Updates

.github/workflows/codespell.yml

@@ -9,11 +9,7 @@
     
     permissions:
       contents: read
-
-    defaults:
-      run:
-        working-directory: docs
-
+    
     jobs:
       codespell:
         name: (Check for spelling errors)
@@ -30,7 +26,7 @@
           - 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

.github/workflows/deploy_docs.yml

@@ -21,10 +21,6 @@ concurrency:
   group: "pages"
   cancel-in-progress: false
 
-defaults:
-  run:
-    working-directory: docs
-
 jobs:
   get-changed-files:
     runs-on: ubuntu-latest
@@ -77,12 +73,11 @@ jobs:
         # 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: make build-docs
         env:
           MKDOCS_GIT_COMMITTERS_APIKEY: ${{ secrets.MKDOCS_GIT_COMMITTERS_APIKEY }}
+
       - name: Check links in notebooks
         env:
           LANGCHAIN_API_KEY: test
@@ -101,13 +96,13 @@ jobs:
               --check-links-ignore "https://python\.langchain\.com/.*" \
               --check-links-ignore "https://openai\.com/.*" \
               --check-links-ignore "https://pepy\.tech/.*" \
-              --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..."

.github/workflows/extract_ignored_words_list.py

@@ -1,6 +1,6 @@
 import toml
 
-pyproject_toml = toml.load("../libs/langgraph/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/run_notebooks.yml

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

.gitignore

@@ -178,4 +178,4 @@ Untitled*.ipynb
 
 Chinook.db
 
-.vercel
+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

@@ -21,7 +21,10 @@ LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [A
 
 ### Why use LangGraph?
 
-LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
+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
@@ -330,10 +333,6 @@ Then we define one normal and one conditional edge. Conditional edge means that
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
 * [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
-## Resources
-
-* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
-
 ## Contributing
 
 For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

docs/.gitignore

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

docs/Makefile

@@ -1,50 +0,0 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text
-
-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-docs: build-typedoc
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
-
-llms-text:
-	poetry run python _scripts/generate_llms_text.py docs/llms-full.txt
-
-install-vercel-deps:
-	curl -sSL https://install.python-poetry.org | python3 -
-	poetry self update 1.8.5
-
-vercel-build-docs: install-vercel-deps
-	poetry install
-	make build-docs
-
-serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
-
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
-
-clean-docs:
-	find ./docs -name "*.ipynb" -type f -delete
-	rm -rf site
-
-## Run format against the project documentation.
-format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
-
-# Check the docs for linting violations
-lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
-
-codespell:
-	./codespell_notebooks.sh .
-
-start-services:
-	docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
-
-stop-services:
-	docker compose -f test-compose.yml down

docs/README.md

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

docs/_scripts/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_llms_text.py

@@ -1,91 +0,0 @@
-"""Experimental script to generate consolidated llms text from the docs."""
-
-import glob
-import os
-import pathlib
-
-from mkdocs.structure.files import File
-from mkdocs.structure.pages import Page
-
-from 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

@@ -22,8 +22,6 @@ class EscapePreprocessor(Preprocessor):
             )
 
         elif cell.cell_type == "code":
-            # Remove noqa comments
-            cell.source = re.sub(r'#\s*noqa.*$', '', cell.source, flags=re.MULTILINE)
             # escape ``` in code
             cell.source = cell.source.replace("```", r"\`\`\`")
             # escape ``` in output

docs/_scripts/notebook_hooks.py

@@ -5,10 +5,9 @@ from typing import Any, Dict
 
 from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
-import posixpath
 
-from generate_api_reference_links import update_markdown_with_imports
 from notebook_convert import convert_notebook
+from generate_api_reference_links import update_markdown_with_imports
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -16,24 +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",
-}
-
-
 class NotebookFile(File):
     def is_documentation_page(self):
         return True
@@ -125,14 +106,7 @@ def _highlight_code_blocks(markdown: str) -> str:
     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"):
@@ -140,76 +114,7 @@ def _on_page_markdown_with_config(
         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)
+    markdown = update_markdown_with_imports(markdown)
     # Apply highlight comments to code blocks
     markdown = _highlight_code_blocks(markdown)
-
-    if remove_base64_images:
-        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/[^;]+;base64,[^\)]+\)", "", markdown)
-
     return markdown
-
-
-def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
-    )
-
-# redirects
-
-HTML_TEMPLATE = """
-<!doctype html>
-<html lang="en">
-<head>
-    <meta charset="utf-8">
-    <title>Redirecting...</title>
-    <link rel="canonical" href="{url}">
-    <meta name="robots" content="noindex">
-    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
-    <meta http-equiv="refresh" content="0; url={url}">
-</head>
-<body>
-Redirecting...
-</body>
-</html>
-"""
-
-
-def write_html(site_dir, old_path, new_path):
-    """Write an HTML file in the site_dir with a meta redirect to the new page"""
-    # Determine all relevant paths
-    old_path_abs = os.path.join(site_dir, old_path)
-    old_dir_abs = os.path.dirname(old_path_abs)
-
-    # Create parent directories if they don't exist
-    if not os.path.exists(old_dir_abs):
-        os.makedirs(old_dir_abs)
-
-    # Write the HTML redirect file in place of the old file
-    content = HTML_TEMPLATE.format(url=new_path)
-    with open(old_path_abs, "w", encoding="utf-8") as f:
-        f.write(content)
-
-
-# Create HTML files for redirects after site dir has been built
-def on_post_build(config):
-    use_directory_urls = config.get("use_directory_urls")
-    for page_old, page_new in REDIRECT_MAP.items():
-        page_old = page_old.replace(".ipynb", ".md")
-        page_new = page_new.replace(".ipynb", ".md")
-        page_new_before_hash, hash, suffix = page_new.partition("#")
-        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
-            os.sep, "/"
-        )
-        new_html_path = File(page_new_before_hash, "", "", True).url
-        new_html_path = (
-            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-            + hash
-            + suffix
-        )
-        write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -7,7 +7,7 @@ import click
 import nbformat
 
 logger = logging.getLogger(__name__)
-NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
+NOTEBOOK_DIRS = ("docs/docs/how-tos","docs/docs/tutorials")
 DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
 
@@ -19,37 +19,36 @@ BLOCKLIST_COMMANDS = (
 )
 
 NOTEBOOKS_NO_CASSETTES = (
-    "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/many-tools.ipynb"
+    "docs/docs/how-tos/visualization.ipynb",
+    "docs/docs/how-tos/many-tools.ipynb"
 )
 
 NOTEBOOKS_NO_EXECUTION = [
     # this uses a user provided project name for langsmith
-    "docs/tutorials/tnt-llm/tnt-llm.ipynb",
+    "docs/docs/tutorials/tnt-llm/tnt-llm.ipynb",
     # this uses langsmith datasets
-    "docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
+    "docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
     # this uses browser APIs
-    "docs/tutorials/web-navigation/web_voyager.ipynb",
+    "docs/docs/tutorials/web-navigation/web_voyager.ipynb",
     # these RAG guides use an ollama model
-    "docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
-    "docs/tutorials/rag/langgraph_crag_local.ipynb",
-    "docs/tutorials/rag/langgraph_self_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_crag_local.ipynb",
+    "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
-    "docs/tutorials/usaco/usaco.ipynb",
+    "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/tutorials/llm-compiler/LLMCompiler.ipynb"
+    "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
+    "docs/docs/tutorials/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"
 ]
 
 
@@ -217,7 +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

@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-"""Create the third party page for the documentation."""
-
-import argparse
-from typing import List
-from typing import TypedDict
-
-import yaml
-
-MARKDOWN = """\
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# 🚀 Prebuilt Libraries
-
-LangGraph includes a prebuilt React agent. For more information on how to use it, 
-check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
-
-If you’re looking for other prebuilt libraries, explore the community-built options 
-below. These libraries can extend LangGraph's functionality in various ways.
-
-## 📚 Available Libraries
-
-{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 |",
-        "| --- | --- | --- | --- |",
-    ]
-    for package in sorted_packages:
-        name = f"**{package['name']}**"
-        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
-        downloads = package["weekly_downloads"] or 0
-        row = f"| {name} | {repo_url} | {package['description']} | {downloads} |"
-        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

@@ -1,95 +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]) -> list[ResolvedPackage]:
-    """Retrieve the monthly download count for a list of packages from PyPIStats."""
-    resolved_packages: list[ResolvedPackage] = []
-
-    for package in packages:
-        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])
-
-        resolved_packages.append(
-            {
-                "name": package["name"],
-                "repo": package["repo"],
-                "weekly_downloads": num_downloads,
-                "description": package["description"],
-            }
-        )
-
-    return resolved_packages
-
-
-
-def main(output_file: str) -> None:
-    """Main function to generate package download information.
-
-    Args:
-        output_file: Path to the output YAML file.
-    """
-    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES)
-
-    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"
-        ),
-    )
-    args = parser.parse_args()
-
-    main(args.output_file)

docs/_scripts/third_party_page/packages.yml

@@ -1,5 +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"

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

@@ -0,0 +1 @@
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

docs/cassettes/stream-updates_e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1.msgpack.zlib

@@ -0,0 +1 @@
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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-tokens_e977406d-7be6-4c9f-9185-5e5551f848f3.msgpack.zlib

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docs/cassettes/streaming-tokens_fdeee9d9-2625-403a-9253-418a0feeed77.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/streaming_c251f809-8922-46ea-bd5b-18264fcc523a.msgpack.zlib

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docs/docs/cloud/quick_start.md

@@ -90,7 +90,7 @@ For this guide, we'll use the pre-built Python [**ReAct Agent**](https://github.
     </figure>
 
 
-## LangGraph Studio Web UI
+## Lagraph Studio Web UI
 
 Once your application is deployed, you can test it in **LangGraph Studio**. 
 

docs/docs/concepts/deployment_options.md

@@ -51,7 +51,7 @@ For more information, please see:
 
     The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
 
-The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
+The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
 
 With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
 

docs/docs/concepts/functional_api.md

@@ -5,21 +5,11 @@
 
 ## Overview
 
-The **Functional API** allows you to add LangGraph's key features -- [persistence](./persistence.md), [memory](./memory.md), [human-in-the-loop](./human_in_the_loop.md), and [streaming](./streaming.md) — to your applications with minimal changes to your existing code.
+The Functional API is an alternative to [Graph API (StateGraph)](low_level.md#stategraph) for development in LangGraph. 
 
-It is designed to integrate these features into existing code that may use standard language primitives for branching and control flow, such as `if` statements, `for` loops, and function calls. Unlike many data orchestration frameworks that require restructuring code into an explicit pipeline or DAG, the Functional API allows you to incorporate these capabilities without enforcing a rigid execution model.  
+If modeling your application with explicit nodes and edges is not useful to you, the Functional API allows you to take advantage of LangGraph's key features for [persistence](persistence.md), [human-in-the-loop](human_in_the_loop.md) workflows, and [streaming](streaming.md) without explicitly specifying state, or control flow in terms of nodes and edges.
 
-The Functional API uses two key building blocks:  
-
-- **`@entrypoint`** – Marks a function as the starting point of a workflow, encapsulating logic and managing execution flow, including handling long-running tasks and interrupts.  
-- **`@task`** – Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously within an entrypoint. Tasks return a future-like object that can be awaited or resolved synchronously.  
-
-This provides a minimal abstraction for building workflows with state management and streaming.
-
-!!! tip 
-
-    For users who prefer a more declarative approach, LangGraph's [Graph API](./low_level.md) allows you to define workflows using a Graph paradigm. Both APIs share the same underlying runtime, so you can use them together in the same application.
-    Please see the [Functional API vs. Graph API](#functional-api-vs-graph-api) section for a comparison of the two paradigms.
+The **Functional API** and the **[Graph API](./low_level.md)** can be used together in the same application, allowing you to intermix the two paradigms if needed.
 
 ## Example
 
@@ -130,6 +120,22 @@ def workflow(topic: str) -> dict:
 
     The workflow has been completed and the review has been added to the essay.
 
+## Functional API vs. Graph API
+
+The **Functional API** and the [Graph APIs (StateGraph)](./low_level.md#stategraph) provide two different paradigms to create in LangGraph. Here are some key differences:
+
+- **Control flow**: The Functional API does not require thinking about graph structure. You can use standard Python constructs to define workflows. This will usually trim the amount of code you need to write.
+- **State management**: The **GraphAPI** requires declaring a [**State**](./low_level.md#state) and may require defining [**reducers**](./low_level.md#reducers) to manage updates to the graph state. `@entrypoint` and `@tasks` do not require explicit state management as their state is scoped to the function and is not shared across functions.
+- **Checkpointing**: Both APIs generate and use checkpoints. In the **Graph API** a new checkpoint is generated after every [superstep](./low_level.md). In the **Functional API**, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint.
+- **Visualization**: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime.
+
+## Building Blocks
+
+The **Functional API** provides two primitives for building workflows:
+
+- **[Entrypoint](#entrypoint)**: An **entrypoint** is a decorator that designates a function as the starting point of a workflow. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](human_in_the_loop.md).
+- **[Task](#task)**: Represents a discrete unit of work, such as an API call or data processing step, that can be executed asynchronously from within an **entrypoint**. Invoking a **task** returns a future-like object, which can be awaited to obtain the result or resolved synchronously.
+
 ## Entrypoint
 
 The [`@entrypoint`][langgraph.func.entrypoint] decorator can be used to create a workflow from a function. It encapsulates workflow logic and manages execution flow, including handling *long-running tasks* and [interrupts](./low_level.md#interrupt).
@@ -140,7 +146,7 @@ An **entrypoint** is defined by decorating a function with the `@entrypoint` dec
 
 The function **must accept a single positional argument**, which serves as the workflow input. If you need to pass multiple pieces of data, use a dictionary as the input type for the first argument.
 
-Decorating a function with an `entrypoint` produces a [`Pregel`][langgraph.pregel.Pregel.stream] instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
+Decorating a function with an `entrypoint` produces a Pregel instance which helps to manage the execution of the workflow (e.g., handles streaming, resumption, and checkpointing).
 
 You will usually want to pass a **checkpointer** to the `@entrypoint` decorator to enable persistence and use features like **human-in-the-loop**.
 
@@ -217,7 +223,7 @@ When declaring an `entrypoint`, you can request access to additional parameters
 
 ### Executing
 
-Using the [`@entrypoint`](#entrypoint) yields a [`Pregel`][langgraph.pregel.Pregel.stream] object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods.
+Using the [`@entrypoint`](#entrypoint) yields a Pregel object that can be executed using the `invoke`, `ainvoke`, `stream`, and `astream` methods.
 
 === "Invoke"
 
@@ -528,14 +534,6 @@ While different runs of a workflow can produce different results, resuming a **s
 
 Idempotency ensures that running the same operation multiple times produces the same result. This helps prevent duplicate API calls and redundant processing if a step is rerun due to a failure. Always place API calls inside **tasks** functions for checkpointing, and design them to be idempotent in case of re-execution. Re-execution can occur if a **task** starts, but does not complete successfully. Then, if the workflow is resumed, the **task** will run again. Use idempotency keys or verify existing results to avoid duplication.
 
-## Functional API vs. Graph API
-
-The **Functional API** and the [Graph APIs (StateGraph)](./low_level.md#stategraph) provide two different paradigms to create applications with LangGraph. Here are some key differences:
-
-- **Control flow**: The Functional API does not require thinking about graph structure. You can use standard Python constructs to define workflows. This will usually trim the amount of code you need to write.
-- **State management**: The **GraphAPI** requires declaring a [**State**](./low_level.md#state) and may require defining [**reducers**](./low_level.md#reducers) to manage updates to the graph state. `@entrypoint` and `@tasks` do not require explicit state management as their state is scoped to the function and is not shared across functions.
-- **Checkpointing**: Both APIs generate and use checkpoints. In the **Graph API** a new checkpoint is generated after every [superstep](./low_level.md). In the **Functional API**, when tasks are executed, their results are saved to an existing checkpoint associated with the given entrypoint instead of creating a new checkpoint.
-- **Visualization**: The Graph API makes it easy to visualize the workflow as a graph which can be useful for debugging, understanding the workflow, and sharing with others. The Functional API does not support visualization as the graph is dynamically generated during runtime.
 
 ## Common Pitfalls
 

docs/docs/concepts/high_level.md

@@ -19,7 +19,7 @@ LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/con
 
 ### Streaming
 
-LangGraph also provides support for [streaming](../how-tos/index.md#streaming) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](../how-tos/streaming.ipynb#updates)) and [tokens from LLM calls](../how-tos/streaming-tokens.ipynb) embedded in an application.
+LangGraph also provides support for [streaming](../how-tos/index.md#streaming) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](../how-tos/stream-updates.ipynb)) and [tokens from LLM calls](../how-tos/streaming-tokens.ipynb) embedded in an application.
 
 ### Debugging and Deployment
 

docs/docs/concepts/index.md

@@ -72,7 +72,7 @@ The LangGraph Platform comprises several components that work together to suppor
 ### Deployment Options
 
 
-- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
+- [Self-Hosted Lite](./self_hosted.md): A free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
 - [Cloud SaaS](./langgraph_cloud.md): Hosted as part of LangSmith.
 - [Bring Your Own Cloud](./bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
 - [Self-Hosted Enterprise](./self_hosted.md): Completely managed by you.

docs/docs/concepts/langgraph_cloud.md

@@ -21,12 +21,6 @@ Resource Allocation:
 
 See the [how-to guide](../cloud/deployment/cloud.md#create-new-deployment) for creating a new deployment.
 
-## Revision
-
-A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
-
-See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
-
 ## Persistence
 
 A dedicated database is automatically created for each deployment. The database serves as the [persistence layer](../concepts/persistence.md) for the deployment.
@@ -47,6 +41,12 @@ Scale down actions are delayed for 30 minutes before any action is taken. In oth
 
 In the future, the autoscaling implementation may evolve to accommodate other metrics such as background run queue size.
 
+## Revision
+
+A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
+
+See the [how-to guide](../cloud/deployment/cloud.md#create-new-revision) for creating a new revision.
+
 ## Asynchronous Deployment
 
 Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
@@ -55,12 +55,6 @@ Infrastructure for [deployments](#deployment) and [revisions](#revision) are pro
 - When a subsequent revision is created for a deployment, there is no database creation step. The deployment time for a subsequent revision is significantly faster compared to the deployment time of the initial revision.
 - The deployment process for each revision contains a build step, which can take up to a few minutes.
 
-## LangSmith Integration
-
-A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployemnt. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING_V2` and `LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set internally, automatically. Traces are created for each run and are emitted to the tracing project automatically.
-
-When a deployment is deleted, the traces and the tracing project are not deleted.
-
 ## Automatic Deletion
 
 Deployments are automatically deleted after 28 consecutive days of non-use (it is in an unused state). A deployment is in an unused state if there are no traces emitted to LangSmith from the deployment after 28 consecutive days. On any given day, if a deployment emits a trace to LangSmith, the counter for consecutive days of non-use is reset.

docs/docs/concepts/low_level.md

@@ -22,6 +22,10 @@ A super-step can be considered a single iteration over the graph nodes. Nodes th
 
 The `StateGraph` class is the main graph class to use. This is parameterized by a user defined `State` object.
 
+### MessageGraph
+
+The `MessageGraph` class is a special type of graph. The `State` of a `MessageGraph` is ONLY a list of messages. This class is rarely used except for chatbots, as most applications require the `State` to be more complex than a list of messages.
+
 ### Compiling your graph
 
 To build your graph, you first define the [state](#state), you then add [nodes](#nodes) and [edges](#edges), and then you compile it. What exactly is compiling your graph and why is it needed?
@@ -372,10 +376,6 @@ def my_node(state: State) -> Command[Literal["my_other_node"]]:
 
     Setting `graph` to `Command.PARENT` will navigate to the closest parent graph.
 
-!!! important "State updates with `Command.PARENT`"
-
-    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](#schema), you **must** define a [reducer](#reducers) for the key you're updating in the parent graph state. See this [example](../how-tos/command.ipynb#navigating-to-a-node-in-a-parent-graph).
-
 This is particularly useful when implementing [multi-agent handoffs](./multi_agent.md#handoffs).
 
 ### Using inside tools

docs/docs/concepts/multi_agent.md

@@ -241,7 +241,7 @@ To address this, you can design your system _hierarchically_. For example, you c
 from typing import Literal
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, MessagesState, START, END
-from langgraph.types import Command
+
 model = ChatOpenAI()
 
 # define team 1 (same as the single supervisor example above)
@@ -286,7 +286,7 @@ team_2_graph = team_2_builder.compile()
 # define top-level supervisor
 
 builder = StateGraph(MessagesState)
-def top_level_supervisor(state: MessagesState) -> Command[Literal["team_1_graph", "team_2_graph", END]]:
+def top_level_supervisor(state: MessagesState):
     # you can pass relevant parts of the state to the LLM (e.g., state["messages"])
     # to determine which team to call next. a common pattern is to call the model
     # with a structured output (e.g. force it to return an output with a "next_team" field)
@@ -297,11 +297,10 @@ def top_level_supervisor(state: MessagesState) -> Command[Literal["team_1_graph"
 
 builder = StateGraph(MessagesState)
 builder.add_node(top_level_supervisor)
-builder.add_node("team_1_graph", team_1_graph)
-builder.add_node("team_2_graph", team_2_graph)
+builder.add_node(team_1_graph)
+builder.add_node(team_2_graph)
+
 builder.add_edge(START, "top_level_supervisor")
-builder.add_edge("team_1_graph", "top_level_supervisor")
-builder.add_edge("team_2_graph", "top_level_supervisor")
 graph = builder.compile()
 ```
 

docs/docs/concepts/persistence.md

@@ -433,7 +433,7 @@ See the [deployment guide](../cloud/deployment/semantic_search.md) for more deta
 
 Under the hood, checkpointing is powered by checkpointer objects that conform to [BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver] interface. LangGraph provides several checkpointer implementations, all implemented via standalone, installable libraries:
 
-* `langgraph-checkpoint`: The base interface for checkpointer savers ([BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver]) and serialization/deserialization interface ([SerializerProtocol][langgraph.checkpoint.serde.base.SerializerProtocol]). Includes in-memory checkpointer implementation ([InMemorySaver][langgraph.checkpoint.memory.InMemorySaver]) for experimentation. LangGraph comes with `langgraph-checkpoint` included.
+* `langgraph-checkpoint`: The base interface for checkpointer savers ([BaseCheckpointSaver][langgraph.checkpoint.base.BaseCheckpointSaver]) and serialization/deserialization interface ([SerializerProtocol][langgraph.checkpoint.serde.base.SerializerProtocol]). Includes in-memory checkpointer implementation ([MemorySaver][langgraph.checkpoint.memory.MemorySaver]) for experimentation. LangGraph comes with `langgraph-checkpoint` included.
 * `langgraph-checkpoint-sqlite`: An implementation of LangGraph checkpointer that uses SQLite database ([SqliteSaver][langgraph.checkpoint.sqlite.SqliteSaver] / [AsyncSqliteSaver][langgraph.checkpoint.sqlite.aio.AsyncSqliteSaver]). Ideal for experimentation and local workflows. Needs to be installed separately.
 * `langgraph-checkpoint-postgres`: An advanced checkpointer that uses Postgres database ([PostgresSaver][langgraph.checkpoint.postgres.PostgresSaver] / [AsyncPostgresSaver][langgraph.checkpoint.postgres.aio.AsyncPostgresSaver]), used in LangGraph Cloud. Ideal for using in production. Needs to be installed separately.
 

docs/docs/concepts/plans.md

@@ -22,7 +22,7 @@ There are three different plans for using it.
 | Real-time streaming of outputs and intermediate steps            | ✅                                           | ✅                                                     | ✅                                                   |
 | Assistants API (configurable templates for LangGraph apps)       | ✅                                           | ✅                                                     | ✅                                                   |
 | Cron scheduling                                                  | --                                          | ✅                                                     | ✅                                                   |
-| LangGraph Studio for prototyping                                 | 	✅                                         | ✅                                                    | ✅                                                  |
+| LangGraph Studio for prototyping                                 | Desktop only                                | Coming Soon!                                          | Coming Soon!                                        |
 | Authentication & authorization to call the LangGraph APIs        | --                                          | Coming Soon!                                          | Coming Soon!                                        |
 | Smart caching to reduce traffic to LLM API                       | --                                          | Coming Soon!                                          | Coming Soon!                                        |
 | Publish/subscribe API for state                                  | --                                          | Coming Soon!                                          | Coming Soon!                                        |

docs/docs/concepts/self_hosted.md

@@ -11,7 +11,7 @@ There are two versions of the self-hosted deployment: [Self-Hosted Enterprise](.
 
 ### Self-Hosted Lite
 
-The Self-Hosted Lite version is a limited version of LangGraph Platform that you can run locally or in a self-hosted manner (up to 1 million nodes executed per year).
+The Self-Hosted Lite version is a limited version of LangGraph Platform that you can run locally or in a self-hosted manner (up to 1 million nodes executed).
 
 When using the Self-Hosted Lite version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.
 

docs/docs/concepts/streaming.md

@@ -7,11 +7,11 @@ LangGraph is built with first class support for streaming. There are several dif
 `.stream` and `.astream` are sync and async methods for streaming back outputs from a graph run.
 There are several different modes you can specify when calling these methods (e.g. `graph.stream(..., mode="...")):
 
-- [`"values"`](../how-tos/streaming.ipynb#values): This streams the full value of the state after each step of the graph.
-- [`"updates"`](../how-tos/streaming.ipynb#updates): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
-- [`"custom"`](../how-tos/streaming.ipynb#custom): This streams custom data from inside your graph nodes.
+- [`"values"`](../how-tos/stream-values.ipynb): This streams the full value of the state after each step of the graph.
+- [`"updates"`](../how-tos/stream-updates.ipynb): This streams the updates to the state after each step of the graph. If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are streamed separately.
+- [`"custom"`](../how-tos/streaming-content.ipynb): This streams custom data from inside your graph nodes.
 - [`"messages"`](../how-tos/streaming-tokens.ipynb): This streams LLM tokens and metadata for the graph node where LLM is invoked.
-- [`"debug"`](../how-tos/streaming.ipynb#debug): This streams as much information as possible throughout the execution of the graph.
+- `"debug"`: This streams as much information as possible throughout the execution of the graph.
 
 You can also specify multiple streaming modes at the same time by passing them as a list. When you do this, the streamed outputs will be tuples `(stream_mode, data)`. For example:
 
@@ -33,7 +33,7 @@ The below visualization shows the difference between the `values` and `updates`
 
 ## Streaming LLM tokens and events (`.astream_events`)
 
-In addition, you can use the `astream_events` method to stream back events that happen _inside_ nodes. This is useful for [streaming tokens of LLM calls](../how-tos/streaming-tokens.ipynb).
+In addition, you can use the [`astream_events`](../how-tos/streaming-events-from-within-tools.ipynb) method to stream back events that happen _inside_ nodes. This is useful for [streaming tokens of LLM calls](../how-tos/streaming-tokens.ipynb).
 
 This is a standard method on all [LangChain objects](https://python.langchain.com/docs/concepts/#runnable-interface). This means that as the graph is executed, certain events are emitted along the way and can be seen if you run the graph using `.astream_events`. 
 
@@ -145,7 +145,7 @@ guide for that [here](../how-tos/streaming-tokens.ipynb).
 
 
 !!! warning "ASYNC IN PYTHON<=3.10"
-    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case.
+    You may fail to see events being emitted from inside a node when using `.astream_events` in Python <= 3.10. If you're using a Langchain RunnableLambda, a RunnableGenerator, or Tool asynchronously inside your node, you will have to propagate callbacks to these objects manually. This is because LangChain cannot automatically propagate callbacks to child objects in this case. Please see examples [here](../how-tos/streaming-content.ipynb) and [here](../how-tos/streaming-events-from-within-tools.ipynb).
 
 
 ## LangGraph Platform

docs/docs/how-tos/agent-handoffs.ipynb

@@ -83,10 +83,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b4864843-00a1-4c88-9a7c-c34e6c31c548",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANTHROPIC_API_KEY:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "import os\n",

docs/docs/how-tos/autogen-integration-functional.ipynb

@@ -1,389 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "100c0c81-6a9f-4ba1-b1a8-42aae82b7172",
-   "metadata": {},
-   "source": [
-    "# How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks\n",
-    "\n",
-    "LangGraph is a framework for building agentic and multi-agent applications. LangGraph can be easily integrated with other agent frameworks. \n",
-    "\n",
-    "The primary reasons you might want to integrate LangGraph with other agent frameworks:\n",
-    "\n",
-    "- create [multi-agent systems](../../concepts/multi_agent) where individual agents are built with different frameworks\n",
-    "- leverage LangGraph to add features like [persistence](../../concepts/persistence), [streaming](../../concepts/streaming), [short and long-term memory](../../concepts/memory) and more\n",
-    "\n",
-    "The simplest way to integrate agents from other frameworks is by calling those agents inside a LangGraph [node](../../concepts/low_level/#nodes):\n",
-    "\n",
-    "```python\n",
-    "import autogen\n",
-    "from langgraph.func import entrypoint, task\n",
-    "\n",
-    "autogen_agent = autogen.AssistantAgent(name=\"assistant\", ...)\n",
-    "user_proxy = autogen.UserProxyAgent(name=\"user_proxy\", ...)\n",
-    "\n",
-    "@task\n",
-    "def call_autogen_agent(messages):\n",
-    "    response = user_proxy.initiate_chat(\n",
-    "        autogen_agent,\n",
-    "        message=messages[-1],\n",
-    "        ...\n",
-    "    )\n",
-    "    ...\n",
-    "\n",
-    "\n",
-    "@entrypoint()\n",
-    "def workflow(messages):\n",
-    "    response = call_autogen_agent(messages).result()\n",
-    "    return response\n",
-    "\n",
-    "\n",
-    "workflow.invoke(\n",
-    "    [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n",
-    "        }\n",
-    "    ]\n",
-    ")\n",
-    "```\n",
-    "\n",
-    "In this guide we show how to build a LangGraph chatbot that integrates with AutoGen, but you can follow the same approach with other frameworks."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b189ceb2-132b-4c7b-81b4-c7b8b062f833",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "62417d3a-94f9-4a52-9962-12639d714966",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install autogen langgraph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d46da41d-0a71-4654-aec8-9e6ad8765236",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_API_KEY:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
-   "metadata": {},
-   "source": [
-    "## Define AutoGen agent\n",
-    "\n",
-    "Here we define our AutoGen agent. Adapted from official tutorial [here](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "524de117-ff09-4b26-bfe8-a9f85a46ffd5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import autogen\n",
-    "import os\n",
-    "\n",
-    "config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
-    "\n",
-    "llm_config = {\n",
-    "    \"timeout\": 600,\n",
-    "    \"cache_seed\": 42,\n",
-    "    \"config_list\": config_list,\n",
-    "    \"temperature\": 0,\n",
-    "}\n",
-    "\n",
-    "autogen_agent = autogen.AssistantAgent(\n",
-    "    name=\"assistant\",\n",
-    "    llm_config=llm_config,\n",
-    ")\n",
-    "\n",
-    "user_proxy = autogen.UserProxyAgent(\n",
-    "    name=\"user_proxy\",\n",
-    "    human_input_mode=\"NEVER\",\n",
-    "    max_consecutive_auto_reply=10,\n",
-    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
-    "    code_execution_config={\n",
-    "        \"work_dir\": \"web\",\n",
-    "        \"use_docker\": False,\n",
-    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
-    "    llm_config=llm_config,\n",
-    "    system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8aa858e2-4acb-4f75-be20-b9ccbbcb5073",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcc478f5-4a35-43f8-bf59-9cb71289cd00",
-   "metadata": {},
-   "source": [
-    "## Create the workflow\n",
-    "\n",
-    "We will now create a LangGraph chatbot graph that calls AutoGen agent."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d129e4e1-3766-429a-b806-cde3d8bc0469",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Literal, TypedDict\n",
-    "\n",
-    "from langchain_core.messages import convert_to_openai_messages, BaseMessage\n",
-    "from langgraph.func import entrypoint, task\n",
-    "from langgraph.graph import add_messages\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "\n",
-    "\n",
-    "@task\n",
-    "def call_autogen_agent(messages: list[BaseMessage]):\n",
-    "    # convert to openai-style messages\n",
-    "    messages = convert_to_openai_messages(messages)\n",
-    "    response = user_proxy.initiate_chat(\n",
-    "        autogen_agent,\n",
-    "        message=messages[-1],\n",
-    "        # pass previous message history as context\n",
-    "        carryover=messages[:-1],\n",
-    "    )\n",
-    "    # get the final response from the agent\n",
-    "    content = response.chat_history[-1][\"content\"]\n",
-    "    return {\"role\": \"assistant\", \"content\": content}\n",
-    "\n",
-    "\n",
-    "# add short-term memory for storing conversation history\n",
-    "checkpointer = MemorySaver()\n",
-    "\n",
-    "\n",
-    "@entrypoint(checkpointer=checkpointer)\n",
-    "def workflow(messages: list[BaseMessage], previous: list[BaseMessage]):\n",
-    "    messages = add_messages(previous or [], messages)\n",
-    "    response = call_autogen_agent(messages).result()\n",
-    "    return entrypoint.final(value=response, save=add_messages(messages, response))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23d629c3-1d6b-40af-adf6-915e15657566",
-   "metadata": {},
-   "source": [
-    "## Run the graph\n",
-    "\n",
-    "We can now run the graph."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a279b667-0f5d-4008-8d43-c806a3f379c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
-      "\n",
-      "Find numbers between 10 and 30 in fibonacci sequence\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
-      "\n",
-      "To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan:\n",
-      "\n",
-      "1. Generate Fibonacci numbers starting from 0.\n",
-      "2. Continue generating until the numbers exceed 30.\n",
-      "3. Collect and print the numbers that are between 10 and 30.\n",
-      "\n",
-      "Let's implement this in Python:\n",
-      "\n",
-      "```python\n",
-      "# filename: fibonacci_range.py\n",
-      "\n",
-      "def fibonacci_sequence():\n",
-      "    a, b = 0, 1\n",
-      "    while a <= 30:\n",
-      "        if 10 <= a <= 30:\n",
-      "            print(a)\n",
-      "        a, b = b, a + b\n",
-      "\n",
-      "fibonacci_sequence()\n",
-      "```\n",
-      "\n",
-      "This script will print the Fibonacci numbers between 10 and 30. Please execute the code to see the result.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
-      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
-      "\n",
-      "exitcode: 0 (execution succeeded)\n",
-      "Code output: \n",
-      "13\n",
-      "21\n",
-      "\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
-      "\n",
-      "The Fibonacci numbers between 10 and 30 are 13 and 21. \n",
-      "\n",
-      "These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n",
-      "\n",
-      "The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n",
-      "\n",
-      "As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n",
-      "\n",
-      "TERMINATE\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "{'call_autogen_agent': {'role': 'assistant', 'content': 'The Fibonacci numbers between 10 and 30 are 13 and 21. \\n\\nThese numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \\n\\nThe sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\\n\\nAs you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\\n\\nTERMINATE'}}\n",
-      "{'workflow': {'role': 'assistant', 'content': 'The Fibonacci numbers between 10 and 30 are 13 and 21. \\n\\nThese numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \\n\\nThe sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\\n\\nAs you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\\n\\nTERMINATE'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# pass the thread ID to persist agent outputs for future interactions\n",
-    "# highlight-next-line\n",
-    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "\n",
-    "for chunk in workflow.stream(\n",
-    "    [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n",
-    "        }\n",
-    "    ],\n",
-    "    # highlight-next-line\n",
-    "    config,\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6cd57b4-d4ee-49f6-be12-318613849669",
-   "metadata": {},
-   "source": [
-    "Since we're leveraging LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) features we can now continue the conversation using the same thread ID -- LangGraph will automatically pass previous history to the AutoGen agent:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e68811a7-962e-4fe3-9f45-9b99ebbe04e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
-      "\n",
-      "Multiply the last number by 3\n",
-      "Context: \n",
-      "Find numbers between 10 and 30 in fibonacci sequence\n",
-      "The Fibonacci numbers between 10 and 30 are 13 and 21. \n",
-      "\n",
-      "These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n",
-      "\n",
-      "The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n",
-      "\n",
-      "As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n",
-      "\n",
-      "TERMINATE\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
-      "\n",
-      "The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n",
-      "\n",
-      "21 * 3 = 63\n",
-      "\n",
-      "TERMINATE\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "{'call_autogen_agent': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\\n\\n21 * 3 = 63\\n\\nTERMINATE'}}\n",
-      "{'workflow': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\\n\\n21 * 3 = 63\\n\\nTERMINATE'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in workflow.stream(\n",
-    "    [\n",
-    "        {\n",
-    "            \"role\": \"user\",\n",
-    "            \"content\": \"Multiply the last number by 3\",\n",
-    "        }\n",
-    "    ],\n",
-    "    # highlight-next-line\n",
-    "    config,\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/how-tos/command.ipynb

@@ -16,10 +16,10 @@
     "!!! info \"Prerequisites\"\n",
     "    This guide assumes familiarity with the following:\n",
     "  \n",
-    "    - [State](../../concepts/low_level#state)\n",
-    "    - [Nodes](../../concepts/low_level#nodes)\n",
-    "    - [Edges](../../concepts/low_level#edges)\n",
-    "    - [Command](../../concepts/low_level#command)\n",
+    "    - [State](../../concepts/low_level/#state)\n",
+    "    - [Nodes](../../concepts/low_level/#nodes)\n",
+    "    - [Edges](../../concepts/low_level/#edges)\n",
+    "    - [Command](../../concepts/low_level/#command)\n",
     "\n",
     "It can be useful to combine control flow (edges) and state updates (nodes). For example, you might want to BOTH perform state updates AND decide which node to go to next in the SAME node. LangGraph provides a way to do so by returning a `Command` object from node functions:\n",
     "\n",
@@ -44,10 +44,6 @@
     "    )\n",
     "```\n",
     "\n",
-    "!!! important \"State updates with `Command.PARENT`\"\n",
-    "\n",
-    "    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../../concepts/low_level#schema), you **must** define a [reducer](../../concepts/low_level#reducers) for the key you're updating in the parent graph state. See this [example](#navigating-to-a-node-in-a-parent-graph) below.\n",
-    "\n",
     "This guide shows how you can do use `Command` to add dynamic control flow in your LangGraph app."
    ]
   },
@@ -228,13 +224,13 @@
      "output_type": "stream",
      "text": [
       "Called A\n",
-      "Called C\n"
+      "Called B\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'foo': 'bc'}"
+       "{'foo': 'ab'}"
       ]
      },
      "execution_count": 5,
@@ -262,16 +258,6 @@
     "Now let's demonstrate how you can navigate from inside a subgraph to a different node in a parent graph. We'll do so by changing `node_a` in the above example into a single-node graph that we'll add as a subgraph to our parent graph."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "6be0aeb9-e138-4adc-a1df-5d743a8eb348",
-   "metadata": {},
-   "source": [
-    "!!! important \"State updates with `Command.PARENT`\"\n",
-    "\n",
-    "    When you send updates from a subgraph node to a parent graph node for a key that's shared by both parent and subgraph [state schemas](../../concepts/low_level#schema), you **must** define a [reducer](../../concepts/low_level#reducers) for the key you're updating in the parent graph state."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -279,14 +265,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import operator\n",
-    "from typing_extensions import Annotated\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    # NOTE: we define a reducer here\n",
-    "    # highlight-next-line\n",
-    "    foo: Annotated[str, operator.add]\n",
+    "# Define the nodes\n",
     "\n",
     "\n",
     "def node_a(state: State):\n",
@@ -304,7 +283,6 @@
     "        goto=goto,\n",
     "        # this tells LangGraph to navigate to node_b or node_c in the parent graph\n",
     "        # NOTE: this will navigate to the closest parent graph relative to the subgraph\n",
-    "        # highlight-next-line\n",
     "        graph=Command.PARENT,\n",
     "    )\n",
     "\n",
@@ -314,17 +292,12 @@
     "\n",
     "def node_b(state: State):\n",
     "    print(\"Called B\")\n",
-    "    # NOTE: since we've defined a reducer, we don't need to manually append\n",
-    "    # new characters to existing 'foo' value. instead, reducer will append these\n",
-    "    # automatically (via operator.add)\n",
-    "    # highlight-next-line\n",
-    "    return {\"foo\": \"b\"}\n",
+    "    return {\"foo\": state[\"foo\"] + \"b\"}\n",
     "\n",
     "\n",
     "def node_c(state: State):\n",
     "    print(\"Called C\")\n",
-    "    # highlight-next-line\n",
-    "    return {\"foo\": \"c\"}"
+    "    return {\"foo\": state[\"foo\"] + \"c\"}"
    ]
   },
   {

docs/docs/how-tos/index.md

@@ -9,24 +9,16 @@ Here you’ll find answers to “How do I...?” types of questions. These guide
 
 ## LangGraph
 
-### Graph API Basics
+### Controllability
+
+LangGraph offers a high level of control over the execution of your graph.
+
+These how-to guides show how to achieve that controllability.
 
-- [How to update graph state from nodes](state-reducers.ipynb)
-- [How to create a sequence of steps](sequence.ipynb)
 - [How to create branches for parallel execution](branching.ipynb)
-- [How to create and control loops with recursion limits](recursion-limit.ipynb)
-- [How to visualize your graph](visualization.ipynb)
-
-### Fine-grained Control
-
-These guides demonstrate LangGraph features that grant fine-grained control over the
-execution of your graph.
-
 - [How to create map-reduce branches for parallel execution](map-reduce.ipynb)
-- [How to update state and jump to nodes in graphs and subgraphs](command.ipynb)
-- [How to add runtime configuration to your graph](configuration.ipynb)
-- [How to add node retries](node-retries.ipynb)
-- [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
+- [How to control graph recursion limit](recursion-limit.ipynb)
+- [How to combine control flow and state updates with Command](command.ipynb)
 
 ### Persistence
 
@@ -89,10 +81,15 @@ See the below guides for how-to implement human-in-the-loop workflows with the (
 
 [Streaming](../concepts/streaming.md) is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.
 
-- [How to stream](streaming.ipynb)
+- [How to stream full state of your graph](stream-values.ipynb)
+- [How to stream state updates of your graph](stream-updates.ipynb)
 - [How to stream LLM tokens](streaming-tokens.ipynb)
-- [How to stream LLM tokens from specific nodes](streaming-specific-nodes.ipynb)
-- [How to stream data from within a tool](streaming-events-from-within-tools.ipynb)
+- [How to stream LLM tokens without LangChain models](streaming-tokens-without-langchain.ipynb)
+- [How to stream custom data](streaming-content.ipynb)
+- [How to configure multiple streaming modes at the same time](stream-multiple.ipynb)
+- [How to stream events from within a tool](streaming-events-from-within-tools.ipynb)
+- [How to stream events from within a tool without LangChain models](streaming-events-from-within-tools-without-langchain.ipynb)
+- [How to stream events from the final node](streaming-from-final-node.ipynb)
 - [How to stream from subgraphs](streaming-subgraphs.ipynb)
 - [How to disable streaming for models that don't support it](disable-streaming.ipynb)
 
@@ -130,7 +127,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
 
 See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for implementations of other multi-agent architectures.
 
-See the below guides for how to implement multi-agent workflows with the (beta)
+See the below guides for how-to implement multi-agent workflows with the (beta)
 [Functional API](../concepts/functional_api.md):
 
 - [How to build a multi-agent network (functional API)](multi-agent-network-functional.ipynb)
@@ -145,15 +142,14 @@ See the below guides for how to implement multi-agent workflows with the (beta)
 ### Other
 
 - [How to run graph asynchronously](async.ipynb)
+- [How to visualize your graph](visualization.ipynb)
+- [How to add runtime configuration to your graph](configuration.ipynb)
+- [How to add node retries](node-retries.ipynb)
 - [How to force tool-calling agent to structure output](react-agent-structured-output.ipynb)
 - [How to pass custom LangSmith run ID for graph runs](run-id-langsmith.ipynb)
+- [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
 - [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
 
-See the below guide for how to integrate with other frameworks using the (beta)
-[Functional API](../concepts/functional_api.md):
-
-- [How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks](autogen-integration-functional.ipynb)
-
 ### Prebuilt ReAct Agent
 
 The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
@@ -303,9 +299,3 @@ These are the guides for resolving common errors you may find while building wit
 - [INVALID_GRAPH_NODE_RETURN_VALUE](../troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)
 - [MULTIPLE_SUBGRAPHS](../troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)
 - [INVALID_CHAT_HISTORY](../troubleshooting/errors/INVALID_CHAT_HISTORY.md)
-
-### LangGraph Platform Troubleshooting
-
-These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.
-
-- [INVALID_LICENSE](../troubleshooting/errors/INVALID_LICENSE.md)

docs/docs/how-tos/multi-agent-multi-turn-convo-functional.ipynb

@@ -88,8 +88,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %%capture --no-stderr\n",
-    "# %pip install -U langgraph langchain-anthropic"
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-anthropic"
    ]
   },
   {
@@ -99,7 +99,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       "ANTHROPIC_API_KEY:  ········\n"
@@ -212,13 +212,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "aa4bdbff-9461-46cc-aee9-8a22d3c3d9ec",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n",
-    "\n",
     "from langchain_core.messages import AIMessage\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "from langgraph.prebuilt import create_react_agent\n",
@@ -275,14 +273,11 @@
     "checkpointer = MemorySaver()\n",
     "\n",
     "\n",
-    "def string_to_uuid(input_string):\n",
-    "    return str(uuid.uuid5(uuid.NAMESPACE_URL, input_string))\n",
-    "\n",
-    "\n",
     "@entrypoint(checkpointer=checkpointer)\n",
     "def multi_turn_graph(messages, previous):\n",
     "    previous = previous or []\n",
     "    messages = add_messages(previous, messages)\n",
+    "\n",
     "    call_active_agent = call_travel_advisor\n",
     "    while True:\n",
     "        agent_messages = call_active_agent(messages).result()\n",
@@ -297,17 +292,7 @@
     "        ai_msg = next(m for m in reversed(agent_messages) if isinstance(m, AIMessage))\n",
     "        if not ai_msg.tool_calls:\n",
     "            user_input = interrupt(value=\"Ready for user input.\")\n",
-    "            # Add user input as a human message\n",
-    "            # NOTE: we generate unique ID for the human message based on its content\n",
-    "            # it's important, since on subsequent invocations previous user input (interrupt) values\n",
-    "            # will be looked up again and we will attempt to add them again here\n",
-    "            # `add_messages` deduplicates messages based on the ID, ensuring correct message history\n",
-    "            human_message = {\n",
-    "                \"role\": \"user\",\n",
-    "                \"content\": user_input,\n",
-    "                \"id\": string_to_uuid(user_input),\n",
-    "            }\n",
-    "            messages = add_messages(messages, [human_message])\n",
+    "            messages = add_messages(messages, [{\"role\": \"user\", \"content\": user_input}])\n",
     "            continue\n",
     "\n",
     "        tool_call = ai_msg.tool_calls[-1]\n",
@@ -333,8 +318,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "2b6fde57-86e3-440e-a7bf-f1e9b5ed9ff2",
+   "execution_count": 5,
+   "id": "161e0cf1-d13a-4026-8f89-bdab67d1ad4d",
    "metadata": {},
    "outputs": [
     {
@@ -344,69 +329,71 @@
       "\n",
       "--- Conversation Turn 1 ---\n",
       "\n",
-      "User: {'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean', 'id': 'f48d82a7-7efa-43f5-ad4c-541758c95f61'}\n",
+      "User: {'role': 'user', 'content': 'i wanna go somewhere warm in the caribbean'}\n",
       "\n",
-      "call_travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Known as \"One Happy Island,\" Aruba offers:\n",
-      "- Year-round warm weather with consistent temperatures around 82°F (28°C)\n",
-      "- Beautiful white sand beaches like Eagle Beach and Palm Beach\n",
-      "- Crystal clear waters perfect for swimming and snorkeling\n",
-      "- Minimal rainfall and location outside the hurricane belt\n",
-      "- Rich culture blending Dutch and Caribbean influences\n",
-      "- Various activities from water sports to desert-like landscape exploration\n",
-      "- Excellent dining and shopping options\n",
+      "call_travel_advisor: Based on the recommendations, Aruba would be an excellent choice for your Caribbean getaway! Aruba is known for its perfect warm weather year-round, with consistent temperatures around 82°F (28°C) and very little rainfall. The island offers:\n",
       "\n",
-      "Would you like me to help you find suitable accommodations in Aruba? I can transfer you to our hotel advisor who can recommend specific hotels based on your preferences.\n",
+      "1. Beautiful white-sand beaches like Eagle Beach and Palm Beach\n",
+      "2. Crystal clear waters perfect for swimming and snorkeling\n",
+      "3. Constant cooling trade winds that make the warm weather comfortable\n",
+      "4. A mix of luxury resorts and boutique hotels\n",
+      "5. Diverse activities from water sports to desert-like terrain exploration\n",
+      "6. Great dining and nightlife options\n",
+      "7. Safe and tourist-friendly environment\n",
+      "\n",
+      "Would you like me to connect you with our hotel advisor to help you find the perfect place to stay in Aruba?\n",
       "\n",
       "--- Conversation Turn 2 ---\n",
       "\n",
       "User: Command(resume='could you recommend a nice hotel in one of the areas and tell me which area it is.')\n",
       "\n",
-      "call_hotel_advisor: I can recommend two excellent options in different areas:\n",
+      "call_hotel_advisor: Based on the recommendations, I can highlight two excellent options in different areas:\n",
       "\n",
       "1. The Ritz-Carlton, Aruba - Located in Palm Beach\n",
-      "- Luxury beachfront resort\n",
-      "- Located in the vibrant Palm Beach area, known for its lively atmosphere\n",
-      "- Close to restaurants, shopping, and nightlife\n",
-      "- Perfect for those who want a more active vacation with plenty of amenities nearby\n",
+      "- Part of the high-rise hotel district\n",
+      "- Luxury beachfront resort with full-service spa\n",
+      "- Multiple restaurants and a casino\n",
+      "- Perfect for those who want to be in the heart of the action\n",
+      "- Close to shopping, dining, and nightlife\n",
       "\n",
       "2. Bucuti & Tara Beach Resort - Located in Eagle Beach\n",
       "- Adults-only boutique resort\n",
-      "- Situated on the quieter Eagle Beach\n",
-      "- Known for its romantic atmosphere and excellent service\n",
-      "- Ideal for couples seeking a more peaceful, intimate setting\n",
+      "- Located on Eagle Beach, voted one of the best beaches in the world\n",
+      "- More serene and romantic atmosphere\n",
+      "- Perfect for couples and those seeking a quieter vacation\n",
+      "- Known for its excellent service and sustainability practices\n",
       "\n",
-      "Would you like more specific information about either of these properties or their locations?\n",
+      "Would you like more specific information about either of these properties or would you like to explore other options in either area?\n",
       "\n",
       "--- Conversation Turn 3 ---\n",
       "\n",
       "User: Command(resume='i like the first one. could you recommend something to do near the hotel?')\n",
       "\n",
-      "call_travel_advisor: Near The Ritz-Carlton in Palm Beach, here are some popular activities you can enjoy:\n",
+      "call_travel_advisor: Near the Ritz-Carlton in Palm Beach, you can enjoy several fantastic activities:\n",
       "\n",
-      "1. Palm Beach Strip - Take a walk along this bustling strip filled with restaurants, shops, and bars\n",
-      "2. Visit the Bubali Bird Sanctuary - Just a short distance away\n",
-      "3. Try your luck at the Stellaris Casino - Located right in The Ritz-Carlton\n",
-      "4. Water Sports at Palm Beach - Right in front of the hotel you can:\n",
-      "   - Go parasailing\n",
-      "   - Try jet skiing\n",
-      "   - Take a sunset sailing cruise\n",
-      "5. Visit the Palm Beach Plaza Mall - High-end shopping just a short walk away\n",
-      "6. Enjoy dinner at Madame Janette's - One of Aruba's most famous restaurants nearby\n",
+      "1. Paseo Herencia Mall - A beautiful outdoor shopping and entertainment center just a short walk away\n",
+      "2. High-Rise Beach Strip - Perfect for beach walks and water sports\n",
+      "3. Bubali Bird Sanctuary - A nature preserve where you can spot local wildlife\n",
+      "4. The Butterfly Farm - A unique attraction featuring hundreds of exotic butterflies\n",
+      "5. Palm Beach Plaza Mall - Great for shopping and dining\n",
+      "6. Various water sports operators offering:\n",
+      "   - Jet skiing\n",
+      "   - Parasailing\n",
+      "   - Snorkeling trips\n",
+      "   - Sunset sailing cruises\n",
       "\n",
-      "Would you like more specific information about any of these activities or other suggestions in the area?\n"
+      "Additionally, the hotel concierge can arrange most activities directly for you. Would you like more specific information about any of these activities?\n"
      ]
     }
    ],
    "source": [
+    "import uuid\n",
+    "\n",
     "thread_config = {\"configurable\": {\"thread_id\": uuid.uuid4()}}\n",
     "\n",
     "inputs = [\n",
     "    # 1st round of conversation,\n",
-    "    {\n",
-    "        \"role\": \"user\",\n",
-    "        \"content\": \"i wanna go somewhere warm in the caribbean\",\n",
-    "        \"id\": str(uuid.uuid4()),\n",
-    "    },\n",
+    "    {\"role\": \"user\", \"content\": \"i wanna go somewhere warm in the caribbean\"},\n",
     "    # Since we're using `interrupt`, we'll need to resume using the Command primitive.\n",
     "    # 2nd round of conversation,\n",
     "    Command(\n",

docs/docs/how-tos/stream-updates.ipynb

@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
+   "metadata": {},
+   "source": [
+    "# How to stream state updates of your graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
+   "metadata": {},
+   "source": [
+    "LangGraph supports multiple streaming modes. The main ones are:\n",
+    "\n",
+    "- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
+    "- `updates`: This streaming mode streams back updates to the graph. This is the **update to the state of the graph** after each node is called.\n",
+    "\n",
+    "This guide covers `stream_mode=\"updates\"`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required package and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc6c48fe",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e7777f9",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "We'll be using a simple ReAct agent for this guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85cf2e23-29f2-40cc-b302-5377b3b49da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
+    "graph = create_react_agent(model, tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956db549-5207-4be1-a823-78311738e3f8",
+   "metadata": {},
+   "source": [
+    "## Stream updates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Receiving update from node: 'agent'\n",
+      "{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd68b3a0-86c3-4afa-9649-1b962a0dd062-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}\n",
+      "\n",
+      "\n",
+      "\n",
+      "Receiving update from node: 'tools'\n",
+      "{'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', tool_call_id='call_kc6cvcEkTAUGRlSHrP4PK9fn')]}\n",
+      "\n",
+      "\n",
+      "\n",
+      "Receiving update from node: 'agent'\n",
+      "{'messages': [AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-009d83c4-b874-4acc-9494-20aba43132b9-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"updates\"):\n",
+    "    for node, values in chunk.items():\n",
+    "        print(f\"Receiving update from node: '{node}'\")\n",
+    "        print(values)\n",
+    "        print(\"\\n\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/stream-values.ipynb

@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
+   "metadata": {},
+   "source": [
+    "# How to stream full state of your graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
+   "metadata": {},
+   "source": [
+    "LangGraph supports multiple streaming modes. The main ones are:\n",
+    "\n",
+    "- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
+    "- `updates`: This streaming mode streams back updates to the graph. This is the **update to the state of the graph** after each node is called.\n",
+    "\n",
+    "This guide covers `stream_mode=\"values\"`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph langchain-openai langchain-community"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaaab1fc",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7939a3c5",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "We'll be using a simple ReAct agent for this guide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ef5a3ec6-0cd0-4541-ab1b-d63ede22720e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Literal\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.runnables import ConfigurableField\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
+    "    \"\"\"Use this to get weather information.\"\"\"\n",
+    "    if city == \"nyc\":\n",
+    "        return \"It might be cloudy in nyc\"\n",
+    "    elif city == \"sf\":\n",
+    "        return \"It's always sunny in sf\"\n",
+    "    else:\n",
+    "        raise AssertionError(\"Unknown city\")\n",
+    "\n",
+    "\n",
+    "tools = [get_weather]\n",
+    "\n",
+    "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
+    "graph = create_react_agent(model, tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "002a715b-e0be-4e89-8d42-f0098882586b",
+   "metadata": {},
+   "source": [
+    "## Stream values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "what's the weather in sf\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "Tool Calls:\n",
+      "  get_weather (call_61VvIzqVGtyxcXi0z6knZkjZ)\n",
+      " Call ID: call_61VvIzqVGtyxcXi0z6knZkjZ\n",
+      "  Args:\n",
+      "    city: sf\n",
+      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+      "Name: get_weather\n",
+      "\n",
+      "It's always sunny in sf\n",
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in San Francisco is currently sunny.\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
+    "    chunk[\"messages\"][-1].pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d73de237-bf45-4fa7-93ef-6dae7eacffc0",
+   "metadata": {},
+   "source": [
+    "If we want to just get the final result, we can use the same method and just keep track of the last value we received"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c122bf15-a489-47bf-b482-a744a54e2cc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
+    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
+    "    final_result = chunk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "316022e5-4c65-48e4-9878-8d94a2425ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='54b39b6f-054b-4306-980b-86905e48a6bc'),\n",
+       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_avoKnK8reERzTUSxrN9cgFxY', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5e6c71d4a8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-f2f43c89-2c96-45f4-975c-2d0f22d0d2d1-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_avoKnK8reERzTUSxrN9cgFxY'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n",
+       "  ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='fc18a798-c7b2-4f73-84fa-8ffdffb6ddcb', tool_call_id='call_avoKnK8reERzTUSxrN9cgFxY'),\n",
+       "  AIMessage(content='The weather in San Francisco is currently sunny. Enjoy the sunshine!', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5e6c71d4a8', 'finish_reason': 'stop', 'logprobs': None}, id='run-21418147-da8e-4738-a076-239377397c40-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0f64ebbe-535c-4b35-a95f-0a7490cfed90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+      "\n",
+      "The weather in San Francisco is currently sunny. Enjoy the sunshine!\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_result[\"messages\"][-1].pretty_print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/streaming-content.ipynb

@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15c4bd28",
+   "metadata": {},
+   "source": [
+    "# How to stream custom data\n",
+    "\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#astream_events\">\n",
+    "                    astream_events API\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
+    "\n",
+    "The most common use case for streaming from inside a node is to stream LLM tokens, but you may also want to stream custom data.\n",
+    "\n",
+    "For example, if you have a long-running tool call, you can dispatch custom events between the steps and use these custom events to monitor progress. You could also surface these custom events to an end user of your application to show them how the current task is progressing.\n",
+    "\n",
+    "You can do so in two ways:\n",
+    "* using graph's `.stream` / `.astream` methods with `stream_mode=\"custom\"`\n",
+    "* emitting custom events using [adispatch_custom_events](https://python.langchain.com/docs/how_to/callbacks_custom_events/).\n",
+    "\n",
+    "Below we'll see how to use both APIs.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's install our required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e1a20f31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12297071",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29814253-ca9b-4844-a8a5-d6b19fbdbdba",
+   "metadata": {},
+   "source": [
+    "## Stream custom data using `.stream / .astream`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b729644a-b65f-4e69-ad45-f2e88ffb4e9d",
+   "metadata": {},
+   "source": [
+    "### Define the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9731c40f-5ce7-460d-b2ad-33185529c99d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import AIMessage\n",
+    "from langgraph.graph import START, StateGraph, MessagesState, END\n",
+    "from langgraph.types import StreamWriter\n",
+    "\n",
+    "\n",
+    "async def my_node(\n",
+    "    state: MessagesState,\n",
+    "    writer: StreamWriter,  # <-- provide StreamWriter to write chunks to be streamed\n",
+    "):\n",
+    "    chunks = [\n",
+    "        \"Four\",\n",
+    "        \"score\",\n",
+    "        \"and\",\n",
+    "        \"seven\",\n",
+    "        \"years\",\n",
+    "        \"ago\",\n",
+    "        \"our\",\n",
+    "        \"fathers\",\n",
+    "        \"...\",\n",
+    "    ]\n",
+    "    for chunk in chunks:\n",
+    "        # write the chunk to be streamed using stream_mode=custom\n",
+    "        writer(chunk)\n",
+    "\n",
+    "    return {\"messages\": [AIMessage(content=\" \".join(chunks))]}\n",
+    "\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(MessagesState)\n",
+    "\n",
+    "workflow.add_node(\"model\", my_node)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_edge(\"model\", END)\n",
+    "\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecd69eed-9624-4640-b0af-c9f82b190900",
+   "metadata": {},
+   "source": [
+    "### Stream content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "00a91b15-82c7-443c-acb6-a7406df15cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Four\n",
+      "score\n",
+      "and\n",
+      "seven\n",
+      "years\n",
+      "ago\n",
+      "our\n",
+      "fathers\n",
+      "...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = [HumanMessage(content=\"What are you thinking about?\")]\n",
+    "async for chunk in app.astream({\"messages\": inputs}, stream_mode=\"custom\"):\n",
+    "    print(chunk, flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7b9f1f0-c170-40dc-9c22-289483dfbc99",
+   "metadata": {},
+   "source": [
+    "You will likely need to use [multiple streaming modes](https://langchain-ai.github.io/langgraph/how-tos/stream-multiple/) as you will\n",
+    "want access to both the custom data and the state updates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f8ed22d4-6ce6-4b04-a68b-2ea516e3ab15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('custom', 'Four')\n",
+      "('custom', 'score')\n",
+      "('custom', 'and')\n",
+      "('custom', 'seven')\n",
+      "('custom', 'years')\n",
+      "('custom', 'ago')\n",
+      "('custom', 'our')\n",
+      "('custom', 'fathers')\n",
+      "('custom', '...')\n",
+      "('updates', {'model': {'messages': [AIMessage(content='Four score and seven years ago our fathers ...', additional_kwargs={}, response_metadata={})]}})\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = [HumanMessage(content=\"What are you thinking about?\")]\n",
+    "async for chunk in app.astream({\"messages\": inputs}, stream_mode=[\"custom\", \"updates\"]):\n",
+    "    print(chunk, flush=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ca976d6a-7c64-4603-8bb4-dee95428c33d",
+   "metadata": {},
+   "source": [
+    "## Stream custom data using `.astream_events`\n",
+    "\n",
+    "If you are already using graph's `.astream_events` method in your workflow, you can also stream custom data by emitting custom events using `adispatch_custom_event`\n",
+    "\n",
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">ASYNC IN PYTHON<=3.10</p>\n",
+    "    <p>\n",
+    "\n",
+    "LangChain cannot automatically propagate configuration, including callbacks necessary for `astream_events()`, to child runnables if you are running async code in python<=3.10. This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "\n",
+    "If you are running python<=3.10, you will need to manually propagate the `RunnableConfig` object to the child runnable in async environments. For an example of how to manually propagate the config, see the implementation of the node below with `adispatch_custom_event`.\n",
+    "\n",
+    "If you are running python>=3.11, the `RunnableConfig` will automatically propagate to child runnables in async environment. However, it is still a good idea to propagate the `RunnableConfig` manually if your code may run in other Python versions.\n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b390a9fe-2d5f-4e82-a1ea-c7c0186b8559",
+   "metadata": {},
+   "source": [
+    "### Define the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "486a01a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableConfig, RunnableLambda\n",
+    "from langchain_core.callbacks.manager import adispatch_custom_event\n",
+    "\n",
+    "\n",
+    "async def my_node(state: MessagesState, config: RunnableConfig):\n",
+    "    chunks = [\n",
+    "        \"Four\",\n",
+    "        \"score\",\n",
+    "        \"and\",\n",
+    "        \"seven\",\n",
+    "        \"years\",\n",
+    "        \"ago\",\n",
+    "        \"our\",\n",
+    "        \"fathers\",\n",
+    "        \"...\",\n",
+    "    ]\n",
+    "    for chunk in chunks:\n",
+    "        await adispatch_custom_event(\n",
+    "            \"my_custom_event\",\n",
+    "            {\"chunk\": chunk},\n",
+    "            config=config,  # <-- propagate config\n",
+    "        )\n",
+    "\n",
+    "    return {\"messages\": [AIMessage(content=\" \".join(chunks))]}\n",
+    "\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(MessagesState)\n",
+    "\n",
+    "workflow.add_node(\"model\", my_node)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_edge(\"model\", END)\n",
+    "\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dcded03-6776-405e-afae-005a3212d3e4",
+   "metadata": {},
+   "source": [
+    "### Stream content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ce773a40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Four|score|and|seven|years|ago|our|fathers|...|"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = [HumanMessage(content=\"What are you thinking about?\")]\n",
+    "async for event in app.astream_events({\"messages\": inputs}, version=\"v2\"):\n",
+    "    tags = event.get(\"tags\", [])\n",
+    "    if event[\"event\"] == \"on_custom_event\" and event[\"name\"] == \"my_custom_event\":\n",
+    "        data = event[\"data\"]\n",
+    "        if data:\n",
+    "            print(data[\"chunk\"], end=\"|\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb

@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "18e6e213-b398-4a7e-b342-ba225e97b424",
+   "metadata": {},
+   "source": [
+    "# How to stream events from within a tool (without LangChain LLMs / tools)\n",
+    "\n",
+    "\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#astream_events\">\n",
+    "                    astream_events API\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
+    "\n",
+    "In this guide, we will demonstrate how to stream tokens from tools used by a custom ReAct agent, without relying on LangChain’s chat models or tool-calling functionalities. \n",
+    "\n",
+    "We will use the OpenAI client library directly for the chat model interaction. The tool execution will be implemented from scratch.\n",
+    "\n",
+    "This showcases how LangGraph can be utilized independently of built-in LangChain components like chat models or tools.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8df7b58",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7d766c7d-34ea-455b-8bcb-f2f12d100e1d",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "### Define a node that will call OpenAI API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d59234f9-173e-469d-a725-c13e0979663e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import AsyncOpenAI\n",
+    "from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
+    "from langchain_core.messages import AIMessageChunk\n",
+    "from langchain_core.runnables.config import (\n",
+    "    ensure_config,\n",
+    "    get_callback_manager_for_config,\n",
+    ")\n",
+    "\n",
+    "openai_client = AsyncOpenAI()\n",
+    "# define tool schema for openai tool calling\n",
+    "\n",
+    "tool = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_items\",\n",
+    "        \"description\": \"Use this tool to look up which items are in the given place.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"place\": {\"type\": \"string\"}},\n",
+    "            \"required\": [\"place\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "async def call_model(state, config=None):\n",
+    "    config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
+    "    callback_manager = get_callback_manager_for_config(config)\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
+    "    response = await openai_client.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\", tools=[tool], stream=True\n",
+    "    )\n",
+    "\n",
+    "    response_content = \"\"\n",
+    "    role = None\n",
+    "\n",
+    "    tool_call_id = None\n",
+    "    tool_call_function_name = None\n",
+    "    tool_call_function_arguments = \"\"\n",
+    "    async for chunk in response:\n",
+    "        delta = chunk.choices[0].delta\n",
+    "        if delta.role is not None:\n",
+    "            role = delta.role\n",
+    "\n",
+    "        if delta.content:\n",
+    "            response_content += delta.content\n",
+    "            llm_run_manager.on_llm_new_token(delta.content)\n",
+    "\n",
+    "        if delta.tool_calls:\n",
+    "            # note: for simplicity we're only handling a single tool call here\n",
+    "            if delta.tool_calls[0].function.name is not None:\n",
+    "                tool_call_function_name = delta.tool_calls[0].function.name\n",
+    "                tool_call_id = delta.tool_calls[0].id\n",
+    "\n",
+    "            # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly\n",
+    "            tool_call_chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=\"\",\n",
+    "                    additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]},\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
+    "            tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
+    "\n",
+    "    if tool_call_function_name is not None:\n",
+    "        tool_calls = [\n",
+    "            {\n",
+    "                \"id\": tool_call_id,\n",
+    "                \"function\": {\n",
+    "                    \"name\": tool_call_function_name,\n",
+    "                    \"arguments\": tool_call_function_arguments,\n",
+    "                },\n",
+    "                \"type\": \"function\",\n",
+    "            }\n",
+    "        ]\n",
+    "    else:\n",
+    "        tool_calls = None\n",
+    "\n",
+    "    response_message = {\n",
+    "        \"role\": role,\n",
+    "        \"content\": response_content,\n",
+    "        \"tool_calls\": tool_calls,\n",
+    "    }\n",
+    "    return {\"messages\": [response_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
+   "metadata": {},
+   "source": [
+    "### Define our tools and a tool-calling node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b90941d8-afe4-42ec-9262-9c3b87c3b1ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from langchain_core.callbacks import adispatch_custom_event\n",
+    "\n",
+    "\n",
+    "async def get_items(place: str) -> str:\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "\n",
+    "    # this can be replaced with any actual streaming logic that you might have\n",
+    "    def stream(place: str):\n",
+    "        if \"bed\" in place:  # For under the bed\n",
+    "            yield from [\"socks\", \"shoes\", \"dust bunnies\"]\n",
+    "        elif \"shelf\" in place:  # For 'shelf'\n",
+    "            yield from [\"books\", \"penciles\", \"pictures\"]\n",
+    "        else:  # if the agent decides to ask about a different place\n",
+    "            yield \"cat snacks\"\n",
+    "\n",
+    "    tokens = []\n",
+    "    for token in stream(place):\n",
+    "        await adispatch_custom_event(\n",
+    "            # this will allow you to filter events by name\n",
+    "            \"tool_call_token_stream\",\n",
+    "            {\n",
+    "                \"function_name\": \"get_items\",\n",
+    "                \"arguments\": {\"place\": place},\n",
+    "                \"tool_output_token\": token,\n",
+    "            },\n",
+    "            # this will allow you to filter events by tags\n",
+    "            config={\"tags\": [\"tool_call\"]},\n",
+    "        )\n",
+    "        tokens.append(token)\n",
+    "\n",
+    "    return \", \".join(tokens)\n",
+    "\n",
+    "\n",
+    "# define mapping to look up functions when running tools\n",
+    "function_name_to_function = {\"get_items\": get_items}\n",
+    "\n",
+    "\n",
+    "async def call_tools(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    tool_call = messages[-1][\"tool_calls\"][0]\n",
+    "    function_name = tool_call[\"function\"][\"name\"]\n",
+    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
+    "    arguments = json.loads(function_arguments)\n",
+    "\n",
+    "    function_response = await function_name_to_function[function_name](**arguments)\n",
+    "    tool_message = {\n",
+    "        \"tool_call_id\": tool_call[\"id\"],\n",
+    "        \"role\": \"tool\",\n",
+    "        \"name\": function_name,\n",
+    "        \"content\": function_response,\n",
+    "    }\n",
+    "    return {\"messages\": [tool_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6685898c-9a1c-4803-a492-bd70574ebe38",
+   "metadata": {},
+   "source": [
+    "### Define our graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Literal\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph import StateGraph, END, START\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, operator.add]\n",
+    "\n",
+    "\n",
+    "def should_continue(state) -> Literal[\"tools\", END]:\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    if last_message[\"tool_calls\"]:\n",
+    "        return \"tools\"\n",
+    "    return END\n",
+    "\n",
+    "\n",
+    "workflow = StateGraph(State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)  # i.e. our \"agent\"\n",
+    "workflow.add_node(\"tools\", call_tools)\n",
+    "workflow.add_conditional_edges(\"model\", should_continue)\n",
+    "workflow.add_edge(\"tools\", \"model\")\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
+   "metadata": {},
+   "source": [
+    "## Stream tokens from within the tool\n",
+    "\n",
+    "Here, we'll use the `astream_events` API to stream back individual events. Please see [astream_events](https://python.langchain.com/docs/concepts/#astream_events) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "45c96a79-4147-42e3-89fd-d942b2b49f6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tool token socks\n",
+      "Tool token shoes\n",
+      "Tool token dust bunnies\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for event in graph.astream_events(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]}, version=\"v2\"\n",
+    "):\n",
+    "    tags = event.get(\"tags\", [])\n",
+    "    if event[\"event\"] == \"on_custom_event\" and \"tool_call\" in tags:\n",
+    "        print(\"Tool token\", event[\"data\"][\"tool_output_token\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/streaming-events-from-within-tools.ipynb

@@ -3,82 +3,50 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "695d935e-b4fe-45a6-a061-a66d32cb832b",
+   "id": "04b012ac-e0b5-483e-a645-d13d0e215aad",
    "metadata": {},
    "source": [
     "# How to stream data from within a tool\n",
     "\n",
-    "!!! info \"Prerequisites\"\n",
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Prerequisites</p>\n",
+    "    <p>\n",
+    "        This guide assumes familiarity with the following:\n",
+    "        <ul>\n",
+    "            <li>            \n",
+    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
+    "                    Streaming\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
+    "                    Chat Models\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
+    "                    Tools\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig\">\n",
+    "                    RunnableConfig\n",
+    "                </a>\n",
+    "            </li>\n",
+    "            <li>\n",
+    "                <a href=\"https://python.langchain.com/docs/concepts/#runnable-interface\">\n",
+    "                    RunnableInterface\n",
+    "                </a>\n",
+    "            </li>\n",
+    "        </ul>\n",
+    "    </p>\n",
+    "</div>\n",
     "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "    - [Tools](https://python.langchain.com/docs/concepts/tools/)\n",
+    "If your graph involves tools that invoke LLMs (or any other LangChain `Runnable` objects like other graphs, `LCEL` chains, or retrievers), you might want to surface partial results during the execution of the tool, especially if the tool takes a longer time to run.\n",
     "\n",
-    "If your graph calls tools that use LLMs or any other streaming APIs, you might want to surface partial results during the execution of the tool, especially if the tool takes a longer time to run.\n",
+    "A common scenario is streaming LLM tokens generated by a tool calling an LLM, though this applies to any use of Runnable objects. \n",
     "\n",
-    "1. To stream **arbitrary** data from inside a tool you can use [`stream_mode=\"custom\"`](../streaming#custom) and `get_stream_writer()`:\n",
-    "\n",
-    "    ```python\n",
-    "    # highlight-next-line\n",
-    "    from langgraph.config import get_stream_writer\n",
-    "    \n",
-    "    def tool(tool_arg: str):\n",
-    "        writer = get_stream_writer()\n",
-    "        for chunk in custom_data_stream():\n",
-    "            # stream any arbitrary data\n",
-    "            # highlight-next-line\n",
-    "            writer(chunk)\n",
-    "        ...\n",
-    "    \n",
-    "    for chunk in graph.stream(\n",
-    "        inputs,\n",
-    "        # highlight-next-line\n",
-    "        stream_mode=\"custom\"\n",
-    "    ):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "2. To stream LLM tokens generated by a tool calling an LLM you can use [`stream_mode=\"messages\"`](../streaming#messages):\n",
-    "\n",
-    "    ```python\n",
-    "    from langgraph.graph import StateGraph, MessagesState\n",
-    "    from langchain_openai import ChatOpenAI\n",
-    "    \n",
-    "    model = ChatOpenAI()\n",
-    "    \n",
-    "    def tool(tool_arg: str):\n",
-    "        model.invoke(tool_arg)\n",
-    "        ...\n",
-    "    \n",
-    "    def call_tools(state: MessagesState):\n",
-    "        tool_call = get_tool_call(state)\n",
-    "        tool_result = tool(**tool_call[\"args\"])\n",
-    "        ...\n",
-    "    \n",
-    "    graph = (\n",
-    "        StateGraph(MessagesState)\n",
-    "        .add_node(call_tools)\n",
-    "        ...\n",
-    "        .compile()\n",
-    "    \n",
-    "    for msg, metadata in graph.stream(\n",
-    "        inputs,\n",
-    "        # highlight-next-line\n",
-    "        stream_mode=\"messages\"\n",
-    "    ):\n",
-    "        print(msg)\n",
-    "    ```\n",
-    "\n",
-    "!!! note \"Using without LangChain\"\n",
-    "\n",
-    "    If you need to stream data from inside tools **without using LangChain**, you can use [`stream_mode=\"custom\"`](../streaming/#custom). Check out the [example below](#example-without-langchain) to learn more.\n",
-    "\n",
-    "!!! warning \"Async in Python < 3.11\"\n",
-    "    \n",
-    "    When using Python < 3.11 with async code, please ensure you manually pass the `RunnableConfig` through to the chat model when invoking it like so: `model.ainvoke(..., config)`.\n",
-    "    The stream method collects all events from your nested code using a streaming tracer passed as a callback. In 3.11 and above, this is automatically handled via [contextvars](https://docs.python.org/3/library/contextvars.html); prior to 3.11, [asyncio's tasks](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task) lacked proper `contextvar` support, meaning that the callbacks will only propagate if you manually pass the config through. We do this in the `call_model` function below.\n",
+    "This guide shows how to stream data from within a tool using the `astream` API with `stream_mode=\"messages\"` and also the more granular `astream_events` API. The `astream` API should be sufficient for most use cases.\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -87,8 +55,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "b364dfe2-010b-4588-8489-fb4d8be1f200",
+   "execution_count": 3,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,18 +66,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_API_KEY:  ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -138,346 +98,156 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ddc3ff-5620-48de-82f0-03b9137410cf",
+   "id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
    "metadata": {},
    "source": [
-    "## Streaming custom data\n",
+    "## Define the graph\n",
     "\n",
-    "We'll use a [prebuilt ReAct agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] for this guide:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f1975577-a485-42bd-b0f1-d3e987faf52b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_core.tools import tool\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "from langgraph.config import get_stream_writer\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "async def get_items(place: str) -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # highlight-next-line\n",
-    "    writer = get_stream_writer()\n",
-    "\n",
-    "    # this can be replaced with any actual streaming logic that you might have\n",
-    "    items = [\"books\", \"penciles\", \"pictures\"]\n",
-    "    for chunk in items:\n",
-    "        # highlight-next-line\n",
-    "        writer({\"custom_tool_data\": chunk})\n",
-    "\n",
-    "    return \", \".join(items)\n",
-    "\n",
-    "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4o-mini\")\n",
-    "tools = [get_items]\n",
-    "# contains `agent` (tool-calling LLM) and `tools` (tool executor) nodes\n",
-    "agent = create_react_agent(llm, tools=tools)"
+    "We'll use a prebuilt ReAct agent for this guide"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa96d572-d15f-4f00-b629-cf25e0b4dece",
+   "id": "9378fd4a-69e4-49e2-b34c-a98a0505ea35",
    "metadata": {},
    "source": [
-    "Let's now invoke our agent with an input that requires a tool call:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8ae5051c-53b9-4c53-87b2-d7263cda3b7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'custom_tool_data': 'books'}\n",
-      "{'custom_tool_data': 'penciles'}\n",
-      "{'custom_tool_data': 'pictures'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = {\n",
-    "    \"messages\": [  # noqa\n",
-    "        {\"role\": \"user\", \"content\": \"what items are in the office?\"}\n",
-    "    ]\n",
-    "}\n",
-    "async for chunk in agent.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6d8fa9fc-19af-47d6-9031-ee1720c51aa2",
-   "metadata": {},
-   "source": [
-    "## Streaming LLM tokens"
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">ASYNC IN PYTHON<=3.10</p>\n",
+    "    <p>\n",
+    "Any Langchain `RunnableLambda`, a `RunnableGenerator`, or `Tool` that invokes other runnables and is running async in python<=3.10, will have to propagate callbacks to child objects **manually**. This is because LangChain cannot automatically propagate callbacks to child objects in this case.\n",
+    "    \n",
+    "This is a common reason why you may fail to see events being emitted from custom runnables or tools.\n",
+    "    </p>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "38eaf453-9773-424d-a110-9e1038a69805",
+   "id": "f1975577-a485-42bd-b0f1-d3e987faf52b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.messages import AIMessageChunk\n",
-    "from langchain_core.runnables import RunnableConfig\n",
+    "from langchain_core.callbacks import Callbacks\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "from langgraph.prebuilt import create_react_agent\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "@tool\n",
     "async def get_items(\n",
     "    place: str,\n",
-    "    # Manually accept config (needed for Python <= 3.10)\n",
-    "    # highlight-next-line\n",
-    "    config: RunnableConfig,\n",
+    "    callbacks: Callbacks,  # <--- Manually accept callbacks (needed for Python <= 3.10)\n",
     ") -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # Attention: when using async, you should be invoking the LLM using ainvoke!\n",
-    "    # If you fail to do so, streaming will NOT work.\n",
-    "    response = await llm.ainvoke(\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "    # Attention when using async, you should be invoking the LLM using ainvoke!\n",
+    "    # If you fail to do so, streaming will not WORK.\n",
+    "    return await llm.ainvoke(\n",
     "        [\n",
     "            {\n",
     "                \"role\": \"user\",\n",
-    "                \"content\": (\n",
-    "                    f\"Can you tell me what kind of items i might find in the following place: '{place}'. \"\n",
-    "                    \"List at least 3 such items separating them by a comma. And include a brief description of each item.\"\n",
-    "                ),\n",
+    "                \"content\": f\"Can you tell me what kind of items i might find in the following place: '{place}'. \"\n",
+    "                \"List at least 3 such items separating them by a comma. And include a brief description of each item..\",\n",
     "            }\n",
     "        ],\n",
-    "        # highlight-next-line\n",
-    "        config,\n",
+    "        {\"callbacks\": callbacks},\n",
     "    )\n",
-    "    return response.content\n",
     "\n",
     "\n",
+    "llm = ChatOpenAI(model_name=\"gpt-4o\")\n",
     "tools = [get_items]\n",
-    "# contains `agent` (tool-calling LLM) and `tools` (tool executor) nodes\n",
     "agent = create_react_agent(llm, tools=tools)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "15cb55cc-b59d-4743-b6a3-13db75414d2c",
+   "metadata": {},
+   "source": [
+    "## Using stream_mode=\"messages\"\n",
+    "\n",
+    "Using `stream_mode=\"messages\"` is a good option if you don't have any complex LCEL logic inside of nodes (or you don't need super granular progress from within the LCEL chain)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "id": "4c9cdad3-3e9a-444f-9d9d-eae20b8d3486",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Certainly|!| Here| are| three| items| you| might| find| in| a| bedroom|:\n",
-      "\n",
-      "|1|.| **|Bed|**|:| The| central| piece| of| furniture| in| a| bedroom|,| typically| consisting| of| a| mattress| supported| by| a| frame|.| It| is| designed| for| sleeping| and| can| vary| in| size| from| twin| to| king|.| Beds| often| have| bedding|,| including| sheets|,| pillows|,| and| comfort|ers|,| to| enhance| comfort|.\n",
-      "\n",
-      "|2|.| **|D|resser|**|:| A| piece| of| furniture| with| drawers| used| for| storing| clothing| and| personal| items|.| Dress|ers| often| have| a| flat| surface| on| top|,| which| can| be| used| for| decorative| items|,| a| mirror|,| or| personal| accessories|.| They| help| keep| the| bedroom| organized| and| clutter|-free|.\n",
-      "\n",
-      "|3|.| **|Night|stand|**|:| A| small| table| or| cabinet| placed| beside| the| bed|,| used| for| holding| items| such| as| a| lamp|,| alarm| clock|,| books|,| or| personal| items|.| Night|stands| provide| convenience| for| easy| access| to| essentials| during| the| night|,| adding| functionality| and| style| to| the| bedroom| decor|.|"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "inputs = {\n",
-    "    \"messages\": [  # noqa\n",
-    "        {\"role\": \"user\", \"content\": \"what items are in the bedroom?\"}\n",
-    "    ]\n",
-    "}\n",
+    "final_message = \"\"\n",
     "async for msg, metadata in agent.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
+    "    {\"messages\": [(\"human\", \"what items are on the shelf?\")]}, stream_mode=\"messages\"\n",
     "):\n",
+    "    # Stream all messages from the tool node\n",
     "    if (\n",
-    "        isinstance(msg, AIMessageChunk)\n",
-    "        and msg.content\n",
-    "        # Stream all messages from the tool node\n",
-    "        # highlight-next-line\n",
+    "        msg.content\n",
+    "        and not isinstance(msg, HumanMessage)\n",
     "        and metadata[\"langgraph_node\"] == \"tools\"\n",
+    "        and not msg.name\n",
     "    ):\n",
-    "        print(msg.content, end=\"|\", flush=True)"
+    "        print(msg.content, end=\"|\", flush=True)\n",
+    "    # Final message should come from our agent\n",
+    "    if msg.content and metadata[\"langgraph_node\"] == \"agent\":\n",
+    "        final_message += msg.content"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "d598d7e2-617d-4c06-bc9a-6a03d5f58499",
+   "id": "81656193-1cbf-4721-a8df-0e316fd510e5",
    "metadata": {},
    "source": [
-    "## Example without LangChain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "780ddcb6-63a7-4c83-a739-bafbe3cd135a",
-   "metadata": {},
-   "source": [
-    "You can also stream data from within tool invocations **without using LangChain**. Below example demonstrates how to do it for a graph with a single tool-executing node. We'll leave it as an exercise for the reader to [implement ReAct agent from scratch](../react-agent-from-scratch) without using LangChain."
+    "## Using stream events API\n",
+    "\n",
+    "For simplicity, the `get_items` tool doesn't use any complex LCEL logic inside it -- it only invokes an LLM.\n",
+    "\n",
+    "However, if the tool were more complex (e.g., using a RAG chain inside it), and you wanted to see more granular events from within the chain, then you can use the astream events API.\n",
+    "\n",
+    "The example below only illustrates how to invoke the API.\n",
+    "\n",
+    "<div class=\"admonition warning\">\n",
+    "    <p class=\"admonition-title\">Use async for the astream events API</p>\n",
+    "    <p>\n",
+    "        You should generally be using `async` code (e.g., using `ainvoke` to invoke the llm) to be able to leverage the astream events API properly.\n",
+    "    </p>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3e8be67f-4bb8-4f14-9fdb-fc60340f3930",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import operator\n",
-    "import json\n",
-    "\n",
-    "from typing import TypedDict\n",
-    "from typing_extensions import Annotated\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "\n",
-    "from openai import AsyncOpenAI\n",
-    "\n",
-    "openai_client = AsyncOpenAI()\n",
-    "model_name = \"gpt-4o-mini\"\n",
-    "\n",
-    "\n",
-    "async def stream_tokens(model_name: str, messages: list[dict]):\n",
-    "    response = await openai_client.chat.completions.create(\n",
-    "        messages=messages, model=model_name, stream=True\n",
-    "    )\n",
-    "    role = None\n",
-    "    async for chunk in response:\n",
-    "        delta = chunk.choices[0].delta\n",
-    "\n",
-    "        if delta.role is not None:\n",
-    "            role = delta.role\n",
-    "\n",
-    "        if delta.content:\n",
-    "            yield {\"role\": role, \"content\": delta.content}\n",
-    "\n",
-    "\n",
-    "# this is our tool\n",
-    "async def get_items(place: str) -> str:\n",
-    "    \"\"\"Use this tool to list items one might find in a place you're asked about.\"\"\"\n",
-    "    # highlight-next-line\n",
-    "    writer = get_stream_writer()\n",
-    "    response = \"\"\n",
-    "    async for msg_chunk in stream_tokens(\n",
-    "        model_name,\n",
-    "        [\n",
-    "            {\n",
-    "                \"role\": \"user\",\n",
-    "                \"content\": (\n",
-    "                    \"Can you tell me what kind of items \"\n",
-    "                    f\"i might find in the following place: '{place}'. \"\n",
-    "                    \"List at least 3 such items separating them by a comma. \"\n",
-    "                    \"And include a brief description of each item.\"\n",
-    "                ),\n",
-    "            }\n",
-    "        ],\n",
-    "    ):\n",
-    "        response += msg_chunk[\"content\"]\n",
-    "        # highlight-next-line\n",
-    "        writer(msg_chunk)\n",
-    "\n",
-    "    return response\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    messages: Annotated[list[dict], operator.add]\n",
-    "\n",
-    "\n",
-    "# this is the tool-calling graph node\n",
-    "async def call_tool(state: State):\n",
-    "    ai_message = state[\"messages\"][-1]\n",
-    "    tool_call = ai_message[\"tool_calls\"][-1]\n",
-    "\n",
-    "    function_name = tool_call[\"function\"][\"name\"]\n",
-    "    if function_name != \"get_items\":\n",
-    "        raise ValueError(f\"Tool {function_name} not supported\")\n",
-    "\n",
-    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
-    "    arguments = json.loads(function_arguments)\n",
-    "\n",
-    "    function_response = await get_items(**arguments)\n",
-    "    tool_message = {\n",
-    "        \"tool_call_id\": tool_call[\"id\"],\n",
-    "        \"role\": \"tool\",\n",
-    "        \"name\": function_name,\n",
-    "        \"content\": function_response,\n",
-    "    }\n",
-    "    return {\"messages\": [tool_message]}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)  # noqa\n",
-    "    .add_node(call_tool)\n",
-    "    .add_edge(START, \"call_tool\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e712d12-841c-4eac-a4d8-d01c73c86c8c",
-   "metadata": {},
-   "source": [
-    "Let's now invoke our graph with an AI message that contains a tool call:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "2c30c7b4-62df-4855-8219-d5e1a1a09be9",
+   "id": "c3acdec9-0a24-4348-921e-435c8ea6f9fe",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sure|!| Here| are| three| common| items| you| might| find| in| a| bedroom|:\n",
+      "|In| a| bedroom|,| you| might| find| the| following| items|:\n",
       "\n",
-      "|1|.| **|Bed|**|:| The| focal| point| of| the| bedroom|,| a| bed| typically| consists| of| a| mattress| resting| on| a| frame|,| and| it| may| include| pillows| and| bedding|.| It| provides| a| comfortable| place| for| sleeping| and| resting|.\n",
+      "|1|.| **|Bed|**|:| The| central| piece| of| furniture| in| a| bedroom|,| typically| consisting| of| a| mattress| on| a| frame|,| where| people| sleep|.| It| often| includes| bedding| such| as| sheets|,| blankets|,| and| pillows| for| comfort|.\n",
       "\n",
-      "|2|.| **|D|resser|**|:| A| piece| of| furniture| with| multiple| drawers|,| a| dresser| is| used| for| storing| clothes|,| accessories|,| and| personal| items|.| It| often| has| a| flat| surface| that| may| be| used| to| display| decorative| items| or| a| mirror|.\n",
+      "|2|.| **|Ward|robe|**|:| A| large|,| tall| cupboard| or| fre|estanding| piece| of| furniture| used| for| storing| clothes|.| It| may| have| hanging| space|,| shelves|,| and| sometimes| drawers| for| organizing| garments| and| accessories|.\n",
       "\n",
-      "|3|.| **|Night|stand|**|:| Also| known| as| a| bedside| table|,| a| night|stand| is| placed| next| to| the| bed| and| typically| holds| items| like| lamps|,| books|,| alarm| clocks|,| and| personal| belongings| for| convenience| during| the| night|.\n",
-      "\n",
-      "|These| items| contribute| to| the| functionality| and| comfort| of| the| bedroom| environment|.|"
+      "|3|.| **|Night|stand|**|:| A| small| table| or| cabinet| placed| beside| the| bed|,| used| for| holding| items| like| a| lamp|,| alarm| clock|,| books|,| or| personal| belongings| that| might| be| needed| during| the| night| or| early| morning|.||"
      ]
     }
    ],
    "source": [
-    "inputs = {\n",
-    "    \"messages\": [\n",
-    "        {\n",
-    "            \"content\": None,\n",
-    "            \"role\": \"assistant\",\n",
-    "            \"tool_calls\": [\n",
-    "                {\n",
-    "                    \"id\": \"1\",\n",
-    "                    \"function\": {\n",
-    "                        \"arguments\": '{\"place\":\"bedroom\"}',\n",
-    "                        \"name\": \"get_items\",\n",
-    "                    },\n",
-    "                    \"type\": \"function\",\n",
-    "                }\n",
-    "            ],\n",
-    "        }\n",
-    "    ]\n",
-    "}\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
-    "async for chunk in graph.astream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
+    "async for event in agent.astream_events(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom.\"}]}, version=\"v2\"\n",
     "):\n",
-    "    print(chunk[\"content\"], end=\"|\", flush=True)"
+    "    if (\n",
+    "        event[\"event\"] == \"on_chat_model_stream\"\n",
+    "        and event[\"metadata\"].get(\"langgraph_node\") == \"tools\"\n",
+    "    ):\n",
+    "        print(event[\"data\"][\"chunk\"].content, end=\"|\", flush=True)"
    ]
   }
  ],
@@ -497,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

docs/docs/how-tos/streaming-specific-nodes.ipynb

@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c5889ca0-6feb-4864-a630-e97ccc2c587e",
-   "metadata": {},
-   "source": [
-    "# How to stream LLM tokens from specific nodes\n",
-    "\n",
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "\n",
-    "A common use case when [streaming LLM tokens](../streaming-tokens) is to only stream them from specific nodes. To do so, you can use `stream_mode=\"messages\"` and filter the outputs by the `langgraph_node` field in the streamed metadata:\n",
-    "\n",
-    "```python\n",
-    "from langgraph.graph import StateGraph\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI()\n",
-    "\n",
-    "def node_a(state: State):\n",
-    "    model.invoke(...)\n",
-    "    ...\n",
-    "\n",
-    "def node_b(state: State):\n",
-    "    model.invoke(...)\n",
-    "    ...\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(node_a)\n",
-    "    .add_node(node_b)\n",
-    "    ...\n",
-    "    .compile()\n",
-    "    \n",
-    "for msg, metadata in graph.stream(\n",
-    "    inputs,\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\"\n",
-    "):\n",
-    "    # stream from 'node_a'\n",
-    "    # highlight-next-line\n",
-    "    if metadata[\"langgraph_node\"] == \"node_a\":\n",
-    "        print(msg)\n",
-    "```\n",
-    "\n",
-    "!!! note \"Streaming from a specific LLM invocation\"\n",
-    "\n",
-    "    If you need to instead filter streamed LLM tokens to a specific LLM invocation, check out [this guide](../streaming-tokens#filter-to-specific-llm-invocation)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcff85bd-8a5d-409e-93d4-e9242b5e976d",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First we need to install the packages required"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "05157237-783c-49de-9f29-7dca3c285647",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "efd22cd2-3152-433b-ad50-65be8ace61d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0ce8c26-f38d-4bdb-89ff-b058e7560019",
-   "metadata": {},
-   "source": [
-    "## Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "419a3c71-7bf6-4656-99b8-b5d61f3f4bf1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import TypedDict\n",
-    "from langgraph.graph import START, StateGraph, MessagesState\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    topic: str\n",
-    "    joke: str\n",
-    "    poem: str\n",
-    "\n",
-    "\n",
-    "def write_joke(state: State):\n",
-    "    topic = state[\"topic\"]\n",
-    "    joke_response = model.invoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a joke about {topic}\"}]\n",
-    "    )\n",
-    "    return {\"joke\": joke_response.content}\n",
-    "\n",
-    "\n",
-    "def write_poem(state: State):\n",
-    "    topic = state[\"topic\"]\n",
-    "    poem_response = model.invoke(\n",
-    "        [{\"role\": \"user\", \"content\": f\"Write a short poem about {topic}\"}]\n",
-    "    )\n",
-    "    return {\"poem\": poem_response.content}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(write_joke)\n",
-    "    .add_node(write_poem)\n",
-    "    # write both the joke and the poem concurrently\n",
-    "    .add_edge(START, \"write_joke\")\n",
-    "    .add_edge(START, \"write_poem\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fed84d5e-ba10-4324-a664-dca263951a33",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In| shadows| soft|,| they| quietly| creep|,|  \n",
-      "|Wh|isk|ered| wonders|,| in| dreams| they| leap|.|  \n",
-      "|With| eyes| like| lantern|s|,| bright| and| wide|,|  \n",
-      "|Myst|eries| linger| where| they| reside|.|  \n",
-      "\n",
-      "|P|aws| that| pat|ter| on| silent| floors|,|  \n",
-      "|Cur|led| in| sun|be|ams|,| they| seek| out| more|.|  \n",
-      "|A| flick| of| a| tail|,| a| leap|,| a| p|ounce|,|  \n",
-      "|In| their| playful| world|,| we| can't| help| but| bounce|.|  \n",
-      "\n",
-      "|Guard|ians| of| secrets|,| with| gentle| grace|,|  \n",
-      "|Each| little| me|ow|,| a| warm| embrace|.|  \n",
-      "|Oh|,| the| joy| that| they| bring|,| so| pure| and| true|,|  \n",
-      "|In| the| heart| of| a| cat|,| there's| magic| anew|.|  |"
-     ]
-    }
-   ],
-   "source": [
-    "for msg, metadata in graph.stream(\n",
-    "    {\"topic\": \"cats\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    # highlight-next-line\n",
-    "    if msg.content and metadata[\"langgraph_node\"] == \"write_poem\":\n",
-    "        print(msg.content, end=\"|\", flush=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/how-tos/streaming-tokens-without-langchain.ipynb

@@ -0,0 +1,355 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b23ced4e-dc29-43be-9f94-0c36bb181b8a",
+   "metadata": {},
+   "source": [
+    "# How to stream LLM tokens (without LangChain LLMs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7044eeb8-4074-4f9c-8a62-962488744557",
+   "metadata": {},
+   "source": [
+    "In this example we will stream tokens from the language model powering an agent. We'll be using OpenAI client library directly, without using LangChain chat models. We will also use a ReAct agent as an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a37f60af-43ea-4aa6-847a-df8cc47065f5",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install the required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "47f79af8-58d8-4a48-8d9a-88823d88701f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langgraph openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cf6b41d-7fcb-40b6-9a72-229cdd00a094",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5bc618",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d02ebb-c2e1-4ef7-b187-810d55139317",
+   "metadata": {},
+   "source": [
+    "## Define model, tools and graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba684f1-d46b-42e4-95cf-9685209a5992",
+   "metadata": {},
+   "source": [
+    "### Define a node that will call OpenAI API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d59234f9-173e-469d-a725-c13e0979663e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import AsyncOpenAI\n",
+    "from langchain_core.language_models.chat_models import ChatGenerationChunk\n",
+    "from langchain_core.messages import AIMessageChunk\n",
+    "from langchain_core.runnables.config import (\n",
+    "    ensure_config,\n",
+    "    get_callback_manager_for_config,\n",
+    ")\n",
+    "\n",
+    "openai_client = AsyncOpenAI()\n",
+    "# define tool schema for openai tool calling\n",
+    "\n",
+    "tool = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_items\",\n",
+    "        \"description\": \"Use this tool to look up which items are in the given place.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\"place\": {\"type\": \"string\"}},\n",
+    "            \"required\": [\"place\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "async def call_model(state, config=None):\n",
+    "    config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
+    "    callback_manager = get_callback_manager_for_config(config)\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]\n",
+    "    response = await openai_client.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\", tools=[tool], stream=True\n",
+    "    )\n",
+    "\n",
+    "    response_content = \"\"\n",
+    "    role = None\n",
+    "\n",
+    "    tool_call_id = None\n",
+    "    tool_call_function_name = None\n",
+    "    tool_call_function_arguments = \"\"\n",
+    "    async for chunk in response:\n",
+    "        delta = chunk.choices[0].delta\n",
+    "        if delta.role is not None:\n",
+    "            role = delta.role\n",
+    "\n",
+    "        if delta.content:\n",
+    "            response_content += delta.content\n",
+    "            # note: we're wrapping the response in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
+    "            chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=delta.content,\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(delta.content, chunk=chunk)\n",
+    "\n",
+    "        if delta.tool_calls:\n",
+    "            # note: for simplicity we're only handling a single tool call here\n",
+    "            if delta.tool_calls[0].function.name is not None:\n",
+    "                tool_call_function_name = delta.tool_calls[0].function.name\n",
+    "                tool_call_id = delta.tool_calls[0].id\n",
+    "\n",
+    "            # note: we're wrapping the tools calls in ChatGenerationChunk so that we can stream this back using stream_mode=\"messages\"\n",
+    "            tool_call_chunk = ChatGenerationChunk(\n",
+    "                message=AIMessageChunk(\n",
+    "                    content=\"\",\n",
+    "                    additional_kwargs={\"tool_calls\": [delta.tool_calls[0].dict()]},\n",
+    "                )\n",
+    "            )\n",
+    "            llm_run_manager.on_llm_new_token(\"\", chunk=tool_call_chunk)\n",
+    "            tool_call_function_arguments += delta.tool_calls[0].function.arguments\n",
+    "\n",
+    "    if tool_call_function_name is not None:\n",
+    "        tool_calls = [\n",
+    "            {\n",
+    "                \"id\": tool_call_id,\n",
+    "                \"function\": {\n",
+    "                    \"name\": tool_call_function_name,\n",
+    "                    \"arguments\": tool_call_function_arguments,\n",
+    "                },\n",
+    "                \"type\": \"function\",\n",
+    "            }\n",
+    "        ]\n",
+    "    else:\n",
+    "        tool_calls = None\n",
+    "\n",
+    "    response_message = {\n",
+    "        \"role\": role,\n",
+    "        \"content\": response_content,\n",
+    "        \"tool_calls\": tool_calls,\n",
+    "    }\n",
+    "    return {\"messages\": [response_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a3877e8-8ace-40d5-ad04-cbf21c6f3250",
+   "metadata": {},
+   "source": [
+    "### Define our tools and a tool-calling node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b756ea32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "\n",
+    "async def get_items(place: str) -> str:\n",
+    "    \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
+    "    if \"bed\" in place:  # For under the bed\n",
+    "        return \"socks, shoes and dust bunnies\"\n",
+    "    if \"shelf\" in place:  # For 'shelf'\n",
+    "        return \"books, penciles and pictures\"\n",
+    "    else:  # if the agent decides to ask about a different place\n",
+    "        return \"cat snacks\"\n",
+    "\n",
+    "\n",
+    "# define mapping to look up functions when running tools\n",
+    "function_name_to_function = {\"get_items\": get_items}\n",
+    "\n",
+    "\n",
+    "async def call_tools(state):\n",
+    "    messages = state[\"messages\"]\n",
+    "\n",
+    "    tool_call = messages[-1][\"tool_calls\"][0]\n",
+    "    function_name = tool_call[\"function\"][\"name\"]\n",
+    "    function_arguments = tool_call[\"function\"][\"arguments\"]\n",
+    "    arguments = json.loads(function_arguments)\n",
+    "\n",
+    "    function_response = await function_name_to_function[function_name](**arguments)\n",
+    "    tool_message = {\n",
+    "        \"tool_call_id\": tool_call[\"id\"],\n",
+    "        \"role\": \"tool\",\n",
+    "        \"name\": function_name,\n",
+    "        \"content\": function_response,\n",
+    "    }\n",
+    "    return {\"messages\": [tool_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6685898c-9a1c-4803-a492-bd70574ebe38",
+   "metadata": {},
+   "source": [
+    "### Define our graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Literal\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langgraph.graph import StateGraph, END, START\n",
+    "\n",
+    "\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[list, operator.add]\n",
+    "\n",
+    "\n",
+    "def should_continue(state) -> Literal[\"tools\", END]:\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    if last_message[\"tool_calls\"]:\n",
+    "        return \"tools\"\n",
+    "    return END\n",
+    "\n",
+    "\n",
+    "workflow = StateGraph(State)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_node(\"model\", call_model)  # i.e. our \"agent\"\n",
+    "workflow.add_node(\"tools\", call_tools)\n",
+    "workflow.add_conditional_edges(\"model\", should_continue)\n",
+    "workflow.add_edge(\"tools\", \"model\")\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d046e2ef-f208-4831-ab31-203b2e75a49a",
+   "metadata": {},
+   "source": [
+    "## Stream tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d6ed3df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': ''}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bed'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "[{'name': 'get_items', 'args': {'place': 'bedroom'}, 'id': 'call_h7g3jsgeRXIOUiaEC0VtM4EI', 'type': 'tool_call'}]\n",
+      "In| the| bedroom|,| you| have| socks|,| shoes|,| and| some| dust| b|unn|ies|.|"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import AIMessageChunk\n",
+    "\n",
+    "first = True\n",
+    "async for msg, metadata in graph.astream(\n",
+    "    {\"messages\": [{\"role\": \"user\", \"content\": \"what's in the bedroom\"}]},\n",
+    "    stream_mode=\"messages\",\n",
+    "):\n",
+    "    if msg.content:\n",
+    "        print(msg.content, end=\"|\", flush=True)\n",
+    "\n",
+    "    if isinstance(msg, AIMessageChunk):\n",
+    "        if first:\n",
+    "            gathered = msg\n",
+    "            first = False\n",
+    "        else:\n",
+    "            gathered = gathered + msg\n",
+    "\n",
+    "        if msg.tool_call_chunks:\n",
+    "            print(gathered.tool_calls)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/how-tos/streaming.ipynb

@@ -1,547 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "76c4b04f-0c03-4321-9d40-38d12c59d088",
-   "metadata": {},
-   "source": [
-    "# How to stream"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15403cdb-441d-43af-a29f-fc15abe03dcc",
-   "metadata": {},
-   "source": [
-    "!!! info \"Prerequisites\"\n",
-    "\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Streaming](../../concepts/streaming/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "\n",
-    "Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs.\n",
-    "\n",
-    "LangGraph is built with first class support for streaming. There are several different ways to stream back outputs from a graph run:\n",
-    "\n",
-    "- `\"values\"`: Emit all values in the state after each step.\n",
-    "- `\"updates\"`: Emit only the node names and updates returned by the nodes after each step.\n",
-    "    If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.\n",
-    "- `\"custom\"`: Emit custom data from inside nodes using `StreamWriter`.\n",
-    "- [`\"messages\"`](../streaming-tokens): Emit LLM messages token-by-token together with metadata for any LLM invocations inside nodes.\n",
-    "- `\"debug\"`: Emit debug events with as much information as possible for each step.\n",
-    "\n",
-    "You can stream outputs from the graph by using `graph.stream(..., stream_mode=<stream_mode>)` method, e.g.:\n",
-    "\n",
-    "=== \"Sync\"\n",
-    "\n",
-    "    ```python\n",
-    "    for chunk in graph.stream(inputs, stream_mode=\"updates\"):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "=== \"Async\"\n",
-    "\n",
-    "    ```python\n",
-    "    async for chunk in graph.astream(inputs, stream_mode=\"updates\"):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "You can also combine multiple streaming mode by providing a list to `stream_mode` parameter:\n",
-    "\n",
-    "=== \"Sync\"\n",
-    "\n",
-    "    ```python\n",
-    "    for chunk in graph.stream(inputs, stream_mode=[\"updates\", \"custom\"]):\n",
-    "        print(chunk)\n",
-    "    ```\n",
-    "\n",
-    "=== \"Async\"\n",
-    "\n",
-    "    ```python\n",
-    "    async for chunk in graph.astream(inputs, stream_mode=[\"updates\", \"custom\"]):\n",
-    "        print(chunk)\n",
-    "    ```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9723cf76-6fe4-4b52-829f-3f28712ddcb7",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "427f8f66-7404-4c7d-a642-af5053b8b28f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install --quiet -U langgraph langchain_openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "03310ce6-e21f-4378-93bf-dd273fdb3e9a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OPENAI_API_KEY:  ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80399508-bad8-43b7-8ec9-4c06ad1774cc",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
-    "    <p style=\"padding-top: 5px;\">\n",
-    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4adbb2-61e8-4bb7-942d-b4dc27ba71ac",
-   "metadata": {},
-   "source": [
-    "Let's define a simple graph with two nodes:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6d4c513-1006-4179-bba9-d858fc952169",
-   "metadata": {},
-   "source": [
-    "## Define graph"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "faeb5ce8-d383-4277-b0a8-322e713638e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import TypedDict\n",
-    "from langgraph.graph import StateGraph, START\n",
-    "\n",
-    "\n",
-    "class State(TypedDict):\n",
-    "    topic: str\n",
-    "    joke: str\n",
-    "\n",
-    "\n",
-    "def refine_topic(state: State):\n",
-    "    return {\"topic\": state[\"topic\"] + \" and cats\"}\n",
-    "\n",
-    "\n",
-    "def generate_joke(state: State):\n",
-    "    return {\"joke\": f\"This is a joke about {state['topic']}\"}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f9b90850-85bf-4391-b6b7-22ad45edaa3b",
-   "metadata": {},
-   "source": [
-    "## Stream all values in the state (stream_mode=\"values\") {#values}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1ed60d4-cf78-4d4d-a660-6879539e168f",
-   "metadata": {},
-   "source": [
-    "Use this to stream **all values** in the state after each step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3daca06a-369b-41e5-8e4e-6edc4d4af3a7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'topic': 'ice cream'}\n",
-      "{'topic': 'ice cream and cats'}\n",
-      "{'topic': 'ice cream and cats', 'joke': 'This is a joke about ice cream and cats'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"values\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "adcb1bdb-f9fa-4d42-87ce-8e25d4290883",
-   "metadata": {},
-   "source": [
-    "## Stream state updates from the nodes (stream_mode=\"updates\") {#updates}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44c55326-d077-4583-ae5b-396f45daf21c",
-   "metadata": {},
-   "source": [
-    "Use this to stream only the **state updates** returned by the nodes after each step. The streamed outputs include the name of the node as well as the update."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eed7d401-37d1-4d15-b6dd-88956fff89e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'refine_topic': {'topic': 'ice cream and cats'}}\n",
-      "{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"updates\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9ed9c68-b7c5-4420-945d-84fa33fcf88f",
-   "metadata": {},
-   "source": [
-    "## Stream debug events (stream_mode=\"debug\") {#debug}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94690715-f86c-42f6-be2d-4df82f6f9a96",
-   "metadata": {},
-   "source": [
-    "Use this to stream **debug events** with as much information as possible for each step. Includes information about tasks that were scheduled to be executed as well as the results of the task executions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cc6354f6-0c39-49cf-a529-b9c6c8713d7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'type': 'task', 'timestamp': '2025-01-28T22:06:34.789803+00:00', 'step': 1, 'payload': {'id': 'eb305d74-3460-9510-d516-beed71a63414', 'name': 'refine_topic', 'input': {'topic': 'ice cream'}, 'triggers': ['start:refine_topic']}}\n",
-      "{'type': 'task_result', 'timestamp': '2025-01-28T22:06:34.790013+00:00', 'step': 1, 'payload': {'id': 'eb305d74-3460-9510-d516-beed71a63414', 'name': 'refine_topic', 'error': None, 'result': [('topic', 'ice cream and cats')], 'interrupts': []}}\n",
-      "{'type': 'task', 'timestamp': '2025-01-28T22:06:34.790165+00:00', 'step': 2, 'payload': {'id': '74355cb8-6284-25e0-579f-430493c1bdab', 'name': 'generate_joke', 'input': {'topic': 'ice cream and cats'}, 'triggers': ['refine_topic']}}\n",
-      "{'type': 'task_result', 'timestamp': '2025-01-28T22:06:34.790337+00:00', 'step': 2, 'payload': {'id': '74355cb8-6284-25e0-579f-430493c1bdab', 'name': 'generate_joke', 'error': None, 'result': [('joke', 'This is a joke about ice cream and cats')], 'interrupts': []}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"debug\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6791da60-0513-43e6-b445-788dd81683bb",
-   "metadata": {},
-   "source": [
-    "## Stream LLM tokens ([stream_mode=\"messages\"](../streaming-tokens)) {#messages}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f45d68b-f7ca-4012-96cc-d276a143f571",
-   "metadata": {},
-   "source": [
-    "Use this to stream **LLM messages token-by-token** together with metadata for any LLM invocations inside nodes or tasks. Let's modify the above example to include LLM calls:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "efa787e1-be4d-433b-a1af-46a9c99ad8f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(model=\"gpt-4o-mini\")\n",
-    "\n",
-    "\n",
-    "def generate_joke(state: State):\n",
-    "    # highlight-next-line\n",
-    "    llm_response = llm.invoke(\n",
-    "        # highlight-next-line\n",
-    "        [\n",
-    "            # highlight-next-line\n",
-    "            {\"role\": \"user\", \"content\": f\"Generate a joke about {state['topic']}\"}\n",
-    "            # highlight-next-line\n",
-    "        ]\n",
-    "        # highlight-next-line\n",
-    "    )\n",
-    "    return {\"joke\": llm_response.content}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c251f809-8922-46ea-bd5b-18264fcc523a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Why| did| the| cat| sit| on| the| ice| cream| cone|?\n",
-      "\n",
-      "|Because| it| wanted| to| be| a| \"|p|urr|-f|ect|\"| scoop|!| 🍦|🐱|"
-     ]
-    }
-   ],
-   "source": [
-    "for message_chunk, metadata in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"messages\",\n",
-    "):\n",
-    "    if message_chunk.content:\n",
-    "        print(message_chunk.content, end=\"|\", flush=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b1912d72-7b68-4810-8b98-d7f3c35fbb6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'langgraph_step': 2,\n",
-       " 'langgraph_node': 'generate_joke',\n",
-       " 'langgraph_triggers': ['refine_topic'],\n",
-       " 'langgraph_path': ('__pregel_pull', 'generate_joke'),\n",
-       " 'langgraph_checkpoint_ns': 'generate_joke:568879bc-8800-2b0d-a5b5-059526a4bebf',\n",
-       " 'checkpoint_ns': 'generate_joke:568879bc-8800-2b0d-a5b5-059526a4bebf',\n",
-       " 'ls_provider': 'openai',\n",
-       " 'ls_model_name': 'gpt-4o-mini',\n",
-       " 'ls_model_type': 'chat',\n",
-       " 'ls_temperature': 0.7}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d1ebeda-4498-40e0-a30a-0844cb491425",
-   "metadata": {},
-   "source": [
-    "## Stream custom data (stream_mode=\"custom\") {#custom}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9ca56cc-d36e-4061-b1f6-9ade4e3e00a0",
-   "metadata": {},
-   "source": [
-    "Use this to stream custom data from inside nodes using [`StreamWriter`][langgraph.types.StreamWriter]."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e3bf6a2b-afe3-4bd3-8474-57cccd994f23",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langgraph.types import StreamWriter\n",
-    "\n",
-    "\n",
-    "# highlight-next-line\n",
-    "def generate_joke(state: State, writer: StreamWriter):\n",
-    "    # highlight-next-line\n",
-    "    writer({\"custom_key\": \"Writing custom data while generating a joke\"})\n",
-    "    return {\"joke\": f\"This is a joke about {state['topic']}\"}\n",
-    "\n",
-    "\n",
-    "graph = (\n",
-    "    StateGraph(State)\n",
-    "    .add_node(refine_topic)\n",
-    "    .add_node(generate_joke)\n",
-    "    .add_edge(START, \"refine_topic\")\n",
-    "    .add_edge(\"refine_topic\", \"generate_joke\")\n",
-    "    .compile()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2ecfb0b0-3311-46f5-9dc8-6c7853373792",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'custom_key': 'Writing custom data while generating a joke'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "for chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=\"custom\",\n",
-    "):\n",
-    "    print(chunk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28e67f4d-fcab-46a8-93e2-b7bee30336c1",
-   "metadata": {},
-   "source": [
-    "## Configure multiple streaming modes {#multiple}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "01ff946a-f38d-42ad-bc71-a2621fab1b6c",
-   "metadata": {},
-   "source": [
-    "Use this to combine multiple streaming modes. The outputs are streamed as tuples `(stream_mode, streamed_output)`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "bf4cab4b-356c-4276-9035-26974abe1efe",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stream mode: updates\n",
-      "{'refine_topic': {'topic': 'ice cream and cats'}}\n",
-      "\n",
-      "\n",
-      "Stream mode: custom\n",
-      "{'custom_key': 'Writing custom data while generating a joke'}\n",
-      "\n",
-      "\n",
-      "Stream mode: updates\n",
-      "{'generate_joke': {'joke': 'This is a joke about ice cream and cats'}}\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for stream_mode, chunk in graph.stream(\n",
-    "    {\"topic\": \"ice cream\"},\n",
-    "    # highlight-next-line\n",
-    "    stream_mode=[\"updates\", \"custom\"],\n",
-    "):\n",
-    "    print(f\"Stream mode: {stream_mode}\")\n",
-    "    print(chunk)\n",
-    "    print(\"\\n\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/index.md

@@ -3,4 +3,4 @@ hide_comments: true
 title: Home
 ---
 
-{!../README.md!}
+{!README.md!}

docs/docs/prebuilt.md

@@ -1,34 +0,0 @@
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# 🚀 Prebuilt Libraries
-
-LangGraph includes a prebuilt React agent. For more information on how to use it, 
-check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
-
-If you’re looking for other prebuilt libraries, explore the community-built options 
-below. These libraries can extend LangGraph's functionality in various ways.
-
-## 📚 Available Libraries
-
-| Name | GitHub URL | Description | Weekly Downloads |
-| --- | --- | --- | --- |
-| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph | 7081 |
-
-## ✨ 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](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) 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! 🚀

docs/docs/reference/config.md

@@ -1,5 +0,0 @@
-::: langgraph.config
-    options:
-      members:
-        - get_store
-        - get_stream_writer

docs/docs/reference/pregel.md

@@ -1,9 +0,0 @@
-::: langgraph.pregel.Pregel
-    options:
-      members:
-        - stream
-        - astream
-        - invoke
-        - ainvoke
-        - update_state
-        - aupdate_state

docs/docs/troubleshooting/errors/INVALID_LICENSE.md

@@ -1,48 +0,0 @@
-# INVALID_LICENSE
-
-This error is raised when license verification fails while attempting to start a self-hosted LangGraph Platform server. This error is specific to the LangGraph Platform and is not related to the open source libraries.
-
-## When This Occurs
-
-This error occurs when running a self-hosted deployment of LangGraph Platform without a valid enterprise license or API key.
-
-## Troubleshooting
-
-### Confirm deployment type
-
-First, confirm the desired mode of deployment.
-
-#### For Local Development
-
-If you're just developing locally, you can use the lightweight in-memory server by running `langgraph dev`.
-See the [local server](../../tutorials/langgraph-platform/local-server.md) docs for more information.
-
-#### For Managed LangGraph Platform
-
-If you would like a fast managed environment, consider the [Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option. This requires no additional license key.
-
-#### For Self-Hosted Lite (Limited Features)
-
-If your deployment is unlikely to see more than 1 million node executions per year and don't need Crons and other enterprise features, consider the [Self-Hosted Lite](../../concepts/deployment_options.md#self-hosted-lite) deployment option.
-
-You can deploy with Self-Hosted Lite by setting a valid `LANGSMITH_API_KEY` in your environment (e.g., in the `.env` file referenced by `langgraph.json`) and building a Docker image. The API key must be associated with an account on a **Plus** plan or greater.
-
-#### For Self-Hosted Enterprise (Full Features)
-
-For full self-hosting, set the `LANGGRAPH_CLOUD_LICENSE_KEY` environment variable. If you are interested in an enterprise license key, please contact the LangChain support team.
-
-For more information on deployment options and their features, see the [Deployment Options](../../concepts/deployment_options.md) documentation.
-
-### Confirm credentials
-
-If you have confirmed that you would like to self-host LangGraph Platform, please verify your credentials.
-
-#### For Self-Hosted Lite
-
-1. Confirm that you have provided a working `LANGSMITH_API_KEY` environment variable in your deployment environment or `.env` file
-2. Confirm the provided API key is associated with an account on a **Plus** or **Enterprise** plan (or equivalent)
-
-#### For Self-Hosted Enterprise
-
-1. Confirm that you have provided a working `LANGGRAPH_CLOUD_LICENSE_KEY` environment variable in your deployment environment or `.env` file
-2. Confirm the key is still valid and has not surpassed its expiration date

docs/docs/troubleshooting/errors/index.md

@@ -1,6 +1,6 @@
 # Error reference
 
-This page contains guides around resolving common errors you may find while building with LangGraph.
+This page contains guides around resolving common errors you may find while building with LangChain.
 Errors referenced below will have an `lc_error_code` property corresponding to one of the below codes when they are thrown in code.
 
 - [GRAPH_RECURSION_LIMIT](./GRAPH_RECURSION_LIMIT.md)
@@ -8,9 +8,3 @@ Errors referenced below will have an `lc_error_code` property corresponding to o
 - [INVALID_GRAPH_NODE_RETURN_VALUE](./INVALID_GRAPH_NODE_RETURN_VALUE.md)
 - [MULTIPLE_SUBGRAPHS](./MULTIPLE_SUBGRAPHS.md)
 - [INVALID_CHAT_HISTORY](./INVALID_CHAT_HISTORY.md)
-
-## LangGraph Platform
-
-These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.
-
-- [INVALID_LICENSE](./INVALID_LICENSE.md)

docs/docs/tutorials/deployment.md

@@ -12,7 +12,7 @@ Get started deploying your LangGraph applications locally or on the cloud with
 
 ## Deployment Options
 
-- [Self-Hosted Lite](../concepts/self_hosted.md): A free (up to 1 million nodes executed per year), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
+- [Self-Hosted Lite](../concepts/self_hosted.md): A free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner
 - [Cloud SaaS](../concepts/langgraph_cloud.md): Hosted as part of LangSmith.
 - [Bring Your Own Cloud](../concepts/bring_your_own_cloud.md): We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud.
 - [Self-Hosted Enterprise](../concepts/self_hosted.md): Completely managed by you.

docs/docs/tutorials/introduction.ipynb

@@ -35,10 +35,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "705d4020-6ee8-44cc-b1a5-8c34e7172fc7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANTHROPIC_API_KEY:  ········\n"
+     ]
+    }
+   ],
    "source": [
     "import getpass\n",
     "import os\n",
@@ -1153,7 +1161,6 @@
     ")\n",
     "graph_builder.add_edge(\"tools\", \"chatbot\")\n",
     "graph_builder.set_entry_point(\"chatbot\")\n",
-    "memory = MemorySaver()\n",
     "graph = graph_builder.compile(checkpointer=memory)\n",
     "```\n",
     "</pre>\n",
@@ -2465,7 +2472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb

@@ -14,7 +14,7 @@
     "\n",
     "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n",
     "\n",
-    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create a specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n",
+    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\". This is an example of a [multi-agent network](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network) architecture.\n",
     "\n",
     "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n",
     "\n",

docs/mkdocs.yml

@@ -56,14 +56,20 @@ plugins:
   - search:
       separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])'
   - autorefs
+  - redirects:
+      redirect_maps:
+        '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'
   - mkdocstrings:
       handlers:
         python:
           import:
             - https://docs.python.org/3/objects.inv
-            - https://python.langchain.com/api_reference/objects.inv
+            - https://api.python.langchain.com/en/latest/objects.inv
           options:
-            enable_inventory: true
             members_order: source
             allow_inspection: true
             heading_level: 2
@@ -98,20 +104,12 @@ nav:
         - how-tos/index.md
         - LangGraph:
           - LangGraph: how-tos#langgraph
-          - Graph API Basics:
-              - Graph API Basics: how-tos#graph-api-basics
-              - how-tos/state-reducers.ipynb
-              - how-tos/sequence.ipynb
-              - how-tos/branching.ipynb
-              - how-tos/recursion-limit.ipynb
-              - how-tos/visualization.ipynb
           - Controllability:
               - Controllability: how-tos#controllability
+              - how-tos/branching.ipynb
               - how-tos/map-reduce.ipynb
+              - how-tos/recursion-limit.ipynb
               - how-tos/command.ipynb
-              - how-tos/configuration.ipynb
-              - how-tos/node-retries.ipynb
-              - how-tos/return-when-recursion-limit-hits.ipynb
           - Persistence:
               - Persistence: how-tos#persistence
               - how-tos/persistence.ipynb
@@ -140,10 +138,15 @@ nav:
               - how-tos/review-tool-calls-functional.ipynb
           - Streaming:
               - Streaming: how-tos#streaming
-              - how-tos/streaming.ipynb
+              - how-tos/stream-values.ipynb
+              - how-tos/stream-updates.ipynb
               - how-tos/streaming-tokens.ipynb
-              - how-tos/streaming-specific-nodes.ipynb
+              - how-tos/streaming-tokens-without-langchain.ipynb
+              - how-tos/streaming-content.ipynb
+              - how-tos/stream-multiple.ipynb
               - how-tos/streaming-events-from-within-tools.ipynb
+              - how-tos/streaming-events-from-within-tools-without-langchain.ipynb
+              - how-tos/streaming-from-final-node.ipynb
               - how-tos/streaming-subgraphs.ipynb
               - how-tos/disable-streaming.ipynb
           - Tool calling:
@@ -174,10 +177,12 @@ nav:
           - Other:
               - Other: how-tos#other
               - how-tos/async.ipynb
+              - how-tos/visualization.ipynb
+              - how-tos/configuration.ipynb
+              - how-tos/node-retries.ipynb
               - how-tos/react-agent-structured-output.ipynb
               - how-tos/run-id-langsmith.ipynb
-              - how-tos/autogen-integration.ipynb
-              - how-tos/autogen-integration-functional.ipynb
+              - how-tos/return-when-recursion-limit-hits.ipynb
           - Prebuilt ReAct Agent:
               - Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
               - how-tos/create-react-agent.ipynb
@@ -198,7 +203,6 @@ nav:
                 - cloud/deployment/custom_docker.md
                 - cloud/deployment/test_locally.md
                 - cloud/deployment/graph_rebuild.md
-                - how-tos/autogen-langgraph-platform.ipynb
             - Deployment:
                 - Deployment: how-tos#deployment
                 - cloud/deployment/cloud.md
@@ -354,7 +358,6 @@ nav:
             - tutorials/auth/resource_auth.md
             - tutorials/auth/add_auth_server.md
     - Resources:
-      - Prebuilt: prebuilt.md
       - FAQ: concepts/faq.md
       - Troubleshooting:
           - Troubleshooting: troubleshooting/errors/index.md
@@ -364,9 +367,8 @@ nav:
           - troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md
           - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
           - troubleshooting/errors/INVALID_CHAT_HISTORY.md
-          - troubleshooting/errors/INVALID_LICENSE.md
       - LangGraph Academy Course: https://academy.langchain.com/courses/intro-to-langgraph
-
+  
   - API reference:
     - Library:
       - Graphs: reference/graphs.md
@@ -377,8 +379,6 @@ nav:
       - Errors: reference/errors.md
       - Types: reference/types.md
       - Constants: reference/constants.md
-      - Pregel: reference/pregel.md
-      - Config: reference/config.md
       - Functional API: reference/func.md
     - LangGraph Platform:
       - Server API: "cloud/reference/api/api_ref.md"

docs/overrides/main.html

@@ -159,24 +159,6 @@
       color: #000000;
     }
 
-    .md-banner a {
-      color: #000000;
-      text-decoration: underline;
-    }
-
-    .md-banner a:hover {
-      color: #000000;
-    }
-
-    /* Dark mode banner links */
-    [data-md-color-scheme="slate"] .md-banner a {
-      color: #000000;
-    }
-
-    [data-md-color-scheme="slate"] .md-banner a:hover {
-      color: #000000;
-    }
-
     /* control the navbar depth */
     [data-md-level="2"] .md-nav {
       display: none;
@@ -215,5 +197,5 @@
 
 
 {% block announce %}
-  <b>Join us at <a href="https://interrupt.langchain.com/" target="_blank" rel="noopener noreferrer"> Interrupt: The Agent AI Conference by LangChain</a> on May 13 & 14 in San Francisco!</b>
+  To learn more about LangGraph, check out our first LangChain Academy course, <em>Introduction to LangGraph</em>, available for free <a href="https://academy.langchain.com/courses/intro-to-langgraph">here</a>.
 {% endblock %}

docs/package.json

@@ -1,8 +0,0 @@
-{
-  "name": "docs",
-  "version": "1.0.0",
-  "license": "MIT",
-  "scripts": {
-    "build": "echo 'export PATH=$PATH:/vercel/.local/bin:$PATH' > ~/.bashrc && source ~/.bashrc && make vercel-build-docs"
-  }
-}

docs/vercel.json

@@ -1,4 +0,0 @@
-{
-  "buildCommand": "yarn build",
-  "outputDirectory": "site"
-}

libs/checkpoint/langgraph/checkpoint/memory/__init__.py

@@ -26,7 +26,7 @@ from langgraph.checkpoint.serde.types import TASKS, ChannelProtocol
 logger = logging.getLogger(__name__)
 
 
-class InMemorySaver(
+class MemorySaver(
     BaseCheckpointSaver[str], AbstractContextManager, AbstractAsyncContextManager
 ):
     """An in-memory checkpoint saver.
@@ -34,7 +34,7 @@ class InMemorySaver(
     This checkpoint saver stores checkpoints in memory using a defaultdict.
 
     Note:
-        Only use `InMemorySaver` for debugging or testing purposes.
+        Only use `MemorySaver` for debugging or testing purposes.
         For production use cases we recommend installing [langgraph-checkpoint-postgres](https://pypi.org/project/langgraph-checkpoint-postgres/) and using `PostgresSaver` / `AsyncPostgresSaver`.
 
     Args:
@@ -44,7 +44,7 @@ class InMemorySaver(
 
             import asyncio
 
-            from langgraph.checkpoint.memory import InMemorySaver
+            from langgraph.checkpoint.memory import MemorySaver
             from langgraph.graph import StateGraph
 
             builder = StateGraph(int)
@@ -52,7 +52,7 @@ class InMemorySaver(
             builder.set_entry_point("add_one")
             builder.set_finish_point("add_one")
 
-            memory = InMemorySaver()
+            memory = MemorySaver()
             graph = builder.compile(checkpointer=memory)
             coro = graph.ainvoke(1, {"configurable": {"thread_id": "thread-1"}})
             asyncio.run(coro)  # Output: 2
@@ -84,7 +84,7 @@ class InMemorySaver(
             self.stack.enter_context(self.storage)  # type: ignore[arg-type]
             self.stack.enter_context(self.writes)  # type: ignore[arg-type]
 
-    def __enter__(self) -> "InMemorySaver":
+    def __enter__(self) -> "MemorySaver":
         return self.stack.__enter__()
 
     def __exit__(
@@ -95,7 +95,7 @@ class InMemorySaver(
     ) -> Optional[bool]:
         return self.stack.__exit__(exc_type, exc_value, traceback)
 
-    async def __aenter__(self) -> "InMemorySaver":
+    async def __aenter__(self) -> "MemorySaver":
         return self.stack.__enter__()
 
     async def __aexit__(
@@ -149,17 +149,15 @@ class InMemorySaver(
                     pending_writes=[
                         (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                     ],
-                    parent_config=(
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
+                    parent_config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": parent_checkpoint_id,
                         }
-                        if parent_checkpoint_id
-                        else None
-                    ),
+                    }
+                    if parent_checkpoint_id
+                    else None,
                 )
         else:
             if checkpoints := self.storage[thread_id][checkpoint_ns]:
@@ -195,17 +193,15 @@ class InMemorySaver(
                     pending_writes=[
                         (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                     ],
-                    parent_config=(
-                        {
-                            "configurable": {
-                                "thread_id": thread_id,
-                                "checkpoint_ns": checkpoint_ns,
-                                "checkpoint_id": parent_checkpoint_id,
-                            }
+                    parent_config={
+                        "configurable": {
+                            "thread_id": thread_id,
+                            "checkpoint_ns": checkpoint_ns,
+                            "checkpoint_id": parent_checkpoint_id,
                         }
-                        if parent_checkpoint_id
-                        else None
-                    ),
+                    }
+                    if parent_checkpoint_id
+                    else None,
                 )
 
     def list(
@@ -311,17 +307,15 @@ class InMemorySaver(
                             ],
                         },
                         metadata=metadata,
-                        parent_config=(
-                            {
-                                "configurable": {
-                                    "thread_id": thread_id,
-                                    "checkpoint_ns": checkpoint_ns,
-                                    "checkpoint_id": parent_checkpoint_id,
-                                }
+                        parent_config={
+                            "configurable": {
+                                "thread_id": thread_id,
+                                "checkpoint_ns": checkpoint_ns,
+                                "checkpoint_id": parent_checkpoint_id,
                             }
-                            if parent_checkpoint_id
-                            else None
-                        ),
+                        }
+                        if parent_checkpoint_id
+                        else None,
                         pending_writes=[
                             (id, c, self.serde.loads_typed(v)) for id, c, v, _ in writes
                         ],
@@ -498,9 +492,6 @@ class InMemorySaver(
         return f"{next_v:032}.{next_h:016}"
 
 
-MemorySaver = InMemorySaver  # Kept for backwards compatibility
-
-
 class PersistentDict(defaultdict):
     """Persistent dictionary with an API compatible with shelve and anydbm.
 

libs/checkpoint/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.11"
+version = "2.0.10"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"

libs/checkpoint/tests/test_memory.py

@@ -9,13 +9,13 @@ from langgraph.checkpoint.base import (
     create_checkpoint,
     empty_checkpoint,
 )
-from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.checkpoint.memory import MemorySaver
 
 
 class TestMemorySaver:
     @pytest.fixture(autouse=True)
     def setup(self) -> None:
-        self.memory_saver = InMemorySaver()
+        self.memory_saver = MemorySaver()
 
         # objects for test setup
         self.config_1: RunnableConfig = {
@@ -138,9 +138,3 @@ class TestMemorySaver:
             c async for c in self.memory_saver.alist(None, filter=query_4)
         ]
         assert len(search_results_4) == 0
-
-
-def test_memory_saver() -> None:
-    from langgraph.checkpoint.memory import MemorySaver
-
-    assert isinstance(MemorySaver(), InMemorySaver)

libs/cli/langgraph_cli/cli.py

@@ -1,10 +1,8 @@
-"""CLI entrypoint for LangGraph API server."""
-
 import os
 import pathlib
 import shutil
 import sys
-from typing import Callable, List, Optional, Sequence, Tuple
+from typing import Callable, Optional, Sequence
 
 import click
 import click.exceptions
@@ -661,7 +659,7 @@ def prepare_args_and_stdin(
     debugger_port: Optional[int] = None,
     debugger_base_url: Optional[str] = None,
     postgres_uri: Optional[str] = None,
-) -> Tuple[List[str], str]:
+):
     # prepare args
     stdin = langgraph_cli.docker.compose(
         capabilities,
@@ -705,8 +703,7 @@ def prepare(
     debugger_port: Optional[int] = None,
     debugger_base_url: Optional[str] = None,
     postgres_uri: Optional[str] = None,
-) -> Tuple[List[str], str]:
-    """Prepare the arguments and stdin for running the LangGraph API server."""
+):
     config_json = langgraph_cli.config.validate_config_file(config_path)
     # pull latest images
     if pull:

libs/cli/langgraph_cli/config.py

@@ -86,42 +86,15 @@ class AuthConfig(TypedDict, total=False):
 
 
 class Config(TypedDict, total=False):
-    """Configuration for langgraph-cli."""
-
     python_version: str
-    """Python version to use."""
-
     node_version: Optional[str]
-    """Node.js version to use."""
-
     pip_config_file: Optional[str]
-    """Path to a pip configuration file."""
-
     dockerfile_lines: list[str]
-    """Additional lines to add to the Dockerfile."""
-
     dependencies: list[str]
-    """Additional Python dependencies to install."""
-
     graphs: dict[str, str]
-    """Mapping of graph names to their definitions."""
-
     env: Union[dict[str, str], str]
-    """Environment variables to set.
-
-    If a dictionary is provided, the keys are environment variable names
-    and the values are the corresponding environment variable values.
-
-    If a string is provided, it is interpreted as a path to a file containing
-    environment variables in the format KEY=VALUE, with one environment variable
-    per line.
-    """
-
     store: Optional[StoreConfig]
-    """Configuration for vector embeddings in store."""
-
     auth: Optional[AuthConfig]
-    """Configuration for authentication."""
 
 
 def _parse_version(version_str: str) -> tuple[int, int]:
@@ -147,7 +120,6 @@ def _parse_node_version(version_str: str) -> int:
 
 
 def validate_config(config: Config) -> Config:
-    """Validate a configuration dictionary."""
     config = (
         {
             "node_version": config.get("node_version"),
@@ -211,21 +183,10 @@ def validate_config(config: Config) -> Config:
             "No graphs found in config. "
             "Add at least one graph to 'graphs' dictionary."
         )
-
-    # Validate auth config
-    if auth_conf := config.get("auth"):
-        if "path" in auth_conf:
-            if ":" not in auth_conf["path"]:
-                raise ValueError(
-                    f"Invalid auth.path format: '{auth_conf['path']}'. "
-                    "Must be in format './path/to/file.py:attribute_name'"
-                )
-
     return config
 
 
 def validate_config_file(config_path: pathlib.Path) -> Config:
-    """Load and validate a configuration file."""
     with open(config_path) as f:
         config = json.load(f)
     validated = validate_config(config)
@@ -268,51 +229,7 @@ def validate_config_file(config_path: pathlib.Path) -> Config:
 
 
 class LocalDeps(NamedTuple):
-    """A container for referencing and managing local Python dependencies.
-
-    A "local dependency" is any entry in the config's `dependencies` list
-    that starts with "." (dot), denoting a relative path
-    to a local directory containing Python code.
-
-    For each local dependency, the system inspects its directory to
-    determine how it should be installed inside the Docker container.
-
-    Specifically, we detect:
-
-    - **Real packages**: Directories containing a `pyproject.toml` or a `setup.py`.
-      These can be installed with pip as a regular Python package.
-    - **Faux packages**: Directories that do not include a `pyproject.toml` or
-      `setup.py` but do contain Python files and possibly an `__init__.py`. For
-      these, the code dynamically generates a minimal `pyproject.toml` in the
-      Docker image so that they can still be installed with pip.
-    - **Requirements files**: If a local dependency directory
-      has a `requirements.txt`, it is tracked so that those dependencies
-      can be installed within the Docker container before installing the local package.
-
-    Attributes:
-        pip_reqs: A list of (host_requirements_path, container_requirements_path)
-            tuples. Each entry points to a local `requirements.txt` file and where
-            it should be placed inside the Docker container before running `pip install`.
-
-        real_pkgs: A dictionary mapping a local directory path (host side) to the
-            same dependency string from the config. These directories contain the
-            necessary files (e.g., `pyproject.toml` or `setup.py`) to be installed
-            as a standard Python package with pip.
-
-        faux_pkgs: A dictionary mapping a local directory path (host side) to a
-            tuple of (dependency_string, container_package_path). For these
-            directories—called "faux packages"—the code will generate a minimal
-            `pyproject.toml` inside the Docker image. This ensures that pip
-            recognizes them as installable packages, even though they do not
-            natively include packaging metadata.
-
-        working_dir: The path inside the Docker container to use as the working
-            directory. If the local dependency `"."` is present in the config, this
-            field captures the path where that dependency will appear in the
-            container (e.g., `/deps/<name>` or similar). Otherwise, it may be `None`.
-    """
-
-    pip_reqs: list[tuple[str, str]]
+    pip_reqs: list[tuple[pathlib.Path, str]]
     real_pkgs: dict[pathlib.Path, str]
     faux_pkgs: dict[pathlib.Path, tuple[str, str]]
     # if . is in dependencies, use it as working_dir
@@ -352,11 +269,8 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
 
     for local_dep in config["dependencies"]:
         if not local_dep.startswith("."):
-            # If the dependency is not a local path, skip it
             continue
 
-        # Verify that the local dependency can be resolved
-        # (e.g., this would raise an informative error if a user mistyped a path).
         resolved = config_path.parent / local_dep
 
         # validate local dependency
@@ -371,22 +285,18 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
                 f"Local dependency '{resolved}' must be a subdirectory of '{config_path.parent}'"
             )
 
-        # Check for pyproject.toml or setup.py
-        # If found, treat as a real package, if not treat as a faux package.
-        # For faux packages, we'll also check for presence of requirements.txt.
+        # if it's installable, add it to local_pkgs
+        # otherwise, add it to faux_pkgs, and create a pyproject.toml
         files = os.listdir(resolved)
         if "pyproject.toml" in files:
-            # real package
             real_pkgs[resolved] = local_dep
             if local_dep == ".":
                 working_dir = f"/deps/{resolved.name}"
         elif "setup.py" in files:
-            # real package
             real_pkgs[resolved] = local_dep
             if local_dep == ".":
                 working_dir = f"/deps/{resolved.name}"
         else:
-            # We could not find a pyproject.toml or setup.py, so treat as a faux package
             if any(file == "__init__.py" for file in files):
                 # flat layout
                 if "-" in resolved.name:
@@ -416,9 +326,6 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
             faux_pkgs[resolved] = (local_dep, container_path)
             if local_dep == ".":
                 working_dir = container_path
-
-            # If the faux package has a requirements.txt, we'll add
-            # the path to the list of requirements to install.
             if "requirements.txt" in files:
                 rfile = resolved / "requirements.txt"
                 pip_reqs.append(
@@ -434,42 +341,6 @@ def _assemble_local_deps(config_path: pathlib.Path, config: Config) -> LocalDeps
 def _update_graph_paths(
     config_path: pathlib.Path, config: Config, local_deps: LocalDeps
 ) -> None:
-    """Remap each graph's import path to the correct in-container path.
-
-    The config may contain entries in `graphs` that look like this:
-        {
-            "my_graph": "./mygraphs/main.py:graph_function"
-        }
-    or
-        {
-            "my_graph": "./src/some_subdir/my_file.py:my_graph"
-        }
-    which indicate a local file (on the host) followed by a colon and a
-    callable/object attribute within that file.
-
-    During the Docker build, local directories are copied into special
-    `/deps/` subdirectories, so they can be installed or referenced in
-    the container. This function updates each graph's import path to
-    reflect its new location **inside** the Docker container.
-
-    Paths inside the container must be POSIX-style paths (even if
-    the host system is Windows).
-
-    Args:
-        config_path: The path to the config file (e.g. `langgraph.json`).
-        config: The validated configuration dictionary.
-        local_deps: An object containing references to local dependencies:
-            - real Python packages (with a `pyproject.toml` or `setup.py`)
-            - “faux” packages that need minimal metadata to be installable
-            - potential `requirements.txt` for local dependencies
-            - container work directory (if "." is in `dependencies`)
-
-    Raises:
-        ValueError: If the import string is not in the format `<module>:<attribute>`
-                    or if the referenced local file is not found in `dependencies`.
-        FileNotFoundError: If the local file (module) does not actually exist on disk.
-        IsADirectoryError: If `module_str` points to a directory instead of a file.
-    """
     for graph_id, import_str in config["graphs"].items():
         module_str, _, attr_str = import_str.partition(":")
         if not module_str or not attr_str:
@@ -477,12 +348,8 @@ def _update_graph_paths(
                 'Import string "{import_str}" must be in format "<module>:<attribute>".'
             )
             raise ValueError(message.format(import_str=import_str))
-
-        # Check for either forward slash or backslash in the module string
-        # to determine if it's a file path.
-        if "/" in module_str or "\\" in module_str:
-            # Resolve the local path properly on the current OS
-            resolved = (config_path.parent / module_str).resolve()
+        if "/" in module_str:
+            resolved = config_path.parent / module_str
             if not resolved.exists():
                 raise FileNotFoundError(f"Could not find local module: {resolved}")
             elif not resolved.is_file():
@@ -490,19 +357,12 @@ def _update_graph_paths(
             else:
                 for path in local_deps.real_pkgs:
                     if resolved.is_relative_to(path):
-                        container_path = (
-                            pathlib.Path("/deps")
-                            / path.name
-                            / resolved.relative_to(path)
-                        )
-                        module_str = container_path.as_posix()
+                        module_str = f"/deps/{path.name}/{resolved.relative_to(path)}"
                         break
                 else:
                     for faux_pkg, (_, destpath) in local_deps.faux_pkgs.items():
                         if resolved.is_relative_to(faux_pkg):
-                            container_subpath = resolved.relative_to(faux_pkg)
-                            # Construct the final path, ensuring POSIX style
-                            module_str = f"{destpath}/{container_subpath.as_posix()}"
+                            module_str = f"{destpath}/{resolved.relative_to(faux_pkg)}"
                             break
                     else:
                         raise ValueError(
@@ -513,51 +373,7 @@ def _update_graph_paths(
             config["graphs"][graph_id] = f"{module_str}:{attr_str}"
 
 
-def _update_auth_path(
-    config_path: pathlib.Path, config: Config, local_deps: LocalDeps
-) -> None:
-    """Update auth.path to use Docker container paths."""
-    auth_conf = config.get("auth")
-    if not auth_conf or not (path_str := auth_conf.get("path")):
-        return
-
-    module_str, sep, attr_str = path_str.partition(":")
-    if not sep or not module_str.startswith("."):
-        return  # Already validated or absolute path
-
-    resolved = config_path.parent / module_str
-    if not resolved.exists():
-        raise FileNotFoundError(f"Auth file not found: {resolved} (from {path_str})")
-    if not resolved.is_file():
-        raise IsADirectoryError(f"Auth path must be a file: {resolved}")
-
-    # Check faux packages first (higher priority)
-    for faux_path, (_, destpath) in local_deps.faux_pkgs.items():
-        if resolved.is_relative_to(faux_path):
-            new_path = f"{destpath}/{resolved.relative_to(faux_path)}:{attr_str}"
-            auth_conf["path"] = new_path
-            return
-
-    # Check real packages
-    for real_path in local_deps.real_pkgs:
-        if resolved.is_relative_to(real_path):
-            new_path = (
-                f"/deps/{real_path.name}/{resolved.relative_to(real_path)}:{attr_str}"
-            )
-            auth_conf["path"] = new_path
-            return
-
-    raise ValueError(
-        f"Auth file '{resolved}' not covered by dependencies.\n"
-        "Add its parent directory to the 'dependencies' array in your config.\n"
-        f"Current dependencies: {config['dependencies']}"
-    )
-
-
-def python_config_to_docker(
-    config_path: pathlib.Path, config: Config, base_image: str
-) -> str:
-    """Generate a Dockerfile from the configuration."""
+def python_config_to_docker(config_path: pathlib.Path, config: Config, base_image: str):
     # configure pip
     pip_install = (
         "PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt"
@@ -573,10 +389,9 @@ def python_config_to_docker(
     # collect dependencies
     pypi_deps = [dep for dep in config["dependencies"] if not dep.startswith(".")]
     local_deps = _assemble_local_deps(config_path, config)
-    # Rewrite graph paths, so they point to the correct location in the Docker container
+
+    # rewrite graph paths
     _update_graph_paths(config_path, config, local_deps)
-    # Rewrite auth path, so it points to the correct location in the Docker container
-    _update_auth_path(config_path, config, local_deps)
 
     pip_pkgs_str = f"RUN {pip_install} {' '.join(pypi_deps)}" if pypi_deps else ""
     if local_deps.pip_reqs:
@@ -619,31 +434,29 @@ RUN set -ex && \\
             ],
         )
     )
-
-    env_vars = []
-
-    if (store_config := config.get("store")) is not None:
-        env_vars.append(f"ENV LANGGRAPH_STORE='{json.dumps(store_config)}'")
-
+    store_config = config.get("store")
+    env_additional_config = (
+        ""
+        if not store_config
+        else f"""
+ENV LANGGRAPH_STORE='{json.dumps(store_config)}'
+"""
+    )
     if (auth_config := config.get("auth")) is not None:
-        env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
+        env_additional_config += f"""
+ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'
+"""
+    return f"""FROM {base_image}:{config['python_version']}
 
-    graphs = config["graphs"]
-    env_vars.append(f"ENV LANGSERVE_GRAPHS='{json.dumps(graphs)}'")
+{os.linesep.join(config["dockerfile_lines"])}
 
-    docker_file_contents = [
-        f"FROM {base_image}:{config['python_version']}",
-        "",
-        os.linesep.join(config["dockerfile_lines"]),
-        "",
-        installs,
-        "",
-        f"RUN {pip_install} -e /deps/*",
-        os.linesep.join(env_vars),
-        "",
-        f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else "",
-    ]
-    return os.linesep.join(docker_file_contents)
+{installs}
+
+RUN {pip_install} -e /deps/*
+{env_additional_config}
+ENV LANGSERVE_GRAPHS='{json.dumps(config["graphs"])}'
+
+{f"WORKDIR {local_deps.working_dir}" if local_deps.working_dir else ""}"""
 
 
 def node_config_to_docker(config_path: pathlib.Path, config: Config, base_image: str):
@@ -685,7 +498,6 @@ ENV LANGGRAPH_STORE='{json.dumps(store_config)}'
         env_additional_config += f"""
 ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'
 """
-
     return f"""FROM {base_image}:{config['node_version']}
 
 {os.linesep.join(config["dockerfile_lines"])}
@@ -713,7 +525,7 @@ def config_to_compose(
     config: Config,
     base_image: str,
     watch: bool = False,
-) -> str:
+):
     env_vars = config["env"].items() if isinstance(config["env"], dict) else {}
     env_vars_str = "\n".join(f'            {k}: "{v}"' for k, v in env_vars)
     env_file_str = (

libs/cli/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.1.71"
+version = "0.1.69"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"

libs/langgraph/README.md

@@ -21,7 +21,10 @@ LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [A
 
 ### Why use LangGraph?
 
-LangGraph powers [production-grade agents](https://www.langchain.com/built-with-langgraph), trusted by Linkedin, Uber, Klarna, GitLab, and many more. LangGraph provides fine-grained control over both the flow and state of your agent applications. It implements a central [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), enabling features that are common to most agent architectures:
+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
@@ -330,10 +333,6 @@ Then we define one normal and one conditional edge. Conditional edge means that
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
 * [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
-## Resources
-
-* [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
-
 ## Contributing
 
 For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).

libs/langgraph/langgraph/config.py

@@ -1,180 +0,0 @@
-import asyncio
-import sys
-
-from langchain_core.runnables import RunnableConfig
-from langchain_core.runnables.config import var_child_runnable_config
-
-from langgraph.constants import CONF, CONFIG_KEY_STORE, CONFIG_KEY_STREAM_WRITER
-from langgraph.store.base import BaseStore
-from langgraph.types import StreamWriter
-
-
-def get_config() -> RunnableConfig:
-    if sys.version_info < (3, 11):
-        try:
-            if asyncio.current_task():
-                raise RuntimeError(
-                    "Python 3.11 or later required to use this in an async context"
-                )
-        except RuntimeError:
-            pass
-    if var_config := var_child_runnable_config.get():
-        return var_config
-    else:
-        raise RuntimeError("Called get_config outside of a runnable context")
-
-
-def get_store() -> BaseStore:
-    """Access LangGraph store from inside a graph node or entrypoint task at runtime.
-
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task], as long as the StateGraph or the [entrypoint][langgraph.func.entrypoint]
-    was initialized with a store, e.g.:
-
-    ```python
-    # with StateGraph
-    graph = (
-        StateGraph(...)
-        ...
-        .compile(store=store)
-    )
-
-    # or with entrypoint
-    @entrypoint(store=store)
-    def workflow(inputs):
-        ...
-    ```
-
-    !!! warning "Async with Python < 3.11"
-
-        If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_store()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
-
-
-    Example: Using with StateGraph
-        ```python
-        from typing_extensions import TypedDict
-        from langgraph.graph import StateGraph, START
-        from langgraph.store.memory import InMemoryStore
-        from langgraph.config import get_store
-
-        store = InMemoryStore()
-        store.put(("values",), "foo", {"bar": 2})
-
-        class State(TypedDict):
-            foo: int
-
-        def my_node(state: State):
-            my_store = get_store()
-            stored_value = my_store.get(("values",), "foo").value["bar"]
-            return {"foo": stored_value + 1}
-
-        graph = (
-            StateGraph(State)
-            .add_node(my_node)
-            .add_edge(START, "my_node")
-            .compile(store=store)
-        )
-
-        graph.invoke({"foo": 1})
-        ```
-
-        ```pycon
-        {'foo': 3}
-        ```
-
-    Example: Using with functional API
-        ```python
-        from langgraph.func import entrypoint, task
-        from langgraph.store.memory import InMemoryStore
-        from langgraph.config import get_store
-
-        store = InMemoryStore()
-        store.put(("values",), "foo", {"bar": 2})
-
-        @task
-        def my_task(value: int):
-            my_store = get_store()
-            stored_value = my_store.get(("values",), "foo").value["bar"]
-            return stored_value + 1
-
-        @entrypoint(store=store)
-        def workflow(value: int):
-            return my_task(value).result()
-
-        workflow.invoke(1)
-        ```
-
-        ```pycon
-        3
-        ```
-    """
-    config = get_config()
-    return config[CONF][CONFIG_KEY_STORE]
-
-
-def get_stream_writer() -> StreamWriter:
-    """Access LangGraph [StreamWriter][langgraph.types.StreamWriter] from inside a graph node or entrypoint task at runtime.
-
-    Can be called from inside any [StateGraph][langgraph.graph.StateGraph] node or
-    functional API [task][langgraph.func.task].
-
-    !!! warning "Async with Python < 3.11"
-
-        If you are using Python < 3.11 and are running LangGraph asynchronously,
-        `get_stream_writer()` won't work since it uses [contextvar](https://docs.python.org/3/library/contextvars.html) propagation (only available in [Python >= 3.11](https://docs.python.org/3/library/asyncio-task.html#asyncio.create_task)).
-
-    Example: Using with StateGraph
-        ```python
-        from typing_extensions import TypedDict
-        from langgraph.graph import StateGraph, START
-        from langgraph.config import get_stream_writer
-
-        class State(TypedDict):
-            foo: int
-
-        def my_node(state: State):
-            my_stream_writer = get_stream_writer()
-            my_stream_writer({"custom_data": "Hello!"})
-            return {"foo": state["foo"] + 1}
-
-        graph = (
-            StateGraph(State)
-            .add_node(my_node)
-            .add_edge(START, "my_node")
-            .compile(store=store)
-        )
-
-        for chunk in graph.stream({"foo": 1}, stream_mode="custom"):
-            print(chunk)
-        ```
-
-        ```pycon
-        {'custom_data': 'Hello!'}
-        ```
-
-    Example: Using with functional API
-        ```python
-        from langgraph.func import entrypoint, task
-        from langgraph.config import get_stream_writer
-
-        @task
-        def my_task(value: int):
-            my_stream_writer = get_stream_writer()
-            my_stream_writer({"custom_data": "Hello!"})
-            return value + 1
-
-        @entrypoint(store=store)
-        def workflow(value: int):
-            return my_task(value).result()
-
-        for chunk in workflow.stream(1, stream_mode="custom"):
-            print(chunk)
-        ```
-
-        ```pycon
-        {'custom_data': 'Hello!'}
-        ```
-    """
-    config = get_config()
-    return config[CONF][CONFIG_KEY_STREAM_WRITER]

libs/langgraph/langgraph/graph/graph.py

@@ -19,6 +19,7 @@ from typing import (
 )
 
 from langchain_core.runnables import Runnable
+from langchain_core.runnables.base import RunnableLike
 from langchain_core.runnables.config import RunnableConfig
 from langchain_core.runnables.graph import Graph as DrawableGraph
 from langchain_core.runnables.graph import Node as DrawableNode
@@ -39,7 +40,7 @@ from langgraph.pregel import Channel, Pregel
 from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.types import All, Checkpointer
-from langgraph.utils.runnable import RunnableCallable, RunnableLike, coerce_to_runnable
+from langgraph.utils.runnable import RunnableCallable, coerce_to_runnable
 
 logger = logging.getLogger(__name__)
 
@@ -417,7 +418,6 @@ class Graph:
         interrupt_before: Optional[Union[All, list[str]]] = None,
         interrupt_after: Optional[Union[All, list[str]]] = None,
         debug: bool = False,
-        name: Optional[str] = None,
     ) -> "CompiledGraph":
         # assign default values
         interrupt_before = interrupt_before or []
@@ -446,7 +446,6 @@ class Graph:
             interrupt_after_nodes=interrupt_after,
             auto_validate=False,
             debug=debug,
-            name=name or "LangGraph",
         )
 
         # attach nodes, edges, and branches

libs/langgraph/langgraph/graph/message.py

@@ -180,15 +180,14 @@ def add_messages(
         if m.id is None:
             m.id = str(uuid.uuid4())
     # merge
+    left_idx_by_id = {m.id: i for i, m in enumerate(left)}
     merged = left.copy()
-    merged_by_id = {m.id: i for i, m in enumerate(merged)}
     ids_to_remove = set()
     for m in right:
-        if (existing_idx := merged_by_id.get(m.id)) is not None:
+        if (existing_idx := left_idx_by_id.get(m.id)) is not None:
             if isinstance(m, RemoveMessage):
                 ids_to_remove.add(m.id)
             else:
-                ids_to_remove.discard(m.id)
                 merged[existing_idx] = m
         else:
             if isinstance(m, RemoveMessage):
@@ -196,7 +195,6 @@ def add_messages(
                     f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
                 )
 
-            merged_by_id[m.id] = len(merged)
             merged.append(m)
     merged = [m for m in merged if m.id not in ids_to_remove]
 

libs/langgraph/langgraph/graph/state.py

@@ -22,6 +22,7 @@ from typing import (
 )
 
 from langchain_core.runnables import Runnable, RunnableConfig
+from langchain_core.runnables.base import RunnableLike
 from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Self
@@ -59,7 +60,7 @@ from langgraph.store.base import BaseStore
 from langgraph.types import All, Checkpointer, Command, RetryPolicy
 from langgraph.utils.fields import get_field_default
 from langgraph.utils.pydantic import create_model
-from langgraph.utils.runnable import RunnableCallable, RunnableLike, coerce_to_runnable
+from langgraph.utils.runnable import RunnableCallable, coerce_to_runnable
 
 logger = logging.getLogger(__name__)
 
@@ -500,7 +501,6 @@ class StateGraph(Graph):
         interrupt_before: Optional[Union[All, list[str]]] = None,
         interrupt_after: Optional[Union[All, list[str]]] = None,
         debug: bool = False,
-        name: Optional[str] = None,
     ) -> "CompiledStateGraph":
         """Compiles the state graph into a `CompiledGraph` object.
 
@@ -571,7 +571,6 @@ class StateGraph(Graph):
             auto_validate=False,
             debug=debug,
             store=store,
-            name=name or "LangGraph",
         )
 
         compiled.attach_node(START, None)

libs/langgraph/langgraph/prebuilt/chat_agent_executor.py

@@ -31,7 +31,7 @@ from langgraph.managed import IsLastStep, RemainingSteps
 from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.prebuilt.tool_node import ToolNode
 from langgraph.store.base import BaseStore
-from langgraph.types import Checkpointer, Send
+from langgraph.types import Checkpointer
 from langgraph.utils.runnable import RunnableCallable
 
 StructuredResponse = Union[dict, BaseModel]
@@ -248,8 +248,6 @@ def create_react_agent(
     interrupt_before: Optional[list[str]] = None,
     interrupt_after: Optional[list[str]] = None,
     debug: bool = False,
-    version: Literal["v1", "v2"] = "v1",
-    name: Optional[str] = None,
 ) -> CompiledGraph:
     """Creates a graph that works with a chat model that utilizes tool calling.
 
@@ -299,18 +297,6 @@ def create_react_agent(
             Should be one of the following: "agent", "tools".
             This is useful if you want to return directly or run additional processing on an output.
         debug: A flag indicating whether to enable debug mode.
-        version: Determines the version of the graph to create.
-            Can be one of:
-
-            - `"v1"`: The tool node processes a single message. All tool
-                calls in the message are executed in parallel within the tool node.
-            - `"v2"`: The tool node processes a tool call.
-                Tool calls are distributed across multiple instances of the tool
-                node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send)
-                API.
-        name: An optional name for the CompiledStateGraph.
-            This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node -
-            particularly useful for building multi-agent systems.
 
     Returns:
         A compiled LangChain runnable that can be used for chat interactions.
@@ -586,10 +572,6 @@ def create_react_agent(
         TimeoutError: Timed out at step 2
         ```
     """
-    if version not in ("v1", "v2"):
-        raise ValueError(
-            f"Invalid version {version}. Supported versions are 'v1' and 'v2'."
-        )
 
     if state_schema is not None:
         required_keys = {"messages", "remaining_steps"}
@@ -636,9 +618,7 @@ def create_react_agent(
     # Define the function that calls the model
     def call_model(state: AgentState, config: RunnableConfig) -> AgentState:
         _validate_chat_history(state["messages"])
-        response = cast(AIMessage, model_runnable.invoke(state, config))
-        # add agent name to the AIMessage
-        response.name = name
+        response = model_runnable.invoke(state, config)
         has_tool_calls = isinstance(response, AIMessage) and response.tool_calls
         all_tools_return_direct = (
             all(call["name"] in should_return_direct for call in response.tool_calls)
@@ -675,9 +655,7 @@ def create_react_agent(
 
     async def acall_model(state: AgentState, config: RunnableConfig) -> AgentState:
         _validate_chat_history(state["messages"])
-        response = cast(AIMessage, await model_runnable.ainvoke(state, config))
-        # add agent name to the AIMessage
-        response.name = name
+        response = await model_runnable.ainvoke(state, config)
         has_tool_calls = isinstance(response, AIMessage) and response.tool_calls
         all_tools_return_direct = (
             all(call["name"] in should_return_direct for call in response.tool_calls)
@@ -766,11 +744,10 @@ def create_react_agent(
             interrupt_before=interrupt_before,
             interrupt_after=interrupt_after,
             debug=debug,
-            name=name,
         )
 
     # Define the function that determines whether to continue or not
-    def should_continue(state: AgentState) -> Union[str, list]:
+    def should_continue(state: AgentState) -> str:
         messages = state["messages"]
         last_message = messages[-1]
         # If there is no function call, then we finish
@@ -778,14 +755,7 @@ def create_react_agent(
             return END if response_format is None else "generate_structured_response"
         # Otherwise if there is, we continue
         else:
-            if version == "v1":
-                return "tools"
-            elif version == "v2":
-                tool_calls = [
-                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
-                    for call in last_message.tool_calls
-                ]
-                return [Send("tools", [tool_call]) for tool_call in tool_calls]
+            return "tools"
 
     # Define a new graph
     workflow = StateGraph(state_schema or AgentState)
@@ -843,7 +813,6 @@ def create_react_agent(
         interrupt_before=interrupt_before,
         interrupt_after=interrupt_after,
         debug=debug,
-        name=name,
     )
 
 

libs/langgraph/langgraph/prebuilt/tool_node.py

@@ -27,6 +27,7 @@ from langchain_core.runnables.config import (
     get_config_list,
     get_executor_for_config,
 )
+from langchain_core.runnables.utils import Input
 from langchain_core.tools import BaseTool, InjectedToolArg
 from langchain_core.tools import tool as create_tool
 from langchain_core.tools.base import get_all_basemodel_annotations
@@ -135,8 +136,6 @@ class ToolNode(RunnableCallable):
     If multiple tool calls are requested, they will be run in parallel. The output will be
     a list of ToolMessages, one for each tool call.
 
-    Tool calls can also be passed directly as a list of `ToolCall` dicts.
-
     Args:
         tools: A sequence of tools that can be invoked by the ToolNode.
         name: The name of the ToolNode in the graph. Defaults to "tools".
@@ -170,28 +169,10 @@ class ToolNode(RunnableCallable):
         return {"messages": result}
     ```
 
-    Tool calls can also be passed directly to a ToolNode. This can be useful when using
-    the Send API, e.g., in a conditional edge:
-
-    ```python
-    def example_conditional_edge(state: dict) -> List[Send]:
-        tool_calls = state["messages"][-1].tool_calls
-        # If tools rely on state or store variables (whose values are not generated
-        # directly by a model), you can inject them into the tool calls.
-        tool_calls = [
-            tool_node.inject_tool_args(call, state, store)
-            for call in last_message.tool_calls
-        ]
-        return [Send("tools", [tool_call]) for tool_call in tool_calls]
-    ```
-
     Important:
-        - The input state can be one of the following:
-            - A dict with a messages key containing a list of messages.
-            - A list of messages.
-            - A list of tool calls.
-        - If operating on a message list, the last message must be an `AIMessage` with
-            `tool_calls` populated.
+        - The state MUST contain a list of messages.
+        - The last message MUST be an `AIMessage`.
+        - The `AIMessage` MUST have `tool_calls` populated.
     """
 
     name: str = "ToolNode"
@@ -229,7 +210,7 @@ class ToolNode(RunnableCallable):
         ],
         config: RunnableConfig,
         *,
-        store: Optional[BaseStore],
+        store: BaseStore,
     ) -> Any:
         tool_calls, input_type = self._parse_input(input, store)
         config_list = get_config_list(config, len(tool_calls))
@@ -239,14 +220,12 @@ class ToolNode(RunnableCallable):
                 *executor.map(self._run_one, tool_calls, input_types, config_list)
             ]
 
-        # preserve existing behavior for non-command tool outputs for backwards
-        # compatibility
+        # preserve existing behavior for non-command tool outputs for backwards compatibility
         if not any(isinstance(output, Command) for output in outputs):
             # TypedDict, pydantic, dataclass, etc. should all be able to load from dict
             return outputs if input_type == "list" else {self.messages_key: outputs}
 
-        # LangGraph will automatically handle list of Command and non-command node
-        # updates
+        # LangGraph will automatically handle list of Command and non-command node updates
         combined_outputs: list[
             Command | list[ToolMessage] | dict[str, list[ToolMessage]]
         ] = []
@@ -259,6 +238,20 @@ class ToolNode(RunnableCallable):
                 )
         return combined_outputs
 
+    def invoke(
+        self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
+    ) -> Any:
+        if "store" not in kwargs:
+            kwargs["store"] = None
+        return super().invoke(input, config, **kwargs)
+
+    async def ainvoke(
+        self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
+    ) -> Any:
+        if "store" not in kwargs:
+            kwargs["store"] = None
+        return await super().ainvoke(input, config, **kwargs)
+
     async def _afunc(
         self,
         input: Union[
@@ -268,7 +261,7 @@ class ToolNode(RunnableCallable):
         ],
         config: RunnableConfig,
         *,
-        store: Optional[BaseStore],
+        store: BaseStore,
     ) -> Any:
         tool_calls, input_type = self._parse_input(input, store)
         outputs = await asyncio.gather(
@@ -296,7 +289,7 @@ class ToolNode(RunnableCallable):
     def _run_one(
         self,
         call: ToolCall,
-        input_type: Literal["list", "dict", "tool_calls"],
+        input_type: Literal["list", "dict"],
         config: RunnableConfig,
     ) -> ToolMessage:
         if invalid_tool_message := self._validate_tool_call(call):
@@ -351,7 +344,7 @@ class ToolNode(RunnableCallable):
     async def _arun_one(
         self,
         call: ToolCall,
-        input_type: Literal["list", "dict", "tool_calls"],
+        input_type: Literal["list", "dict"],
         config: RunnableConfig,
     ) -> ToolMessage:
         if invalid_tool_message := self._validate_tool_call(call):
@@ -411,16 +404,11 @@ class ToolNode(RunnableCallable):
             dict[str, Any],
             BaseModel,
         ],
-        store: Optional[BaseStore],
-    ) -> Tuple[list[ToolCall], Literal["list", "dict", "tool_calls"]]:
+        store: BaseStore,
+    ) -> Tuple[list[ToolCall], Literal["list", "dict"]]:
         if isinstance(input, list):
-            if isinstance(input[-1], dict) and input[-1].get("type") == "tool_call":
-                input_type = "tool_calls"
-                tool_calls = input
-                return tool_calls, input_type
-            else:
-                input_type = "list"
-                message: AnyMessage = input[-1]
+            input_type = "list"
+            message: AnyMessage = input[-1]
         elif isinstance(input, dict) and (messages := input.get(self.messages_key, [])):
             input_type = "dict"
             message = messages[-1]
@@ -435,7 +423,7 @@ class ToolNode(RunnableCallable):
             raise ValueError("Last message is not an AIMessage")
 
         tool_calls = [
-            self.inject_tool_args(call, input, store) for call in message.tool_calls
+            self._inject_tool_args(call, input, store) for call in message.tool_calls
         ]
         return tool_calls, input_type
 
@@ -496,9 +484,7 @@ class ToolNode(RunnableCallable):
         }
         return tool_call
 
-    def _inject_store(
-        self, tool_call: ToolCall, store: Optional[BaseStore]
-    ) -> ToolCall:
+    def _inject_store(self, tool_call: ToolCall, store: BaseStore) -> ToolCall:
         store_arg = self.tool_to_store_arg[tool_call["name"]]
         if not store_arg:
             return tool_call
@@ -515,7 +501,7 @@ class ToolNode(RunnableCallable):
         }
         return tool_call
 
-    def inject_tool_args(
+    def _inject_tool_args(
         self,
         tool_call: ToolCall,
         input: Union[
@@ -523,23 +509,8 @@ class ToolNode(RunnableCallable):
             dict[str, Any],
             BaseModel,
         ],
-        store: Optional[BaseStore],
+        store: BaseStore,
     ) -> ToolCall:
-        """Injects the state and store into the tool call.
-
-        Tool arguments with types annotated as `InjectedState` and `InjectedStore` are
-        ignored in tool schemas for generation purposes. This method injects them into
-        tool calls for tool invocation.
-
-        Args:
-            tool_call (ToolCall): The tool call to inject state and store into.
-            input (Union[list[AnyMessage], dict[str, Any], BaseModel]): The input state
-                to inject.
-            store (Optional[BaseStore]): The store to inject.
-
-        Returns:
-            ToolCall: The tool call with injected state and store.
-        """
         if tool_call["name"] not in self.tools_by_name:
             return tool_call
 
@@ -549,14 +520,11 @@ class ToolNode(RunnableCallable):
         return tool_call_with_store
 
     def _validate_tool_command(
-        self,
-        command: Command,
-        call: ToolCall,
-        input_type: Literal["list", "dict", "tool_calls"],
+        self, command: Command, call: ToolCall, input_type: Literal["list", "dict"]
     ) -> Command:
         if isinstance(command.update, dict):
             # input type is dict when ToolNode is invoked with a dict input (e.g. {"messages": [AIMessage(..., tool_calls=[...])]})
-            if input_type not in ("dict", "tool_calls"):
+            if input_type != "dict":
                 raise ValueError(
                     f"Tools can provide a dict in Command.update only when using dict with '{self.messages_key}' key as ToolNode input, "
                     f"got: {command.update} for tool '{call['name']}'"