cells to standardized format.
-
-    We want to update markdown links code cells by linking to markdown
-    files rather than assuming that the link is to the finalized HTML.
-
-    This code is needed temporarily since the markdown links that are present
-    in ipython notebooks do not follow the same conventions as regular markdown
-    files in mkdocs (which should link to a .md file).
-    """
-
-    # Define the regex pattern in parts for clarity:
-    pattern = (
-        r"(?<!!)"  # Negative lookbehind: ensure the link is not an image (i.e., doesn't start with "!")
-        r"\["  # Literal '[' indicating the start of the link text.
-        r"(?P<text>[^\]]*)"  # Named group 'text': match any characters except ']', representing the link text.
-        r"\]"  # Literal ']' indicating the end of the link text.
-        r"\("  # Literal '(' indicating the start of the URL.
-        r"(?![^\)]*//)"  # Negative lookahead: ensure that the URL does not contain '//' (skip absolute URLs).
-        r"(?P<url>[^)]*)"  # Named group 'url': match any characters except ')', representing the URL.
-        r"\)"  # Literal ')' indicating the end of the URL.
-    )
-
-    def custom_replacement(match):
-        """logic will correct the link format used in ipython notebooks
-
-        Ipython notebooks were being converted directly into HTML links
-        instead of markdown links that retain the markdown extension.
-
-        It needs to handle the following cases:
-        - optional fragments (e.g., `#section`)
-            e.g., `[text](url/#section)` -> `[text](url.md#section)`
-            e.g., `[text](url#section)` -> `[text](url.md#section)`
-        - relative paths (e.g., `../path/to/file`) need to be denested by 1 level
-        """
-        text = match.group("text")
-        url = match.group("url")
-
-        if url.startswith("../"):
-            # we strip the "../" from the start of the URL
-            # We only need to denest one level.
-            url = url[3:]
-
-        url = url.rstrip("/")  # Strip `/` from the end of the URL
-
-        # if url has a fragment
-        if "#" in url:
-            url, fragment = url.split("#")
-            url = url.rstrip("/")
-            # Strip `/` from the end of the URL
-            return f"[{text}]({url}.md#{fragment})"
-        # Otherwise add the .md extension
-        return f"[{text}]({url}.md)"
-
-    return re.sub(
-        pattern,
-        custom_replacement,
-        markdown,
-    )
-
-
 class EscapePreprocessor(Preprocessor):
-    def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.markdown_exec_migration = markdown_exec_migration
-
     def preprocess_cell(self, cell, resources, cell_index):
         if cell.cell_type == "markdown":
-            if not self.markdown_exec_migration:
-                # Old logic is to convert ipynb links to HTML links
-                cell.source = re.sub(
-                    r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
-                    r'<a href="\2">\1</a>',
-                    cell.source,
-                )
-            else:
-                cell.source = _convert_links_in_markdown(cell.source)
-
+            # rewrite markdown links to html links (excluding image links)
+            cell.source = re.sub(
+                r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
+                r'<a href="\2">\1</a>',
+                cell.source,
+            )
             # Fix image paths in <img> tags
             cell.source = re.sub(
                 r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
             )
 
         elif cell.cell_type == "code":
-            # Determine if the cell has bash or cell magic
-            source = cell.source
-            is_exec = not (
-                source.startswith("%") or source.startswith("!") or _uses_input(source)
-            )
-            cell.metadata["exec"] = is_exec
-
-            if self.markdown_exec_migration:
-                # For markdown exec migration we'll re-write cell magic as bash commands
-                if source.startswith("%%"):
-                    cell.source = _rewrite_cell_magic(source)
-                    cell.metadata["language"] = "shell"
-
-                cell.metadata["has_output"] = _has_output(source)
-
-            # Remove noqa comments
-            cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
             # escape ``` in code
-            # This is needed because the markdown exporter will wrap code blocks in
-            # triple backticks, which will break the markdown output if the code block
-            # contains triple backticks.
             cell.source = cell.source.replace("```", r"\`\`\`")
             # escape ``` in output
             if "outputs" in cell:
@@ -353,11 +115,9 @@ exporter = MarkdownExporter(
 
 def convert_notebook(
     notebook_path: Path,
-    mode: Literal["markdown", "exec"] = "markdown",
-) -> str:
+) -> Path:
     with open(notebook_path) as f:
         nb = nbformat.read(f, as_version=4)
 
-    nb.metadata.mode = mode
     body, _ = exporter.from_notebook_node(nb)
     return body

docs/_scripts/notebook_hooks.py

@@ -1,14 +1,13 @@
 import logging
 import os
-import posixpath
 import re
 from typing import Any, Dict
 
 from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
-from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
+from notebook_convert import convert_notebook
+from generate_api_reference_links import update_markdown_with_imports
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -16,26 +15,6 @@ logger.setLevel(logging.INFO)
 DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
 
 
-REDIRECT_MAP = {
-    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
-    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
-    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
-    # cloud redirects
-    "cloud/index.md": "concepts/index.md#langgraph-platform",
-    "cloud/how-tos/index.md": "how-tos/index.md#langgraph-platform",
-    "cloud/concepts/api.md": "concepts/langgraph_server.md",
-    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
-    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
-    # misc
-    "prebuilt.md": "agents/prebuilt.md"
-}
-
-
 class NotebookFile(File):
     def is_documentation_page(self):
         return True
@@ -59,29 +38,6 @@ def on_files(files: Files, **kwargs: Dict[str, Any]):
     return new_files
 
 
-def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
-    """Add the path to the code blocks."""
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
-        r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
-        r"(?P=indent)```"  # Match closing backticks with the same indentation
-    )
-
-    def replace_code_block_header(match: re.Match) -> str:
-        indent = match.group("indent")
-        language = match.group("language")
-        attributes = match.group("attributes").rstrip()
-
-        if 'exec="on"' not in attributes:
-            # Return original code block
-            return match.group(0)
-
-        code = match.group("code")
-        return f'{indent}```{language} {attributes} path="{page.file.src_path}"\n{code}{indent}```'
-
-    return code_block_pattern.sub(replace_code_block_header, markdown)
-
-
 def _highlight_code_blocks(markdown: str) -> str:
     """Find code blocks with highlight comments and add hl_lines attribute.
 
@@ -96,7 +52,7 @@ def _highlight_code_blocks(markdown: str) -> str:
     # existing hl_lines for Python and JavaScript
     # Pattern to find code blocks with highlight comments, handling optional indentation
     code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
+        r"(?P<indent>[ \t]*)```(?P<language>py|python|js|javascript)(?!\s+hl_lines=)\n"
         r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
         r"(?P=indent)```"  # Match closing backticks with the same indentation
     )
@@ -105,13 +61,6 @@ def _highlight_code_blocks(markdown: str) -> str:
         indent = match.group("indent")
         language = match.group("language")
         code_block = match.group("code")
-        attributes = match.group("attributes").rstrip()
-
-        # Account for a case where hl_lines is manually specified
-        if "hl_lines" in attributes:
-            # Return original code block
-            return match.group(0)
-
         lines = code_block.split("\n")
         highlighted_lines = []
 
@@ -137,121 +86,35 @@ def _highlight_code_blocks(markdown: str) -> str:
         # Reconstruct the new code block
         new_code_block = "\n".join(lines_to_keep)
 
-        # Construct the full code block that also includes
-        # the fenced code block syntax.
-        opening_fence = f"```{language}"
-
-        if attributes:
-            opening_fence += f" {attributes}"
-
         if highlighted_lines:
-            opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
-
-        return (
-            # The indent and opening fence
-            f"{indent}{opening_fence}\n"
-            # The indent and terminating \n is already included in the code block
-            f"{new_code_block}"
-            f"{indent}```"
-        )
+            return (
+                f'{indent}```{language} hl_lines="{" ".join(highlighted_lines)}"\n'
+                # The indent and terminating \n is already included in the code block
+                f'{new_code_block}'
+                f'{indent}```'
+            )
+        else:
+            return (
+                f"{indent}```{language}\n"
+                # The indent and terminating \n is already included in the code block
+                f"{new_code_block}"
+                f"{indent}```"
+            )
 
     # Replace all code blocks in the markdown
     markdown = code_block_pattern.sub(replace_highlight_comments, markdown)
     return markdown
 
 
-def _on_page_markdown_with_config(
-    markdown: str,
-    page: Page,
-    *,
-    add_api_references: bool = True,
-    remove_base64_images: bool = False,
-    **kwargs: Any,
-) -> str:
+def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
     if DISABLED:
         return markdown
-
     if page.file.src_path.endswith(".ipynb"):
-        # logger.info("Processing Jupyter notebook: %s", page.file.src_path)
+        logger.info("Processing Jupyter notebook: %s", page.file.src_path)
         markdown = convert_notebook(page.file.abs_src_path)
 
     # Append API reference links to code blocks
-    if add_api_references:
-        markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
+    markdown = update_markdown_with_imports(markdown)
     # Apply highlight comments to code blocks
     markdown = _highlight_code_blocks(markdown)
-
-    # Add file path as an attribute to code blocks that are executable.
-    # This file path is used to associate fixtures with the executable code
-    # which can be used in CI to test the docs without making network requests.
-    markdown = _add_path_to_code_blocks(markdown, page)
-
-    if remove_base64_images:
-        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^)]+\)", "", markdown)
-
     return markdown
-
-
-def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
-    )
-
-
-# redirects
-
-HTML_TEMPLATE = """
-<!doctype html>
-<html lang="en">
-<head>
-    <meta charset="utf-8">
-    <title>Redirecting...</title>
-    <link rel="canonical" href="{url}">
-    <meta name="robots" content="noindex">
-    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
-    <meta http-equiv="refresh" content="0; url={url}">
-</head>
-<body>
-Redirecting...
-</body>
-</html>
-"""
-
-
-def write_html(site_dir, old_path, new_path):
-    """Write an HTML file in the site_dir with a meta redirect to the new page"""
-    # Determine all relevant paths
-    old_path_abs = os.path.join(site_dir, old_path)
-    old_dir_abs = os.path.dirname(old_path_abs)
-
-    # Create parent directories if they don't exist
-    if not os.path.exists(old_dir_abs):
-        os.makedirs(old_dir_abs)
-
-    # Write the HTML redirect file in place of the old file
-    content = HTML_TEMPLATE.format(url=new_path)
-    with open(old_path_abs, "w", encoding="utf-8") as f:
-        f.write(content)
-
-
-# Create HTML files for redirects after site dir has been built
-def on_post_build(config):
-    use_directory_urls = config.get("use_directory_urls")
-    for page_old, page_new in REDIRECT_MAP.items():
-        page_old = page_old.replace(".ipynb", ".md")
-        page_new = page_new.replace(".ipynb", ".md")
-        page_new_before_hash, hash, suffix = page_new.partition("#")
-        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
-            os.sep, "/"
-        )
-        new_html_path = File(page_new_before_hash, "", "", True).url
-        new_html_path = (
-            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-            + hash
-            + suffix
-        )
-        write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -7,7 +7,7 @@ import click
 import nbformat
 
 logger = logging.getLogger(__name__)
-NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
+NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
 DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
 
@@ -19,39 +19,36 @@ BLOCKLIST_COMMANDS = (
 )
 
 NOTEBOOKS_NO_CASSETTES = (
-    "docs/how-tos/visualization.ipynb",
+    "docs/docs/how-tos/visualization.ipynb",
+    "docs/docs/how-tos/many-tools.ipynb"
 )
 
 NOTEBOOKS_NO_EXECUTION = [
     # this uses a user provided project name for langsmith
-    "docs/tutorials/tnt-llm/tnt-llm.ipynb",
+    "docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
     # this uses langsmith datasets
-    "docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
+    "docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
     # this uses browser APIs
-    "docs/tutorials/web-navigation/web_voyager.ipynb",
+    "docs/docs/tutorials/web-navigation/web_voyager.ipynb",
     # these RAG guides use an ollama model
-    "docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
-    "docs/tutorials/rag/langgraph_crag_local.ipynb",
-    "docs/tutorials/rag/langgraph_self_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
-    "docs/tutorials/usaco/usaco.ipynb",
+    "docs/docs/tutorials/usaco/usaco.ipynb",
     # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
-    "docs/how-tos/autogen-integration.ipynb",
-    "docs/how-tos/autogen-integration-functional.ipynb",
+    "docs/docs/how-tos/autogen-integration.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
-    "docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
-    "docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
-    "docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
-    "docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
-    "docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
-    "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
-    "docs/tutorials/tot/tot.ipynb",
-    "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/streaming-specific-nodes.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb",
-    "docs/tutorials/customer-support/customer-support.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
-    "docs/how-tos/many-tools.ipynb",  # relies on openai embeddings, doesn't play well w/ VCR
+    "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
+    "docs/docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
+    "docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
+    "docs/docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
+    "docs/docs/tutorials/tot/tot.ipynb",
+    "docs/docs/how-tos/visualization.ipynb",
+    "docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
 ]
 
 
@@ -88,12 +85,6 @@ def has_blocklisted_command(code: str, metadata: dict) -> bool:
             return True
     return False
 
-def add_mermaid_retries(code: str) -> str:
-    return code.replace(
-        "draw_mermaid_png()",
-        "draw_mermaid_png(max_retries=10, retry_delay=2.0)"
-    )
-
 
 def add_vcr_to_notebook(
     notebook: nbformat.NotebookNode, cassette_prefix: str
@@ -188,15 +179,6 @@ def add_vcr_to_notebook(
     return notebook
 
 
-def add_mermaid_retries_to_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
-    for cell in notebook.cells:
-        if cell.cell_type != "code":
-            continue
-
-        cell.source = add_mermaid_retries(cell.source)
-    return notebook
-
-
 def process_notebooks(should_comment_install_cells: bool) -> None:
     for directory in NOTEBOOK_DIRS:
         for root, _, files in os.walk(directory):
@@ -218,8 +200,6 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                             notebook, cassette_prefix=cassette_prefix
                         )
 
-                    notebook = add_mermaid_retries_to_notebook(notebook)
-
                     if notebook_path in NOTEBOOKS_NO_EXECUTION:
                         # Add a cell at the beginning to indicate that this notebook should not be executed
                         warning_cell = nbformat.v4.new_markdown_cell(
@@ -236,7 +216,7 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                 except Exception as e:
                     logger.error(f"Error processing {notebook_path}: {e}")
     
-    with open("notebooks_no_execution.json", "w") as f:
+    with open(os.path.join(DOCS_PATH, "notebooks_no_execution.json"), "w") as f:
         json.dump(NOTEBOOKS_NO_EXECUTION, f)
 
 

docs/_scripts/third_party_page/create_third_party_page.py

@@ -1,138 +0,0 @@
-#!/usr/bin/env python
-"""Create the third party page for the documentation."""
-
-import argparse
-from typing import List
-from typing import TypedDict
-
-import yaml
-
-MARKDOWN = """\
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# Community Agents
-
-If you’re looking for other prebuilt libraries, explore the community-built options 
-below. These libraries can extend LangGraph's functionality in various ways.
-
-## 📚 Available Libraries
-
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-{library_list}
-
-## ✨ Contributing Your Library
-
-Have you built an awesome open-source library using LangGraph? We'd love to feature 
-your project on the official LangGraph documentation pages! 🏆
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
-
-**Guidelines**
-
-- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
-  for JavaScript/TypeScript, etc.) 📦
-- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
-  the Functional API (exposing an `entrypoint`).
-- The package must include documentation (e.g., a `README.md` or docs site) 
-  explaining how to use it.
-  
-We'll review your contribution and merge it in!
-
-Thanks for contributing! 🚀
-"""
-
-
-class ResolvedPackage(TypedDict):
-    name: str
-    """The name of the package."""
-    repo: str
-    """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    weekly_downloads: int | None
-    """The weekly download count of the package."""
-    description: str
-    """A brief description of what the package does."""
-
-
-def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
-    """Generate the markdown content for the third party page.
-
-    Args:
-        resolved_packages: A list of resolved package information.
-        language: str
-
-    Returns:
-        The markdown content as a string.
-    """
-    # Update the URL to the actual file once the initial version is merged
-    if language == "python":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraph/blob/main/docs"
-            "/_scripts/third_party_page/packages.yml"
-        )
-    elif language == "js":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
-            "/_scripts/third_party/packages.yml"
-        )
-    else:
-        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
-
-    sorted_packages = sorted(
-        resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
-    )
-    rows = [
-        "| Name | GitHub URL | Description | Weekly Downloads | Stars |",
-        "| --- | --- | --- | --- | --- |",
-    ]
-    for package in sorted_packages:
-        name = f"**{package['name']}**"
-        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
-        stars_badge = (
-            f"https://img.shields.io/github/stars/{package['repo']}?style=social"
-        )
-        stars = f"![GitHub stars]({stars_badge})"
-        downloads = package["weekly_downloads"] or "-"
-        row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
-        rows.append(row)
-    markdown_content = MARKDOWN.format(
-        library_list="\n".join(rows), langgraph_url=langgraph_url
-    )
-    return markdown_content
-
-
-def main(input_file: str, output_file: str, language: str) -> None:
-    """Main function to create the third party page.
-
-    Args:
-        input_file: Path to the input YAML file containing resolved package information.
-        output_file: Path to the output file for the third party page.
-        language: The language for which to generate the third party page.
-    """
-    # Parse the input YAML file
-    with open(input_file, "r") as f:
-        resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
-
-    markdown_content = generate_markdown(resolved_packages, language)
-
-    # Write the markdown content to the output file
-    with open(output_file, "w", encoding="utf-8") as f:
-        f.write(markdown_content)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Create the third party page.")
-    parser.add_argument(
-        "input_file",
-        help="Path to the input YAML file containing resolved package information.",
-    )
-    parser.add_argument(
-        "output_file", help="Path to the output file for the third party page."
-    )
-    parser.add_argument(
-        "--language",
-        choices=["python", "js"],
-        default="python",
-        help="The language for which to generate the third party page. Defaults to 'python'.",
-    )
-    args = parser.parse_args()
-
-    main(args.input_file, args.output_file, args.language)

docs/_scripts/third_party_page/get_download_stats.py

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

docs/_scripts/third_party_page/packages.yml

@@ -1,41 +0,0 @@
-#A list of third-party packages to surface on the third-party page.
-packages:
-  - name: "trustcall"
-    repo: "hinthornw/trustcall"
-    description: "Tenacious tool calling built on LangGraph."
-  - name: "breeze-agent"
-    repo: "andrestorres123/breeze-agent"
-    description: "A streamlined research system built inspired on STORM and built on LangGraph."
-  - name: "langgraph-supervisor"
-    repo: "langchain-ai/langgraph-supervisor-py"
-    description: "Build supervisor multi-agent systems with LangGraph."
-  - name: "langmem"
-    repo: "langchain-ai/langmem"
-    description: "Build agents that learn and adapt from interactions over time."
-  - name: "langchain-mcp-adapters"
-    repo: "langchain-ai/langchain-mcp-adapters"
-    description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-  - name: "open-deep-research"
-    repo: "langchain-ai/open_deep_research"
-    description: "Open source assistant for iterative web research and report writing."
-  - name: "langgraph-swarm"
-    repo: "langchain-ai/langgraph-swarm-py"
-    description: "Build swarm-style multi-agent systems using LangGraph."
-  - name: "delve-taxonomy-generator"
-    repo: "andrestorres123/delve"
-    description: "A taxonomy generator for unstructured data"
-  - name: "nodeology"
-    repo: "xyin-anl/Nodeology"
-    description: "Enable researcher to build scientific workflows easily with simplified interface."
-  - name: "langgraph-bigtool"
-    repo: "langchain-ai/langgraph-bigtool"
-    description: "Build LangGraph agents with large numbers of tools."
-  - name: "ai-data-science-team"
-    repo: "business-science/ai-data-science-team"
-    description: "An AI-powered data science team of agents to help you perform common data science tasks 10X faster."
-  - name: "langgraph-reflection"
-    repo: "langchain-ai/langgraph-reflection"
-    description: "LangGraph agent that runs a reflection step."
-  - name: "langgraph-codeact"
-    repo: "langchain-ai/langgraph-codeact"
-    description: "LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling."

docs/cassettes/create-react-agent-manage-message-history_26c53429-90ba-4d0b-abb9-423d9120ad26.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/stream-multiple_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/streaming-events-from-within-tools_8ae5051c-53b9-4c53-87b2-d7263cda3b7b.msgpack.zlib

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docs/cassettes/streaming-from-final-node_55d60dfa-96e3-442f-9924-0c99f46baed8.msgpack.zlib

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docs/cassettes/streaming-from-final-node_68ac2c7f.msgpack.zlib

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docs/cassettes/streaming-tokens-without-langchain_d6ed3df5.msgpack.zlib

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docs/cassettes/streaming-tokens_96050fba.msgpack.zlib

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docs/cassettes/streaming-tokens_c9e0df34-6020-445e-8ecd-ca4239e9b22b.msgpack.zlib

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docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib

@@ -1 +0,0 @@
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docs/codespell_notebooks.sh

@@ -1,17 +1,6 @@
 ERROR_FOUND=0
 for file in $(find $1 -name "*.ipynb" | grep -v ".ipynb_checkpoints"); do
-    # Adding regexp to ignore base64 strings
-    OUTPUT=$(cat "$file" | jupytext --from ipynb --to py:percent | codespell --ignore-regex='[A-Za-z0-9+/=]{25,}' -)
-    if [ -n "$OUTPUT" ]; then
-        echo "Errors found in $file"
-        echo "$OUTPUT"
-        ERROR_FOUND=1
-    fi
-done
-
-for file in $(find $1 -name "*.md"); do
-    # Adding regexp to ignore base64 strings
-    OUTPUT=$(cat "$file" | codespell --ignore-regex='[A-Za-z0-9+/=]{25,}' -)
+    OUTPUT=$(cat "$file" | jupytext --from ipynb --to py:percent | codespell -)
     if [ -n "$OUTPUT" ]; then
         echo "Errors found in $file"
         echo "$OUTPUT"

docs/docs/adopters.md

@@ -1,31 +0,0 @@
-# 🦜🕸️ Companies using LangGraph
-
-This list of companies using LangGraph and their success stories is compiled from public sources. If your company uses LangGraph, we'd love for you to share your story and add it to the list. You’re also welcome to contribute updates based on publicly available information from other companies, such as blog posts or press releases.
-
-
-| Company | Industry | Use case | Reference |
-| --- | --- | --- | --- |
-| [AirTop](https://www.airtop.ai/) | Software & Technology (GenAI Native) | Browser automation for AI agents | [Case study, 2024](https://blog.langchain.dev/customers-airtop/) |
-| [AppFolio](https://www.appfolio.com/) | Real Estate | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-appfolio/) |
-| [Athena Intelligence](https://www.athenaintel.com/) | Software & Technology (GenAI Native) | Research & summarization | [Case study, 2024](https://blog.langchain.dev/customers-athena-intelligence/) |
-| [Captide](https://www.captide.co/) | Software & Technology (GenAI Native) | Data extraction | [Case study, 2025](https://blog.langchain.dev/how-captide-is-redefining-equity-research-with-agentic-workflows-built-on-langgraph-and-langsmith/) |
-| [Cisco Outshift](https://outshift.cisco.com/) | Software & Technology | DevOps | [Blog post, 2025](https://outshift.cisco.com/blog/build-react-agent-application-for-devops-tasks-using-rest-apis) |
-| [C.H. Robinson](https://www.chrobinson.com/en-us/) | Logistics | Automation | [Case study, 2025](https://blog.langchain.dev/customers-chrobinson/) |
-| [Elastic](https://www.elastic.co/) | Software & Technology | Copilot for domain-specific task | [Blog post, 2025](https://www.elastic.co/blog/elastic-security-generative-ai-features) |
-| [GitLab](https://about.gitlab.com/) | Software & Technology | Code generation | [Duo workflow docs](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/duo_workflow/) |
-| [Inconvo](https://inconvo.ai/?ref=blog.langchain.dev) | Software & Technology | Code generation | [Case study, 2025](https://blog.langchain.dev/customers-inconvo/) |
-| [Infor](https://infor.com/) | Software & Technology | GenAI embedded product experiences; customer support; copilot | [Case study, 2025](https://blog.langchain.dev/customers-infor/) |
-| [Klarna](https://www.klarna.com/) | Fintech | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/customers-klarna/) |
-| [Komodo Health](https://www.komodohealth.com/) | Healthcare | Copilot for domain-specific task | [Blog post](https://www.komodohealth.com/perspectives/new-gen-ai-assistant-empowers-the-enterprise/) |
-| [LinkedIn](https://www.linkedin.com/) | Social Media | Code generation; Search & discovery | [Blog post, 2025](https://www.linkedin.com/blog/engineering/ai/practical-text-to-sql-for-data-analytics); [Blog post, 2024](https://www.linkedin.com/blog/engineering/generative-ai/behind-the-platform-the-journey-to-create-the-linkedin-genai-application-tech-stack) |
-| [Minimal](https://gominimal.ai/) | E-commerce | Customer support | [Case study, 2025](https://blog.langchain.dev/how-minimal-built-a-multi-agent-customer-support-system-with-langgraph-langsmith/) |
-| [OpenRecovery](https://www.openrecovery.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-openrecovery/) |
-| [Qodo](https://www.qodo.ai/) | Software & Technology (GenAI Native) | Code generation | [Blog post, 2025](https://www.qodo.ai/blog/why-we-chose-langgraph-to-build-our-coding-agent/) |
-| [Rakuten](https://www.rakuten.com/) | E-commerce / Fintech | Copilot for domain-specific task | [Blog post, 2025](https://rakuten.today/blog/from-ai-hype-to-real-world-tools-rakuten-teams-up-with-langchain.html) |
-| [Replit](https://replit.com/) | Software & Technology | Code generation | [Blog post, 2024](https://blog.langchain.dev/customers-replit/); [Breakout agent story, 2024](https://www.langchain.com/breakoutagents/replit); [Fireside chat video, 2024](https://www.youtube.com/watch?v=ViykMqljjxU) |
-| [Rexera](https://www.rexera.com/) | Real Estate (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-rexera/) |
-| [Tradestack](https://www.tradestack.uk/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Case study, 2024](https://blog.langchain.dev/customers-tradestack/) |
-| [Uber](https://www.uber.com/) | Transportation | Developer productivity; Code generation | [Presentation, 2024](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/); [Video, 2024](https://www.youtube.com/watch?v=8rkA5vWUE4Y) |
-| [Unify](https://www.unifygtm.com/) | Software & Technology (GenAI Native) | Copilot for domain-specific task | [Blog post, 2024](https://blog.langchain.dev/unify-launches-agents-for-account-qualification-using-langgraph-and-langsmith/) |
-| [Vizient](https://www.vizientinc.com/) | Healthcare | Copilot for domain-specific task | [Case study, 2025](https://blog.langchain.dev/p/3d2cd58c-13a5-4df9-bd84-7d54ed0ed82c/) |
-| [Vodafone](https://www.vodafone.com/) | Telecommunications | Code generation; internal search | [Case study, 2025](https://blog.langchain.dev/customers-vodafone/) |

docs/docs/agents/agents.md

@@ -1,205 +0,0 @@
-# Agents
-
-## What is an agent?
-
-An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
-
-The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
-
-<figure markdown="1">
-![image](./assets/agent.png){: style="max-height:400px"}
-<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
-</figure>
-
-## Basic configuration
-
-Use [`create_react_agent`](https://python.langchain.com/docs/api_reference/langgraph.prebuilt.chat_agent_executor/#create-react-agent) to instantiate an agent:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def get_weather(city: str) -> str:  # (1)!
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",  # (2)!
-    tools=[get_weather],  # (3)!
-    prompt="You are a helpful assistant"  # (4)!
-)
-
-# Run the agent
-agent.invoke({"messages": "what is the weather in sf"})
-```
-
-1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
-2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the [models](./models.md) page.
-3. Provide a list of tools for the model to use.
-4. Provide a system prompt (instructions) to the language model used by the agent.
-
-## LLM configuration
-
-Use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) to configure an LLM with specific parameters,
-such as temperature:
-
-```python
-from langchain.chat_models import init_chat_model
-from langgraph.prebuilt import create_react_agent
-
-# highlight-next-line
-model = init_chat_model(
-    "anthropic:claude-3-7-sonnet-latest",
-    # highlight-next-line
-    temperature=0
-)
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model,
-    tools=[get_weather],
-)
-```
-
-See the [models](./models.md) page for more information on how to configure LLMs.
-
-## Custom Prompts
-
-Prompts instruct the LLM how to behave. They can be:
-
-* **Static**: A string is interpreted as a **system message**
-* **Dynamic**: a list of messages generated at **runtime** based on input or configuration
-
-### Static prompts
-
-Define a fixed prompt string or list of messages.
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # A static prompt that never changes
-    # highlight-next-line
-    prompt="Never answer questions about the weather."
-)
-
-agent.invoke(
-    {"messages": "what is the weather in sf"},
-)
-```
-
-### Dynamic prompts
-
-Define a function that returns a message list based on the agent's state and configuration:
-
-```python
-from langchain_core.messages import AnyMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.prebuilt import create_react_agent
-
-# highlight-next-line
-def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config.get("configurable", {}).get("user_name")
-    system_msg = f"You are a helpful assistant. Address the user as {user_name}."
-    return [{"role": "system", "content": system_msg}] + state["messages"]
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    prompt=prompt
-)
-
-agent.invoke(
-    {"messages": "what is the weather in sf"},
-    # highlight-next-line
-    config={"configurable": {"user_name": "John Smith"}}
-)
-```
-
-1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
-
-    - Information passed at runtime, like a `user_id` or API credentials (using `config`).
-    - Internal agent state updated during a multi-step reasoning process (using `state`).
-
-    Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
-
-See the [context](./context.md) page for more information.
-
-## Memory
-
-To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
-
-```python
-from langgraph.prebuilt import create_react_agent
-from langgraph.checkpoint.memory import InMemorySaver
-
-# highlight-next-line
-checkpointer = InMemorySaver()
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    checkpointer=checkpointer  # (1)!
-)
-
-# Run the agent
-# highlight-next-line
-config = {"configurable": {"thread_id": "1"}}
-sf_response = agent.invoke(
-    {"messages": "what is the weather in sf"},
-    # highlight-next-line
-    config  # (2)!
-)
-ny_response = agent.invoke(
-    {"messages": "what about new york?"},
-    # highlight-next-line
-    config
-)
-```
-
-1. `checkpointer` allows the agent to store its state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities.
-2. Pass configuration with `thread_id` to be able to resume the same conversation on future agent invocations.
-
-When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using `InMemorySaver`).
-
-Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.
-
-Please see the [memory guide](./memory.md) for more details on how to work with memory.
-
-
-## Structured output
-
-To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.
-
-```python
-from pydantic import BaseModel
-from langgraph.prebuilt import create_react_agent
-
-class WeatherResponse(BaseModel):
-    conditions: str
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    response_format=WeatherResponse  # (1)!
-)
-
-response = agent.invoke({"messages": "what is the weather in sf"})
-
-# highlight-next-line
-response["structured_response"]
-```
-
-1. When `response_format` is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response.
-
-    To provide a system prompt to this LLM, use a tuple `(prompt, schema)`, e.g., `response_format=(prompt, WeatherResponse)`.
-
-!!! Note "LLM post-processing"
-
-    Structured output requires an additional call to the LLM to format the response according to the schema.
-

docs/docs/agents/context.md

@@ -1,287 +0,0 @@
-# Context
-
-Agents often require more than a list of messages to function effectively. They need **context**.
-
-Context includes *any* data outside the message list that can shape agent behavior or tool execution. This can be:
-
-- Information passed at runtime, like a `user_id` or API credentials.
-- Internal state updated during a multi-step reasoning process.
-- Persistent memory or facts from previous interactions.
-
-LangGraph provides **three** primary ways to supply context:
-
-| Type                                                                         | Description                                   | Mutable? | Lifetime                |
-|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
-| [**Config**](#config-static-context)                                         | data passed at the start of a run             | ❌        | per run                 |
-| [**State**](#state-mutable-context)                                          | dynamic data that can change during execution | ✅        | per run or conversation |
-| [**Long-term Memory (Store)**](#long-term-memory-cross-conversation-context) | data that can be shared between conversations | ✅        | across conversations    |
-
-You can use context to:
-
-- Adjust the system prompt the model sees
-- Feed tools with necessary inputs
-- Track facts during an ongoing conversation
-
-## Providing Runtime Context
-
-Use this when you need to inject data into an agent at runtime.
-
-### Config (static context)
-
-Config is for immutable data like user metadata or API keys. Use
-when you have values that don't change mid-run.
-
-Specify configuration using a key called **"configurable"** which is reserved
-for this purpose:
-
-```python
-agent.invoke(
-    {"messages": "hi!"},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-### State (mutable context)
-
-State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
-
-```python
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-agent = create_react_agent(
-    # Other agent parameters...
-    # highlight-next-line
-    state_schema=CustomState,
-)
-
-agent.invoke({
-    "messages": "hi!",
-    "user_name": "Jane"
-})
-```
-
-!!! tip "Turning on memory"
-
-    Please see the [memory guide](./memory.md) for more details on how to enable memory. This is a powerful feature that allows you to persist the agent's state across multiple invocations.
-    Otherwise, the state is scoped only to a single agent run.
-
-
-
-### Long-Term Memory (cross-conversation context)
-
-For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](./memory.md).
-
-## Customizing Prompts with Context
-
-Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.
-
-Common use cases:
-
-- Personalization
-- Role or goal customization
-- Conditional behavior (e.g., user is admin)
-
-=== "Using config"
-
-    ```python
-    from langchain_core.messages import AnyMessage
-    from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
-    from langgraph.prebuilt.chat_agent_executor import AgentState
-
-    def prompt(
-        state: AgentState,
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> list[AnyMessage]:
-        # highlight-next-line
-        user_name = config.get("configurable", {}).get("user_name")
-        system_msg = f"You are a helpful assistant. User's name is {user_name}"
-        return [{"role": "system", "content": system_msg}] + state["messages"]
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-        # highlight-next-line
-        prompt=prompt
-    )
-
-    agent.invoke(
-        ...,
-        # highlight-next-line
-        config={"configurable": {"user_name": "John Smith"}}
-    )
-    ```
-
-=== "Using state"
-
-    ```python
-    from langchain_core.messages import AnyMessage
-    from langchain_core.runnables import RunnableConfig
-    from langgraph.prebuilt import create_react_agent
-    from langgraph.prebuilt.chat_agent_executor import AgentState
-
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_name: str
-
-    def prompt(
-        # highlight-next-line
-        state: CustomState
-    ) -> list[AnyMessage]:
-        # highlight-next-line
-        user_name = state["user_name"]
-        system_msg = f"You are a helpful assistant. User's name is {user_name}"
-        return [{"role": "system", "content": system_msg}] + state["messages"]
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[...],
-        # highlight-next-line
-        state_schema=CustomState,
-        # highlight-next-line
-        prompt=prompt
-    )
-
-    agent.invoke({
-        "messages": "hi!",
-        # highlight-next-line
-        "user_name": "John Smith"
-    })
-    ```
-
-## Tools
-
-Tools can access context through special parameter **annotations**.
-
-* Use `RunnableConfig` for config access
-* Use `Annotated[StateSchema, InjectedState]` for agent state
-
-
-!!! tip 
-
-    These annotations prevent LLMs from attempting to fill in the values. These parameters will be **hidden** from the LLM.
-
-=== "Using config"
-
-    ```python
-    def get_user_info(
-        # highlight-next-line
-        config: RunnableConfig,
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = config.get("configurable", {}).get("user_id")
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-    )
-
-    agent.invoke(
-        {"messages": "look up user information"},
-        # highlight-next-line
-        config={"configurable": {"user_id": "user_123"}}
-    )
-    ```
-
-=== "Using State"
-
-    ```python
-    from typing import Annotated
-    from langgraph.prebuilt import InjectedState
-
-    class CustomState(AgentState):
-        # highlight-next-line
-        user_id: str
-
-    def get_user_info(
-        # highlight-next-line
-        state: Annotated[CustomState, InjectedState]
-    ) -> str:
-        """Look up user info."""
-        # highlight-next-line
-        user_id = state["user_id"]
-        return "User is John Smith" if user_id == "user_123" else "Unknown user"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_user_info],
-        # highlight-next-line
-        state_schema=CustomState,
-    )
-
-    agent.invoke({
-        "messages": "look up user information",
-        # highlight-next-line
-        "user_id": "user_123"
-    })
-    ```
-
-
-## Update context from tools
-
-Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
-
-```python
-from typing import Annotated
-from langchain_core.tools import InjectedToolCallId
-from langchain_core.messages import ToolMessage
-from langgraph.prebuilt import InjectedState
-from langgraph.types import Command
-
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-def get_user_info(
-    # highlight-next-line
-    tool_call_id: Annotated[str, InjectedToolCallId],
-    # highlight-next-line
-    config: RunnableConfig
-) -> Command:
-    """Look up user info."""
-    # highlight-next-line
-    user_id = config.get("configurable", {}).get("user_id")
-    name = "John Smith" if user_id == "user_123" else "Unknown user"
-    return Command(update={
-        # highlight-next-line
-        "user_name": name,
-        # update the message history
-        # highlight-next-line
-        "messages": [
-            ToolMessage(
-                "Successfully looked up user information",
-                # highlight-next-line
-                tool_call_id=tool_call_id
-            )
-        ]
-    })
-
-def greet(
-    # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
-) -> str:
-    """Use this to greet the user once you found their info."""
-    user_name = state["user_name"]
-    return f"Hello {user_name}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info, greet],
-    # highlight-next-line
-    state_schema=CustomState
-)
-
-agent.invoke(
-    {"messages": "greet the user"},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).

docs/docs/agents/deployment.md

@@ -1,83 +0,0 @@
-# Deployment
-
-To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.
-
-Features: 
-
-* 🖥️ Local server for development
-* 🧩 Studio Web UI for visual debugging
-* ☁️ Cloud and 🔧 self-hosted deployment options
-* 📊 LangSmith integration for tracing and observability
-
-!!! info "Requirements" 
-
-    - ✅ You **must** have a [LangSmith account](https://www.langchain.com/langsmith). You can sign up for **free** and get started with the free tier.
-
-## Create a LangGraph app
-
-```bash
-pip install -U "langgraph-cli[inmem]"
-langgraph new path/to/your/app --template new-langgraph-project-python
-```
-
-This will create an empty LangGraph project. You can modify it by replacing the code in `src/agent/graph.py` with your agent code. For example:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-graph = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    prompt="You are a helpful assistant"
-)
-```
-
-### Install dependencies
-
-In the root of your new LangGraph app, install the dependencies in `edit` mode so your local changes are used by the server:
-
-```shell
-pip install -e .
-```
-
-### Create an `.env` file
-
-You will find a `.env.example` in the root of your new LangGraph app. Create
-a `.env` file in the root of your new LangGraph app and copy the contents of the `.env.example` file into it, filling in the necessary API keys:
-
-```bash
-LANGSMITH_API_KEY=lsv2...
-ANTHROPIC_API_KEY=sk-
-```
-
-## Launch LangGraph server locally
-
-```shell
-langgraph dev
-```
-
-This will start up the LangGraph API server locally. If this runs successfully, you should see something like:
-
->    Ready!
-> 
->    - API: [http://localhost:2024](http://localhost:2024/)
->     
->    - Docs: http://localhost:2024/docs
->     
->    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-
-See this [tutorial](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) to learn more about running LangGraph app locally.
-
-## LangGraph Studio Web UI
-
-LangGraph Studio Web is a specialized UI that you can connect to LangGraph API server to enable visualization, interaction, and debugging of your application locally. Test your graph in the LangGraph Studio Web UI by visiting the URL provided in the output of the `langgraph dev` command.
-
->    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-
-## Deployment
-
-Once your LangGraph app is running locally, you can deploy it using LangGraph Cloud or self-hosted options. Refer to the [deployment options guide](../tutorials/deployment.md) for detailed instructions on all supported deployment models.

docs/docs/agents/evals.md

@@ -1,119 +0,0 @@
-# Evals
-
-To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:
-
-```python
-def evaluator(*, outputs: dict, reference_outputs: dict):
-    # compare agent outputs against reference outputs
-    output_messages = outputs["messages"]
-    reference_messages = reference["messages"]
-    score = compare_messages(output_messages, reference_messages)
-    return {"key": "evaluator_score", "score": score}
-```
-
-To get started, you can use prebuilt evaluators from `AgentEvals` package:
-
-```bash
-pip install -U agentevals
-```
-
-## Create evaluator
-
-A common way to evaluate agent performance is by comparing its trajectory (the order in which it calls its tools) against a reference trajectory:
-
-```python
-import json
-# highlight-next-line
-from agentevals.trajectory.match import create_trajectory_match_evaluator
-
-outputs = [
-    {
-        "role": "assistant",
-        "tool_calls": [
-            {
-                "function": {
-                    "name": "get_weather",
-                    "arguments": json.dumps({"city": "san francisco"}),
-                }
-            },
-            {
-                "function": {
-                    "name": "get_directions",
-                    "arguments": json.dumps({"destination": "presidio"}),
-                }
-            }
-        ],
-    }
-]
-reference_outputs = [
-    {
-        "role": "assistant",
-        "tool_calls": [
-            {
-                "function": {
-                    "name": "get_weather",
-                    "arguments": json.dumps({"city": "san francisco"}),
-                }
-            },
-        ],
-    }
-]
-
-# Create the evaluator
-evaluator = create_trajectory_match_evaluator(
-    # highlight-next-line
-    trajectory_match_mode="superset",  # (1)!
-)
-
-# Run the evaluator
-result = evaluator(
-    outputs=outputs, reference_outputs=reference_outputs
-)
-```
-
-1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)
-
-
-As a next step, learn more about how to [customize trajectory match evaluator](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#agent-trajectory-match).
-
-### LLM-as-a-judge
-
-You can use LLM-as-a-judge evaluator that uses an LLM to compare the trajectory against the reference outputs and output a score:
-
-```python
-import json
-from agentevals.trajectory.llm import (
-    # highlight-next-line
-    create_trajectory_llm_as_judge,
-    TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE
-)
-
-evaluator = create_trajectory_llm_as_judge(
-    prompt=TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
-    model="openai:o3-mini"
-)
-```
-
-## Run evaluator
-
-To run an evaluator, you will first need to create a [LangSmith dataset](https://docs.smith.langchain.com/evaluation/concepts#datasets). To use the prebuilt AgentEvals evaluators, you will need a dataset with the following schema:
-
-- **input**: `{"messages": [...]}` input messages to call the agent with.
-- **output**: `{"messages": [...]}` expected message history in the agent output. For trajectory evaluation, you can choose to keep only assistant messages.
-
-```python
-from langsmith import Client
-from langgraph.prebuilt import create_react_agent
-from agentevals.trajectory.match import create_trajectory_match_evaluator
-
-client = Client()
-agent = create_react_agent(...)
-evaluator = create_trajectory_match_evaluator(...)
-
-experiment_results = client.evaluate(
-    lambda inputs: agent.invoke(inputs),
-    # replace with your dataset name
-    data="<Name of your dataset>",
-    evaluators=[evaluator]
-)
-```

docs/docs/agents/human-in-the-loop.md

@@ -1,227 +0,0 @@
-# Human-in-the-loop
-
-To review, edit and approve tool calls in an agent you can use LangGraph's built-in [human-in-the-loop](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
-
-LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.
-
-This is possible because the agent state is **checkpointed into a database**, which allows the system to persist execution context and later resume the workflow, continuing from where it left off.
-
-For a deeper dive into the **human-in-the-loop** concept, see the [concept guide](../concepts/human_in_the_loop.md).
-
-<figure markdown="1">
-![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
-<figcaption>
-A human can review and edit the output from the agent before proceeding. This is particularly critical in applications where the tool calls requested may be sensitive or require human oversight.
-</figcaption>
-</figure>
-
-
-## Review tool calls
-
-To add a human approval step to a tool:
-
-1. Use `interrupt()` in the tool to pause execution.
-2. Resume with a `Command(resume=...)` to continue based on human input.
-
-```python
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.types import interrupt
-from langgraph.prebuilt import create_react_agent
-
-# An example of a sensitive tool that requires human review / approval
-def book_hotel(hotel_name: str):
-    """Book a hotel"""
-    # highlight-next-line
-    response = interrupt(  # (1)!
-        f"Trying to call `book_hotel` with args {{'hotel_name': {hotel_name}}}. "
-        "Please approve or suggest edits."
-    )
-    if response["type"] == "accept":
-        pass
-    elif response["type"] == "edit":
-        hotel_name = response["args"]["hotel_name"]
-    else:
-        raise ValueError(f"Unknown response type: {response['type']}")
-    return f"Successfully booked a stay at {hotel_name}."
-
-# highlight-next-line
-checkpointer = InMemorySaver() # (2)!
-
-agent = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    tools=[book_hotel],
-    # highlight-next-line
-    checkpointer=checkpointer, # (3)!
-)
-```
-
-1. The [`interrupt` function][langgraph.types.interrupt] pauses the agent graph at a specific node. In this case, we call `interrupt()` at the beginning of the tool function, which pauses the graph at the node that executes the tool. The information inside `interrupt()` (e.g., tool calls) can be presented to a human, and the graph can be resumed with the user input (tool call approval, edit or feedback).
-2. The `InMemorySaver` is used to store the agent state at every step in the tool calling loop. This enables [short-term memory](./memory.md#short-term-memory) and [human-in-the-loop](./human-in-the-loop.md) capabilities. In this example, we use `InMemorySaver` to store the agent state in memory. In a production application, the agent state will be stored in a database.
-3. Initialize the agent with the `checkpointer`.
-
-Run the agent with the `stream()` method, passing the `config` object to specify the thread ID. This allows the agent to resume the same conversation on future invocations.
-
-```python
-config = {
-   "configurable": {
-      # highlight-next-line
-      "thread_id": "1"
-   }
-}
-
-for chunk in agent.stream(
-    {"messages": "book a stay at McKittrick hotel"},
-    # highlight-next-line
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-> You should see that the agent runs until it reaches the `interrupt()` call, at which point it pauses and waits for human input.
-
-Resume the agent with a `Command(resume=...)` to continue based on human input.
-
-```python
-from langgraph.types import Command
-
-for chunk in agent.stream(
-    # highlight-next-line
-    Command(resume={"type": "accept"}),  # (1)!
-    # Command(resume={"type": "edit", "args": {"hotel_name": "McKittrick Hotel"}}),
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-1. The [`interrupt` function][langgraph.types.interrupt] is used in conjunction with the [`Command`](../reference/types.md#langgraph.types.Command) object to resume the graph with a value provided by the human.
-
-## Using with Agent Inbox
-
-You can create a wrapper to add interrupts to *any* tool. 
-
-The example below provides a reference implementation compatible with [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox) and [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui).
-
-```python title="Wrapper that adds human-in-the-loop to any tool"
-from typing import Callable
-from langchain_core.tools import BaseTool, tool as create_tool
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import interrupt 
-from langgraph.prebuilt.interrupt import HumanInterruptConfig, HumanInterrupt
-
-def add_human_in_the_loop(
-    tool: Callable | BaseTool,
-    *,
-    interrupt_config: HumanInterruptConfig = None,
-) -> BaseTool:
-   """Wrap a tool to support human-in-the-loop review.""" 
-    if not isinstance(tool, BaseTool):
-        tool = create_tool(tool)
-
-    if interrupt_config is None:
-        interrupt_config = {
-            "allow_accept": True,
-            "allow_edit": True,
-            "allow_respond": True,
-        }
-
-    @create_tool(  # (1)!
-        tool.name,
-        description=tool.description,
-        args_schema=tool.args_schema
-    )
-    def call_tool_with_interrupt(config: RunnableConfig, **tool_input):
-        request: HumanInterrupt = {
-            "action_request": {
-                "action": tool.name,
-                "args": tool_input
-            },
-            "config": interrupt_config,
-            "description": "Please review the tool call"
-        }
-        # highlight-next-line
-        response = interrupt([request])[0]  # (2)!
-        # approve the tool call
-        if response["type"] == "accept":
-            tool_response = tool.invoke(tool_input, config)
-        # update tool call args
-        elif response["type"] == "edit":
-            tool_input = response["args"]["args"]
-            tool_response = tool.invoke(tool_input, config)
-        # respond to the LLM with user feedback
-        elif response["type"] == "response":
-            user_feedback = response["args"]
-            tool_response = user_feedback
-        else:
-            raise ValueError(f"Unsupported interrupt response type: {response['type']}")
-
-        return tool_response
-
-    return call_tool_with_interrupt
-```
-
-1. This wrapper creates a new tool that calls `interrupt()` **before** executing the wrapped tool.
-2. `interrupt()` is using special input and output format that's expected by [Agent Inbox UI](https://github.com/langchain-ai/agent-inbox):
-    - a list of [`HumanInterrupt`][langgraph.prebuilt.interrupt.HumanInterrupt] objects is sent to `AgentInbox` render interrupt information to the end user
-    - resume value is provided by `AgentInbox` as a list (i.e., `Command(resume=[...])`)
-
-You can use the `add_human_in_the_loop` wrapper to add `interrupt()` to any tool without having to add it *inside* the tool:
-
-```python
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.prebuilt import create_react_agent
-
-# highlight-next-line
-checkpointer = InMemorySaver()
-
-def book_hotel(hotel_name: str):
-   """Book a hotel"""
-   return f"Successfully booked a stay at {hotel_name}."
-
-
-agent = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    tools=[
-        # highlight-next-line
-        add_human_in_the_loop(book_hotel), # (1)!
-    ],
-    # highlight-next-line
-    checkpointer=checkpointer,
-)
-
-config = {"configurable": {"thread_id": "1"}}
-
-# Run the agent
-for chunk in agent.stream(
-    {"messages": "book a stay at McKittrick hotel"},
-    # highlight-next-line
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-1. The `add_human_in_the_loop` wrapper is used to add `interrupt()` to the tool. This allows the agent to pause execution and wait for human input before proceeding with the tool call.
-
-> You should see that the agent runs until it reaches the `interrupt()` call, 
->  at which point it pauses and waits for human input.
-
-Resume the agent with a `Command(resume=...)`  to continue based on human input.
-
-```python
-from langgraph.types import Command 
-
-for chunk in agent.stream(
-    # highlight-next-line
-    Command(resume=[{"type": "accept"}]),
-    # Command(resume=[{"type": "edit", "args": {"args": {"hotel_name": "McKittrick Hotel"}}}]),
-    config
-):
-    print(chunk)
-    print("\n")
-```
-
-## Additional resources
-
-* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)

docs/docs/agents/mcp.md

@@ -1,94 +0,0 @@
-# MCP Integration
-
-[Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
-
-![MCP](./assets/mcp.png)
-
-Install the `langchain-mcp-adapters` library to use MCP tools in LangGraph:
-
-```bash
-pip install langchain-mcp-adapters
-```
-
-## Use MCP tools
-
-The `langchain-mcp-adapters` package enables agents to use tools defined across one or more MCP servers.
-
-```python title="Agent using tools defined on MCP servers"
-# highlight-next-line
-from langchain_mcp_adapters.client import MultiServerMCPClient
-from langgraph.prebuilt import create_react_agent
-
-# highlight-next-line
-async with MultiServerMCPClient(
-    {
-        "math": {
-            "command": "python",
-            # Replace with absolute path to your math_server.py file
-            "args": ["/path/to/math_server.py"],
-            "transport": "stdio",
-        },
-        "weather": {
-            # Ensure your start your weather server on port 8000
-            "url": "http://localhost:8000/sse",
-            "transport": "sse",
-        }
-    }
-) as client:
-    agent = create_react_agent(
-        "anthropic:claude-3-7-sonnet-latest",
-        # highlight-next-line
-        client.get_tools()
-    )
-    math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
-    weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
-```
-
-## Custom MCP servers
-
-To create your own MCP servers, you can use the `mcp` library. This library provides a simple way to define tools and run them as servers.
-
-Install the MCP library:
-
-```bash
-pip install mcp
-```
-Use the following reference implementations to test your agent with MCP tool servers.
-
-```python title="Example Math Server (stdio transport)"
-from mcp.server.fastmcp import FastMCP
-
-mcp = FastMCP("Math")
-
-@mcp.tool()
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-@mcp.tool()
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers"""
-    return a * b
-
-if __name__ == "__main__":
-    mcp.run(transport="stdio")
-```
-
-```python title="Example Weather Server (SSE transport)"
-from mcp.server.fastmcp import FastMCP
-
-mcp = FastMCP("Weather")
-
-@mcp.tool()
-async def get_weather(location: str) -> str:
-    """Get weather for location."""
-    return "It's always sunny in New York"
-
-if __name__ == "__main__":
-    mcp.run(transport="sse")
-```
-
-## Additional resources
-
-- [MCP documentation](https://modelcontextprotocol.io/introduction)
-- [MCP Transport documentation](https://modelcontextprotocol.io/docs/concepts/transports)

docs/docs/agents/memory.md

@@ -1,262 +0,0 @@
-# Memory
-
-LangGraph supports two types of memory essential for building conversational agents:
-
-- **[Short-term memory](#short-term-memory)**: Tracks the ongoing conversation by maintaining message history within a session.
-- **[Long-term memory](#long-term-memory)**: Stores user-specific or application-level data across sessions.
-
-This guide demonstrates how to use both memory types with agents in LangGraph. For a deeper
-understanding of memory concepts, refer to the [LangGraph memory documentation](../concepts/memory.md).
-
-<figure markdown="1">
-![image](./assets/memory.png){: style="max-height:400px"}
-<figcaption>Both <strong>short-term</strong> and <strong>long-term</strong> memory require persistent storage to maintain continuity across LLM interactions. In production environments, this data is typically stored in a database.</figcaption>
-</figure>
-
-!!! note "Terminology"
-
-    In LangGraph:
-
-    - *Short-term memory* is also referred to as **thread-level memory**.
-    - *Long-term memory* is also called **cross-thread memory**.
-
-    A [thread](../concepts/persistence.md#threads) represents a sequence of related runs
-    grouped by the same `thread_id`.
-
-## Short-term memory
-
-Short-term memory enables agents to track multi-turn conversations. To use it, you must:
-
-1. Provide a `checkpointer` when creating the agent. The `checkpointer` enables [persistence](../concepts/persistence.md) of the agent's state.
-2. Supply a `thread_id` in the config when running the agent. The `thread_id` is a unique identifier for the conversation session.
-
-```python
-from langgraph.prebuilt import create_react_agent
-from langgraph.checkpoint.memory import InMemorySaver
-
-# highlight-next-line
-checkpointer = InMemorySaver() # (1)!
-
-
-def get_weather(city: str) -> str:
-    """Get weather for a given city."""
-    return f"It's always sunny in {city}!"
-
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    checkpointer=checkpointer # (2)!
-)
-
-# Run the agent
-config = {
-    "configurable": {
-        # highlight-next-line
-        "thread_id": "1"  # (3)!
-    }
-}
-
-sf_response = agent.invoke(
-    {"messages": "what is the weather in sf"},
-    # highlight-next-line
-    config
-)
-
-# Continue the conversation using the same thread_id
-ny_response = agent.invoke(
-    {"messages": "what about new york?"},
-    # highlight-next-line
-    config # (4)!
-)
-```
-
-1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
-2. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations. Please note that 
-3. A unique `thread_id` is provided in the config. This ID is used to identify the conversation session. The value is controlled by the user and can be any string.
-4. The agent will continue the conversation using the same `thread_id`. This will allow the agent to infer that the user is asking specifically about the **weather** in New York. 
-
-When the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, allowing the agent to infer that the user is asking specifically about the **weather** in New York.
-
-!!! Note "LangGraph Platform providers a production-ready checkpointer"
-
-    If you're using [LangGraph Platform](./deployment.md), during deployment your checkpointer will be automatically configured to use a production-ready database.
-
-### Message history summarization 
-
-<figure markdown="1">
-![image](./assets/summary.png){: style="max-height:400px"}
-<figcaption>Message history can grow quickly and exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.
-</figcaption>
-</figure>
-
-Long conversations can exceed the LLM's context window. To handle this, you can summarize older messages by specifying a [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent], such as the prebuilt [`SummarizationNode`](https://langchain-ai.github.io/langmem/reference/short_term/#langmem.short_term.SummarizationNode):
-
-```python
-from langchain_anthropic import ChatAnthropic
-from langmem.short_term import SummarizationNode
-from langchain_core.messages.utils import count_tokens_approximately
-from langgraph.prebuilt import create_react_agent
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.checkpoint.memory import InMemorySaver
-from typing import Any
-
-model = ChatAnthropic(model="claude-3-7-sonnet-latest")
-
-summarization_node = SummarizationNode( # (1)!
-    token_counter=count_tokens_approximately,
-    model=model,
-    max_tokens=384,
-    max_summary_tokens=128,
-    output_messages_key="llm_input_messages",
-)
-
-class State(AgentState):
-    # NOTE: we're adding this key to keep track of previous summary information
-    # to make sure we're not summarizing on every LLM call
-    # highlight-next-line
-    context: dict[str, Any]  # (2)!
-
-
-checkpointer = InMemorySaver() # (3)!
-
-agent = create_react_agent(
-    model=model,
-    tools=tools,
-    # highlight-next-line
-    pre_model_hook=summarization_node, # (4)!
-    # highlight-next-line
-    state_schema=State, # (5)!
-    checkpointer=checkpointer,
-)
-```
-
-1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
-2. The `context` key is added to the agent's state. The key contains book-keeping information for the summarization node. It is used to keep track of the last summary information and ensure that the agent doesn't summarize on every LLM call, which can be inefficient.
-3. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations.
-4. The `pre_model_hook` is set to the `SummarizationNode`. This node will summarize the message history before sending it to the LLM. The summarization node will automatically handle the summarization process and update the agent's state with the new summary. You can replace this with a custom implementation if you prefer. Please see the [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] API reference for more details.
-5. The `state_schema` is set to the `State` class, which is the custom state that contains an extra `context` key.
-
-To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
-
-## Long-term memory
-
-Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
-
-To use long-term memory, you need to:
-
-1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
-2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
-
-### Reading
-
-```python title="A tool the agent can use to look up user information"
-from langgraph.config import get_store
-from langgraph.prebuilt import create_react_agent
-from langgraph.store.memory import InMemoryStore
-
-# highlight-next-line
-store = InMemoryStore() # (1)!
-
-# highlight-next-line
-store.put(  # (2)!
-    ("users",),  # (3)!
-    "user_123",  # (4)!
-    {
-        "name": "John Smith",
-        "language": "English",
-    } # (5)!
-)
-
-def get_user_info(config: RunnableConfig) -> str:
-    """Look up user info."""
-    # Same as that provided to `create_react_agent`
-    # highlight-next-line
-    store = get_store() # (6)!
-    user_id = config.get("configurable", {}).get("user_id")
-    # highlight-next-line
-    user_info = store.get(("users",), user_id) # (7)!
-    return str(user_info.value) if user_info else "Unknown user"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info],
-    # highlight-next-line
-    store=store # (8)!
-)
-
-# Run the agent
-agent.invoke(
-    {"messages": "look up user information"},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
-2. For this example, we write some sample data to the store using the `put` method. Please see the [BaseStore.put][langgraph.store.base.BaseStore.put] API reference for more details.
-3. The first argument is the namespace. This is used to group related data together. In this case, we are using the `users` namespace to group user data.
-4. A key within the namespace. This example uses a user ID for the key.
-5. The data that we want to store for the given user.
-6. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
-7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
-8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
-
-### Writing
-
-```python title="Example of a tool that updates user information"
-from typing import TypedDict
-
-from langgraph.config import get_store
-from langgraph.prebuilt import create_react_agent
-from langgraph.store.memory import InMemoryStore
-
-store = InMemoryStore() # (1)!
-
-class UserInfo(TypedDict): # (2)!
-    name: str
-
-def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
-    """Save user info."""
-    # Same as that provided to `create_react_agent`
-    # highlight-next-line
-    store = get_store() # (4)!
-    user_id = config.get("configurable", {}).get("user_id")
-    # highlight-next-line
-    store.put(("users",), user_id, user_info) # (5)!
-    return "Successfully saved user info."
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[save_user_info],
-    # highlight-next-line
-    store=store
-)
-
-# Run the agent
-agent.invoke(
-    {"messages": "My name is John Smith"},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}} # (6)!
-)
-
-# You can access the store directly to get the value
-store.get(("users",), "user_123").value
-```
-
-1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
-2. The `UserInfo` class is a `TypedDict` that defines the structure of the user information. The LLM will use this to format the response according to the schema.
-3. The `save_user_info` function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
-4. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
-5. The `put` method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
-6. The `user_id` is passed in the config. This is used to identify the user whose information is being updated.
-
-### Prebuilt memory tools
-
-**LangMem** is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the [LangMem documentation](https://langchain-ai.github.io/langmem/) for usage examples.
-
-
-## Additional resources
-
-* [Memory in LangGraph](../concepts/memory.md)

docs/docs/agents/models.md

@@ -1,69 +0,0 @@
-# Models
-
-This page describes how to configure the chat model used by an agent.
-
-## Tool calling support
-
-To enable tool-calling agents, the underlying LLM must support [tool calling](https://python.langchain.com/docs/concepts/tool_calling/).
-
-Compatible models can be found in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/chat/).
-
-## Specifying a model by name
-
-You can configure an agent with a model name string:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-agent = create_react_agent(
-    # highlight-next-line
-    model="anthropic:claude-3-7-sonnet-latest",
-    # other parameters
-)
-```
-
-## Using `init_chat_model`
-
-The [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) utility simplifies model initialization with configurable parameters:
-
-```python
-from langchain.chat_models import init_chat_model
-
-model = init_chat_model(
-    "anthropic:claude-3-7-sonnet-latest",
-    temperature=0,
-    max_tokens=2048
-)
-```
-
-Refer to the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for advanced options.
-
-## Using provider-specific LLMs 
-
-If a model provider is not available via `init_chat_model`, you can instantiate the provider's model class directly. The model must implement the [BaseChatModel interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) and support tool calling:
-
-```python
-from langchain_anthropic import ChatAnthropic
-from langgraph.prebuilt import create_react_agent
-
-model = ChatAnthropic(
-    model="claude-3-7-sonnet-latest",
-    temperature=0,
-    max_tokens=2048
-)
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model,
-    # other parameters
-)
-```
-
-!!! note "Illustrative example" 
-
-    The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.
-
-## Additional resources
-
-- [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
-- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)

docs/docs/agents/multi-agent.md

@@ -1,275 +0,0 @@
-# Multi-agent
-
-A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).
-
-In multi-agent systems, agents need to communicate between each other. They do so via [handoffs](#handoffs) — a primitive that describes which agent to hand control to and the payload to send to that agent.
-
-Two of the most popular multi-agent architectures are:
-
-- [supervisor](#supervisor) — individual agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements.
-- [swarm](#swarm) — agents dynamically hand off control to one another based on their specializations. The system remembers which agent was last active, ensuring that on subsequent interactions, the conversation resumes with that agent.
-
-## Supervisor
-
-![Supervisor](./assets/supervisor.png)
-
-Use [`langgraph-supervisor`](https://github.com/langchain-ai/langgraph-supervisor-py) library to create a supervisor multi-agent system:
-
-```bash
-pip install langgraph-supervisor
-```
-
-```python
-from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
-# highlight-next-line
-from langgraph_supervisor import create_supervisor
-
-def book_hotel(hotel_name: str):
-    """Book a hotel"""
-    return f"Successfully booked a stay at {hotel_name}."
-
-def book_flight(from_airport: str, to_airport: str):
-    """Book a flight"""
-    return f"Successfully booked a flight from {from_airport} to {to_airport}."
-
-flight_assistant = create_react_agent(
-    model="openai:gpt-4o",
-    tools=[book_flight],
-    prompt="You are a flight booking assistant",
-    # highlight-next-line
-    name="flight_assistant"
-)
-
-hotel_assistant = create_react_agent(
-    model="openai:gpt-4o",
-    tools=[book_hotel],
-    prompt="You are a hotel booking assistant",
-    # highlight-next-line
-    name="hotel_assistant"
-)
-
-# highlight-next-line
-supervisor = create_supervisor(
-    agents=[flight_assistant, hotel_assistant],
-    model=ChatOpenAI(model="gpt-4o"),
-    prompt="You manage a hotel booking assistant and a flight booking assistant. Assign work to them."
-).compile()
-
-for chunk in supervisor.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
-    print(chunk)
-    print("\n")
-```
-
-## Swarm
-
-![Swarm](./assets/swarm.png)
-
-Use [`langgraph-swarm`](https://github.com/langchain-ai/langgraph-swarm-py) library to create a swarm multi-agent system:
-
-```bash
-pip install langgraph-swarm
-```
-
-```python
-from langgraph.prebuilt import create_react_agent
-# highlight-next-line
-from langgraph_swarm import create_swarm, create_handoff_tool
-
-transfer_to_hotel_assistant = create_handoff_tool(
-    agent_name="hotel_assistant",
-    description="Transfer user to the hotel-booking assistant.",
-)
-transfer_to_flight_assistant = create_handoff_tool(
-    agent_name="flight_assistant",
-    description="Transfer user to the flight-booking assistant.",
-)
-
-flight_assistant = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    # highlight-next-line
-    tools=[book_flight, transfer_to_hotel_assistant],
-    prompt="You are a flight booking assistant",
-    # highlight-next-line
-    name="flight_assistant"
-)
-hotel_assistant = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    # highlight-next-line
-    tools=[book_hotel, transfer_to_flight_assistant],
-    prompt="You are a hotel booking assistant",
-    # highlight-next-line
-    name="hotel_assistant"
-)
-
-# highlight-next-line
-swarm = create_swarm(
-    agents=[flight_assistant, hotel_assistant],
-    default_active_agent="flight_assistant"
-).compile()
-
-for chunk in supervisor.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
-    print(chunk)
-    print("\n")
-```
-
-## Handoffs
-
-A common pattern in multi-agent interactions is **handoffs**, where one agent *hands off* control to another. Handoffs allow you to specify:
-
-- **destination**: target agent to navigate to
-- **payload**: information to pass to that agent
-
-This is used both by `langgraph-supervisor` (supervisor hands off to individual agents) and `langgraph-swarm` (an individual agent can hand off to other agents).
-
-To implement handoffs with `create_react_agent`, you need to:
-
-1. Create a special tool that can transfer control to a different agent
-
-    ```python
-    def transfer_to_bob():
-        """Transfer to bob."""
-        return Command(
-            # name of the agent (node) to go to
-            # highlight-next-line
-            goto="bob",
-            # data to send to the agent
-            # highlight-next-line
-            update={"messages": [...]},
-            # indicate to LangGraph that we need to navigate to
-            # agent node in a parent graph
-            # highlight-next-line
-            graph=Command.PARENT,
-        )
-    ```
-
-1. Create individual agents that have access to handoff tools:
-
-    ```python
-    flight_assistant = create_react_agent(
-        ..., tools=[book_flight, transfer_to_hotel_assistant]
-    )
-    hotel_assistant = create_react_agent(
-        ..., tools=[book_hotel, transfer_to_flight_assistant]
-    )
-    ```
-
-1. Define a parent graph that contains individual agents as nodes:
-
-    ```python
-    from langgraph.graph import StateGraph, MessagesState
-    multi_agent_graph = (
-        StateGraph(MessagesState)
-        .add_node(flight_assistant)
-        .add_node(hotel_assistant)
-        ...
-    )
-    ```
-
-Putting this together, here is how you can implement a simple multi-agent system with two agents — a flight booking assistant and a hotel booking assistant:
-
-```python
-from typing import Annotated
-from langchain_core.tools import tool, InjectedToolCallId
-from langgraph.prebuilt import create_react_agent, InjectedState
-from langgraph.graph import StateGraph, START, MessagesState
-from langgraph.types import Command
-
-def create_handoff_tool(*, agent_name: str, description: str | None = None):
-    name = f"transfer_to_{agent_name}"
-    description = description or f"Transfer to {agent_name}"
-
-    @tool(name, description=description)
-    def handoff_tool(
-        # highlight-next-line
-        state: Annotated[MessagesState, InjectedState], # (1)!
-        # highlight-next-line
-        tool_call_id: Annotated[str, InjectedToolCallId],
-    ) -> Command:
-        tool_message = {
-            "role": "tool",
-            "content": f"Successfully transferred to {agent_name}",
-            "name": name,
-            "tool_call_id": tool_call_id,
-        }
-        return Command(  # (2)!
-            # highlight-next-line
-            goto=agent_name,  # (3)!
-            # highlight-next-line
-            update={"messages": state["messages"] + [tool_message]},  # (4)!
-            # highlight-next-line
-            graph=Command.PARENT,  # (5)!
-        )
-    return handoff_tool
-
-# Handoffs
-transfer_to_hotel_assistant = create_handoff_tool(
-    agent_name="hotel_assistant",
-    description="Transfer user to the hotel-booking assistant.",
-)
-transfer_to_flight_assistant = create_handoff_tool(
-    agent_name="flight_assistant",
-    description="Transfer user to the flight-booking assistant.",
-)
-
-# Simple agent tools
-def book_hotel(hotel_name: str):
-    """Book a hotel"""
-    return f"Successfully booked a stay at {hotel_name}."
-
-def book_flight(from_airport: str, to_airport: str):
-    """Book a flight"""
-    return f"Successfully booked a flight from {from_airport} to {to_airport}."
-
-# Define agents
-flight_assistant = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    # highlight-next-line
-    tools=[book_flight, transfer_to_hotel_assistant],
-    prompt="You are a flight booking assistant",
-    # highlight-next-line
-    name="flight_assistant"
-)
-hotel_assistant = create_react_agent(
-    model="anthropic:claude-3-5-sonnet-latest",
-    # highlight-next-line
-    tools=[book_hotel, transfer_to_flight_assistant],
-    prompt="You are a hotel booking assistant",
-    # highlight-next-line
-    name="hotel_assistant"
-)
-
-# Define multi-agent graph
-multi_agent_graph = (
-    StateGraph(MessagesState)
-    .add_node(flight_assistant)
-    .add_node(hotel_assistant)
-    .add_edge(START, "flight_assistant")
-    .compile()
-)
-
-# Run the multi-agent graph
-for chunk in multi_agent_graph.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
-    print(chunk)
-    print("\n")
-```
-
-1. Access agent's state
-2. The `Command` primitive allows specifying a state update and a node transition as a single operation, making it useful for implementing handoffs.
-3. Name of the agent or node to hand off to.
-4. Take the agent's messages and **add** them to the parent's **state** as part of the handoff. The next agent will see the parent state.
-5. Indicate to LangGraph that we need to navigate to agent node in a **parent** multi-agent graph.
-
-!!! Note
-    This handoff implementation assumes that:
-
-    - each agent receives overall message history (across all agents) in the multi-agent system as its input
-    - each agent outputs its internal messages history to the overall message history of the multi-agent system
-
-    Check out LangGraph [supervisor](https://github.com/langchain-ai/langgraph-supervisor-py#customizing-handoff-tools) and [swarm](https://github.com/langchain-ai/langgraph-swarm-py#customizing-handoff-tools) documentation to learn how to customize handoffs.

docs/docs/agents/overview.md

@@ -1,38 +0,0 @@
----
-title: Overview
----
-
-# Agent development with LangGraph
-
-**LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
-
-## Key features
-
-LangGraph includes several capabilities essential for building robust, production-ready agentic systems:
-
-- [**Memory integration**](./memory.md): Native support for *short-term* (session-based) and *long-term* (persistent across sessions) memory, enabling stateful behaviors in chatbots and assistants.
-- [**Human-in-the-loop control**](./human-in-the-loop.md): Execution can pause *indefinitely* to await human feedback—unlike websocket-based solutions limited to real-time interaction. This enables asynchronous approval, correction, or intervention at any point in the workflow.
-- [**Streaming support**](./streaming.md): Real-time streaming of agent state, model tokens, tool outputs, or combined streams.
-- [**Deployment tooling**](./deployment.md): Includes infrastructure-free deployment tools. [**LangGraph Platform**](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/) supports testing, debugging, and deployment.
-    - **[Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)**: A visual IDE for inspecting and debugging workflows.
-    - Supports multiple [**deployment options**](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for production.
-
-## High-level building blocks
-
-LangGraph comes with a set of prebuilt components that implement common agent behaviors and workflows. These abstractions are built on top of the LangGraph framework, offering a faster path to production while remaining flexible for advanced customization.
-
-Using LangGraph for agent development allows you to focus on your application's logic and behavior, instead of building and maintaining the supporting infrastructure for state, memory, and human feedback.
-
-## Package ecosystem
-
-The high-level components are organized into several packages, each with a specific focus.
-
-| Package                                    | Description                                                                 | Installation                            |
-|--------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------|
-| `langgraph-prebuilt` (part of `langgraph`) | Prebuilt components to [**create agents**](./agents.md)                     | `pip install -U langgraph langchain`    |
-| `langgraph-supervisor`                     | Tools for building [**supervisor**](./multi-agent.md#supervisor) agents     | `pip install -U langgraph-supervisor`   |
-| `langgraph-swarm`                          | Tools for building a [**swarm**](./multi-agent.md#swarm) multi-agent system | `pip install -U langgraph-swarm`        |
-| `langchain-mcp-adapters`                   | Interfaces to [**MCP servers**](./mcp.md) for tool and resource integration | `pip install -U langchain-mcp-adapters` |
-| `langmem`                                  | Agent memory management: [**short-term and long-term**](./memory.md)        | `pip install -U langmem`                |
-| `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
-

docs/docs/agents/prebuilt.md

@@ -1,7 +0,0 @@
-# Community Agents
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
-
-[//]: # (This file is stub. Do not edit this file directly!)
-[//]: # (1. Update the `packages.yml` file in the `docs/_scripts/third_party_page` directory.)
-[//]: # (2. From the /docs directory, run `make build-prebuilt` to generate an updated version of this file for testing locally.)

docs/docs/agents/run_agents.md

@@ -1,159 +0,0 @@
-# Running agents
-
-
-Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](#streaming) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
-
-
-## Basic usage
-
-Agents can be executed in two primary modes:
-
-- **Synchronous** using `.invoke()` or `.stream()`
-- **Asynchronous** using `await .invoke()` or `async for` with `.astream()`
-
-=== "Sync invocation"
-    ```python
-    from langgraph.prebuilt import create_react_agent
-
-    agent = create_react_agent(...)
-
-    # highlight-next-line
-    response = agent.invoke({"messages": "what is the weather in sf"})
-    ```
-
-=== "Async invocation"
-    ```python
-    from langgraph.prebuilt import create_react_agent
-
-    agent = create_react_agent(...)
-    # highlight-next-line
-    response = await agent.ainvoke({"messages": "what is the weather in sf"})
-    ```
-
-## Inputs and outputs
-
-Agents use a language model that expects a list of `messages` as an input. Therefore, agent inputs and outputs are stored as a list of `messages` under the `messages` key in the agent [state](../concepts/low_level.md#working-with-messages-in-graph-state).
-
-## Input format
-
-Agent input must be a dictionary with a `messages` key. Supported formats are:
-
-| Format             | Example                                                                                                                       |
-|--------------------|-------------------------------------------------------------------------------------------------------------------------------|
-| String             | `{"messages": "Hello"}`  — Interpreted as a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage) |
-| Message dictionary | `{"messages": {"role": "user", "content": "Hello"}}`                                                                          |
-| List of messages   | `{"messages": [{"role": "user", "content": "Hello"}]}`                                                                        |
-| With custom state  | `{"messages": [{"role": "user", "content": "Hello"}], "user_name": "Alice"}` — If using a custom `state_schema`               |
-
-Messages are automatically converted into LangChain's internal message format. You can read
-more about [LangChain messages](https://python.langchain.com/docs/concepts/messages/#langchain-messages) in the LangChain documentation.
-
-!!! tip "Using custom agent state"
-
-    You can provide additional fields defined in your agent’s state schema directly in the input dictionary. This allows dynamic behavior based on runtime data or prior tool outputs.  
-    See the [context guide](./context.md) for full details.
-
-!!! note
-
-    A string input for `messages` is converted to a [HumanMessage](https://python.langchain.com/docs/concepts/messages/#humanmessage). This behavior differs from the `prompt` parameter in `create_react_agent`, which is interpreted as a [SystemMessage](https://python.langchain.com/docs/concepts/messages/#systemmessage) when passed as a string.
-
-
-## Output format
-
-Agent output is a dictionary containing:
-
-- `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
-- Optionally, `structured_response` if [structured output](./agents.md#structured-output) is configured.
-- If using a custom `state_schema`, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
-
-See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.
-
-## Streaming output
-
-Agents support streaming responses for more responsive applications. This includes:
-
-- **Progress updates** after each step
-- **LLM tokens** as they're generated
-- **Custom tool messages** during execution
-
-Streaming is available in both sync and async modes:
-
-=== "Sync streaming"
-
-    ```python
-    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
-        stream_mode="updates"
-    ):
-        print(chunk)
-    ```
-
-=== "Async streaming"
-
-    ```python
-    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
-        stream_mode="updates"
-    ):
-        print(chunk)
-    ```
-
-!!! tip
-
-    For full details, see the [streaming guide](./streaming.md).
-
-## Max iterations
-
-To control agent execution and avoid infinite loops, set a recursion limit. This defines the maximum number of steps the agent can take before raising a `GraphRecursionError`. You can configure `recursion_limit` at runtime or when defining agent via `.with_config()`:
-
-=== "Runtime"
-
-    ```python
-    from langgraph.errors import GraphRecursionError
-    from langgraph.prebuilt import create_react_agent
-
-    max_iterations = 3
-    # highlight-next-line
-    recursion_limit = 2 * max_iterations + 1
-    agent = create_react_agent(
-        model="anthropic:claude-3-5-haiku-latest",
-        tools=[get_weather]
-    )
-
-    try:
-        response = agent.invoke(
-            {"messages": "what's the weather in sf"},
-            # highlight-next-line
-            {"recursion_limit": recursion_limit},
-        )
-    except GraphRecursionError:
-        print("Agent stopped due to max iterations.")
-    ```
-
-=== "`.with_config()`"
-
-    ```python
-    from langgraph.errors import GraphRecursionError
-    from langgraph.prebuilt import create_react_agent
-
-    max_iterations = 3
-    # highlight-next-line
-    recursion_limit = 2 * max_iterations + 1
-    agent = create_react_agent(
-        model="anthropic:claude-3-5-haiku-latest",
-        tools=[get_weather]
-    )
-    # highlight-next-line
-    agent_with_recursion_limit = agent.with_config(recursion_limit=recursion_limit)
-
-    try:
-        response = agent_with_recursion_limit.invoke(
-            {"messages": "what's the weather in sf"},
-        )
-    except GraphRecursionError:
-        print("Agent stopped due to max iterations.")
-    ```
-
-## Additional Resources
-
-* [Async programming in LangChain](https://python.langchain.com/docs/concepts/async)

docs/docs/agents/streaming.md

@@ -1,208 +0,0 @@
-# Streaming
-
-Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
-
-1. [**Agent progress**](#agent-progress) — get updates after each node in the agent graph is executed.
-2. [**LLM tokens**](#llm-tokens) — stream tokens as they are generated by the language model.
-3. [**Custom updates**](#tool-updates) — emit custom data from tools during execution (e.g., "Fetched 10/100 records")
-
-You can stream [more than one type of data](#stream-multiple-modes) at a time. 
-
-
-<figure markdown="1">
-![image](./assets/fast_parrot.png){: style="max-height:300px"}
-<figcaption>
-Waiting is for pigeons.
-</figcaption>
-</figure>
-
-## Agent progress
-
-To stream agent progress, use the [`stream()`][langgraph.graph.state.CompiledStateGraph.stream] or [`astream()`][langgraph.graph.state.CompiledStateGraph.astream] methods with [`stream_mode="updates"`](https://langchain-ai.github.io/langgraph/how-tos/streaming/#updates). This emits an event after every agent step.
-
-For example, if you have an agent that calls a tool once, you should see the following updates:
-
-* **LLM node**: AI message with tool call requests
-* **Tool node**: Tool message with execution result
-* **LLM node**: Final AI response
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="updates"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## LLM tokens
-
-To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    for token, metadata in agent.stream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-    # highlight-next-line
-    async for token, metadata in agent.astream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="messages"
-    ):
-        print("Token", token)
-        print("Metadata", metadata)
-        print("\n")
-    ```
-
-## Tool updates
-
-To stream updates from tools as they are executed, you can use [get_stream_writer][langgraph.config.get_stream_writer].
-
-=== "Sync"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    # highlight-next-line
-    from langgraph.config import get_stream_writer
-
-    def get_weather(city: str) -> str:
-        """Get weather for a given city."""
-        # highlight-next-line
-        writer = get_stream_writer()
-        # stream any arbitrary data
-        # highlight-next-line
-        writer(f"Looking up data for city: {city}")
-        return f"It's always sunny in {city}!"
-
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode="custom"
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-!!! Note
-    If you add `get_stream_writer` inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. 
-
-## Stream multiple modes
-
-You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "messages", "custom"]`:
-
-=== "Sync"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    for stream_mode, chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-=== "Async"
-
-    ```python
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[get_weather],
-    )
-
-    async for stream_mode, chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
-        # highlight-next-line
-        stream_mode=["updates", "messages", "custom"]
-    ):
-        print(chunk)
-        print("\n")
-    ```
-
-## Additional resources
-
-* [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)

docs/docs/agents/tools.md

@@ -1,268 +0,0 @@
-# Tools
-
-[Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
-
-You can either [define your own tools](#define-simple-tools) or use [prebuilt integrations](#prebuilt-tools) that LangChain provides.
-
-## Define simple tools
-
-You can pass a vanilla function to `create_react_agent` to use as a tool:
-
-```python
-from langgraph.prebuilt import create_react_agent
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-create_react_agent(
-    model="anthropic:claude-3-7-sonnet",
-    tools=[multiply]
-)
-```
-
-`create_react_agent` automatically converts vanilla functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
-
-## Customize tools
-
-For more control over tool behavior, use the `@tool` decorator:
-
-```python
-# highlight-next-line
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool("multiply_tool", parse_docstring=True)
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers.
-
-    Args:
-        a: First operand
-        b: Second operand
-    """
-    return a * b
-```
-
-You can also define a custom input schema using Pydantic:
-
-```python
-from pydantic import BaseModel, Field
-
-class MultiplyInputSchema(BaseModel):
-    """Multiply two numbers"""
-    a: int = Field(description="First operand")
-    b: int = Field(description="Second operand")
-
-# highlight-next-line
-@tool("multiply_tool", args_schema=MultiplyInputSchema)
-def multiply(a: int, b: int) -> int:
-   return a * b
-```
-
-For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).
-
-## Hide arguments from the model
-
-Some tools require runtime-only arguments (e.g., user ID or session context) that should not be controllable by the model.
-
-You can put these arguments in the `state` or `config` of the agent, and access
-this information inside the tool:
-
-```python
-from langgraph.prebuilt import InjectedState
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langchain_core.runnables import RunnableConfig
-
-def my_tool(
-    # This will be populated by an LLM
-    tool_arg: str,
-    # access information that's dynamically updated inside the agent
-    # highlight-next-line
-    state: Annotated[AgentState, InjectedState],
-    # access static data that is passed at agent invocation
-    # highlight-next-line
-    config: RunnableConfig,
-) -> str:
-    """My tool."""
-    do_something_with_state(state["messages"])
-    do_something_with_config(config)
-    ...
-```
-
-## Disable parallel tool calling
-
-Some model providers support executing multiple tools in parallel, but
-allow users to disable this feature.
-
-For supported providers, you can disable parallel tool calling by setting `parallel_tool_calls=False` via the `model.bind_tools()` method:
-
-```python
-from langchain.chat_models import init_chat_model
-
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-def multiply(a: int, b: int) -> int:
-    """Multiply two numbers."""
-    return a * b
-
-model = init_chat_model("anthropic:claude-3-5-sonnet-latest", temperature=0)
-tools = [add, multiply]
-agent = create_react_agent(
-    # disable parallel tool calls
-    # highlight-next-line
-    model=model.bind_tools(tools, parallel_tool_calls=False),
-    tools=tools
-)
-
-agent.invoke({"messages": "what's 3 + 5 and 4 * 7? make both calculations in parallel"})
-```
-
-## Return tool results directly
-
-Use `return_direct=True` to return tool results immediately and stop the agent loop:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def add(a: int, b: int) -> int:
-    """Add two numbers"""
-    return a + b
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[add]
-)
-
-agent.invoke({"messages": "what's 3 + 5?"})
-```
-
-## Force tool use
-
-To force the agent to use specific tools, you can set the `tool_choice` option in `model.bind_tools()`:
-
-```python
-from langchain_core.tools import tool
-
-# highlight-next-line
-@tool(return_direct=True)
-def greet(user_name: str) -> int:
-    """Greet user."""
-    return f"Hello {user_name}!"
-
-tools = [greet]
-
-agent = create_react_agent(
-    # highlight-next-line
-    model=model.bind_tools(tools, tool_choice={"type": "tool", "name": "greet"}),
-    tools=tools
-)
-
-agent.invoke({"messages": "Hi, I am Bob"})
-```
-
-!!! Warning "Avoid infinite loops"
-
-    Forcing tool usage without stopping conditions can create infinite loops. Use one of the following safeguards:
-
-    - Mark the tool with [`return_direct=True`](#return-tool-results-directly) to end the loop after execution.
-    - Set [`recursion_limit`](../concepts/low_level.md#recursion-limit) to restrict the number of execution steps.
-
-## Handle tool errors
-
-By default, the agent will catch all exceptions raised during tool calls and will pass those as tool messages to the LLM. To control how the errors are handled, you can use the prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] — the node that executes tools inside `create_react_agent` — via its `handle_tool_errors` parameter:
-
-=== "Enable error handling (default)"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # Run with error handling (default)
-    agent = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=[multiply]
-    )
-    agent.invoke({"messages": "what's 42 x 7?"})
-    ```
-
-=== "Disable error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=False  # (1)!
-    )
-    agent_no_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_no_error_handling.invoke({"messages": "what's 42 x 7?"})
-    ```
-
-    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-=== "Custom error handling"
-
-    ```python
-    from langgraph.prebuilt import create_react_agent, ToolNode
-
-    def multiply(a: int, b: int) -> int:
-        """Multiply two numbers."""
-        if a == 42:
-            raise ValueError("The ultimate error")
-        return a * b
-
-    # highlight-next-line
-    tool_node = ToolNode(
-        [multiply],
-        # highlight-next-line
-        handle_tool_errors=(
-            "Can't use 42 as a first operand, you must switch operands!"  # (1)!
-        )
-    )
-    agent_custom_error_handling = create_react_agent(
-        model="anthropic:claude-3-7-sonnet-latest",
-        tools=tool_node
-    )
-    agent_custom_error_handling.invoke({"messages": "what's 42 x 7?"})
-    ```
-
-    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
-
-See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
-
-## Prebuilt tools
-
-LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
-
-You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
-
-Some commonly used tool categories include:
-
-- **Search**: Bing, SerpAPI, Tavily
-- **Code interpreters**: Python REPL, Node.js REPL
-- **Databases**: SQL, MongoDB, Redis
-- **Web data**: Web scraping and browsing
-- **APIs**: OpenWeatherMap, NewsAPI, and others
-
-These integrations can be configured and added to your agents using the same `tools` parameter shown in the examples above.
-

docs/docs/agents/ui.md

@@ -1,31 +0,0 @@
-# UI
-
-You can use a prebuilt chat UI for interacting with any LangGraph agent through the [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui). Using the [deployed version](https://agentchat.vercel.app) is the quickest way to get started, and allows you to interact with both local and deployed graphs.
-
-## Run agent in UI
-
-First, set up LangGraph API server [locally](./deployment.md#launch-langgraph-server-locally) or deploy your agent on [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/quick_start/).
-
-Then, navigate to [Agent Chat UI](https://agentchat.vercel.app), or clone the repository and [run the dev server locally](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#setup):
-
-<video controls src="../assets/base-chat-ui.mp4" type="video/mp4"></video>
-
-!!! Tip
-
-    UI has out-of-box support for rendering tool calls, and tool result messages. To customize what messages are shown, see the [Hiding Messages in the Chat](https://github.com/langchain-ai/agent-chat-ui?tab=readme-ov-file#hiding-messages-in-the-chat) section in the Agent Chat UI documentation.
-
-## Add human-in-the-loop
-
-Agent Chat UI has full support for [human-in-the-loop](../concepts/human_in_the_loop.md) workflows. To try it out, replace the agent code in `src/agent/graph.py` (from the [deployment](./deployment.md) guide) with this [agent implementation](./human-in-the-loop.md#using-with-agent-inbox):
-
-<video controls src="../assets/interrupt-chat-ui.mp4" type="video/mp4"></video>
-
-!!! Important
-
-    Agent Chat UI works best if your LangGraph agent interrupts using the [`HumanInterrupt` schema][langgraph.prebuilt.interrupt.HumanInterrupt]. If you do not use that schema, the Agent Chat UI will be able to render the input passed to the `interrupt` function, but it will not have full support for resuming your graph.
-
-## Generative UI
-
-You can also use generative UI in the Agent Chat UI.
-
-Generative UI allows you to define [React](https://react.dev/) components, and push them to the UI from the LangGraph server. For more documentation on building generative UI LangGraph agents, read [these docs](https://langchain-ai.github.io/langgraph/cloud/how-tos/generative_ui_react/).

docs/docs/cloud/deployment/cloud.md

@@ -1,6 +1,6 @@
-# How to Deploy to Cloud SaaS (Beta)
+# How to Deploy to LangGraph Cloud
 
-Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.
+LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith" target="_blank">LangSmith</a>. To deploy a LangGraph Cloud API, navigate to the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>.
 
 ## Prerequisites
 
@@ -92,28 +92,3 @@ Starting from the `LangGraph Platform` view...
 1. Check/uncheck checkbox to `Automatically update deployment on push to branch`.
     1. Branch creation/deletion and tag creation/deletion events will not trigger an update. Only pushes to an existing branch will trigger an update.
     1. Pushes in quick succession to a branch will not trigger subsequent updates. In the future, this functionality may be changed/improved.
-
-## Add or Remove GitHub Repositories
-
-After installing and authorizing LangChain's `hosted-langserve` GitHub app, repository access for the app can be modified to add new repositories or remove existing repositories. If a new repository is created, it may need to be added explicitly.
-
-1. From the GitHub profile, navigate to `Settings` > `Applications` > `hosted-langserve` > click `Configure`.
-1. Under `Repository access`, select `All repositories` or `Only select repositories`. If `Only select repositories` is selected, new repositories must be explicitly added.
-1. Click `Save`.
-1. When creating a new deployment, the list of GitHub repositories in the dropdown menu will be updated to reflect the repository access changes.
-
-## Whitelisting IP Addresses
-
-All traffic from `LangGraph Platform` deployments created after January 6th 2025 will come through a NAT gateway.
-This NAT gateway will have several static ip addresses depending on the region you are deploying in. Refer to the table below for the list of IP addresses to whitelist:
-
-| US             | EU             |
-|----------------|----------------|
-| 35.197.29.146  | 34.13.192.67   |
-| 34.145.102.123 | 34.147.105.64  |
-| 34.169.45.153  | 34.90.22.166   |
-| 34.82.222.17   | 34.147.36.213  |
-| 35.227.171.135 | 34.32.137.113  | 
-| 34.169.88.30   | 34.91.238.184  |
-| 34.19.93.202   | 35.204.101.241 |
-| 34.19.34.50    | 35.204.48.32   |

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -1,56 +0,0 @@
-# How to Deploy Self-Hosted Control Plane (Beta)
-
-Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
-
-## Prerequisites
-
-1. You are using Kubernetes.
-1. You have self-hosted LangSmith deployed.
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster has access to.
-1. `KEDA` is installed on your cluster.
-
-         helm repo add kedacore https://kedacore.github.io/charts 
-         helm install keda kedacore/keda --namespace keda --create-namespace
-
-1. Ingress Configuration (recommended)
-    1. Install `Ingress Nginx` to serve as a reverse proxy for your deployment.
-
-            helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
-            helm repo update
-            helm install ingress-nginx ingress-nginx/ingress-nginx
-
-    1. Provision a root domain that will suffix all domains for your workloads (e.g. `us.langgraph.app`).
-    1. Provision wildcard certificates to terminate TLS for your deployments.
-    1. Note: If this step is skipped, you will need to provision domains/certs for each of your deployments.
-
-1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
-
-## Setup
-
-1. As part of configuring your Self-Hosted LangSmith instance, you enable the `langgraphPlatform` option. This will provision a few key resources.
-    1. `listener`: This is a service that listens to the [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
-    1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
-    1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
-    1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
-1. Two additional images will be used by the chart.
-
-        hostBackendImage:
-          repository: "docker.io/langchain/hosted-langserve-backend"
-          pullPolicy: IfNotPresent
-          tag: "0.9.80"
-        operatorImage:
-          repository: "docker.io/langchain/langgraph-operator"
-          pullPolicy: IfNotPresent
-          tag: "aa9dff4"
-
-1. In your `values.yaml` file, enable the `langgraphPlatform` option.
-
-        config:
-          langgraphPlatform:
-            enabled: true
-            langgraphPlatformLicenseKey: "YOUR_LANGGRAPH_PLATFORM_LICENSE_KEY"
-            rootDomain: "YOUR_ROOT_DOMAIN"
-
-1. You can also configure base templates for your agents by overriding the base templates [here](https://github.com/langchain-ai/helm/blob/main/charts/langsmith/values.yaml#L898).
-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -1,53 +0,0 @@
-# How to Deploy Self-Hosted Data Plane (Beta)
-
-Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
-
-## Prerequisites
-
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster or Amazon ECS cluster has access to.
-
-## Kubernetes
-
-### Prerequisites
-1. `KEDA` is installed on your cluster.
-
-        helm repo add kedacore https://kedacore.github.io/charts 
-        helm install keda kedacore/keda --namespace keda --create-namespace
-
-1. A valid `Ingress` controller is install on your cluster.
-1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
-
-### Setup
-
-1. You give us your LangSmith organization ID. We will enable the Self-Hosted Data Plane for your organization.
-1. We provide you a [Helm chart](https://github.com/langchain-ai/helm/tree/main/charts/langgraph-dataplane) which you run to setup your Kubernetes cluster. This chart contains a few important components.
-    1. `langgraph-listener`: This is a service that listens to LangChain's [control plane](../../concepts/langgraph_control_plane.md) for changes to your deployments and creates/updates downstream CRDs.
-    1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph Platform deployment.
-    1. `langgraph-platform-operator`: This operator handles changes to your LangGraph Platform CRDs.
-1. Configure your `langgraph-dataplane-values.yaml` file.
-
-        config:
-          langgraphPlatformLicenseKey: "" # Your LangGraph Platform license key
-          langsmithApiKey: "" # API Key of your Workspace
-          langsmithWorkspaceId: "" # Workspace ID
-          hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU
-          smithBackendUrl: "https://api.smith.langchain.com" # Only override this if on EU
-
-1. Deploy `langgraph-dataplane` Helm chart.
-
-        helm repo add langchain https://langchain-ai.github.io/helm/
-        helm repo update
-        helm upgrade -i langgraph-dataplane langchain/langgraph-dataplane --values langgraph-dataplane-values.yaml
-
-1. If successful, you will see two services start up in your namespace.
-
-        NAME                                          READY   STATUS              RESTARTS   AGE
-        langgraph-dataplane-listener-7fccd788-wn2dx   0/1     Running             0          9s
-        langgraph-dataplane-redis-0                   0/1     ContainerCreating   0          9s
-
-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
-
-## Amazon ECS
-
-Coming soon!

docs/docs/cloud/deployment/semantic_search.md

@@ -17,7 +17,7 @@ This guide explains how to add semantic search to your LangGraph deployment's cr
     ...
     "store": {
         "index": {
-            "embed": "openai:text-embedding-3-small",
+            "embed": "openai:text-embeddings-3-small",
             "dims": 1536,
             "fields": ["$"]
         }
@@ -27,7 +27,7 @@ This guide explains how to add semantic search to your LangGraph deployment's cr
 
 This configuration:
 
-- Uses OpenAI's text-embedding-3-small model for generating embeddings
+- Uses OpenAI's text-embeddings-3-small model for generating embeddings
 - Sets the embedding dimension to 1536 (matching the model's output)
 - Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
 

docs/docs/cloud/deployment/setup.md

@@ -36,20 +36,21 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
 ```
-langgraph>=0.2.56,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
+langgraph>=0.2.56,<0.3.0
+langgraph-checkpoint>=2.0.5,<3.0
 langchain-core>=0.2.38,<0.4.0
 langsmith>=0.1.63
 orjson>=3.9.7
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
-sse-starlette>=2.1.0,<2.2.0
+sse-starlette>=2.1.0
 uvloop>=0.18.0
 httptools>=0.5.0
-jsonschema-rs>=0.20.0
+jsonschema-rs>=0.16.3
+croniter>=1.0.1
 structlog>=23.1.0
+redis>=5.0.0,<6.0.0
 ```
 
 Example `requirements.txt` file:

docs/docs/cloud/deployment/setup_pyproject.md

@@ -36,20 +36,21 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
 ```
-langgraph>=0.2.56,<0.4.0
-langgraph-sdk>=0.1.53
-langgraph-checkpoint>=2.0.15,<3.0
+langgraph>=0.2.56,<0.3.0
+langgraph-checkpoint>=2.0.5,<3.0
 langchain-core>=0.2.38,<0.4.0
 langsmith>=0.1.63
 orjson>=3.9.7
 httpx>=0.25.0
 tenacity>=8.0.0
 uvicorn>=0.26.0
-sse-starlette>=2.1.0,<2.2.0
+sse-starlette>=2.1.0
 uvloop>=0.18.0
 httptools>=0.5.0
-jsonschema-rs>=0.20.0
+jsonschema-rs>=0.16.3
+croniter>=1.0.1
 structlog>=23.1.0
+redis>=5.0.0,<6.0.0
 ```
 
 Example `pyproject.toml` file:
@@ -64,7 +65,7 @@ license = "MIT"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.9"
+python = ">=3.9.0,<3.13"
 langgraph = "^0.2.0"
 langchain-fireworks = "^0.1.3"
 

docs/docs/cloud/deployment/standalone_container.md

@@ -1,110 +0,0 @@
-# How to Deploy a Standalone Container
-
-Before deploying, review the [conceptual guide for the Standalone Container](../../concepts/langgraph_standalone_container.md) deployment option.
-
-## Prerequisites
-
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`).
-1. The following environment variables are needed for a standalone container deployment.
-    1. `REDIS_URI`: Connection details to a Redis instance. Redis will be used as a pub-sub broker to enable streaming real time output from background runs. The value of `REDIS_URI` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
-
-        !!! Note "Shared Redis Instance"
-            Multiple self-hosted deployments can share the same Redis instance. For example, for `Deployment A`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/1` and for `Deployment B`, `REDIS_URI` can be set to `redis://<hostname_1>:<port>/2`.
-
-            `1` and `2` are different database numbers within the same instance, but `<hostname_1>` is shared. **The same database number cannot be used for separate deployments**.
-
-    1. `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics. The value of `DATABASE_URI` must be a valid [Postgres connection URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
-
-        !!! Note "Shared Postgres Instance"
-            Multiple self-hosted deployments can share the same Postgres instance. For example, for `Deployment A`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_1>?host=<hostname_1>` and for `Deployment B`, `DATABASE_URI` can be set to `postgres://<user>:<password>@/<database_name_2>?host=<hostname_1>`.
-
-            `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.
-
-    1. `LANGSMITH_API_KEY`: (if using [Lite](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangSmith API key. This will be used to authenticate ONCE at server start up.
-    1. `LANGGRAPH_CLOUD_LICENSE_KEY`: (if using [Enterprise](../../concepts/langgraph_data_plane.md#lite-vs-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
-    1. `LANGSMITH_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGSMITH_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
-
-## Kubernetes (Helm)
-
-Use this [Helm chart](https://github.com/langchain-ai/helm/blob/main/charts/langgraph-cloud/README.md) to deploy a LangGraph Server to a Kubernetes cluster.
-
-## Docker
-
-Run the following `docker` command:
-```shell
-docker run \
-    --env-file .env \
-    -p 8123:8000 \
-    -e REDIS_URI="foo" \
-    -e DATABASE_URI="bar" \
-    -e LANGSMITH_API_KEY="baz" \
-    my-image
-```
-
-!!! note
-
-    * You need to replace `my-image` with the name of the image you built in the prerequisite steps (from `langgraph build`)
-    and you should provide appropriate values for `REDIS_URI`, `DATABASE_URI`, and `LANGSMITH_API_KEY`.
-    * If your application requires additional environment variables, you can pass them in a similar way.
-
-## Docker Compose
-
-Docker Compose YAML file:
-```yml
-volumes:
-    langgraph-data:
-        driver: local
-services:
-    langgraph-redis:
-        image: redis:6
-        healthcheck:
-            test: redis-cli ping
-            interval: 5s
-            timeout: 1s
-            retries: 5
-    langgraph-postgres:
-        image: postgres:16
-        ports:
-            - "5433:5432"
-        environment:
-            POSTGRES_DB: postgres
-            POSTGRES_USER: postgres
-            POSTGRES_PASSWORD: postgres
-        volumes:
-            - langgraph-data:/var/lib/postgresql/data
-        healthcheck:
-            test: pg_isready -U postgres
-            start_period: 10s
-            timeout: 1s
-            retries: 5
-            interval: 5s
-    langgraph-api:
-        image: ${IMAGE_NAME}
-        ports:
-            - "8123:8000"
-        depends_on:
-            langgraph-redis:
-                condition: service_healthy
-            langgraph-postgres:
-                condition: service_healthy
-        env_file:
-            - .env
-        environment:
-            REDIS_URI: redis://langgraph-redis:6379
-            LANGSMITH_API_KEY: ${LANGSMITH_API_KEY}
-            POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable
-```
-
-You can run the command `docker compose up` with this Docker Compose file in the same folder.
-
-This will launch a LangGraph Server on port `8123` (if you want to change this, you can change this by changing the ports in the `langgraph-api` volume). You can test if the application is healthy by running:
-
-```shell
-curl --request GET --url 0.0.0.0:8123/ok
-```
-Assuming everything is running correctly, you should see a response like:
-
-```shell
-{"ok":true}
-```

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

@@ -1,31 +0,0 @@
-# Testing local agents with remote traces
-
-## Overview
-
-A common workflow when debugging production-deployed agents is to test the same thread against a local version of the same agent, which may have modifications.
-
-To support this, LangGraph Studio, in combination with LangSmith, allows you to clone remote threads traced in LangSmith into your locally running agent. This cloned thread can then be used to re-run specific nodes within Studio.
-
-## Requirements
-
-!!! info "Prerequisites"
-
-    - langgraph>=0.3.18
-    - langgraph-api>=0.0.32
-
-- A thread traced in LangSmith.
-- A locally running agent. See [here](../../how-tos/local-studio.md) for setup instructions.
-  - Note that your local agent must be using the above specified `langgraph` and `langgraph-api` versions.
-  - The nodes present in the remote trace must exist in at least one of the graphs in your local agent.
-
-## Cloning Thread
-
-First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".
-
-![Run in Studio](../img/run_in_studio.png){width=1200}
-
-This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.
-
-Once selected, a will a new thread in your local agent will be created and the thread history will be reconstruced to reflect the original trace.
-
-Alternatively, if your trace originates from an agent deployed on LangGraph Platform, you can "View original thread" to open Studio with the actual deployed thread.

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

@@ -1,366 +0,0 @@
-# How to implement Generative User Interfaces with LangGraph
-
-!!! info "Prerequisites"
-
-    - [LangGraph Platform](../../concepts/langgraph_platform.md)
-    - [LangGraph Server](../../concepts/langgraph_server.md)
-    - [`useStream()` React Hook](./use_stream_react.md)
-
-Generative user interfaces (Generative UI) allows agents to go beyond text and generate rich user interfaces. This enables creating more interactive and context-aware applications where the UI adapts based on the conversation flow and AI responses.
-
-![Generative UI Sample](./img/generative_ui_sample.jpg)
-
-LangGraph Platform supports colocating your React components with your graph code. This allows you to focus on building specific UI components for your graph while easily plugging into existing chat interfaces such as [Agent Chat](https://agentchat.vercel.app) and loading the code only when actually needed.
-
-## Tutorial
-
-### 1. Define and configure UI components
-
-First, create your first UI component. For each component you need to provide an unique identifier that will be used to reference the component in your graph code.
-
-```tsx title="src/agent/ui.tsx"
-const WeatherComponent = (props: { city: string }) => {
-  return <div>Weather for {props.city}</div>;
-};
-
-export default {
-  weather: WeatherComponent,
-};
-```
-
-Next, define your UI components in your `langgraph.json` configuration:
-
-```json
-{
-  "node_version": "20",
-  "graphs": {
-    "agent": "./src/agent/index.ts:graph"
-  },
-  "ui": {
-    "agent": "./src/agent/ui.tsx"
-  }
-}
-```
-
-The `ui` section points to the UI components that will be used by graphs. By default, we recommend using the same key as the graph name, but you can split out the components however you like, see [Customise the namespace of UI components](#customise-the-namespace-of-ui-components) for more details.
-
-LangGraph Platform will automatically bundle your UI components code and styles and serve them as external assets that can be loaded by the `LoadExternalComponent` component. Some dependencies such as `react` and `react-dom` will be automatically excluded from the bundle.
-
-CSS and Tailwind 4.x is also supported out of the box, so you can freely use Tailwind classes as well as `shadcn/ui` in your UI components.
-
-=== "`src/agent/ui.tsx`"
-
-    ```tsx
-    import "./styles.css";
-
-    const WeatherComponent = (props: { city: string }) => {
-      return <div className="bg-red-500">Weather for {props.city}</div>;
-    };
-
-    export default {
-      weather: WeatherComponent,
-    };
-    ```
-
-=== "`src/agent/styles.css`"
-
-    ```css
-    @import "tailwindcss";
-    ```
-
-### 2. Send the UI components in your graph
-
-=== "Python"
-
-    ```python title="src/agent.py"
-    import uuid
-    from typing import Annotated, Sequence, TypedDict
-
-    from langchain_core.messages import AIMessage, BaseMessage
-    from langchain_openai import ChatOpenAI
-    from langgraph.graph import StateGraph
-    from langgraph.graph.message import add_messages
-    from langgraph.graph.ui import AnyUIMessage, ui_message_reducer, push_ui_message
-
-
-    class AgentState(TypedDict):  # noqa: D101
-        messages: Annotated[Sequence[BaseMessage], add_messages]
-        ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
-
-
-    async def weather(state: AgentState):
-        class WeatherOutput(TypedDict):
-            city: str
-
-        weather: WeatherOutput = (
-            await ChatOpenAI(model="gpt-4o-mini")
-            .with_structured_output(WeatherOutput)
-            .with_config({"tags": ["nostream"]})
-            .ainvoke(state["messages"])
-        )
-
-        message = AIMessage(
-            id=str(uuid.uuid4()),
-            content=f"Here's the weather for {weather['city']}",
-        )
-
-        # Emit UI elements associated with the message
-        push_ui_message("weather", weather, message=message)
-        return {"messages": [message]}
-
-
-    workflow = StateGraph(AgentState)
-    workflow.add_node(weather)
-    workflow.add_edge("__start__", "weather")
-    graph = workflow.compile()
-    ```
-
-=== "JS"
-
-    Use the `typedUi` utility to emit UI elements from your agent nodes:
-
-    ```typescript title="src/agent/index.ts"
-    import {
-      typedUi,
-      uiMessageReducer,
-    } from "@langchain/langgraph-sdk/react-ui/server";
-
-    import { ChatOpenAI } from "@langchain/openai";
-    import { v4 as uuidv4 } from "uuid";
-    import { z } from "zod";
-
-    import type ComponentMap from "./ui.js";
-
-    import {
-      Annotation,
-      MessagesAnnotation,
-      StateGraph,
-      type LangGraphRunnableConfig,
-    } from "@langchain/langgraph";
-
-    const AgentState = Annotation.Root({
-      ...MessagesAnnotation.spec,
-      ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
-    });
-
-    export const graph = new StateGraph(AgentState)
-      .addNode("weather", async (state, config) => {
-        // Provide the type of the component map to ensure
-        // type safety of `ui.push()` calls as well as
-        // pushing the messages to the `ui` and sending a custom event as well.
-        const ui = typedUi<typeof ComponentMap>(config);
-
-        const weather = await new ChatOpenAI({ model: "gpt-4o-mini" })
-          .withStructuredOutput(z.object({ city: z.string() }))
-          .withConfig({ tags: ["nostream"] })
-          .invoke(state.messages);
-
-        const response = {
-          id: uuidv4(),
-          type: "ai",
-          content: `Here's the weather for ${weather.city}`,
-        };
-
-        // Emit UI elements associated with the AI message
-        ui.push({ name: "weather", props: weather }, { message: response });
-
-        return { messages: [response] };
-      })
-      .addEdge("__start__", "weather")
-      .compile();
-    ```
-
-### 3. Handle UI elements in your React application
-
-On the client side, you can use `useStream()` and `LoadExternalComponent` to display the UI elements.
-
-```tsx title="src/app/page.tsx"
-"use client";
-
-import { useStream } from "@langchain/langgraph-sdk/react";
-import { LoadExternalComponent } from "@langchain/langgraph-sdk/react-ui";
-
-export default function Page() {
-  const { thread, values } = useStream({
-    apiUrl: "http://localhost:2024",
-    assistantId: "agent",
-  });
-
-  return (
-    <div>
-      {thread.messages.map((message) => (
-        <div key={message.id}>
-          {message.content}
-          {values.ui
-            ?.filter((ui) => ui.metadata?.message_id === message.id)
-            .map((ui) => (
-              <LoadExternalComponent key={ui.id} stream={thread} message={ui} />
-            ))}
-        </div>
-      ))}
-    </div>
-  );
-}
-```
-
-Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI components from LangGraph Platform and render them in a shadow DOM, thus ensuring style isolation from the rest of your application.
-
-## How-to guides
-
-### Show loading UI when components are loading
-
-You can provide a fallback UI to be rendered when the components are loading.
-
-```tsx
-<LoadExternalComponent
-  stream={thread}
-  message={ui}
-  fallback={<div>Loading...</div>}
-/>
-```
-
-### Provide custom components on the client side
-
-If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangGraph Platform.
-
-```tsx
-const clientComponents = {
-  weather: WeatherComponent,
-};
-
-<LoadExternalComponent
-  stream={thread}
-  message={ui}
-  components={clientComponents}
-/>;
-```
-
-### Customise the namespace of UI components.
-
-By default `LoadExternalComponent` will use the `assistantId` from `useStream()` hook to fetch the code for UI components. You can customise this by providing a `namespace` prop to the `LoadExternalComponent` component.
-
-=== "`src/app/page.tsx`"
-
-    ```tsx
-    <LoadExternalComponent
-      stream={thread}
-      message={ui}
-      namespace="custom-namespace"
-    />
-    ```
-
-=== "`langgraph.json`"
-
-    ```json
-    {
-      "ui": {
-        "custom-namespace": "./src/agent/ui.tsx"
-      }
-    }
-    ```
-
-### Access and interact with the thread state from the UI component
-
-You can access the thread state inside the UI component by using the `useStreamContext` hook.
-
-```tsx
-import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";
-
-const WeatherComponent = (props: { city: string }) => {
-  const { thread, submit } = useStreamContext();
-  return (
-    <>
-      <div>Weather for {props.city}</div>
-
-      <button
-        onClick={() => {
-          const newMessage = {
-            type: "human",
-            content: `What's the weather in ${props.city}?`,
-          };
-
-          submit({ messages: [newMessage] });
-        }}
-      >
-        Retry
-      </button>
-    </>
-  );
-};
-```
-
-### Pass additional context to the client components
-
-You can pass additional context to the client components by providing a `meta` prop to the `LoadExternalComponent` component.
-
-```tsx
-<LoadExternalComponent stream={thread} message={ui} meta={{ userId: "123" }} />
-```
-
-Then, you can access the `meta` prop in the UI component by using the `useStreamContext` hook.
-
-```tsx
-import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";
-
-const WeatherComponent = (props: { city: string }) => {
-  const { meta } = useStreamContext<
-    { city: string },
-    { MetaType: { userId?: string } }
-  >();
-
-  return (
-    <div>
-      Weather for {props.city} (user: {meta?.userId})
-    </div>
-  );
-};
-```
-
-### Streaming UI updates before the node execution is finished
-
-You can stream UI updates before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook.
-
-```tsx
-import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";
-
-const { thread, submit } = useStream({
-  apiUrl: "http://localhost:2024",
-  assistantId: "agent",
-  onCustomEvent: (event, options) => {
-    options.mutate((prev) => {
-      const ui = uiMessageReducer(prev.ui ?? [], event);
-      return { ...prev, ui };
-    });
-  },
-});
-```
-
-### Remove UI messages from state
-
-Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `remove_ui_message` / `ui.delete` with the ID of the UI message.
-
-=== "Python"
-
-    ```python
-    from langgraph.graph.ui import push_ui_message, delete_ui_message
-
-    # push message
-    message = push_ui_message("weather", {"city": "London"})
-
-    # remove said message
-    delete_ui_message(message["id"])
-    ```
-
-=== "JS"
-
-    ```tsx
-    // push message
-    const message = ui.push({ name: "weather", props: { city: "London" } });
-
-    // remove said message
-    ui.delete(message.id);
-    ```
-
-## Learn more
-
-- [JS/TS SDK Reference](../reference/sdk/js_ts_sdk_ref.md)

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

@@ -63,7 +63,7 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
         "messages": [
             {
                 "role": "user",
-                "content": "Ask the user where they are, then look up the weather there",
+                "content": "Use the search tool to ask the user where they are, then look up the weather there",
             }
         ]
     }
@@ -85,7 +85,8 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
       messages: [
         {
           role: "human",
-          content: "Ask the user where they are, then look up the weather there" }
+          content: "Use the search tool to ask the user where they are, then look up the weather there"
+        }
       ]
     };
 
@@ -114,7 +115,7 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Ask the user where they are, then look up the weather there\"}]},
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Use the search tool to ask the user where they are, then look up the weather there\"}]},
        \"interrupt_before\": [\"ask_human\"],
        \"stream_mode\": [
          \"updates\"