first pass at data storage docs

* docs: fix assistants links

* fix another page

.github/workflows/_test_release.yml

@@ -13,7 +13,7 @@ env:
 
 jobs:
   build:
-    if: github.ref == 'refs/heads/main'
+    if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v0'
     runs-on: ubuntu-latest
 
     outputs:

.github/workflows/deploy_docs.yml

@@ -4,9 +4,11 @@ on:
   push:
     branches:
       - main
+      - v0
   pull_request:
     branches:
       - main
+      - v0
   workflow_dispatch:
 
 permissions:
@@ -82,9 +84,9 @@ jobs:
         run: make llms-text
       - name: Build site
         run: |
-          # If this is main branch, then we want to download stats. we do this 
+          # If this is v0 branch, then we want to download stats. we do this 
           # with the env variable DOWNLOAD_STATS=true
-          if [ "${{ github.ref }}" == "refs/heads/main" ]; then
+          if [ "${{ github.ref }}" == "refs/heads/v0" ]; then
             DOWNLOAD_STATS=true make build-docs
           else
             make build-docs
@@ -144,8 +146,8 @@ jobs:
           fi
 
       - name: Configure GitHub Pages
-        if: github.ref == 'refs/heads/main'
-        uses: actions/configure-pages@v4
+        if: github.ref == 'refs/heads/v0'
+        uses: actions/configure-pages@v5
 
       - name: Upload Pages Artifact
         # if: github.ref == 'refs/heads/main'
@@ -154,6 +156,6 @@ jobs:
           path: ./docs/site/
 
       - name: Deploy to GitHub Pages
-        if: github.ref == 'refs/heads/main'
+        if: github.ref == 'refs/heads/v0'
         id: deployment
         uses: actions/deploy-pages@v4

.github/workflows/release.yml

@@ -13,7 +13,7 @@ env:
 
 jobs:
   build:
-    if: github.ref == 'refs/heads/main'
+    if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v0'
     runs-on: ubuntu-latest
 
     outputs:

README.md

@@ -74,7 +74,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.

docs/_scripts/generate_llms_text.py

@@ -1,10 +1,16 @@
 """Experimental script to generate consolidated llms text from the docs."""
 
+import asyncio
 import glob
 import os
-from typing import TypedDict, List
+from typing import TypedDict, List, Optional
+import pydantic
+import re
+from pydantic import BaseModel, Field
+from langchain_core.rate_limiters import InMemoryRateLimiter
 
 import yaml
+from langchain.chat_models import init_chat_model
 from mkdocs.structure.files import File
 from mkdocs.structure.pages import Page
 from yaml import SafeLoader
@@ -16,7 +22,49 @@ HERE = os.path.dirname(os.path.abspath(__file__))
 SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
 
 
-def generate_full_llms_text(output_file: str) -> str:
+async def convert_ipynb_to_md(file_path: str) -> Optional[str]:
+    """Process a file (markdown or notebook) to markdown format.
+
+    Args:
+        file_path: Path to the file to process
+
+    Returns:
+        Processed markdown content if successful, None otherwise
+    """
+    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={},
+    )
+
+    try:
+        # 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
+        )
+        # Remove self-closing img tags <img ... />
+        processed_content = re.sub(r"<img[^>]*/>", "", processed_content)
+        # Remove img tags with content <img ...>...</img>
+        processed_content = re.sub(
+            r"<img[^>]*>.*?</img>", "", processed_content, flags=re.DOTALL
+        )
+        return processed_content
+    except Exception as e:
+        print(f"Error processing file {file_path}: {e}")
+        return None
+
+
+async def generate_full_llms_text(output_file: str) -> None:
     """Generate a consolidated text file from markdown/notebook files for LLM training.
 
     Args:
@@ -24,11 +72,9 @@ def generate_full_llms_text(output_file: str) -> str:
     """
     # Collect all markdown and notebook files
     all_files = 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)
     )
@@ -38,30 +84,14 @@ def generate_full_llms_text(output_file: str) -> str:
 
     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)
+    # Process files concurrently
+    tasks = [convert_ipynb_to_md(file_path) for file_path in all_files]
+    results = await asyncio.gather(*tasks)
 
-        # 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
-        )
+    # Combine results with file paths
+    for file_path, processed_content in zip(all_files, results):
         if processed_content:
+            rel_path = os.path.relpath(file_path, SOURCE_DIR)
             # Add file name
             all_content.append(f"---\n{rel_path}\n---")
             # Add content
@@ -86,6 +116,7 @@ class NavItem(TypedDict):
     title: str
     url: str
     hierarchy: tuple[str, ...]
+    description: str
 
 
 def _flatten_nav(
@@ -98,7 +129,14 @@ def _flatten_nav(
                 new_path = path + (title,)
                 if isinstance(node, str):
                     # Leaf page
-                    flat.append({"title": title, "url": node, "hierarchy": new_path})
+                    flat.append(
+                        {
+                            "title": title,
+                            "url": node,
+                            "hierarchy": new_path,
+                            "description": "",
+                        }
+                    )
                 elif isinstance(node, list):
                     # Dive in, carrying along the updated path
                     flat.extend(_flatten_nav(node, new_path))
@@ -109,14 +147,82 @@ def _flatten_nav(
         elif isinstance(item, str):
             # Bare string entry → use itself as title, and as URL
             new_path = path + (item,)
-            flat.append({"title": item, "url": item, "hierarchy": new_path})
+            flat.append(
+                {"title": item, "url": item, "hierarchy": new_path, "description": ""}
+            )
         else:
             raise TypeError(f"Unexpected item type {type(item)} in nav")
     return flat
 
 
-def generate_nav_links_text(output_file: str, *, replace_links: bool = False) -> None:
-    """Generate a text file containing navigation structure and links from mkdocs.yaml."""
+class PageInfo(BaseModel):
+    title: str = Field(description="The title of the page")
+    description: str = Field(
+        description="A short description of the page no longer than 3 sentences "
+        "explaining the kind of content that can be found in the page."
+    )
+
+
+async def process_nav_items(nav_items: list[NavItem]) -> list[NavItem]:
+    """Open the contents of each nav item and come up with a better title and description."""
+    rate_limiter = InMemoryRateLimiter(requests_per_second=10)
+    model = init_chat_model("gpt-4o-mini", temperature=0.0, rate_limiter=rate_limiter)
+    model = model.with_structured_output(PageInfo)
+
+    async def process_single_item(item: NavItem) -> NavItem:
+        path = item["url"]
+        file_path = os.path.join(SOURCE_DIR, path)
+
+        # Process the file content (handles both markdown and notebooks)
+        if path.endswith(".ipynb"):
+            content = await convert_ipynb_to_md(file_path)
+        else:
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+
+        if not content:
+            return item
+
+        # Generate a better title and description
+        response = await model.ainvoke(
+            [
+                {
+                    "role": "system",
+                    "content": "You are a technical documentation writer. "
+                    "You are given a markdown page of documentation. "
+                    "Please come up with an appropriate title and "
+                    "description for the page. The description should "
+                    "be a short summary of the page content that is "
+                    "no longer than 3 sentences.",
+                },
+                {
+                    "role": "user",
+                    "content": "The markdown page is as follows:\n\n" + content,
+                },
+            ]
+        )
+        return {
+            "title": response.title,
+            "url": item["url"],
+            "hierarchy": item["hierarchy"],
+            "description": response.description,
+        }
+
+    # Remove any items that start with http:// or https:// looking only for
+    # local file at this stages.
+    nav_items = [
+        item for item in nav_items if not item["url"].startswith(("http://", "https://"))
+    ]
+    # Process items in parallel
+    tasks = [process_single_item(item) for item in nav_items]
+    new_nav_items = await asyncio.gather(*tasks)
+    return new_nav_items
+
+
+async def generate_nav_links_text(
+    output_file: str, *, replace_links: bool = False
+) -> None:
+    """Generate llms.txt from mkdocs.yaml."""
     # Get path to mkdocs.yaml relative to this script
     script_dir = os.path.dirname(os.path.abspath(__file__))
     mkdocs_path = os.path.join(os.path.dirname(script_dir), "mkdocs.yml")
@@ -129,15 +235,15 @@ def generate_nav_links_text(output_file: str, *, replace_links: bool = False) ->
     nav = config.get("nav", [])
     flattened = _flatten_nav(nav)
 
+    processed_nav = await process_nav_items(flattened)
+
     with open(output_file, "w") as f:
         current_section = None
-        for item in flattened:
+        for item in processed_nav:
             # Get the top-level section (first item in hierarchy)
             section = item["hierarchy"][0]
 
-            if section not in {
-                "Guides", "Examples", "Resources"
-            }:
+            if section not in {"Guides", "Examples", "Resources"}:
                 continue
 
             # If we're starting a new section, add a heading
@@ -145,15 +251,7 @@ def generate_nav_links_text(output_file: str, *, replace_links: bool = False) ->
                 f.write(f"\n# {section}\n\n")
                 current_section = section
 
-            # Add the item as a bullet point with title and link
-            # Include full hierarchy path in title, separated by " > "
-            hierarchy_path = " > ".join(item["hierarchy"][1:])
-            title = (
-                f"{item['title']} ({hierarchy_path})"
-                if hierarchy_path
-                else item["title"]
-            )
-
+            title = item["title"]
             # Process URL based on replace_links flag
             url = item["url"]
             if replace_links:
@@ -163,7 +261,7 @@ def generate_nav_links_text(output_file: str, *, replace_links: bool = False) ->
                 url = url.rstrip("/") + "/"
                 url = f"https://langchain-ai.github.io/langgraph/{url}"
 
-            f.write(f"- [{title}]({url})\n")
+            f.write(f"- [{title}]({url}): {item['description']}\n")
 
 
 if __name__ == "__main__":
@@ -188,6 +286,10 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
     if args.link_only:
-        generate_nav_links_text(args.output_file, replace_links=args.replace_links)
+        coro = generate_nav_links_text(
+            args.output_file, replace_links=args.replace_links
+        )
     else:
-        generate_full_llms_text(args.output_file)
+        coro = generate_full_llms_text(args.output_file)
+
+    asyncio.run(coro)

docs/_scripts/notebook_hooks.py

@@ -1,9 +1,16 @@
+"""mkdocs hooks for adding custom logic to documentation pipeline.
+
+Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
+"""
+
 import logging
 import os
 import posixpath
 import re
 from typing import Any, Dict
 
+from bs4 import BeautifulSoup
+from mkdocs.config.defaults import MkDocsConfig
 from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
@@ -71,7 +78,7 @@ REDIRECT_MAP = {
     "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
     "cloud/how-tos/human_in_the_loop_edit_state.md": "cloud/how-tos/add-human-in-the-loop.md",
     "cloud/how-tos/human_in_the_loop_user_input.md": "cloud/how-tos/add-human-in-the-loop.md",
-    "concepts/platform_architecture.md": "langgraph/concepts/langgraph_cloud#architecture",
+    "concepts/platform_architecture.md": "concepts/langgraph_cloud#architecture",
     # cloud streaming redirects
     "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
     "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
@@ -101,8 +108,7 @@ REDIRECT_MAP = {
     "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
     "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md",
     # assistant redirects
-    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md"
-
+    "cloud/how-tos/assistant_versioning.md": "cloud/how-tos/configuration_cloud.md",
 }
 
 
@@ -292,7 +298,7 @@ Redirecting...
 """
 
 
-def write_html(site_dir, old_path, new_path):
+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)
@@ -308,6 +314,52 @@ def write_html(site_dir, old_path, new_path):
         f.write(content)
 
 
+def _inject_gtm(html: str) -> str:
+    """Inject Google Tag Manager code into the HTML.
+
+    Code to inject Google Tag Manager noscript tag immediately after <body>.
+
+    This is done via hooks rather than via a template because the MkDocs material
+    theme does not seem to allow placing the code immediately after the <body> tag
+    without modifying the template files directly.
+
+    Args:
+        html: The HTML content to modify.
+
+    Returns:
+        The modified HTML content with GTM code injected.
+    """
+    # Code was copied from Google Tag Manager setup instructions.
+    gtm_code = """
+<!-- Google Tag Manager (noscript) -->
+<noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-T35S4S46"
+height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
+<!-- End Google Tag Manager (noscript) -->
+"""
+    soup = BeautifulSoup(html, "html.parser")
+    body = soup.body
+    if body:
+        # Insert the GTM code as raw HTML at the top of <body>
+        body.insert(0, BeautifulSoup(gtm_code, "html.parser"))
+        return str(soup)
+    else:
+        return html  # fallback if no <body> found
+
+
+def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
+    """Inject Google Tag Manager noscript tag immediately after <body>.
+
+    Args:
+        output: The HTML output of the page.
+        page: The page instance.
+        config: The MkDocs configuration object.
+
+    Returns:
+        modified HTML output with GTM code injected.
+    """
+    return _inject_gtm(output)
+
+
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):
     use_directory_urls = config.get("use_directory_urls")
@@ -324,4 +376,4 @@ def on_post_build(config):
             + hash
             + suffix
         )
-        write_html(config["site_dir"], old_html_path, new_html_path)
+        _write_html(config["site_dir"], old_html_path, new_html_path)

docs/docs/agents/agents.md

@@ -15,7 +15,7 @@ This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable*
 
 Before you start this tutorial, ensure you have the following:
 
-- An [Anthropic](https://console.anthropic.com/settings/admin-keys) API key 
+- An [Anthropic](https://console.anthropic.com/settings/keys) API key 
 
 ## 1. Install dependencies
 

docs/docs/agents/memory.md

@@ -82,7 +82,7 @@ ny_response = agent.invoke(
 ```
 
 1. The `InMemorySaver` is a checkpointer that stores the agent's state in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [checkpointer documentation](../reference/checkpoints.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready checkpointer for you.
-2. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations. Please note that 
+2. The `checkpointer` is passed to the agent. This enables the agent to persist its state across invocations.
 3. A unique `thread_id` is provided in the config. This ID is used to identify the conversation session. The value is controlled by the user and can be any string.
 4. The agent will continue the conversation using the same `thread_id`. This will allow the agent to infer that the user is asking specifically about the **weather** in New York. 
 

docs/docs/agents/overview.md

@@ -53,3 +53,139 @@ The high-level components are organized into several packages, each with a speci
 | `langmem`                                  | Agent memory management: [**short-term and long-term**](./memory.md)        | `pip install -U langmem`                |
 | `agentevals`                               | Utilities to [**evaluate agent performance**](./evals.md)                   | `pip install -U agentevals`             |
 
+## Visualize an agent graph
+
+Use the following tool to visualize the graph generated by
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]
+and to view an outline of the corresponding code.
+It allows you to explore the infrastructure of the agent as defined by the presence of:
+
+* [`tools`](../agents/tools.md): A list of tools (functions, APIs, or other callable objects) that the agent can use to perform tasks.
+* [`pre_model_hook`](../how-tos/create-react-agent-manage-message-history.ipynb): A function that is called before the model is invoked. It can be used to condense messages or perform other preprocessing tasks.
+* `post_model_hook`: A function that is called after the model is invoked. It can be used to implement guardrails, human-in-the-loop flows, or other postprocessing tasks.
+* [`response_format`](../agents/agents.md#6-configure-structured-output): A data structure used to constrain the type of the final output, e.g., a `pydantic` `BaseModel`.
+
+<div class="agent-layout">
+  <div class="agent-graph-features-container">
+    <div class="agent-graph-features">
+      <h3 class="agent-section-title">Features</h3>
+      <label><input type="checkbox" id="tools" checked> <code>tools</code></label>
+      <label><input type="checkbox" id="pre_model_hook"> <code>pre_model_hook</code></label>
+      <label><input type="checkbox" id="post_model_hook"> <code>post_model_hook</code></label>
+      <label><input type="checkbox" id="response_format"> <code>response_format</code></label>
+    </div>
+  </div>
+
+  <div class="agent-graph-container">
+    <h3 class="agent-section-title">Graph</h3>
+    <img id="agent-graph-img" src="../assets/react_agent_graphs/0001.svg" alt="graph image" style="max-width: 100%;"/>
+  </div>
+</div>
+
+
+The following code snippet shows how to create the above agent (and underlying graph) with
+[`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent]:
+
+<div class="language-python">
+  <pre><code id="agent-code" class="language-python"></code></pre>
+</div>
+
+
+<script>
+function getCheckedValue(id) {
+  return document.getElementById(id).checked ? "1" : "0";
+}
+
+function getKey() {
+  return [
+    getCheckedValue("response_format"),
+    getCheckedValue("post_model_hook"),
+    getCheckedValue("pre_model_hook"),
+    getCheckedValue("tools")
+  ].join("");
+}
+
+function generateCodeSnippet({ tools, pre, post, response }) {
+  const lines = [
+    "from langgraph.prebuilt import create_react_agent",
+    "from langchain_openai import ChatOpenAI"
+  ];
+
+  if (response) lines.push("from pydantic import BaseModel");
+
+  lines.push("", 'model = ChatOpenAI("o4-mini")', "");
+
+  if (tools) {
+    lines.push(
+      "def tool() -> None:",
+      '    """Testing tool."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (pre) {
+    lines.push(
+      "def pre_model_hook() -> None:",
+      '    """Pre-model hook."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (post) {
+    lines.push(
+      "def post_model_hook() -> None:",
+      '    """Post-model hook."""',
+      "    ...",
+      ""
+    );
+  }
+
+  if (response) {
+    lines.push(
+      "class ResponseFormat(BaseModel):",
+      '    """Response format for the agent."""',
+      "    result: str",
+      ""
+    );
+  }
+
+  lines.push("agent = create_react_agent(");
+  lines.push("    model,");
+
+  if (tools) lines.push("    tools=[tool],");
+  if (pre) lines.push("    pre_model_hook=pre_model_hook,");
+  if (post) lines.push("    post_model_hook=post_model_hook,");
+  if (response) lines.push("    response_format=ResponseFormat,");
+
+  lines.push(")", "", "agent.get_graph().draw_mermaid_png()");
+
+  return lines.join("\n");
+}
+
+async function render() {
+  const key = getKey();
+  document.getElementById("agent-graph-img").src = `../assets/react_agent_graphs/${key}.svg`;
+
+  const state = {
+    tools: document.getElementById("tools").checked,
+    pre: document.getElementById("pre_model_hook").checked,
+    post: document.getElementById("post_model_hook").checked,
+    response: document.getElementById("response_format").checked
+  };
+
+  document.getElementById("agent-code").textContent = generateCodeSnippet(state);
+}
+
+function initializeWidget() {
+  render(); // no need for `await` here
+  document.querySelectorAll(".agent-graph-features input").forEach((input) => {
+    input.addEventListener("change", render);
+  });
+}
+
+// Init for both full reload and SPA nav (used by MkDocs Material)
+window.addEventListener("DOMContentLoaded", initializeWidget);
+document$.subscribe(initializeWidget);
+</script>

docs/docs/agents/tools.md

@@ -280,7 +280,21 @@ LangGraph allows access to short-term and long-term memory from tools. See [Memo
 
 ## Prebuilt tools
 
-LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
+You can use prebuilt tools from model providers by passing a dictionary with tool specs to the `tools` parameter of `create_react_agent`. For example, to use the `web_search_preview` tool from OpenAI:
+
+```python
+from langgraph.prebuilt import create_react_agent
+
+agent = create_react_agent(
+    model="openai:gpt-4o-mini", 
+    tools=[{"type": "web_search_preview"}]
+)
+response = agent.invoke(
+    {"messages": ["What was a positive news story from today?"]}
+)
+```
+
+Additionally, LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
 
 You can browse the full list of available integrations in the [LangChain integrations directory](https://python.langchain.com/docs/integrations/tools/).
 

docs/docs/assets/react-agent-graphs.json

@@ -0,0 +1,19 @@
+{
+    "0000": "graph TD;\n\t__start__ --> agent;\n\tagent --> __end__;",
+    "0001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\ttools --> agent;",
+    "0010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tpre_model_hook --> agent;\n\tagent --> __end__;",
+    "0011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> __end__;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
+    "0100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> __end__;",
+    "0101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;",
+    "0110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpre_model_hook --> agent;\n\tpost_model_hook --> __end__;",
+    "0111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> __end__;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;",
+    "1000": "graph TD;\n\t__start__ --> agent;\n\tagent --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
+    "1001": "graph TD;\n\t__start__ --> agent;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1010": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1011": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent -.-> generate_structured_response;\n\tagent -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;",
+    "1100": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tgenerate_structured_response --> __end__;",
+    "1101": "graph TD;\n\t__start__ --> agent;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> agent;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> tools;\n\ttools --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1110": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook --> generate_structured_response;\n\tpre_model_hook --> agent;\n\tgenerate_structured_response --> __end__;",
+    "1111": "graph TD;\n\t__start__ --> pre_model_hook;\n\tagent --> post_model_hook;\n\tpost_model_hook -.-> generate_structured_response;\n\tpost_model_hook -.-> pre_model_hook;\n\tpost_model_hook -.-> tools;\n\tpre_model_hook --> agent;\n\ttools --> pre_model_hook;\n\tgenerate_structured_response --> __end__;"
+}
+  

docs/docs/cloud/deployment/cloud.md

@@ -62,6 +62,15 @@ Starting from the `LangGraph Platform` view...
 1. In the panel, select the `Server` tab to view server logs for the revision. Server logs are only available after a revision has been deployed.
 1. Within the `Server` tab, adjust the date/time range picker as needed. By default, the date/time range picker is set to the `Last 7 days`.
 
+## View Deployment Metrics
+
+Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
+
+1. In the left-hand navigation panel, select `LangGraph Platform`. The `LangGraph Platform` view contains a list of existing LangGraph Platform deployments.
+1. Select an existing deployment to monitor.
+1. Select the `Monitoring` tab to view the deployment metrics. See a list of [all available metrics](../../concepts/langgraph_control_plane.md#monitoring).
+1. Within the `Monitoring` tab, use the date/time range picker as needed. By default, the date/time range picker is set to the `Last 15 minutes`.
+
 ## Interrupt Revision
 
 Interrupting a revision will stop deployment of the revision.

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -2,8 +2,8 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 
@@ -30,18 +30,17 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
     1. `LangGraphPlatform CRD`: A CRD for LangGraph Platform deployments. This contains the spec for managing an instance of a LangGraph platform deployment.
     1. `operator`: This operator handles changes to your LangGraph Platform CRDs.
     1. `host-backend`: This is the [control plane](../../concepts/langgraph_control_plane.md).
-1. Two additional images will be used by the chart.
+1. Two additional images will be used by the chart. Use the images that are specified in the latest release.
 
         hostBackendImage:
           repository: "docker.io/langchain/hosted-langserve-backend"
           pullPolicy: IfNotPresent
-          tag: "0.9.80"
         operatorImage:
           repository: "docker.io/langchain/langgraph-operator"
           pullPolicy: IfNotPresent
-          tag: "aa9dff4"
 
-1. In your `values.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
+1. In your config file for langsmith (usually `langsmith_config.yaml`, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
+
         config:
           langgraphPlatform:
             enabled: true

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -2,8 +2,8 @@
 
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Prerequisites
 

docs/docs/cloud/deployment/setup.md

@@ -129,9 +129,6 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```
 
-!!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
-
 Example file directory:
 
 ```bash

docs/docs/cloud/deployment/setup_javascript.md

@@ -155,10 +155,6 @@ const workflow = new StateGraph(MessagesAnnotation)
 export const graph = workflow.compile();
 ```
 
-!!! info "Assign `CompiledGraph` to Variable"
-
-    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).
-
 Example file directory:
 
 ```bash

docs/docs/cloud/deployment/setup_pyproject.md

@@ -142,9 +142,6 @@ workflow.add_edge("action", "agent")
 graph = workflow.compile()
 ```
 
-!!! warning "Assign `CompiledGraph` to Variable"
-    The build process for LangGraph Platform requires that the `CompiledGraph` object be assigned to a variable at the top-level of a Python module.
-
 Example file directory:
 
 ```bash

docs/docs/cloud/how-tos/studio/manage_assistants.md

@@ -2,7 +2,7 @@
 
 !!! info "Prerequisites"
 
-    - [Assistants Overview](../../concepts/assistants.md)
+    - [Assistants Overview](../../../concepts/assistants.md)
 
 LangGraph Studio lets you view, edit, and update your assistants, and allows you to run your graph using these assistant configurations.
 

docs/docs/cloud/how-tos/studio/run_evals.md

@@ -0,0 +1,57 @@
+# Run experiments over a dataset
+
+LangGraph Studio supports evaluations by allowing you to run your assistant over a pre-defined LangSmith dataset. This enables you to understand how your application performs over a variety of inputs, compare the results to reference outputs, and score the results using [evaluators](../../../agents/evals.md).
+
+This guide shows you how to run an experiment end-to-end from Studio.
+
+---
+
+## Prerequisites
+
+Before running an experiment, ensure you have the following:
+
+1.  **A LangSmith dataset**: Your dataset should contain the inputs you want to test and optionally, reference outputs for comparison.
+
+    - The schema for the inputs must match the required input schema for the assistant. For more information on schemas, see [here](../../../concepts/low_level.md#schema).
+    - For more on creating datasets, see [How to Manage Datasets](https://docs.smith.langchain.com/evaluation/how_to_guides/manage_datasets_in_application#set-up-your-dataset).
+
+2.  **(Optional) Evaluators**: You can attach evaluators (e.g., LLM-as-a-Judge, heuristics, or custom functions) to your dataset in LangSmith. These will run automatically after the graph has processed all inputs.
+
+    - To learn more, read about [Evaluation Concepts](https://docs.smith.langchain.com/evaluation/concepts#evaluators).
+
+3.  **A running application**: The experiment can be run against:
+    - An application deployed on [LangGraph Platform](../../quick_start.md).
+    - A locally running application started via the [langgraph-cli](../../../tutorials/langgraph-platform/local-server.md).
+
+---
+
+## Step-by-step guide
+
+### 1. Launch the experiment
+
+Click the **Run experiment** button in the top right corner of the Studio page.
+
+### 2. Select your dataset
+
+In the modal that appears, select the dataset (or a specific dataset split) to use for the experiment and click **Start**.
+
+### 3. Monitor the progress
+
+All of the inputs in the dataset will now be run against the active assistant. Monitor the experiment's progress via the badge in the top right corner.
+
+You can continue to work in Studio while the experiment runs in the background. Click the arrow icon button at any time to navigate to LangSmith and view the detailed experiment results.
+
+---
+
+## Troubleshooting
+
+### "Run experiment" button is disabled
+
+If the "Run experiment" button is disabled, check the following:
+
+- **Deployed application**: If your application is deployed on LangGraph Platform, you may need to create a new revision to enable this feature.
+- **Local development server**: If you are running your application locally, make sure you have upgraded to the latest version of the `langgraph-cli` (`pip install -U langgraph-cli`). Additionally, ensure you have tracing enabled by setting the `LANGSMITH_API_KEY` in your project's `.env` file.
+
+### Evaluator results are missing
+
+When you run an experiment, any attached evaluators are scheduled for execution in a queue. If you don't see results immediately, it likely means they are still pending.

docs/docs/cloud/reference/cli.md

@@ -40,9 +40,10 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
     | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`dependencies`</span>     | **Required**. Array of dependencies for LangGraph Platform API server. Dependencies can be one of the following: <ul><li>A single period (`"."`), which will look for local Python packages.</li><li>The directory path where `pyproject.toml`, `setup.py` or `requirements.txt` is located.</br></br>For example, if `requirements.txt` is located in the root of the project directory, specify `"./"`. If it's located in a subdirectory called `local_package`, specify `"./local_package"`. Do not specify the string `"requirements.txt"` itself.</li><li>A Python package name.</li></ul> |
-    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and returns an instance of `langgraph.graph.state.StateGraph` or `langgraph.graph.state.CompiledStateGraph`. See [how to rebuild a graph at runtime](../../cloud/deployment/graph_rebuild.md) for more details.</li></ul>                                    |
     | <span style="white-space: nowrap;">`auth`</span>             | _(Added in v0.0.11)_ Auth configuration containing the path to your authentication handler. Example: `./your_package/auth.py:auth`, where `auth` is an instance of `langgraph_sdk.Auth`. See [authentication guide](../../concepts/auth.md) for details.                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`base_image`</span>       | Optional. Base image to use for the LangGraph API server. Defaults to `langchain/langgraph-api` or `langchain/langgraphjs-api`. Use this to pin your builds to a particular version of the langgraph API, such as `"langchain/langgraph-server:0.2"`. See https://hub.docker.com/r/langchain/langgraph-server/tags for more details. (added in `langgraph-cli==0.2.8`) |
+    | <span style="white-space: nowrap;">`image_distro`</span>     | Optional. Linux distribution for the base image. Must be either `"debian"` or `"wolfi"`. If omitted, defaults to `"debian"`. Available in `langgraph-cli>=0.2.11`.|
     | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
     | <span style="white-space: nowrap;">`ui`</span>               | Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file. (added in `langgraph-cli==0.1.84`)                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
@@ -57,7 +58,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
 
     | Key                                                          | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
     | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and creates an instance of `StateGraph` / `CompiledStateGraph`.</li></ul>                                    |
+    | <span style="white-space: nowrap;">`graphs`</span>           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./src/graph.ts:variable`, where `variable` is an instance of `CompiledStateGraph`</li><li>`./src/graph.ts:makeGraph`, where `makeGraph` is a function that takes a config dictionary (`LangGraphRunnableConfig`) and returns an instance of `StateGraph` or `CompiledStateGraph`. See [how to rebuild a graph at runtime](../../cloud/deployment/graph_rebuild.md) for more details.</li></ul>                                    |
     | <span style="white-space: nowrap;">`env`</span>              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
     | <span style="white-space: nowrap;">`store`</span>            | Configuration for adding semantic search and/or time-to-live (TTL) to the BaseStore. Contains the following fields: <ul><li>`index` (optional): Configuration for semantic search indexing with fields `embed`, `dims`, and optional `fields`.</li><li>`ttl` (optional): Configuration for item expiration. An object with optional fields: `refresh_on_read` (boolean, defaults to `true`), `default_ttl` (float, lifespan in **minutes**, defaults to no expiration), and `sweep_interval_minutes` (integer, how often to check for expired items, defaults to no sweeping).</li></ul> |
     | <span style="white-space: nowrap;">`node_version`</span>     | Specify `node_version: 20` to use LangGraph.js.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
@@ -79,6 +80,20 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     }
     ```
 
+    #### Using Wolfi Base Images
+
+    You can specify the Linux distribution for your base image using the `image_distro` field. Valid options are `debian` or `wolfi`. Wolfi is the recommended option as it provides smaller and more secure images. This is available in `langgraph-cli>=0.2.11`.
+
+    ```json
+    {
+      "dependencies": ["."],
+      "graphs": {
+        "chat": "./chat/graph.py:graph"
+      },
+      "image_distro": "wolfi"
+    }
+    ```
+
     #### Adding semantic search to the store
 
     All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.

docs/docs/cloud/reference/env_var.md

@@ -123,3 +123,12 @@ Defaults to `''`.
 Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
 
 Defaults to `False`.
+
+## `MOUNT_PREFIX`
+
+!!! info "Only Allowed in Self-Hosted Deployments"
+    The `MOUNT_PREFIX` environment variable is only allowed in Self-Hosted Deployment models, LangGraph Platform SaaS will not allow this environment variable.
+
+Set `MOUNT_PREFIX` to serve the LangGraph Server under a specific path prefix. This is useful for deployments where the server is behind a reverse proxy or load balancer that requires a specific path prefix.
+
+For example, if the server is to be served under `https://example.com/langgraph`, set `MOUNT_PREFIX` to `/langgraph`.

docs/docs/concepts/data_storage_and_privacy.md

@@ -0,0 +1,127 @@
+# Data Storage and Privacy
+
+This document provides a comprehensive overview of what data is stored, collected, and processed when using LangGraph, particularly with the CLI tools like `langgraph dev`.
+
+## What Data is Stored
+
+### CLI Telemetry (Opt-out)
+
+By default, the LangGraph CLI collects minimal analytics data to help improve the tool:
+
+**Data Collected:**
+- CLI command used (e.g., `dev`, `up`, `build`)
+- CLI version
+- Operating system type and version
+- Python version
+- Anonymized parameter usage (boolean flags indicating non-default options were used)
+
+**Data NOT Collected:**
+- Actual parameter values
+- File contents or paths
+- Personal information
+- Code or graph implementations
+- API keys or sensitive data
+
+**How to Opt Out:**
+Set the environment variable `LANGGRAPH_CLI_NO_ANALYTICS=1` to disable all CLI analytics collection.
+
+### LangSmith Integration (Opt-in)
+
+When a `LANGSMITH_API_KEY` is provided (not required):
+- Metadata on number of runs executed
+- Current API version being run
+- Trace data (if tracing is enabled)
+
+This data is only sent when explicitly configured with LangSmith credentials.
+
+### Tracing Data (Opt-in)
+
+When tracing is enabled:
+- Execution traces are logged to the configured tracing backend
+- This requires explicit configuration and is not enabled by default
+
+## What Data is NOT Stored Remotely
+
+- **Checkpoints**: Stored locally in your development environment
+- **Memory store data**: Persisted locally, not transmitted
+- **Graph state**: Remains in your local environment
+- **Application data**: Your actual application logic and data stay local
+
+## Local Data Storage
+
+### Development Mode (`langgraph dev`)
+
+When using `langgraph dev`:
+- State is persisted to a local directory
+- Checkpoints are stored locally for debugging and development
+- No remote storage or transmission of your application data
+
+### Checkpoints and State Persistence
+
+LangGraph automatically persists:
+- **Checkpoints**: Snapshots of graph state at each execution step
+- **Thread data**: Conversation/execution history organized by thread IDs
+- **Graph state**: Node outputs, intermediate results, and execution metadata
+- **Memory/Store data**: Information that persists across multiple threads
+
+**Storage Locations:**
+- **Local development**: Local directory (configurable)
+- **Docker deployment**: Local Docker volumes
+- **LangGraph Platform**: Managed database infrastructure
+
+## Security and Encryption
+
+### Data Encryption
+
+- Checkpointers can optionally encrypt all persisted state
+- Encryption uses AES encryption via `EncryptedSerializer`
+- When `LANGGRAPH_AES_KEY` environment variable is present, encryption is automatically enabled on LangGraph Platform
+
+### Data Retention
+
+- **TTL (Time-to-Live)**: Configurable automatic cleanup of old data
+- **Default TTL**: Can be set in minutes for automatic expiration
+- **Automatic sweeping**: Expired data is automatically removed at configurable intervals
+
+## Privacy Controls
+
+### Environment Variables
+
+Key environment variables for controlling data collection and storage:
+
+- `LANGGRAPH_CLI_NO_ANALYTICS=1`: Disable CLI analytics collection
+- `LANGGRAPH_AES_KEY`: Enable automatic encryption of stored data
+- `LANGSMITH_TRACING=false`: Disable tracing to LangSmith (self-hosted deployments)
+- `LANGSMITH_API_KEY`: Enable LangSmith integration (opt-in)
+
+### Logging Controls
+
+- `LOG_LEVEL`: Control verbosity of logs
+- `LOG_JSON`: Format logs as JSON
+- Various other logging configuration options
+
+## Security Policy
+
+For security vulnerabilities:
+- Report through the huntr.com bounty program
+- LangGraph is in-scope for security bounties
+- Security contact: `security@langchain.dev`
+
+## Best Practices for Privacy
+
+1. **Review Analytics**: Set `LANGGRAPH_CLI_NO_ANALYTICS=1` if you prefer not to share usage analytics
+2. **Enable Encryption**: Use `LANGGRAPH_AES_KEY` for sensitive data
+3. **Configure TTL**: Set appropriate data retention policies
+4. **Monitor Tracing**: Only enable tracing when needed and review what data is being sent
+5. **Environment Variables**: Audit your environment variables to ensure proper privacy controls
+
+## Summary
+
+LangGraph is designed with privacy in mind:
+- Minimal data collection (analytics can be disabled)
+- Local storage by default for development
+- Optional encryption for sensitive data
+- Clear opt-in requirements for external services
+- Comprehensive privacy controls through environment variables
+
+Your application data, checkpoints, and state remain under your control and are not transmitted unless you explicitly configure external services like LangSmith.

docs/docs/concepts/deployment_options.md

@@ -40,8 +40,8 @@ For more information, please see:
 
 ## Self-Hosted Data Plane
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.
 
@@ -56,8 +56,8 @@ For more information, please see:
 
 ## Self-Hosted Control Plane
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../concepts/plans.md) plan.
 
 The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option give you full control and responsibility of the control plane and data plane infrastructure.
 

docs/docs/concepts/langgraph_control_plane.md

@@ -19,6 +19,7 @@ From the control plane UI, you can:
 - Update a deployment.
 - Update environment variables for a deployment.
 - View build and server logs of a deployment.
+- View deployment metrics such as CPU and memory usage.
 - Delete a deployment.
 
 The Control Plane UI is embedded in [LangSmith](https://docs.smith.langchain.com/langgraph_cloud).
@@ -47,17 +48,22 @@ This section describes various features of the control plane.
 
 For simplicity, the control plane offers two deployment types with different resource allocations: `Development` and `Production`.
 
-| **Deployment Type** | **CPU** | **Memory** | **Scaling**         |
-|---------------------|---------|------------|---------------------|
-| Development         | 1 CPU   | 1 GB       | Up to 1 container   |
-| Production          | 2 CPU   | 2 GB       | Up to 10 containers |
+| **Deployment Type** | **CPU/Memory**  | **Scaling**         | **Database**                                                                     |
+|---------------------|-----------------|---------------------|----------------------------------------------------------------------------------|
+| Development         | 1 CPU, 1 GB RAM | Up to 1 container   | 10 GB disk, no backups                                                           |
+| Production          | 2 CPU, 2 GB RAM | Up to 10 containers | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
 
 CPU and memory resources are per container.
 
-!!! info "For [Cloud SaaS](../concepts/langgraph_cloud.md)"
+!!! warning "Immutable Deployment Type"
+
+    Once a deployment is created, the deployment type cannot be changed.
+
+!!! info "Resource Customization"
     For `Production` type deployments, resources can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support@langchain.dev to request an increase in resources.
 
-!!! info 
+    For `Development` types deployments, database disk size can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](../how-tos/ttl/configure_ttl.md) should be configured to manage disk usage. Contact support@langchain.dev to request an increase in resources.
+
     Resources for [Self-Hosted Data Plane](../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../concepts/langgraph_self_hosted_control_plane.md) deployments can be fully customized.
 
 ### Database Provisioning
@@ -83,6 +89,17 @@ Infrastructure for deployments and revisions are provisioned and deployed asynch
 
 The control plane and [LangGraph Data Plane](./langgraph_data_plane.md) "listener" application coordinate to achieve asynchronous deployments.
 
+### Monitoring
+
+After a deployment is ready, the control plane monitors the deployment and records various metrics, such as:
+
+- CPU and memory usage of the deployment.
+- Number of container restarts.
+- Number of replicas (this will increase with [autoscaling](../concepts/langgraph_data_plane.md#autoscaling)).
+- [Postgres](../concepts/langgraph_data_plane.md#postgres) CPU, memory usage, and disk usage.
+
+These metrics are displayed as charts in the Control Plane UI.
+
 ### LangSmith Integration
 
 A [LangSmith](https://docs.smith.langchain.com/) tracing project is automatically created for each deployment. The tracing project has the same name as the deployment. When creating a deployment, the `LANGCHAIN_TRACING` and `LANGSMITH_API_KEY`/`LANGCHAIN_API_KEY` environment variables do not need to be specified; they are set automatically by the control plane.

docs/docs/concepts/langgraph_self_hosted_control_plane.md

@@ -2,8 +2,8 @@
 
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
-!!! important "Beta"
-    The Self-Hosted Control Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Control Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/docs/concepts/langgraph_self_hosted_data_plane.md

@@ -7,8 +7,8 @@ search:
 
 There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).
 
-!!! important "Beta"
-    The Self-Hosted Data Plane deployment option is currently in beta stage.
+!!! info "Important"
+    The Self-Hosted Data Plane deployment option is currently in beta stage and requires an [Enterprise](../../concepts/plans.md) plan.
 
 ## Requirements
 

docs/docs/concepts/langgraph_studio.md

@@ -21,9 +21,10 @@ Key features of LangGraph Studio:
 
 - Visualize your graph architecture
 - [Run and interact with your agent](../cloud/how-tos/invoke_studio.md)
-- [Manage assistants](../cloud/how-tos/studio/manage_assistants.md.md)
+- [Manage assistants](../cloud/how-tos/studio/manage_assistants.md)
 - [Manage threads](../cloud/how-tos/threads_studio.md)
 - [Iterate on prompts](../cloud/how-tos/iterate_graph_studio.md)
+- [Run experiments over a dataset](../cloud/how-tos/studio/run_evals.md)
 - Manage [long term memory](memory.md)
 - Debug agent state via [time travel](time-travel.md)
 
@@ -41,4 +42,4 @@ Chat mode is a simpler UI for iterating on and testing chat-specific agents. It
 
 ## Learn more
 
-- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.
+- See this guide on how to [get started](../cloud/how-tos/studio/quick_start.md) with LangGraph Studio.

docs/docs/concepts/low_level.md

@@ -197,19 +197,25 @@ In LangGraph, nodes are typically python functions (sync or async) where the **f
 Similar to `NetworkX`, you add these nodes to a graph using the [add_node][langgraph.graph.StateGraph.add_node] method:
 
 ```python
+from typing_extensions import TypedDict
+
 from langchain_core.runnables import RunnableConfig
 from langgraph.graph import StateGraph
 
-builder = StateGraph(dict)
+class State(TypedDict):
+    input: str
+    results: str
+
+builder = StateGraph(State)
 
 
-def my_node(state: dict, config: RunnableConfig):
+def my_node(state: State, config: RunnableConfig):
     print("In node: ", config["configurable"]["user_id"])
     return {"results": f"Hello, {state['input']}!"}
 
 
 # The second argument is optional
-def my_other_node(state: dict):
+def my_other_node(state: State):
     return state
 
 

docs/docs/concepts/persistence.md

@@ -470,9 +470,34 @@ If the checkpointer is used with asynchronous graph execution (i.e. executing th
 
 ### Serializer
 
-When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects. 
+When checkpointers save the graph state, they need to serialize the channel values in the state. This is done using serializer objects.
 `langgraph_checkpoint` defines [protocol][langgraph.checkpoint.serde.base.SerializerProtocol] for implementing serializers provides a default implementation ([JsonPlusSerializer][langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer]) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
 
+#### Encryption
+
+Checkpointers can optionally encrypt all persisted state. To enable this, pass an instance of [`EncryptedSerializer`][langgraph.checkpoint.serde.encrypted.EncryptedSerializer] to the `serde` argument of any `BaseCheckpointSaver` implementation. The easiest way to create an encrypted serializer is via [`from_pycryptodome_aes`][langgraph.checkpoint.serde.encrypted.EncryptedSerializer.from_pycryptodome_aes], which reads the AES key from the `LANGGRAPH_AES_KEY` environment variable (or accepts a `key` argument):
+
+```python
+import sqlite3
+
+from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+serde = EncryptedSerializer.from_pycryptodome_aes()  # reads LANGGRAPH_AES_KEY
+checkpointer = SqliteSaver(sqlite3.connect("checkpoint.db"), serde=serde)
+```
+
+```python
+from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
+from langgraph.checkpoint.postgres import PostgresSaver
+
+serde = EncryptedSerializer.from_pycryptodome_aes()
+checkpointer = PostgresSaver.from_conn_string("postgresql://...", serde=serde)
+checkpointer.setup()
+```
+
+When running on LangGraph Platform, encryption is automatically enabled whenever `LANGGRAPH_AES_KEY` is present, so you only need to provide the environment variable. Other encryption schemes can be used by implementing [`CipherProtocol`][langgraph.checkpoint.serde.base.CipherProtocol] and supplying it to `EncryptedSerializer`.
+
 ## Capabilities
 
 ### Human-in-the-loop

docs/docs/concepts/sdk.md

@@ -5,7 +5,7 @@ search:
 
 # LangGraph SDK
 
-LangGraph Platform provides both a Python SDK for interacting with [LangGraph Server](./langgraph_server.md).
+LangGraph Platform provides both a Python and JS SDK for interacting with [LangGraph Server](./langgraph_server.md).
 
 !!! tip "Python SDK reference"
   

docs/docs/how-tos/graph-api.ipynb

@@ -2235,7 +2235,7 @@
     "    if termination_condition(state):\n",
     "        return END\n",
     "    else:\n",
-    "        return \"a\"\n",
+    "        return \"b\"\n",
     "\n",
     "builder.add_edge(START, \"a\")\n",
     "builder.add_conditional_edges(\"a\", route)\n",
@@ -2950,16 +2950,6 @@
     "    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 the example below."
    ]
   },
-  {
-   "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,

docs/docs/llms.txt

@@ -1,150 +1,149 @@
 
 # Guides
 
-- [index.md (index.md)](https://langchain-ai.github.io/langgraph/index/)
-- [Quickstart (Get started > Quickstart)](https://langchain-ai.github.io/langgraph/agents/agents/)
-- [concepts/why-langgraph.md (Get started > LangGraph basics > concepts/why-langgraph.md)](https://langchain-ai.github.io/langgraph/concepts/why-langgraph/)
-- [Build a basic chatbot (Get started > LangGraph basics > Build a basic chatbot)](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/)
-- [tutorials/get-started/2-add-tools.md (Get started > LangGraph basics > tutorials/get-started/2-add-tools.md)](https://langchain-ai.github.io/langgraph/tutorials/get-started/2-add-tools/)
-- [tutorials/get-started/3-add-memory.md (Get started > LangGraph basics > tutorials/get-started/3-add-memory.md)](https://langchain-ai.github.io/langgraph/tutorials/get-started/3-add-memory/)
-- [Add human-in-the-loop (Get started > LangGraph basics > Add human-in-the-loop)](https://langchain-ai.github.io/langgraph/tutorials/get-started/4-human-in-the-loop/)
-- [tutorials/get-started/5-customize-state.md (Get started > LangGraph basics > tutorials/get-started/5-customize-state.md)](https://langchain-ai.github.io/langgraph/tutorials/get-started/5-customize-state/)
-- [tutorials/get-started/6-time-travel.md (Get started > LangGraph basics > tutorials/get-started/6-time-travel.md)](https://langchain-ai.github.io/langgraph/tutorials/get-started/6-time-travel/)
-- [Deployment (Get started > Deployment)](https://langchain-ai.github.io/langgraph/tutorials/deployment/)
-- [Overview (Prebuilt agents > Overview)](https://langchain-ai.github.io/langgraph/agents/overview/)
-- [agents/run_agents.md (Prebuilt agents > agents/run_agents.md)](https://langchain-ai.github.io/langgraph/agents/run_agents/)
-- [agents/streaming.md (Prebuilt agents > agents/streaming.md)](https://langchain-ai.github.io/langgraph/agents/streaming/)
-- [agents/models.md (Prebuilt agents > agents/models.md)](https://langchain-ai.github.io/langgraph/agents/models/)
-- [agents/tools.md (Prebuilt agents > agents/tools.md)](https://langchain-ai.github.io/langgraph/agents/tools/)
-- [agents/mcp.md (Prebuilt agents > agents/mcp.md)](https://langchain-ai.github.io/langgraph/agents/mcp/)
-- [agents/context.md (Prebuilt agents > agents/context.md)](https://langchain-ai.github.io/langgraph/agents/context/)
-- [agents/memory.md (Prebuilt agents > agents/memory.md)](https://langchain-ai.github.io/langgraph/agents/memory/)
-- [agents/human-in-the-loop.md (Prebuilt agents > agents/human-in-the-loop.md)](https://langchain-ai.github.io/langgraph/agents/human-in-the-loop/)
-- [agents/multi-agent.md (Prebuilt agents > agents/multi-agent.md)](https://langchain-ai.github.io/langgraph/agents/multi-agent/)
-- [agents/evals.md (Prebuilt agents > agents/evals.md)](https://langchain-ai.github.io/langgraph/agents/evals/)
-- [agents/deployment.md (Prebuilt agents > agents/deployment.md)](https://langchain-ai.github.io/langgraph/agents/deployment/)
-- [agents/ui.md (Prebuilt agents > agents/ui.md)](https://langchain-ai.github.io/langgraph/agents/ui/)
-- [Overview (LangGraph framework > Agent architectures > Overview)](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/)
-- [Workflows & agents (LangGraph framework > Agent architectures > Workflows & agents)](https://langchain-ai.github.io/langgraph/tutorials/workflows/)
-- [Overview (LangGraph framework > Graphs > Overview)](https://langchain-ai.github.io/langgraph/concepts/low_level/)
-- [Runtime overview (LangGraph framework > Graphs > Runtime overview)](https://langchain-ai.github.io/langgraph/concepts/pregel/)
-- [Use the Graph API (LangGraph framework > Graphs > Use the Graph API)](https://langchain-ai.github.io/langgraph/how-tos/graph-api/)
-- [Overview (LangGraph framework > Streaming > Overview)](https://langchain-ai.github.io/langgraph/concepts/streaming/)
-- [Stream outputs (LangGraph framework > Streaming > Stream outputs)](https://langchain-ai.github.io/langgraph/how-tos/streaming/)
-- [Overview (LangGraph framework > Persistence > Overview)](https://langchain-ai.github.io/langgraph/concepts/persistence/)
-- [concepts/durable_execution.md (LangGraph framework > Persistence > concepts/durable_execution.md)](https://langchain-ai.github.io/langgraph/concepts/durable_execution/)
-- [how-tos/persistence.ipynb (LangGraph framework > Persistence > how-tos/persistence.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
-- [Overview (LangGraph framework > Memory > Overview)](https://langchain-ai.github.io/langgraph/concepts/memory/)
-- [Manage memory (LangGraph framework > Memory > Manage memory)](https://langchain-ai.github.io/langgraph/how-tos/memory/)
-- [Overview (LangGraph framework > Human-in-the-loop > Overview)](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/)
-- [how-tos/human_in_the_loop/add-human-in-the-loop.md (LangGraph framework > Human-in-the-loop > how-tos/human_in_the_loop/add-human-in-the-loop.md)](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/add-human-in-the-loop/)
-- [Overview (LangGraph framework > Breakpoints > Overview)](https://langchain-ai.github.io/langgraph/concepts/breakpoints/)
-- [how-tos/human_in_the_loop/breakpoints.ipynb (LangGraph framework > Breakpoints > how-tos/human_in_the_loop/breakpoints.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/)
-- [Overview (LangGraph framework > Time travel > Overview)](https://langchain-ai.github.io/langgraph/concepts/time-travel/)
-- [how-tos/human_in_the_loop/time-travel.ipynb (LangGraph framework > Time travel > how-tos/human_in_the_loop/time-travel.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
-- [Overview (LangGraph framework > Tools > Overview)](https://langchain-ai.github.io/langgraph/concepts/tools/)
-- [how-tos/tool-calling.ipynb (LangGraph framework > Tools > how-tos/tool-calling.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
-- [Overview (LangGraph framework > Subgraphs > Overview)](https://langchain-ai.github.io/langgraph/concepts/subgraphs/)
-- [how-tos/subgraph.ipynb (LangGraph framework > Subgraphs > how-tos/subgraph.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/subgraph/)
-- [Overview (LangGraph framework > Multi-agent > Overview)](https://langchain-ai.github.io/langgraph/concepts/multi_agent/)
-- [how-tos/multi_agent.ipynb (LangGraph framework > Multi-agent > how-tos/multi_agent.ipynb)](https://langchain-ai.github.io/langgraph/how-tos/multi_agent/)
-- [Overview (LangGraph framework > Functional API > Overview)](https://langchain-ai.github.io/langgraph/concepts/functional_api/)
-- [how-tos/use-functional-api.md (LangGraph framework > Functional API > how-tos/use-functional-api.md)](https://langchain-ai.github.io/langgraph/how-tos/use-functional-api/)
-- [Overview (LangGraph Platform > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/)
-- [Quickstart (LangGraph Platform > Get started > Quickstart)](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/)
-- [Deployment quickstart (LangGraph Platform > Get started > Deployment quickstart)](https://langchain-ai.github.io/langgraph/cloud/quick_start/)
-- [Overview (LangGraph Platform > Components > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_components/)
-- [Overview (LangGraph Platform > Components > LangGraph Server > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/)
-- [Overview (LangGraph Platform > Components > LangGraph Server > Application structure > Overview)](https://langchain-ai.github.io/langgraph/concepts/application_structure/)
-- [cloud/deployment/setup.md (LangGraph Platform > Components > LangGraph Server > Application structure > cloud/deployment/setup.md)](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/)
-- [cloud/deployment/setup_pyproject.md (LangGraph Platform > Components > LangGraph Server > Application structure > cloud/deployment/setup_pyproject.md)](https://langchain-ai.github.io/langgraph/cloud/deployment/setup_pyproject/)
-- [cloud/deployment/setup_javascript.md (LangGraph Platform > Components > LangGraph Server > Application structure > cloud/deployment/setup_javascript.md)](https://langchain-ai.github.io/langgraph/cloud/deployment/setup_javascript/)
-- [cloud/deployment/custom_docker.md (LangGraph Platform > Components > LangGraph Server > Application structure > cloud/deployment/custom_docker.md)](https://langchain-ai.github.io/langgraph/cloud/deployment/custom_docker/)
-- [LangGraph CLI (LangGraph Platform > Components > LangGraph CLI)](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/)
-- [Overview (LangGraph Platform > Components > LangGraph Studio > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/)
-- [Quickstart (LangGraph Platform > Components > LangGraph Studio > Quickstart)](https://langchain-ai.github.io/langgraph/cloud/how-tos/studio/quick_start/)
-- [cloud/how-tos/invoke_studio.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/invoke_studio.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/)
-- [cloud/how-tos/studio/manage_assistants.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/studio/manage_assistants.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/studio/manage_assistants/)
-- [cloud/how-tos/threads_studio.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/threads_studio.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/threads_studio/)
-- [cloud/how-tos/iterate_graph_studio.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/iterate_graph_studio.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/iterate_graph_studio/)
-- [cloud/how-tos/clone_traces_studio.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/clone_traces_studio.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/clone_traces_studio/)
-- [cloud/how-tos/datasets_studio.md (LangGraph Platform > Components > LangGraph Studio > cloud/how-tos/datasets_studio.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/datasets_studio/)
-- [LangGraph SDK (LangGraph Platform > Components > LangGraph SDK)](https://langchain-ai.github.io/langgraph/concepts/sdk/)
-- [Add semantic search (LangGraph Platform > Data management > Add semantic search)](https://langchain-ai.github.io/langgraph/cloud/deployment/semantic_search/)
-- [Add TTLs (LangGraph Platform > Data management > Add TTLs)](https://langchain-ai.github.io/langgraph/how-tos/ttl/configure_ttl/)
-- [Overview (LangGraph Platform > Authentication & access control > Overview)](https://langchain-ai.github.io/langgraph/concepts/auth/)
-- [how-tos/auth/custom_auth.md (LangGraph Platform > Authentication & access control > how-tos/auth/custom_auth.md)](https://langchain-ai.github.io/langgraph/how-tos/auth/custom_auth/)
-- [how-tos/auth/openapi_security.md (LangGraph Platform > Authentication & access control > how-tos/auth/openapi_security.md)](https://langchain-ai.github.io/langgraph/how-tos/auth/openapi_security/)
-- [Overview (LangGraph Platform > Assistants > Overview)](https://langchain-ai.github.io/langgraph/concepts/assistants/)
-- [cloud/how-tos/configuration_cloud.md (LangGraph Platform > Assistants > cloud/how-tos/configuration_cloud.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/)
-- [Overview (LangGraph Platform > Threads > Overview)](https://langchain-ai.github.io/langgraph/cloud/concepts/threads/)
-- [cloud/how-tos/use_threads.md (LangGraph Platform > Threads > cloud/how-tos/use_threads.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/use_threads/)
-- [Overview (LangGraph Platform > Runs > Overview)](https://langchain-ai.github.io/langgraph/cloud/concepts/runs/)
-- [cloud/how-tos/background_run.md (LangGraph Platform > Runs > cloud/how-tos/background_run.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/background_run/)
-- [cloud/how-tos/same-thread.md (LangGraph Platform > Runs > cloud/how-tos/same-thread.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/same-thread/)
-- [cloud/how-tos/cron_jobs.md (LangGraph Platform > Runs > cloud/how-tos/cron_jobs.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/cron_jobs/)
-- [cloud/how-tos/stateless_runs.md (LangGraph Platform > Runs > cloud/how-tos/stateless_runs.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/stateless_runs/)
-- [cloud/how-tos/configurable_headers.md (LangGraph Platform > Runs > cloud/how-tos/configurable_headers.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/configurable_headers/)
-- [Overview (LangGraph Platform > Streaming > Overview)](https://langchain-ai.github.io/langgraph/cloud/concepts/streaming/)
-- [cloud/how-tos/streaming.md (LangGraph Platform > Streaming > cloud/how-tos/streaming.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/streaming/)
-- [Human-in-the-loop (LangGraph Platform > Human-in-the-loop)](https://langchain-ai.github.io/langgraph/cloud/how-tos/add-human-in-the-loop/)
-- [Breakpoints (LangGraph Platform > Breakpoints)](https://langchain-ai.github.io/langgraph/cloud/how-tos/human_in_the_loop_breakpoint/)
-- [Time travel (LangGraph Platform > Time travel)](https://langchain-ai.github.io/langgraph/cloud/how-tos/human_in_the_loop_time_travel/)
-- [MCP (LangGraph Platform > MCP)](https://langchain-ai.github.io/langgraph/concepts/server-mcp/)
-- [Overview (LangGraph Platform > Double-texting > Overview)](https://langchain-ai.github.io/langgraph/concepts/double_texting/)
-- [cloud/how-tos/interrupt_concurrent.md (LangGraph Platform > Double-texting > cloud/how-tos/interrupt_concurrent.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/interrupt_concurrent/)
-- [cloud/how-tos/rollback_concurrent.md (LangGraph Platform > Double-texting > cloud/how-tos/rollback_concurrent.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/rollback_concurrent/)
-- [cloud/how-tos/reject_concurrent.md (LangGraph Platform > Double-texting > cloud/how-tos/reject_concurrent.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/reject_concurrent/)
-- [cloud/how-tos/enqueue_concurrent.md (LangGraph Platform > Double-texting > cloud/how-tos/enqueue_concurrent.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/enqueue_concurrent/)
-- [Overview (LangGraph Platform > Webhooks > Overview)](https://langchain-ai.github.io/langgraph/cloud/concepts/webhooks/)
-- [cloud/how-tos/webhooks.md (LangGraph Platform > Webhooks > cloud/how-tos/webhooks.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/webhooks/)
-- [Overview (LangGraph Platform > Cron jobs > Overview)](https://langchain-ai.github.io/langgraph/cloud/concepts/cron_jobs/)
-- [cloud/how-tos/cron_jobs.md (LangGraph Platform > Cron jobs > cloud/how-tos/cron_jobs.md)](https://langchain-ai.github.io/langgraph/cloud/how-tos/cron_jobs/)
-- [how-tos/http/custom_lifespan.md (LangGraph Platform > Server customization > how-tos/http/custom_lifespan.md)](https://langchain-ai.github.io/langgraph/how-tos/http/custom_lifespan/)
-- [how-tos/http/custom_middleware.md (LangGraph Platform > Server customization > how-tos/http/custom_middleware.md)](https://langchain-ai.github.io/langgraph/how-tos/http/custom_middleware/)
-- [how-tos/http/custom_routes.md (LangGraph Platform > Server customization > how-tos/http/custom_routes.md)](https://langchain-ai.github.io/langgraph/how-tos/http/custom_routes/)
-- [Overview (LangGraph Platform > Deployment > Overview)](https://langchain-ai.github.io/langgraph/concepts/deployment_options/)
-- [Data plane (LangGraph Platform > Deployment > Data plane)](https://langchain-ai.github.io/langgraph/concepts/langgraph_data_plane/)
-- [Control plane (LangGraph Platform > Deployment > Control plane)](https://langchain-ai.github.io/langgraph/concepts/langgraph_control_plane/)
-- [Overview (LangGraph Platform > Deployment > Deployment options > Cloud SaaS > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_cloud/)
-- [Deploy Cloud SaaS (LangGraph Platform > Deployment > Deployment options > Cloud SaaS > Deploy Cloud SaaS)](https://langchain-ai.github.io/langgraph/cloud/deployment/cloud/)
-- [Overview (LangGraph Platform > Deployment > Deployment options > Self-Hosted Data Plane > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_self_hosted_data_plane/)
-- [Deploy Self-Hosted Data Plane (LangGraph Platform > Deployment > Deployment options > Self-Hosted Data Plane > Deploy Self-Hosted Data Plane)](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_data_plane/)
-- [Overview (LangGraph Platform > Deployment > Deployment options > Self-Hosted Control Plane > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_self_hosted_control_plane/)
-- [Deploy Self-Hosted Control Plane (LangGraph Platform > Deployment > Deployment options > Self-Hosted Control Plane > Deploy Self-Hosted Control Plane)](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_control_plane/)
-- [Overview (LangGraph Platform > Deployment > Deployment options > Standalone Container > Overview)](https://langchain-ai.github.io/langgraph/concepts/langgraph_standalone_container/)
-- [Deploy Standalone Container (LangGraph Platform > Deployment > Deployment options > Standalone Container > Deploy Standalone Container)](https://langchain-ai.github.io/langgraph/cloud/deployment/standalone_container/)
-- [Scalability & resilience (LangGraph Platform > Deployment > Scalability & resilience)](https://langchain-ai.github.io/langgraph/concepts/scalability_and_resilience/)
-- [Plans & pricing (LangGraph Platform > Deployment > Plans & pricing)](https://langchain-ai.github.io/langgraph/concepts/plans/)
+- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/index/): This page provides an overview of the LangGraph project, including its logo and essential scripts for functionality within MkDocs. It also includes a reference to the README.md file for detailed information about the project. The content is designed to be user-friendly and visually appealing.
+- [LangGraph Quickstart Guide](https://langchain-ai.github.io/langgraph/agents/agents/): This quickstart guide provides step-by-step instructions for setting up and using LangGraph's prebuilt components to create agentic systems. It covers prerequisites, installation, agent creation, configuration of language models, and advanced features like memory and structured output. Ideal for developers looking to leverage LangGraph for building intelligent agents.
+- [Getting Started with LangGraph: Building AI Agents](https://langchain-ai.github.io/langgraph/concepts/why-langgraph/): This page provides an overview of LangGraph, a platform designed for developers to create adaptable AI agents. It highlights key features such as reliability, extensibility, and streaming support, and offers a series of tutorials to help users build a support chatbot with various capabilities. By following the tutorials, developers will learn to implement essential functionalities like conversation state management and human-in-the-loop controls.
+- [Building a Basic Chatbot with LangGraph](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/): This tutorial guides you through the process of creating a basic chatbot using LangGraph. It covers prerequisites, installation of necessary packages, and step-by-step instructions to set up a state machine for the chatbot. By the end of the tutorial, you will have a functional chatbot that can engage in simple conversations.
+- [Integrating Web Search Tools into Your Chatbot](https://langchain-ai.github.io/langgraph/tutorials/get-started/2-add-tools/): This tutorial guides you through the process of enhancing your chatbot's capabilities by integrating a web search tool, specifically the Tavily Search Engine. It covers prerequisites, installation, configuration, and the implementation of the search tool within a LangGraph-based chatbot. By the end, you'll have a functional chatbot that can retrieve real-time information to answer user queries beyond its training data.
+- [Implementing Memory in Chatbots with LangGraph](https://langchain-ai.github.io/langgraph/tutorials/get-started/3-add-memory/): This page provides a comprehensive guide on how to add memory functionality to chatbots using LangGraph's persistent checkpointing feature. It details the steps to create a `MemorySaver` checkpointer, compile the graph, and interact with the chatbot to maintain context across multiple interactions. Additionally, it explains how to inspect the state of the chatbot and highlights the advantages of checkpointing over simple memory solutions.
+- [Implementing Human-in-the-Loop Controls in LangGraph](https://langchain-ai.github.io/langgraph/tutorials/get-started/4-human-in-the-loop/): This page provides a comprehensive guide on adding human-in-the-loop controls to LangGraph workflows, enabling agents to pause execution for human input. It details the use of the `interrupt` function to facilitate user feedback and outlines the steps to integrate a `human_assistance` tool into a chatbot. Additionally, the tutorial covers graph compilation, visualization, and resuming execution with human input.
+- [Customizing State in LangGraph for Enhanced Chatbot Functionality](https://langchain-ai.github.io/langgraph/tutorials/get-started/5-customize-state/): This tutorial guides you through the process of adding custom fields to the state in LangGraph, enabling complex behaviors in your chatbot without relying solely on message lists. You will learn how to implement human-in-the-loop controls to verify information before it is stored in the state. By the end of this tutorial, you will have a deeper understanding of state management and how to enhance your chatbot's capabilities.
+- [Implementing Time Travel in LangGraph Chatbots](https://langchain-ai.github.io/langgraph/tutorials/get-started/6-time-travel/): This page provides a comprehensive guide on utilizing the time travel functionality in LangGraph to enhance chatbot interactions. It covers how to rewind, add steps, and replay the state history of a chatbot, allowing users to explore different outcomes and fix mistakes. Additionally, it includes code snippets and practical examples to help developers implement these features effectively.
+- [LangGraph Deployment Options](https://langchain-ai.github.io/langgraph/tutorials/deployment/): This page outlines the various options available for deploying LangGraph applications, including local testing and different cloud-based solutions. It details free deployment methods such as Local and Standalone Container (Lite), as well as production options like Cloud SaaS and self-hosted solutions. Each deployment method is linked to further documentation for in-depth guidance.
+- [Agent Development with LangGraph](https://langchain-ai.github.io/langgraph/agents/overview/): This page provides an overview of agent development using LangGraph, highlighting its prebuilt components and capabilities for building agent-based applications. It explains the structure of an agent, key features such as memory integration and human-in-the-loop control, and outlines the package ecosystem available for developers. With LangGraph, users can focus on application logic while leveraging robust infrastructure for state management and feedback.
+- [Guide to Running Agents in LangGraph](https://langchain-ai.github.io/langgraph/agents/run_agents/): This page provides a comprehensive overview of how to execute agents in LangGraph, detailing both synchronous and asynchronous methods. It covers input and output formats, streaming capabilities, and how to manage execution limits to prevent infinite loops. Additionally, it includes code examples and links to further resources for deeper understanding.
+- [Streaming Data in LangGraph](https://langchain-ai.github.io/langgraph/agents/streaming/): This page provides an overview of streaming data types in LangGraph, including agent progress, LLM tokens, and custom updates. It includes code examples for both synchronous and asynchronous streaming methods. Additionally, it covers how to stream multiple modes and disable streaming when necessary.
+- [Configuring Chat Models for Agents](https://langchain-ai.github.io/langgraph/agents/models/): This page provides detailed instructions on how to configure various chat models for use with agents in LangChain. It covers model initialization, tool calling support, and how to specify models from different providers such as OpenAI, Anthropic, Azure, Google Gemini, and AWS Bedrock. Additionally, it includes information on disabling streaming, adding model fallbacks, and links to further resources.
+- [Using Tools in LangChain](https://langchain-ai.github.io/langgraph/agents/tools/): This page provides an overview of how to define, customize, and manage tools within the LangChain framework. It covers creating simple tools, handling tool errors, and utilizing prebuilt integrations for enhanced functionality. Additionally, it discusses advanced features such as memory management and controlling tool behavior during agent execution.
+- [Integrating MCP with LangGraph Agents](https://langchain-ai.github.io/langgraph/agents/mcp/): This page provides a comprehensive guide on how to integrate the Model Context Protocol (MCP) with LangGraph agents using the `langchain-mcp-adapters` library. It includes installation instructions, example code for using MCP tools, and guidance on creating custom MCP servers. Additional resources for further reading on MCP are also provided.
+- [Understanding Context in LangGraph Agents](https://langchain-ai.github.io/langgraph/agents/context/): This page provides an overview of how to supply context to agents in LangGraph, detailing the three primary types: Config, State, and Long-Term Memory. It explains how to use these context types to enhance agent behavior, customize prompts, and access context in tools. Additionally, it includes code examples for implementing context in various scenarios.
+- [Understanding Memory in LangGraph for Conversational Agents](https://langchain-ai.github.io/langgraph/agents/memory/): This documentation page provides an overview of the two types of memory supported by LangGraph: short-term and long-term memory. It explains how to implement these memory types in conversational agents, including code examples and best practices for managing message history. Additionally, it covers the use of persistent storage and tools for enhancing memory functionality.
+- [Implementing Human-in-the-Loop in LangGraph](https://langchain-ai.github.io/langgraph/agents/human-in-the-loop/): This documentation page provides a comprehensive guide on how to implement Human-in-the-Loop (HIL) features in LangGraph, allowing for human review and approval of tool calls in agents. It covers the use of the `interrupt()` function to pause execution for human input, along with practical examples and code snippets. Additionally, it explains how to create a wrapper to add HIL capabilities to any tool seamlessly.
+- [Building Multi-Agent Systems](https://langchain-ai.github.io/langgraph/agents/multi-agent/): This page provides an overview of multi-agent systems, detailing how to create and manage them using supervisor and swarm architectures. It includes practical examples of implementing a flight and hotel booking assistant using the LangGraph libraries. Additionally, the page explains the concept of handoffs between agents, allowing for seamless communication and task delegation.
+- [Evaluating Agent Performance with LangSmith](https://langchain-ai.github.io/langgraph/agents/evals/): This page provides a comprehensive guide on how to evaluate the performance of agents using the LangSmith evaluations framework. It includes instructions on defining evaluator functions, utilizing prebuilt evaluators from the AgentEvals package, and running evaluations with specific datasets. Additionally, it covers different evaluation techniques, including trajectory matching and using LLMs as judges.
+- [Deploying Your LangGraph Agent](https://langchain-ai.github.io/langgraph/agents/deployment/): This page provides a comprehensive guide on how to deploy a LangGraph agent, including setting up a LangGraph app for both local development and production. It covers essential features, installation steps, and configuration requirements, along with instructions for launching the local server and utilizing the LangGraph Studio Web UI for debugging. Additionally, it offers links to further resources for deployment options.
+- [Agent Chat UI Documentation](https://langchain-ai.github.io/langgraph/agents/ui/): This page provides comprehensive guidance on using the Agent Chat UI for interacting with LangGraph agents. It covers setup instructions, features like human-in-the-loop workflows, and the integration of generative UI components. Users can find links to relevant resources and tips for customizing their chat experience.
+- [Overview of Agent Architectures in LLM Applications](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/): This page provides a comprehensive overview of various agent architectures used in large language model (LLM) applications, highlighting their control flows and functionalities. It discusses key concepts such as routers, tool-calling agents, memory management, and planning, along with customization options for specific tasks. Additionally, it covers advanced features like human-in-the-loop, parallelization, subgraphs, and reflection mechanisms to enhance agent performance.
+- [Understanding Workflows and Agents in LangGraph](https://langchain-ai.github.io/langgraph/tutorials/workflows/): This documentation page provides an in-depth overview of workflows and agents within LangGraph, highlighting their differences and use cases. It covers various patterns for building agentic systems, including setup instructions, building blocks, and advanced concepts like prompt chaining, parallelization, and routing. Additionally, it offers practical examples and code snippets to help users implement these workflows effectively.
+- [Understanding LangGraph: Core Concepts and Components](https://langchain-ai.github.io/langgraph/concepts/low_level/): This documentation page provides an in-depth overview of the core concepts of LangGraph, focusing on how agent workflows are modeled as graphs. It covers essential components such as States, Nodes, and Edges, and explains how they interact to create complex workflows. Additionally, it discusses graph compilation, message handling, and configuration options to enhance the functionality of your graphs.
+- [LangGraph Runtime Overview](https://langchain-ai.github.io/langgraph/concepts/pregel/): This page provides a comprehensive overview of the LangGraph runtime, specifically focusing on the Pregel execution model. It details the structure and functionality of actors and channels within the Pregel framework, along with examples of how to implement applications. Additionally, it introduces high-level APIs for creating Pregel applications using StateGraph and Functional API.
+- [Using the LangGraph API: A Comprehensive Guide](https://langchain-ai.github.io/langgraph/how-tos/graph-api/): This documentation provides a detailed overview of how to utilize the LangGraph Graph API, covering essential concepts such as state management, node creation, and control flow. It includes practical examples for building sequences, branches, and loops, as well as advanced features like retry policies and async execution. Additionally, the guide offers insights into visualizing graphs and integrating with external tools.
+- [LangGraph Streaming System](https://langchain-ai.github.io/langgraph/concepts/streaming/): This page provides an overview of the streaming capabilities of LangGraph, enabling real-time updates for enhanced user experiences. It details the types of data that can be streamed, including workflow progress, LLM tokens, and custom updates. Additionally, it outlines various functionalities and modes available for streaming within the LangGraph framework.
+- [Streaming Outputs in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming/): This documentation page provides an overview of how to utilize the streaming capabilities of LangGraph, including synchronous and asynchronous streaming methods. It covers various stream modes, such as updates, values, and custom data, along with examples of how to implement them in your graphs. Additionally, it discusses the integration of Large Language Models (LLMs) and how to handle streaming outputs effectively.
+- [LangGraph Persistence and Checkpointing](https://langchain-ai.github.io/langgraph/concepts/persistence/): This page provides an in-depth overview of the persistence layer in LangGraph, focusing on the use of checkpointers to save graph states at each super-step. It covers key concepts such as threads, checkpoints, state retrieval, and memory management, along with practical examples and code snippets. Additionally, it discusses advanced features like time travel, fault tolerance, and the integration of memory stores for cross-thread information retention.
+- [Understanding Durable Execution in LangGraph](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): This page provides an overview of durable execution, a technique that allows workflows to save their progress and resume from key points. It details the requirements for implementing durable execution in LangGraph, including the use of persistence and tasks to ensure deterministic and consistent replay. Additionally, it covers how to handle pausing, resuming, and recovering workflows effectively.
+- [Implementing Memory in LangGraph for AI Applications](https://langchain-ai.github.io/langgraph/how-tos/persistence/): This documentation page provides a comprehensive guide on adding persistence to AI applications using LangGraph. It covers both short-term and long-term memory implementations, including code examples for managing conversation context and user-specific data. Additionally, it discusses the use of various storage backends and semantic search capabilities for enhanced memory management.
+- [Understanding Memory in AI Agents](https://langchain-ai.github.io/langgraph/concepts/memory/): This documentation page provides an in-depth overview of memory types in AI agents, focusing on short-term and long-term memory. It explains how these memory types can be implemented and managed within applications using LangGraph, including techniques for handling conversation history and storing memories. Additionally, it discusses the importance of memory in enhancing user interactions and the various strategies for writing and updating memories.
+- [Memory Management in LangGraph for AI Applications](https://langchain-ai.github.io/langgraph/how-tos/memory/): This page provides an overview of memory management in LangGraph, focusing on short-term and long-term memory functionalities essential for conversational agents. It includes detailed instructions on how to implement memory strategies such as trimming, summarizing, and deleting messages to optimize conversation tracking without exceeding context limits. Code examples are provided to illustrate the implementation of these memory management techniques.
+- [Human-in-the-Loop Workflows in LangGraph](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): This page provides an overview of the human-in-the-loop (HIL) capabilities within LangGraph, highlighting how human intervention can enhance automated processes. It details key features such as persistent execution state and flexible integration points, along with typical use cases for validating outputs and providing context. Additionally, it outlines the implementation of HIL through specific functions and primitives.
+- [Implementing Human-in-the-Loop Workflows with Interrupts](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/add-human-in-the-loop/): This documentation page provides a comprehensive guide on using the `interrupt` function in LangGraph to facilitate human-in-the-loop workflows. It covers the implementation details, design patterns, and best practices for pausing graph execution to gather human input, as well as how to resume execution with that input. Additionally, it highlights common pitfalls and offers extended examples to illustrate various use cases.
+- [Understanding Breakpoints in LangGraph](https://langchain-ai.github.io/langgraph/concepts/breakpoints/): This page provides an overview of breakpoints in LangGraph, which allow users to pause graph execution at specific points for inspection. It explains how breakpoints utilize the persistence layer to save the graph state and how execution can be resumed after inspection. An illustrative example is included to demonstrate the concept visually.
+- [Using Breakpoints in Graph Execution](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): This page provides a comprehensive guide on how to implement breakpoints in graph execution for debugging purposes. It covers the requirements for setting breakpoints, the difference between static and dynamic breakpoints, and includes code examples for both compile-time and run-time configurations. Additionally, it explains how to manage breakpoints in subgraphs.
+- [Time Travel Functionality in LangGraph](https://langchain-ai.github.io/langgraph/concepts/time-travel/): This page explains the time travel feature in LangGraph, which allows users to analyze and debug decision-making processes in non-deterministic systems. It outlines how to understand reasoning, debug mistakes, and explore alternative solutions by resuming execution from prior checkpoints. The functionality enables users to create new forks in the execution history for deeper insights.
+- [Using Time-Travel in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/): This page provides a comprehensive guide on how to implement time-travel functionality in LangGraph. It outlines the steps to run a graph, identify checkpoints, modify graph states, and resume execution from specific checkpoints. Additionally, an example workflow is included to illustrate the process of generating and modifying jokes using LangGraph.
+- [Integrating Tools with AI Models](https://langchain-ai.github.io/langgraph/concepts/tools/): This page provides an overview of how AI models can interact with external systems using tool calling. It explains the concept of tools, their integration with chat models, and how to create or use prebuilt tools for various applications. Additionally, it highlights the importance of relevance in tool invocation and offers links to further resources and guides.
+- [Using Tools in LangChain](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/): This documentation page provides a comprehensive guide on how to create and utilize tools within the LangChain framework. It covers defining simple and customized tools, managing tool arguments, accessing configuration and state, and integrating tools with chat models and agents. Additionally, it discusses error handling and strategies for managing a large number of tools.
+- [Understanding Subgraphs in LangGraph](https://langchain-ai.github.io/langgraph/concepts/subgraphs/): This page provides an overview of subgraphs in LangGraph, explaining their role as encapsulated nodes within larger graphs. It discusses the benefits of using subgraphs, such as facilitating multi-agent systems and enabling independent team work. Additionally, it outlines the communication methods between parent graphs and subgraphs, detailing scenarios involving shared and different state schemas.
+- [Using Subgraphs in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/subgraph/): This guide provides an overview of how to effectively use subgraphs within LangGraph, including communication methods between parent graphs and subgraphs. It covers shared and different state schemas, setup instructions, and examples for implementing subgraphs in multi-agent systems. Additionally, it discusses persistence, state management, and streaming outputs from subgraphs.
+- [Understanding Multi-Agent Systems](https://langchain-ai.github.io/langgraph/concepts/multi_agent/): This page provides an in-depth overview of multi-agent systems, focusing on the architecture and benefits of using multiple independent agents to manage complex applications. It discusses various multi-agent architectures, including network, supervisor, and hierarchical models, as well as communication strategies and state management techniques for effective agent interaction.
+- [Building Multi-Agent Systems with LangGraph](https://langchain-ai.github.io/langgraph/how-tos/multi_agent/): This guide provides an overview of how to build multi-agent systems using LangGraph, focusing on the implementation of handoffs for agent communication. It covers the creation of independent agents, the use of handoffs to transfer control and data between agents, and examples of prebuilt multi-agent architectures. Additionally, it includes code snippets and best practices for managing agent interactions and state.
+- [Understanding the Functional API in LangGraph](https://langchain-ai.github.io/langgraph/concepts/functional_api/): This documentation page provides an overview of the Functional API in LangGraph, detailing its key features such as persistence, memory, and human-in-the-loop capabilities. It explains how to define workflows using the `@entrypoint` and `@task` decorators, along with examples and best practices for implementing workflows with state management and streaming. Additionally, it compares the Functional API with the Graph API, highlighting their differences and use cases.
+- [Functional API Documentation](https://langchain-ai.github.io/langgraph/how-tos/use-functional-api/): This page provides comprehensive guidance on using the Functional API, including creating workflows, handling parallel execution, and integrating with other APIs. It covers various features such as retry policies, caching, and human-in-the-loop workflows, along with practical examples. Additionally, it discusses memory management strategies for both short-term and long-term use cases.
+- [Overview of LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/): The LangGraph Platform is designed for developing, deploying, and managing long-running agent workflows with ease. This page outlines the platform's features, including streaming support, background runs, and memory management, which enhance the performance and reliability of agent applications. Additionally, it provides links to resources for getting started and deploying agents effectively.
+- [LangGraph Platform Quickstart Guide](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/): This quickstart guide provides step-by-step instructions for running a LangGraph application locally. It covers prerequisites, installation of the LangGraph CLI, app creation, dependency installation, and launching the server. Additionally, it includes testing your application using the LangGraph Studio and API.
+- [LangGraph Platform Deployment Quickstart](https://langchain-ai.github.io/langgraph/cloud/quick_start/): This quickstart guide provides step-by-step instructions for deploying an application on the LangGraph Platform using GitHub. It covers prerequisites, repository creation, deployment procedures, and testing your application and API. Follow these steps to successfully set up and run your application in the LangGraph environment.
+- [Overview of LangGraph Platform Components](https://langchain-ai.github.io/langgraph/concepts/langgraph_components/): This page provides a comprehensive overview of the various components that make up the LangGraph Platform. It details the functionalities of each component, including the LangGraph Server, CLI, Studio, SDKs, and the control and data planes. Users can learn how these components work together to facilitate the development, deployment, and management of LangGraph applications.
+- [LangGraph Server Documentation](https://langchain-ai.github.io/langgraph/concepts/langgraph_server/): This page provides an overview of the LangGraph Server, an API designed for creating and managing agent-based applications. It details the server versions, application structure, deployment components, and the use of assistants, persistence, and task queues. Additionally, it includes links to further resources and guides for effective deployment and usage.
+- [LangGraph Application Structure Guide](https://langchain-ai.github.io/langgraph/concepts/application_structure/): This page provides an overview of the structure of a LangGraph application, detailing the essential components such as the configuration file, dependencies, graphs, and environment variables. It includes examples of directory structures for both Python and JavaScript applications, as well as guidance on how to specify the necessary information for deployment. Additionally, it covers key concepts related to the configuration file and the role of dependencies and environment variables in the application.
+- [Setting Up a LangGraph Application with requirements.txt](https://langchain-ai.github.io/langgraph/cloud/deployment/setup/): This guide provides step-by-step instructions for configuring a LangGraph application for deployment using a requirements.txt file to manage dependencies. It covers essential topics such as specifying dependencies, defining environment variables, and creating the LangGraph configuration file. Additionally, it includes examples and tips for alternative setup methods.
+- [Setting Up a LangGraph Application with pyproject.toml](https://langchain-ai.github.io/langgraph/cloud/deployment/setup_pyproject/): This guide provides step-by-step instructions for configuring a LangGraph application using the `pyproject.toml` file for dependency management. It covers the necessary components, including specifying dependencies, environment variables, and defining graphs, along with examples and best practices. Additionally, it offers tips for alternative setups and links to further resources for deployment.
+- [Setting Up a LangGraph.js Application](https://langchain-ai.github.io/langgraph/cloud/deployment/setup_javascript/): This guide provides step-by-step instructions for configuring a LangGraph.js application for deployment on the LangGraph Platform or for self-hosting. It covers essential topics such as specifying dependencies, environment variables, defining graphs, and creating the necessary configuration file. By following this walkthrough, users will learn how to structure their application and prepare it for deployment.
+- [Customizing Your Dockerfile in LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/custom_docker/): This page provides a guide on how to customize your Dockerfile by adding additional commands through the `langgraph.json` configuration file. It explains how to specify the `dockerfile_lines` key to include necessary dependencies, such as installing system packages and Python libraries. An example is provided to illustrate the process of integrating the Pillow library for image processing.
+- [LangGraph CLI Documentation](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/): This page provides an overview of the LangGraph CLI, a command-line tool for building and running the LangGraph API server locally. It includes installation instructions, a list of core commands, and their descriptions to help users effectively utilize the CLI for development and deployment. For further details, users can refer to the LangGraph CLI Reference.
+- [LangGraph Studio Documentation](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/): This page provides an overview of LangGraph Studio, an IDE for visualizing, interacting with, and debugging agentic systems that utilize the LangGraph Server API. It outlines the prerequisites for using the studio, key features, and the two operational modes: Graph mode and Chat mode. Additionally, it includes links to further resources for getting started with LangGraph Studio.
+- [Getting Started with LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/studio/quick_start/): This page provides a comprehensive guide on how to connect and use LangGraph Studio with both deployed applications on the LangGraph Platform and local development servers. It includes instructions for installation, running the server, accessing the Studio UI, and debugging options. Additionally, troubleshooting tips and next steps for further exploration of LangGraph Studio features are also provided.
+- [Running Applications: A Comprehensive Guide](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/): This page provides a detailed guide on how to submit a run to your application, covering both Graph and Chat modes. It includes instructions on specifying input, managing assistants, enabling streaming, and using breakpoints. Additionally, it offers tips for running applications from specific checkpoints in existing threads.
+- [Managing Assistants in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/studio/manage_assistants/): This page provides guidance on how to manage assistants within LangGraph Studio, including viewing, editing, and updating assistant configurations. It covers both Graph mode and Chat mode, detailing how to activate assistants and make changes to their settings. Users will learn how to navigate the interface to effectively manage their assistant configurations for graph runs.
+- [Managing Threads in Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/threads_studio/): This page provides a comprehensive guide on how to view and edit threads within the Studio application. It covers both Graph and Chat modes, detailing the steps to create new threads, view thread history, and edit thread states. Additionally, it includes links to related concepts for further learning.
+- [Modifying Prompts in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/iterate_graph_studio/): This page provides guidance on how to modify prompts within LangGraph Studio using two methods: direct node editing and the LangSmith Playground interface. It details the configuration options available for nodes, including `langgraph_nodes` and `langgraph_type`, along with examples for both Pydantic models and dataclasses. Additionally, it outlines the steps for editing prompts in the UI and utilizing the LangSmith Playground for testing LLM calls.
+- [Debugging LangSmith Traces in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/clone_traces_studio/): This guide provides step-by-step instructions for opening and debugging LangSmith traces in LangGraph Studio. It covers how to deploy threads and test local agents with remote traces, ensuring a seamless debugging experience. Additionally, it outlines the requirements for local agents and the process for cloning threads for local testing.
+- [How to Add Nodes to LangSmith Datasets](https://langchain-ai.github.io/langgraph/cloud/how-tos/datasets_studio/): This guide provides step-by-step instructions on how to add examples from nodes in the thread log to LangSmith datasets. It covers selecting threads, choosing nodes, and editing inputs/outputs before adding them to the dataset. Additionally, it includes links to further resources on evaluating intermediate steps.
+- [LangGraph SDK Documentation](https://langchain-ai.github.io/langgraph/concepts/sdk/): This page provides an overview of the LangGraph SDK, including installation instructions for both Python and JavaScript. It details the synchronous and asynchronous client options available for interacting with the LangGraph Server. Additionally, it offers links to further resources and references for the SDK.
+- [Integrating Semantic Search in LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/semantic_search/): This guide provides step-by-step instructions on how to implement semantic search in your LangGraph deployment. It covers prerequisites, configuration of the store, and usage examples for searching memories and documents by semantic similarity. Additionally, it includes information on using custom embeddings and querying via the LangGraph SDK.
+- [Configuring Time-to-Live (TTL) in LangGraph Applications](https://langchain-ai.github.io/langgraph/how-tos/ttl/configure_ttl/): This guide provides detailed instructions on how to configure Time-to-Live (TTL) settings for checkpoints and store items in LangGraph applications. It covers the necessary configurations in the `langgraph.json` file, including strategies for managing data lifecycle and memory. Additionally, it explains how to combine TTL configurations and override them at runtime.
+- [LangGraph Authentication & Access Control Overview](https://langchain-ai.github.io/langgraph/concepts/auth/): This page provides a comprehensive guide to the authentication and authorization mechanisms within the LangGraph Platform. It explains the core concepts of authentication versus authorization, outlines default security models, and details the system architecture involved in user identity management. Additionally, it covers implementation examples for authentication and authorization handlers, along with common access patterns and supported resources.
+- [Custom Authentication Setup for LangGraph Platform](https://langchain-ai.github.io/langgraph/how-tos/auth/custom_auth/): This guide provides step-by-step instructions on how to implement custom authentication in your LangGraph Platform application. It covers the necessary prerequisites, implementation details, configuration updates, and client connection methods. The guide is applicable to both managed and Enterprise self-hosted deployments, but not to Lite self-hosted plans.
+- [Documenting API Authentication in OpenAPI for LangGraph](https://langchain-ai.github.io/langgraph/how-tos/auth/openapi_security/): This guide provides instructions on how to customize the security schema for your LangGraph Platform API documentation using OpenAPI. It covers default security schemes for both LangGraph Platform and self-hosted deployments, as well as how to implement custom authentication. Additionally, it includes examples for OAuth2 and API key authentication, along with testing procedures.
+- [Managing Assistants in LangGraph](https://langchain-ai.github.io/langgraph/concepts/assistants/): This page provides an overview of how to create and manage assistants within the LangGraph Platform, which allows for separate configuration of agents without altering the core graph logic. It covers the prerequisites, configuration options, and versioning of assistants, highlighting their role in optimizing agent performance for different tasks. Additionally, it includes links to relevant API references and how-to guides for further assistance.
+- [Managing Assistants in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/configuration_cloud/): This documentation page provides a comprehensive guide on how to create, configure, and manage assistants using the LangGraph SDK and Platform UI. It includes code examples in Python and JavaScript, as well as instructions for creating new versions and using previous versions of assistants. Additionally, it covers the process of utilizing assistants in various environments.
+- [Understanding Threads in LangGraph](https://langchain-ai.github.io/langgraph/cloud/concepts/threads/): This page provides an overview of threads in the LangGraph framework, detailing how they accumulate the state of runs and the importance of checkpoints. It explains the process of creating threads and retrieving their current and historical states. Additionally, it offers links to further resources on threads, checkpoints, and the LangGraph API for managing thread states.
+- [Managing Threads in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/use_threads/): This documentation page provides a comprehensive guide on how to create, view, and inspect threads using the LangGraph SDK. It includes detailed instructions for creating empty threads, copying existing threads, and initializing threads with prepopulated states. Additionally, it covers how to list and inspect threads, including filtering and sorting options.
+- [Understanding Runs in LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/concepts/runs/): This page provides an overview of what constitutes a run in the LangGraph Platform, including its input, configuration, and metadata. It also highlights the ability to execute runs on threads and offers links to the API reference for managing runs.
+- [Starting Background Runs for Your Agent](https://langchain-ai.github.io/langgraph/cloud/how-tos/background_run/): This guide provides step-by-step instructions on how to initiate background runs for your agent using Python, JavaScript, and CURL. It covers the setup process, checking current runs, starting new runs, and retrieving the final results. By following this documentation, users can efficiently manage long-running jobs within their applications.
+- [Running Multiple Agents on the Same Thread in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/same-thread/): This documentation page explains how to run multiple agents on the same thread using the LangGraph Platform. It provides step-by-step examples in Python, JavaScript, and CURL to create agents, run them on a thread, and demonstrate how the second agent can utilize the context from the first agent's responses. By following the examples, users can learn to effectively manage multiple agents and their interactions.
+- [Scheduling Cron Jobs with LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/cron_jobs/): This page provides a comprehensive guide on how to schedule cron jobs using the LangGraph Platform. It includes setup instructions for different programming languages, examples of creating and deleting cron jobs, and details on managing stateless cron jobs. Users will learn how to automate tasks such as sending weekly emails without writing custom scripts.
+- [Guide to Stateless Runs in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/stateless_runs/): This page provides a comprehensive guide on how to implement stateless runs using the LangGraph Platform. It includes setup instructions for various programming languages, examples of streaming results, and methods for waiting for stateless results. Users will learn how to execute runs without maintaining persistent state, making their applications more efficient.
+- [Configurable Headers in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/configurable_headers/): This page provides guidance on how to configure headers dynamically in the LangGraph platform to modify agent behavior and permissions. It details how to include or exclude specific headers in the runtime configuration using the `langgraph.json` file. Additionally, it explains how to access these headers within your graph and offers an option to opt-out of configurable headers.
+- [Streaming in LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/concepts/streaming/): This page provides an overview of streaming capabilities within the LangGraph Platform, detailing the various streaming modes available for LLM applications. It includes instructions for creating streaming runs, handling stateless runs, and joining active background runs. Additionally, code examples in Python, JavaScript, and cURL are provided to illustrate the implementation of these features.
+- [Streaming Outputs with LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/how-tos/streaming/): This documentation page provides detailed instructions on how to stream outputs from the LangGraph API server using the LangGraph SDK in Python, JavaScript, and cURL. It covers various streaming modes, including updates, values, and custom data, along with examples for each mode. Additionally, it explains how to handle subgraphs, debug information, and LLM tokens during streaming.
+- [Human-in-the-Loop Workflows in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/add-human-in-the-loop/): This page provides an overview of the human-in-the-loop (HIL) capabilities in LangGraph, allowing for human intervention in automated processes. It details the `interrupt` function, which pauses execution for human input, and includes examples in Python, JavaScript, and cURL for implementing HIL workflows. Additionally, it links to further resources for understanding and utilizing HIL features effectively.
+- [Using Breakpoints in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/human_in_the_loop_breakpoint/): This page provides an overview of how to set and use breakpoints in LangGraph to pause graph execution for inspection. It includes examples for setting breakpoints at compile time and run time in Python, JavaScript, and cURL. Additionally, it offers guidance on resuming execution after hitting a breakpoint.
+- [Using Time Travel in LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/human_in_the_loop_time_travel/): This page provides a comprehensive guide on how to utilize the time travel functionality in LangGraph, allowing users to resume execution from previous checkpoints. It outlines the steps to run a graph, identify checkpoints, modify graph states, and resume execution. Additionally, the page includes code examples in Python, JavaScript, and cURL for practical implementation.
+- [Model Context Protocol (MCP) Endpoint Documentation](https://langchain-ai.github.io/langgraph/concepts/server-mcp/): This page provides comprehensive documentation on the Model Context Protocol (MCP) endpoint available in LangGraph Server. It covers the requirements for using MCP, how to expose agents as MCP tools, and includes examples for connecting with MCP-compliant clients in various programming languages. Additionally, it outlines session behavior, authentication, and instructions for disabling the MCP endpoint.
+- [Managing Double Texting in LangGraph](https://langchain-ai.github.io/langgraph/concepts/double_texting/): This page provides an overview of how to handle double texting scenarios in LangGraph, where users may send multiple messages before the first has completed. It outlines four strategies: Reject, Enqueue, Interrupt, and Rollback, each with links to detailed configuration guides. Prerequisites for implementing these strategies include having the LangGraph Server set up.
+- [Using the Interrupt Option in Double Texting](https://langchain-ai.github.io/langgraph/cloud/how-tos/interrupt_concurrent/): This guide provides detailed instructions on how to utilize the `interrupt` option for double texting, allowing users to interrupt a prior run of a graph and start a new one. It includes setup instructions, code examples in Python, JavaScript, and CURL, as well as guidance on viewing run results and verifying the status of interrupted runs. Familiarity with double texting is assumed, and a link to a conceptual guide is provided for further understanding.
+- [Using the Rollback Option in Double Texting](https://langchain-ai.github.io/langgraph/cloud/how-tos/rollback_concurrent/): This guide provides detailed instructions on how to utilize the `rollback` option in double texting, which allows users to interrupt a previous run and start a new one while permanently deleting the prior run from the database. It includes setup instructions, code examples in Python, JavaScript, and CURL, and demonstrates how to view run results and verify the deletion of the original run. Familiarity with double texting is assumed, and a link to a conceptual guide is provided for further reading.
+- [Using the Reject Option in Double Texting](https://langchain-ai.github.io/langgraph/cloud/how-tos/reject_concurrent/): This guide provides an overview of the `reject` option in double texting, which prevents new runs of a graph from starting while an original run is still in progress. It includes setup instructions, code examples in Python, JavaScript, and CURL, and demonstrates how to handle errors when attempting to create concurrent runs. Additionally, it shows how to view the results of the original run after the rejection.
+- [Using the Enqueue Option for Double Texting](https://langchain-ai.github.io/langgraph/cloud/how-tos/enqueue_concurrent/): This guide provides an overview of the `enqueue` option for double texting, which allows interruptions to be queued and executed in the order they are received. It includes setup instructions, code examples in Python, JavaScript, and CURL for creating runs, and methods for viewing run results. Familiarity with double texting concepts is assumed, and a helper function for output formatting is also provided.
+- [Understanding Webhooks in LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/concepts/webhooks/): This page provides an overview of webhooks and their role in enabling event-driven communication between LangGraph Platform applications and external services. It explains how to use the `webhook` parameter in various endpoints to trigger requests upon the completion of API calls. For further details, a link to a comprehensive how-to guide is also included.
+- [Using Webhooks with LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/how-tos/webhooks/): This documentation page provides a comprehensive guide on how to implement webhooks in the LangGraph Platform to receive updates after API calls. It includes details on supported endpoints, setup instructions for different programming languages, and examples of how to specify webhook parameters in API requests. Additionally, it covers security measures and testing tools for verifying webhook functionality.
+- [Scheduling Tasks with Cron Jobs on LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/concepts/cron_jobs/): This page provides an overview of how to use cron jobs on the LangGraph Platform to run assistants on a defined schedule. It explains the process of setting up a cron job, including specifying the schedule, assistant, and input. Additionally, it includes links to a how-to guide and API reference for further details.
+- [Scheduling Cron Jobs with LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/cron_jobs/): This page provides a comprehensive guide on how to use cron jobs with the LangGraph Platform to automate graph executions on a schedule. It includes setup instructions for various programming languages, examples of creating and deleting cron jobs, and tips for managing stateless cron jobs. Users will learn how to efficiently schedule tasks without manual intervention, ensuring timely execution of automated processes.
+- [Adding Custom Lifespan Events in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/http/custom_lifespan/): This page provides a guide on how to implement custom lifespan events in your LangGraph Platform applications, specifically for Python deployments. It covers the initialization and cleanup of resources during server startup and shutdown using FastAPI. Additionally, it includes code examples and configuration steps to help you integrate these events into your application.
+- [Adding Custom Middleware to LangGraph Platform](https://langchain-ai.github.io/langgraph/how-tos/http/custom_middleware/): This page provides a step-by-step guide on how to add custom middleware to your server when deploying agents to the LangGraph Platform. It covers the necessary code implementation using FastAPI, configuration settings in `langgraph.json`, and instructions for testing and deploying your application. Additionally, it offers links to related topics such as custom routes and lifespan events for further customization.
+- [Adding Custom Routes in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/http/custom_routes/): This page provides a step-by-step guide on how to add custom routes to your LangGraph platform application using a Starlette or FastAPI app. It includes instructions for creating a new app, configuring the `langgraph.json` file, and testing the server locally. Additionally, it explains how custom routes can override default endpoints and offers suggestions for further customization.
+- [LangGraph Deployment Options](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): This page outlines the various deployment options available for the LangGraph Platform, including Cloud SaaS, Self-Hosted Data Plane, Self-Hosted Control Plane, and Standalone Container. Each option is described in detail, highlighting key features, management responsibilities, and compatibility. A comparison table is also provided to help users choose the best deployment strategy for their needs.
+- [LangGraph Data Plane Overview](https://langchain-ai.github.io/langgraph/concepts/langgraph_data_plane/): This page provides a comprehensive overview of the LangGraph Data Plane, detailing its components including the server infrastructure, listener application, and data management systems like Postgres and Redis. It also covers key features such as autoscaling, static IP addresses, and custom configurations for Postgres and Redis. Additionally, the page outlines telemetry, licensing, and tracing functionalities relevant to different deployment options.
+- [LangGraph Control Plane Overview](https://langchain-ai.github.io/langgraph/concepts/langgraph_control_plane/): This page provides a comprehensive overview of the LangGraph Control Plane, detailing its UI and API functionalities for managing LangGraph Servers. It covers deployment types, environment variables, database provisioning, and asynchronous deployment processes. Additionally, it highlights the integration with LangSmith for tracing projects.
+- [Cloud SaaS Deployment Guide](https://langchain-ai.github.io/langgraph/concepts/langgraph_cloud/): This page provides a comprehensive guide on deploying the LangGraph Server using the Cloud SaaS model. It outlines the roles of the control plane and data plane, detailing their functionalities and management. Additionally, it includes an architectural diagram to illustrate the deployment structure.
+- [Deployment Guide for LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/deployment/cloud/): This page provides a comprehensive guide on how to deploy applications to the LangGraph Platform using GitHub repositories. It covers prerequisites, steps for creating new deployments and revisions, managing deployment settings, and viewing logs. Additionally, it includes instructions for whitelisting IP addresses and modifying GitHub repository access.
+- [Self-Hosted Data Plane Deployment Guide](https://langchain-ai.github.io/langgraph/concepts/langgraph_self_hosted_data_plane/): This page provides an overview of the Self-Hosted Data Plane deployment option, which allows users to manage their data plane infrastructure while offloading control plane management to LangChain. It outlines the requirements, architecture, and supported compute platforms for deployment. Additionally, it includes important information regarding the beta status of this deployment option.
+- [Deploying a Self-Hosted Data Plane](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_data_plane/): This page provides a comprehensive guide on deploying a Self-Hosted Data Plane using Kubernetes and Amazon ECS. It outlines the prerequisites, setup steps, and configuration details necessary for a successful deployment. Additionally, it highlights the current beta status of this deployment option.
+- [Self-Hosted Control Plane Deployment Guide](https://langchain-ai.github.io/langgraph/concepts/langgraph_self_hosted_control_plane/): This page provides an overview of the Self-Hosted Control Plane deployment option, currently in beta. It outlines the requirements, architecture, and compute platforms supported for deploying the control and data planes in your cloud environment. Additionally, it includes important links and resources for managing your self-hosted infrastructure.
+- [Deploying a Self-Hosted Control Plane](https://langchain-ai.github.io/langgraph/cloud/deployment/self_hosted_control_plane/): This page provides a comprehensive guide on deploying a Self-Hosted Control Plane using Kubernetes. It outlines the prerequisites, setup steps, and configuration details necessary for a successful deployment. Additionally, it highlights the beta status of this deployment option and includes links to relevant resources for further assistance.
+- [Deploying LangGraph Server with Standalone Container](https://langchain-ai.github.io/langgraph/concepts/langgraph_standalone_container/): This page provides a comprehensive guide on deploying a LangGraph Server using the Standalone Container option. It outlines the architecture, supported compute platforms, and differences between Lite and Enterprise server versions. Users will find essential information on managing the data plane infrastructure without a control plane.
+- [Deploying a Standalone Container with LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/standalone_container/): This documentation provides a comprehensive guide on deploying a standalone container for the LangGraph application. It covers prerequisites, environment variable configurations, and deployment methods using Docker and Docker Compose. Additionally, it includes instructions for deploying on Kubernetes using Helm.
+- [Scalability and Resilience of LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/scalability_and_resilience/): This page provides an overview of the scalability and resilience features of the LangGraph Platform. It details how the platform handles server and queue scalability, as well as the mechanisms in place for ensuring resilience during both graceful and hard shutdowns. Additionally, it covers the resilience strategies employed for Postgres and Redis to maintain service availability.
+- [LangGraph Platform Plans Overview](https://langchain-ai.github.io/langgraph/concepts/plans/): This page provides an overview of the different plans available for the LangGraph Platform, including Developer, Plus, and Enterprise options. Each plan offers varying deployment options, usage limits, and features tailored to different user needs. For detailed pricing and related resources, links to additional documentation are also included.
 
 # Examples
 
-- [Agentic RAG (Agentic RAG)](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/)
-- [Agent Supervisor (Agent Supervisor)](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/)
-- [SQL agent (SQL agent)](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
-- [Graph runs in LangSmith (Graph runs in LangSmith)](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/)
-- [tutorials/auth/getting_started.md (LangGraph Platform > Authentication > tutorials/auth/getting_started.md)](https://langchain-ai.github.io/langgraph/tutorials/auth/getting_started/)
-- [tutorials/auth/resource_auth.md (LangGraph Platform > Authentication > tutorials/auth/resource_auth.md)](https://langchain-ai.github.io/langgraph/tutorials/auth/resource_auth/)
-- [tutorials/auth/add_auth_server.md (LangGraph Platform > Authentication > tutorials/auth/add_auth_server.md)](https://langchain-ai.github.io/langgraph/tutorials/auth/add_auth_server/)
-- [Rebuild graph at runtime (LangGraph Platform > Rebuild graph at runtime)](https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/)
-- [Use RemoteGraph (LangGraph Platform > Use RemoteGraph)](https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/)
-- [Deploy CrewAI, AutoGen, and other frameworks (LangGraph Platform > Deploy CrewAI, AutoGen, and other frameworks)](https://langchain-ai.github.io/langgraph/how-tos/autogen-langgraph-platform/)
-- [Integrate LangGraph into a React app (LangGraph Platform > Front-end and generative UI > Integrate LangGraph into a React app)](https://langchain-ai.github.io/langgraph/cloud/how-tos/use_stream_react/)
-- [Implement generative UI with LangGraph (LangGraph Platform > Front-end and generative UI > Implement generative UI with LangGraph)](https://langchain-ai.github.io/langgraph/cloud/how-tos/generative_ui_react/)
+- [Building an Agentic RAG System](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/): This tutorial guides you through the process of creating a retrieval agent (RAG) system using LangChain and LangGraph. You will learn how to fetch and preprocess documents, create a retriever tool, and build an agentic RAG that intelligently decides when to retrieve information or respond directly to user queries. By the end, you'll have a functional system capable of semantic search and context-aware responses.
+- [Building a Multi-Agent Supervisor System](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/): This tutorial guides you through the process of creating a multi-agent supervisor system using specialized agents for research and math tasks. You will learn how to set up the environment, create individual worker agents, and implement a supervisor that orchestrates their interactions. By the end, you'll have a fully functional multi-agent architecture capable of handling complex queries.
+- [Building a SQL Agent with LangChain](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/): This tutorial provides a step-by-step guide on how to create a SQL agent capable of answering questions about a SQL database. It covers the setup of necessary dependencies, configuration of a SQLite database, and the implementation of a prebuilt agent that interacts with the database to generate and execute queries. Additionally, it discusses customizing the agent for more control over its behavior.
+- [Custom Run ID, Tags, and Metadata for LangSmith Graph Runs](https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/): This guide provides instructions on how to pass a custom run ID and set tags and metadata for graph runs in LangSmith. It covers prerequisites, configuration options, and includes code examples for setting up and running a graph with LangGraph. Additionally, it explains how to view and filter traces in the LangSmith platform.
+- [Custom Authentication Setup for Chatbots](https://langchain-ai.github.io/langgraph/tutorials/auth/getting_started/): This tutorial guides you through the process of setting up custom authentication for a chatbot using the LangGraph platform. You will learn how to implement token-based security to control user access, starting with a basic example and preparing for more advanced authentication methods in future tutorials. By the end, you'll have a functional chatbot that restricts access to authenticated users.
+- [Implementing Private Conversations in Chatbots](https://langchain-ai.github.io/langgraph/tutorials/auth/resource_auth/): This tutorial guides you through extending a chatbot to enable private conversations for each user by implementing resource-level access control. You'll learn how to add authorization handlers to ensure users can only access their own threads and test the functionality to confirm proper access restrictions. Additionally, the tutorial covers scoped authorization handlers for more granular control over resource access.
+- [Integrating OAuth2 Authentication with Supabase](https://langchain-ai.github.io/langgraph/tutorials/auth/add_auth_server/): This tutorial guides you through replacing hard-coded tokens with real user accounts using OAuth2 for secure authentication in your LangGraph application. You'll learn how to set up Supabase as your identity provider, implement token validation, and ensure proper user authorization. By the end, you'll have a production-ready authentication system that allows users to securely access their own data.
+- [Rebuilding Graphs at Runtime in LangGraph](https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/): This guide explains how to rebuild your graph at runtime with different configurations in LangGraph. It covers the necessary prerequisites, how to define graphs, and the steps to modify your graph-making function for dynamic behavior based on user input. Additionally, it provides examples of both static and dynamic graph configurations.
+- [Interacting with RemoteGraph in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/): This documentation page provides a comprehensive guide on how to interact with a LangGraph Platform deployment using the RemoteGraph interface. It covers the initialization of RemoteGraph, invoking the graph both asynchronously and synchronously, and utilizing it as a subgraph. Additionally, it includes code examples in Python and JavaScript to facilitate understanding and implementation.
+- [Deploying Agents on LangGraph Platform](https://langchain-ai.github.io/langgraph/how-tos/autogen-langgraph-platform/): This page provides a comprehensive guide on how to deploy agents like AutoGen and CrewAI using the LangGraph Platform. It covers the necessary setup, agent definition, and wrapping the agent in a LangGraph node for deployment. Additionally, it highlights the benefits of using LangGraph for scalable infrastructure and memory support.
+- [Integrating LangGraph with React: A Comprehensive Guide](https://langchain-ai.github.io/langgraph/cloud/how-tos/use_stream_react/): This documentation provides a detailed guide on how to integrate the LangGraph platform into your React applications using the `useStream()` hook. It covers installation, key features, example implementations, and customization options for building chat experiences. Additionally, it includes advanced topics such as event handling, TypeScript support, and managing conversation threads.
+- [Implementing Generative User Interfaces with LangGraph](https://langchain-ai.github.io/langgraph/cloud/how-tos/generative_ui_react/): This documentation provides a comprehensive guide on how to implement Generative User Interfaces (Generative UI) using the LangGraph platform. It covers prerequisites, step-by-step tutorials for defining UI components, sending them in graphs, and handling them in React applications. Additionally, it includes how-to guides for customizing components and managing UI state effectively.
 
 # Resources
 
-- [concepts/faq.md (concepts/faq.md)](https://langchain-ai.github.io/langgraph/concepts/faq/)
-- [Template applications (Template applications)](https://langchain-ai.github.io/langgraph/concepts/template_applications/)
-- [llms.txt (llms.txt)](https://langchain-ai.github.io/langgraph/llms-txt-overview/)
-- [agents/prebuilt.md (agents/prebuilt.md)](https://langchain-ai.github.io/langgraph/agents/prebuilt/)
-- [troubleshooting/errors/index.md (Troubleshooting > Errors > troubleshooting/errors/index.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/index/)
-- [troubleshooting/errors/GRAPH_RECURSION_LIMIT.md (Troubleshooting > Errors > troubleshooting/errors/GRAPH_RECURSION_LIMIT.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/GRAPH_RECURSION_LIMIT/)
-- [troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md (Troubleshooting > Errors > troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE/)
-- [troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md (Troubleshooting > Errors > troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE/)
-- [troubleshooting/errors/MULTIPLE_SUBGRAPHS.md (Troubleshooting > Errors > troubleshooting/errors/MULTIPLE_SUBGRAPHS.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/MULTIPLE_SUBGRAPHS/)
-- [troubleshooting/errors/INVALID_CHAT_HISTORY.md (Troubleshooting > Errors > troubleshooting/errors/INVALID_CHAT_HISTORY.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_CHAT_HISTORY/)
-- [troubleshooting/errors/INVALID_LICENSE.md (Troubleshooting > Errors > troubleshooting/errors/INVALID_LICENSE.md)](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_LICENSE/)
-- [LangGraph Studio (Troubleshooting > LangGraph Studio)](https://langchain-ai.github.io/langgraph/troubleshooting/studio/)
-- [LangGraph Academy course (Learn > LangGraph Academy course)](https://langchain-ai.github.io/langgraph/https://academy.langchain.com/courses/intro-to-langgraph/)
-- [Case studies (Learn > Case studies)](https://langchain-ai.github.io/langgraph/adopters/)
+- [LangGraph FAQ](https://langchain-ai.github.io/langgraph/concepts/faq/): This FAQ page provides answers to common questions about LangGraph, an orchestration framework for complex agentic systems. It covers topics such as the differences between LangGraph and LangChain, performance impacts, open-source status, and compatibility with various LLMs. Additionally, it outlines the distinctions between LangGraph and LangGraph Platform, including features and deployment options.
+- [Getting Started with LangGraph Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): This page provides an overview of open source reference applications known as templates, designed to help users quickly build applications with LangGraph. It includes installation instructions for the LangGraph CLI, a list of available templates with their descriptions, and guidance on creating and deploying a new LangGraph app. Users can find links to repositories for each template and next steps for customizing their applications.
+- [Guide to Using llms.txt and llms-full.txt for LLMs](https://langchain-ai.github.io/langgraph/llms-txt-overview/): This page provides an overview of the `llms.txt` and `llms-full.txt` formats, which facilitate access to programming documentation for large language models (LLMs) and agents. It outlines the differences between the two formats, usage instructions via an MCP server, and best practices for integrating these files into integrated development environments (IDEs). Additionally, it highlights considerations for managing large documentation files effectively.
+- [Community Agents for LangGraph](https://langchain-ai.github.io/langgraph/agents/prebuilt/): This page provides a list of community-built libraries that extend the functionality of LangGraph. Each entry includes the library name, GitHub URL, a brief description, and additional metrics like weekly downloads and stars. Additionally, it outlines how to contribute your own library to the LangGraph documentation.
+- [LangGraph Error Reference Guide](https://langchain-ai.github.io/langgraph/troubleshooting/errors/index/): This page serves as a comprehensive reference for resolving common errors encountered while using the LangGraph platform. It includes a list of error codes and links to detailed guides for troubleshooting specific issues. Users can find solutions for errors related to graph recursion, concurrent updates, node return values, and more.
+- [Handling Recursion Limits in LangGraph](https://langchain-ai.github.io/langgraph/troubleshooting/errors/GRAPH_RECURSION_LIMIT/): This page provides guidance on managing recursion limits in LangGraph's StateGraph. It explains how to identify potential infinite loops in your graph and offers solutions for increasing the recursion limit when working with complex graphs. Additionally, it includes code examples to illustrate the concepts discussed.
+- [Handling INVALID_CONCURRENT_GRAPH_UPDATE in LangGraph](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE/): This page explains the INVALID_CONCURRENT_GRAPH_UPDATE error that occurs in LangGraph when multiple nodes attempt to update the same state property concurrently. It provides an example of how this error can arise and offers a solution by using a reducer to combine values from parallel node executions. Additionally, troubleshooting tips are included to help resolve this issue.
+- [Handling Invalid Node Return Values in LangGraph](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE/): This page provides guidance on the error encountered when a LangGraph node returns a non-dict value. It includes an example of incorrect node implementation and the resulting error message. Additionally, troubleshooting tips are offered to ensure that all nodes return the expected dictionary format.
+- [Handling Multiple Subgraphs in LangGraph](https://langchain-ai.github.io/langgraph/troubleshooting/errors/MULTIPLE_SUBGRAPHS/): This page discusses the limitations of calling multiple subgraphs within a single LangGraph node when checkpointing is enabled. It provides troubleshooting tips to resolve related errors, including suggestions for compiling subgraphs without checkpointing and using the Send API for graph calls.
+- [Handling INVALID_CHAT_HISTORY Error in create_react_agent](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_CHAT_HISTORY/): This page provides an overview of the INVALID_CHAT_HISTORY error encountered in the create_react_agent function when a malformed list of messages is passed. It outlines the potential causes of the error and offers troubleshooting steps to resolve it. Users can learn how to properly invoke the graph and manage tool calls to avoid this issue.
+- [Handling INVALID_LICENSE Error in LangGraph Platform](https://langchain-ai.github.io/langgraph/troubleshooting/errors/INVALID_LICENSE/): This page provides guidance on troubleshooting the INVALID_LICENSE error encountered when starting a self-hosted LangGraph Platform server. It outlines the scenarios in which this error may occur and offers solutions based on different deployment types. Additionally, it includes steps to verify the necessary credentials for successful deployment.
+- [LangGraph Studio Troubleshooting Guide](https://langchain-ai.github.io/langgraph/troubleshooting/studio/): This page provides troubleshooting solutions for common connection issues encountered in LangGraph Studio, particularly with Safari and Brave browsers. It also addresses potential graph edge issues and offers methods to define routing paths for conditional edges. Users can find step-by-step instructions for resolving these issues using Cloudflare Tunnel and browser settings.
+- [LangGraph Case Studies](https://langchain-ai.github.io/langgraph/adopters/): This page provides a comprehensive list of companies that have successfully implemented LangGraph, showcasing their unique use cases and the benefits they have achieved. Each entry includes links to detailed case studies or blog posts for further reading. If your company uses LangGraph, you are encouraged to share your success story to contribute to this growing collection.

docs/docs/reference/checkpoints.md

@@ -12,12 +12,18 @@
     options:
       members:
         - SerializerProtocol
+        - CipherProtocol
 
 ::: langgraph.checkpoint.serde.jsonplus
     options:
       members:
         - JsonPlusSerializer
 
+::: langgraph.checkpoint.serde.encrypted
+    options:
+      members:
+        - EncryptedSerializer
+
 ::: langgraph.checkpoint.memory
 
 ::: langgraph.checkpoint.sqlite
@@ -32,4 +38,4 @@
 ::: langgraph.checkpoint.postgres.aio
     options:
       members:
-        - AsyncPostgresSaver
+        - AsyncPostgresSaver

docs/docs/stylesheets/agent_graph_widget.css

@@ -0,0 +1,57 @@
+.agent-layout {
+    display: flex;
+    flex-wrap: nowrap;
+    gap: 1rem;
+    align-items: flex-start;
+    margin-top: 1rem;
+}
+
+.agent-layout h3 {
+    margin: 0;
+}
+
+.agent-graph-features {
+    display: flex;
+    flex-direction: column;
+    gap: 0.5rem;
+    padding: 1rem;
+    max-width: 300px;
+    flex-shrink: 0;
+
+    border: 1px solid var(--md-default-fg-color--lightest);
+    border-radius: 0.5rem;
+    background-color: var(--md-default-bg-color);
+}
+
+.agent-graph-features label {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    font-size: 0.9rem;
+    color: var(--md-typeset-color);
+    cursor: pointer;
+}
+
+.agent-graph-features input[type="checkbox"] {
+    accent-color: var(--md-accent-fg-color);
+    transform: scale(1.2);
+}
+
+.agent-graph-container {
+    flex: 1 1 50%;
+    max-width: 70%;
+    padding: 1rem;
+
+    overflow: auto;
+    height: auto;
+    box-sizing: border-box;
+
+    border: 1px solid var(--md-default-fg-color--lightest);
+    border-radius: 0.5rem;
+    background-color: var(--md-default-bg-color);
+}
+
+.agent-graph-container img {
+    display: block;
+    margin: 0 auto;
+}

docs/docs/tutorials/get-started/1-build-basic-chatbot.md

@@ -6,7 +6,7 @@ In this tutorial, you will build a basic chatbot. This chatbot is the basis for
 
 Before you start this tutorial, ensure you have access to a LLM that supports
 tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
-[Anthropic](https://console.anthropic.com/settings/admin-keys), or
+[Anthropic](https://console.anthropic.com/settings/keys), or
 [Google Gemini](https://ai.google.dev/gemini-api/docs/api-key).
 
 ## 1. Install packages

docs/docs/tutorials/get-started/2-add-tools.md

@@ -146,7 +146,7 @@ graph_builder.add_node("tools", tool_node)
 
 !!! note
 
-    If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode).
+    If you do not want to build this yourself in the future, you can use LangGraph's prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/agents/#langgraph.prebuilt.tool_node.ToolNode).
 
 ## 6. Define the `conditional_edges`
 

docs/docs/tutorials/overview.md

@@ -0,0 +1,21 @@
+# Examples
+
+The pages in this section provide end-to-end examples for the following topics:
+
+## General 
+
+- [Agentic RAG](./rag/langgraph_adaptive_rag.ipynb)
+- [Agent Supervisor](./multi_agent/agent_supervisor.ipynb)
+- [SQL agent](./sql-agent.ipynb)
+- [Graph runs in LangSmith](../how-tos/run-id-langsmith.ipynb)
+
+## LangGraph Platform
+
+- [Set up custom authentication](./auth/getting_started.md)
+- [Make conversations private](./auth/resource_auth.md)
+- [Connect an authentication provider](./auth/add_auth_server.md)
+- [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md)
+- [Use RemoteGraph](../how-tos/use-remote-graph.md)
+- [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-langgraph-platform.ipynb)
+- [Integrate LangGraph into a React app](../cloud/how-tos/use_stream_react.md)
+- [Implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)

docs/mkdocs.yml

@@ -19,7 +19,6 @@ theme:
     - content.tabs.link
     - content.action.edit
     - content.tooltips
-    - header.autohide
     - navigation.indexes
     - navigation.footer
     - navigation.instant
@@ -28,7 +27,6 @@ theme:
     - navigation.instant.progress
     - navigation.path
     - navigation.tabs
-    - navigation.tabs.sticky
     - navigation.top
     - navigation.prune
     - navigation.tracking
@@ -181,10 +179,12 @@ nav:
           - cloud/how-tos/studio/manage_assistants.md
           - cloud/how-tos/threads_studio.md
           - cloud/how-tos/iterate_graph_studio.md
+          - cloud/how-tos/studio/run_evals.md
           - cloud/how-tos/clone_traces_studio.md
           - cloud/how-tos/datasets_studio.md
         - LangGraph SDK: concepts/sdk.md
       - Data management:
+        - Data storage & privacy: concepts/data_storage_and_privacy.md
         - Add semantic search: cloud/deployment/semantic_search.md
         - Add TTLs: how-tos/ttl/configure_ttl.md
       - Authentication & access control:
@@ -366,16 +366,6 @@ markdown_extensions:
 hooks:
   - _scripts/notebook_hooks.py
 extra:
-  consent:
-    title: Cookie consent
-    actions:
-      - accept
-      - reject
-    description: >- 
-      We use cookies to recognize your repeated visits and preferences, as well
-      as to measure the effectiveness of our documentation and whether users
-      find what they're searching for. <strong>Clicking "Accept" makes our
-      documentation better. Thank you!</strong> ❤️
   social:
     - icon: fontawesome/brands/js
       link: https://langchain-ai.github.io/langgraphjs/
@@ -383,25 +373,6 @@ extra:
       link: https://github.com/langchain-ai/langgraph
     - icon: fontawesome/brands/twitter
       link: https://twitter.com/LangChainAI
-  analytics:
-    provider: google
-    property: G-G8X6ELZYE0
-    feedback:
-      title: Was this page helpful?
-      ratings:
-        - icon: material/emoticon-happy-outline
-          name: This page was helpful
-          data: 1
-          note: >-
-            Thanks for your feedback!
-        - icon: material/emoticon-sad-outline
-          name: This page could be improved
-          data: 0
-          note: >-
-            Thanks for your feedback! Please help us improve this page by adding to the discussion below.
-  shared_analytics:
-    provider: google
-    property: G-47WX3HKKY2
 validation:
   # https://www.mkdocs.org/user-guide/configuration/
   # We are still raising for omitted files because they determine the breadcrumbs for pages.
@@ -418,3 +389,5 @@ extra_css:
   - stylesheets/version_admonitions.css
   - stylesheets/logos.css
   - stylesheets/sticky_navigation.css
+  - stylesheets/agent_graph_widget.css
+

docs/overrides/main.html

@@ -1,5 +1,16 @@
 {% extends "base.html" %}
 
+{% block analytics %}
+<!-- Google Tag Manager -->
+<script>(function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':
+new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],
+j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
+'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);
+})(window,document,'script','dataLayer','GTM-T35S4S46');</script>
+<!-- End Google Tag Manager -->
+{% endblock %}
+
+
 {% block extrahead %}
   <meta name="algolia-site-verification" content="165B7E7C89E49946" />
   <style>
@@ -185,7 +196,6 @@
   </style>
 {% endblock %}
 
-
 {% block content %}
 <div class="notebook-links">
   {% if page.nb_url %}
@@ -209,7 +219,6 @@
   {% endif %}
 {% endblock %}
 
-
 {% block announce %}
 <strong>We are growing and hiring for multiple roles for LangChain, LangGraph and LangSmith. <a href="https://www.langchain.com/careers" target="_blank" rel="noopener noreferrer"> Join our team!</a></strong>
 {% endblock %}

docs/uv.lock

@@ -2590,7 +2590,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "../libs/langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -2891,7 +2891,7 @@ test = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.1.8"
+version = "0.2.2"
 source = { editable = "../libs/prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -2917,11 +2917,12 @@ dev = [
     { name = "pytest-mock" },
     { name = "pytest-watcher" },
     { name = "ruff" },
+    { name = "syrupy" },
 ]
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.69"
+version = "0.1.70"
 source = { editable = "../libs/sdk-py" }
 dependencies = [
     { name = "httpx" },
@@ -2947,8 +2948,8 @@ dev = [
 
 [[package]]
 name = "langgraph-supervisor"
-version = "0.0.21"
-source = { git = "https://github.com/langchain-ai/langgraph-supervisor-py#6367bebd5462ac899e7def931ac6ab9cc6a9b070" }
+version = "0.0.25"
+source = { git = "https://github.com/langchain-ai/langgraph-supervisor-py#79380b5c21d3170e2d20dc6c55149ee057a306b1" }
 dependencies = [
     { name = "langchain-core" },
     { name = "langgraph" },

libs/langgraph/README.md

@@ -74,7 +74,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 - [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/overview/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
 - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.

libs/langgraph/langgraph/graph/message.py

@@ -23,6 +23,7 @@ from langchain_core.messages import (
 )
 from typing_extensions import TypedDict
 
+from langgraph.constants import CONF, CONFIG_KEY_SEND
 from langgraph.graph.state import StateGraph
 
 Messages = Union[list[MessageLikeRepresentation], MessageLikeRepresentation]
@@ -294,3 +295,52 @@ def _format_messages(messages: Sequence[BaseMessage]) -> list[BaseMessage]:
         return list(messages)
     else:
         return convert_to_messages(convert_to_openai_messages(messages))
+
+
+def push_message(
+    message: Union[MessageLikeRepresentation, BaseMessageChunk],
+    *,
+    state_key: Optional[str] = "messages",
+) -> AnyMessage:
+    """Write a message manually to the `messages` / `messages-tuple` stream mode.
+
+    Will automatically write to the channel specified in the `state_key` unless `state_key` is `None`.
+    """
+
+    from langchain_core.callbacks.base import (
+        BaseCallbackHandler,
+        BaseCallbackManager,
+    )
+
+    from langgraph.config import get_config
+    from langgraph.constants import NS_SEP
+    from langgraph.pregel.messages import StreamMessagesHandler
+
+    config = get_config()
+    message = next(x for x in convert_to_messages([message]))
+
+    if message.id is None:
+        raise ValueError("Message ID is required")
+
+    if isinstance(config["callbacks"], BaseCallbackManager):
+        manager = config["callbacks"]
+        handlers = manager.handlers
+    elif isinstance(config["callbacks"], list) and all(
+        isinstance(x, BaseCallbackHandler) for x in config["callbacks"]
+    ):
+        handlers = config["callbacks"]
+
+    if stream_handler := next(
+        (x for x in handlers if isinstance(x, StreamMessagesHandler)), None
+    ):
+        metadata = config["metadata"]
+        message_meta = (
+            tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP)),
+            metadata,
+        )
+        stream_handler._emit(message_meta, message, dedupe=False)
+
+    if state_key:
+        config[CONF][CONFIG_KEY_SEND]([(state_key, message)])
+
+    return message

libs/langgraph/langgraph/graph/schema_utils.py

@@ -1,268 +0,0 @@
-import functools
-import logging
-import weakref
-from dataclasses import is_dataclass
-from inspect import isclass
-from typing import (
-    Annotated,
-    Any,
-    Callable,
-    Optional,
-    Union,
-    get_args,
-    get_origin,
-    get_type_hints,
-)
-
-from pydantic import BaseModel, ConfigDict, TypeAdapter
-from typing_extensions import is_typeddict
-
-__all__ = ["SchemaCoercionMapper"]
-
-logger = logging.getLogger(__name__)
-
-
-_cache: weakref.WeakKeyDictionary[type[Any], dict[int, "SchemaCoercionMapper"]] = (
-    weakref.WeakKeyDictionary()
-)
-
-
-class SchemaCoercionMapper:
-    """Lightweight coercion of *dict* → *BaseModel* instances."""
-
-    def __new__(
-        cls,
-        schema: type[Any],
-        type_hints: Optional[dict[str, Any]] = None,
-        *,
-        max_depth: int = 12,
-    ) -> "SchemaCoercionMapper":
-        by_depth = _cache.setdefault(schema, {})
-        if max_depth in by_depth:
-            return by_depth[max_depth]
-        inst = super().__new__(cls)
-        by_depth[max_depth] = inst
-        return inst
-
-    def __init__(
-        self,
-        schema: type[BaseModel],
-        type_hints: Optional[dict[str, Any]] = None,
-        *,
-        max_depth: int = 12,
-    ) -> None:
-        if hasattr(self, "_initialised"):
-            return
-        self._initialised = True
-
-        self.schema = schema
-        self.max_depth = max_depth
-
-        self.type_hints = (
-            type_hints
-            if type_hints is not None
-            else get_type_hints(schema, localns={schema.__name__: schema})
-        )
-
-        if issubclass(schema, BaseModel):
-            self._fields = {
-                n: self.type_hints.get(n, f.annotation)
-                for n, f in schema.model_fields.items()
-            }
-            self._construct: Callable[..., Any] = schema.model_construct
-            unhandled_attrs = ("validators", "field_validators", "root_validators")
-            if (decorators := getattr(schema, "__pydantic_decorators__", None)) and any(
-                getattr(decorators, attr, None) for attr in unhandled_attrs
-            ):
-                self.coerce = lambda v, _: schema.model_validate(v)
-            else:
-                self.coerce = self._coerce
-        else:
-            raise TypeError("Schema must be a Pydantic V2 model.")
-
-        self._field_coercers: Optional[dict[str, Callable[[Any, int], Any]]] = None
-
-    def __call__(self, input_data: Any, depth: Optional[int] = None) -> Any:
-        return self.coerce(input_data, depth)
-
-    def _coerce(self, input_data: Any, depth: Optional[int] = None) -> Any:
-        if depth is None:
-            depth = self.max_depth
-        if not isinstance(input_data, dict) or depth <= 0:
-            return input_data
-
-        if self._field_coercers is None:
-            self._field_coercers = {
-                n: self._build_coercer(t, depth - 1) for n, t in self._fields.items()
-            }
-
-        processed: dict[str, Any] = {}
-        for k, v in input_data.items():
-            fn = self._field_coercers.get(k)
-            processed[k] = fn(v, depth - 1) if fn else v
-        return self._construct(**processed)
-
-    def _build_coercer(
-        self, field_type: Any, depth: int, *, throw: bool = False
-    ) -> Callable[[Any, Any], Any]:
-        if depth == 0:
-            return self._passthrough
-
-        origin = get_origin(field_type)
-
-        if (field_type in _IDENTITY_TYPES) or (origin in _IDENTITY_TYPES):
-            return self._passthrough
-
-        if origin is Annotated:
-            real_type, *_ = get_args(field_type)
-            sub = self._build_coercer(real_type, depth - 1)
-            return lambda v, d: sub(v, d)
-
-        if isclass(field_type):
-            # This is needed bcs. of issubclass issues on older versions of python
-            try:
-                is_bm_subclass = issubclass(field_type, BaseModel)
-            except TypeError:
-                # python < 3.11 issue.
-                is_bm_subclass = False
-            if is_bm_subclass:
-                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
-                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
-
-        if origin is list:
-            args = get_args(field_type)
-            if len(args) != 1:
-                return self._passthrough
-            sub = self._build_coercer(args[0], depth - 1)
-
-            def list_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple)):
-                    return v
-                return [sub(x, d - 1) for x in v]
-
-            return list_coercer
-
-        if origin is set or field_type is set:
-            args = get_args(field_type)
-            if len(args) > 1:
-                return self._passthrough
-            elif len(args) == 1:
-                sub = self._build_coercer(args[0], depth - 1)
-            else:
-                sub = None  # type: ignore
-
-            def set_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, (list, tuple, set)):
-                    return v
-                if sub is None:
-                    return set(v)
-                return {sub(x, d - 1) for x in v}
-
-            return set_coercer
-        if origin is dict or field_type is dict:
-            args = get_args(field_type)
-            if len(args) != 2:
-
-                def dict_coercer(v: Any, d: Any) -> Any:
-                    if not isinstance(v, dict):
-                        if throw:
-                            raise TypeError(f"Expected dict, got {type(v)}")
-                    return v
-
-                return dict_coercer
-            k_sub = self._build_coercer(args[0], depth - 1)
-            v_sub = self._build_coercer(args[1], depth - 1)
-
-            def dict_coercer(v: Any, d: Any) -> Any:
-                if not isinstance(v, dict):
-                    if throw:
-                        raise TypeError(f"Expected dict, got {type(v)}")
-                    return v
-                return {k_sub(k, d - 1): v_sub(val, d - 1) for k, val in v.items()}
-
-            return dict_coercer
-
-        if origin is tuple:
-            elem_types = get_args(field_type)
-            if not elem_types:
-                return self._passthrough
-            subs = [self._build_coercer(t, depth - 1) for t in elem_types]
-            return lambda v, d: (
-                tuple(
-                    subs[i](v[i] if i < len(v) else None, d - 1)
-                    for i in range(len(subs))
-                )
-                if isinstance(v, (list, tuple))
-                else v
-            )
-
-        if origin is Union:
-            uargs = get_args(field_type)
-            subs, none_in_union = [], False
-            for ix, arg in enumerate(uargs):
-                if arg is type(None):
-                    none_in_union = True
-                else:
-                    subs.append(
-                        self._build_coercer(arg, depth - 1, throw=ix < len(uargs) - 1)
-                    )
-
-            def union_coercer(v: Any, d: Any) -> Any:
-                if v is None and none_in_union:
-                    return None
-                err = None
-                for sp in subs:
-                    try:
-                        return sp(v, d - 1)
-                    except TypeError as e:
-                        err = e
-                if err:
-                    raise err
-                return v
-
-            return union_coercer
-
-        adapter_fn = _get_adapter(field_type)
-        return lambda v, _d: adapter_fn(v)
-
-    @staticmethod
-    def _passthrough(v: Any, _d: Any) -> Any:  # noqa: D401
-        return v
-
-
-_adapter_cache: dict[Any, Callable[[Any], Any]] = {}
-
-
-_IDENTITY_TYPES: tuple[type[Any], ...] = (
-    int,
-    float,
-    str,
-    bool,
-    bytes,
-    bytearray,
-    complex,
-    memoryview,
-    type(None),
-)
-
-
-@functools.lru_cache(maxsize=2048)
-def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
-    try:
-        config = (
-            None
-            if (issubclass(tp, BaseModel) or is_dataclass(tp) or is_typeddict(tp))
-            else ConfigDict(arbitrary_types_allowed=True)
-        )
-    except TypeError:
-        config = None
-    return TypeAdapter(tp, config=config).validate_python
-
-
-def _get_adapter(tp: Any) -> Callable[[Any], Any]:
-    try:
-        return _adapter_cache[tp]
-    except KeyError:
-        fn = _adapter_for(tp)
-        _adapter_cache[tp] = fn
-        return fn

libs/langgraph/langgraph/graph/state.py

@@ -64,7 +64,6 @@ from langgraph.graph.graph import (
     Graph,
     Send,
 )
-from langgraph.graph.schema_utils import SchemaCoercionMapper
 from langgraph.managed.base import (
     ChannelKeyPlaceholder,
     ChannelTypePlaceholder,
@@ -320,12 +319,18 @@ class StateGraph(Graph):
 
         Example:
             ```python
+            from typing_extensions import TypedDict
+
+            from langchain_core.runnables import RunnableConfig
             from langgraph.graph import START, StateGraph
 
-            def my_node(state, config):
+            class State(TypedDict):
+                x: int
+
+            def my_node(state: State, config: RunnableConfig) -> State:
                 return {"x": state["x"] + 1}
 
-            builder = StateGraph(dict)
+            builder = StateGraph(State)
             builder.add_node(my_node)  # node name will be 'my_node'
             builder.add_edge(START, "my_node")
             graph = builder.compile()
@@ -335,7 +340,7 @@ class StateGraph(Graph):
 
         Example: Customize the name:
             ```python
-            builder = StateGraph(dict)
+            builder = StateGraph(State)
             builder.add_node("my_fair_node", my_node)
             builder.add_edge(START, "my_fair_node")
             graph = builder.compile()
@@ -1042,11 +1047,8 @@ def _pick_mapper(
 ) -> Optional[Callable[[Any], Any]]:
     if state_keys == ["__root__"]:
         return None
-    if isclass(schema):
-        if issubclass(schema, dict):
-            return None
-        if issubclass(schema, BaseModel):
-            return SchemaCoercionMapper(schema, type_hints=type_hints)
+    if isclass(schema) and issubclass(schema, dict):
+        return None
     return partial(_coerce_state, schema)
 
 

libs/langgraph/langgraph/graph/ui.py

@@ -54,7 +54,7 @@ def push_ui_message(
     id: Optional[str] = None,
     metadata: Optional[dict[str, Any]] = None,
     message: Optional[AnyMessage] = None,
-    state_key: str = "ui",
+    state_key: Optional[str] = "ui",
     merge: bool = False,
 ) -> UIMessage:
     """Push a new UI message to update the UI state.
@@ -111,7 +111,8 @@ def push_ui_message(
     }
 
     writer(evt)
-    config[CONF][CONFIG_KEY_SEND]([(state_key, evt)])
+    if state_key:
+        config[CONF][CONFIG_KEY_SEND]([(state_key, evt)])
 
     return evt
 

libs/langgraph/langgraph/pregel/__init__.py

@@ -2214,12 +2214,14 @@ class Pregel(PregelProtocol):
             validate_keys(output_keys, self.channels)
         interrupt_before = interrupt_before or self.interrupt_before_nodes
         interrupt_after = interrupt_after or self.interrupt_after_nodes
-        stream_mode = stream_mode if stream_mode is not None else self.stream_mode
+        if stream_mode is None and CONFIG_KEY_TASK_ID in config.get(CONF, {}):
+            # if being called as a node in another graph, default to values mode
+            # but don't overwrite stream_mode arg if provided
+            stream_mode = ["values"]
+        elif stream_mode is None:
+            stream_mode = self.stream_mode
         if not isinstance(stream_mode, list):
             stream_mode = [stream_mode]
-        if CONFIG_KEY_TASK_ID in config.get(CONF, {}):
-            # if being called as a node in another graph, always use values mode
-            stream_mode = ["values"]
         if self.checkpointer is False:
             checkpointer: BaseCheckpointSaver | None = None
         elif CONFIG_KEY_CHECKPOINTER in config.get(CONF, {}):

libs/langgraph/langgraph/pregel/loop.py

@@ -135,7 +135,6 @@ P = ParamSpec("P")
 INPUT_DONE = object()
 INPUT_RESUMING = object()
 INPUT_SHOULD_VALIDATE = object()
-SPECIAL_CHANNELS = (ERROR, INTERRUPT, SCHEDULED)
 WritesT = Sequence[tuple[str, Any]]
 
 
@@ -509,9 +508,14 @@ class PregelLoop(LoopProtocol):
                     **read_channels(self.channels, self.stream_keys)
                 )
             # produce values output
-            self._emit(
-                "values", map_output_values, self.output_keys, writes, self.channels
-            )
+            if not updated_channels.isdisjoint(
+                (self.output_keys,)
+                if isinstance(self.output_keys, str)
+                else self.output_keys
+            ):
+                self._emit(
+                    "values", map_output_values, self.output_keys, writes, self.channels
+                )
             # clear pending writes
             self.checkpoint_pending_writes.clear()
             # "not skip_done_tasks" only applies to first tick after resuming
@@ -887,7 +891,7 @@ class PregelLoop(LoopProtocol):
                 and self.checkpoint_pending_writes
                 and any(task.writes for task in self.tasks.values())
             ):
-                mv_writes, _ = apply_writes(
+                mv_writes, updated_channels = apply_writes(
                     self.checkpoint,
                     self.channels,
                     self.tasks.values(),
@@ -896,13 +900,18 @@ class PregelLoop(LoopProtocol):
                 )
                 for key, values in mv_writes.items():
                     self._update_mv(key, values)
-                self._emit(
-                    "values",
-                    map_output_values,
-                    self.output_keys,
-                    [w for t in self.tasks.values() for w in t.writes],
-                    self.channels,
-                )
+                if not updated_channels.isdisjoint(
+                    (self.output_keys,)
+                    if isinstance(self.output_keys, str)
+                    else self.output_keys
+                ):
+                    self._emit(
+                        "values",
+                        map_output_values,
+                        self.output_keys,
+                        [w for t in self.tasks.values() for w in t.writes],
+                        self.channels,
+                    )
             # emit INTERRUPT if exception is empty (otherwise emitted by put_writes)
             if exc_value is not None and (not exc_value.args or not exc_value.args[0]):
                 self._emit(

libs/langgraph/langgraph/pregel/retry.py

@@ -41,7 +41,7 @@ def run_with_retry(
         except ParentCommand as exc:
             ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
             cmd = exc.args[0]
-            if cmd.graph == ns:
+            if cmd.graph in (ns, task.name):
                 # this command is for the current graph, handle it
                 for w in task.writers:
                     w.invoke(cmd, config)
@@ -137,7 +137,7 @@ async def arun_with_retry(
         except ParentCommand as exc:
             ns: str = config[CONF][CONFIG_KEY_CHECKPOINT_NS]
             cmd = exc.args[0]
-            if cmd.graph == ns:
+            if cmd.graph in (ns, task.name):
                 # this command is for the current graph, handle it
                 for w in task.writers:
                     w.invoke(cmd, config)

libs/langgraph/langgraph/pregel/runner.py

@@ -56,6 +56,10 @@ EXCLUDED_FRAME_FNAMES = (
     "concurrent/futures/_base.py",
 )
 
+SKIP_RERAISE_SET: weakref.WeakSet[Union[concurrent.futures.Future, asyncio.Future]] = (
+    weakref.WeakSet()
+)
+
 
 class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
     event: E
@@ -100,7 +104,8 @@ class FuturesDict(Generic[F, E], dict[F, Optional[PregelExecutableTask]]):
         fut: F,
     ) -> None:
         try:
-            self.callback()(task, _exception(fut))  # type: ignore[misc]
+            if cb := self.callback():
+                cb(task, _exception(fut))
         finally:
             with self.lock:
                 self.done.add(fut)
@@ -165,7 +170,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                         ),
                     },
                 )
@@ -207,7 +211,6 @@ class PregelRunner:
                         futures=weakref.ref(futures),
                         schedule_task=schedule_task,
                         submit=self.submit,
-                        reraise=reraise,
                     ),
                 },
                 __reraise_on_exit__=reraise,
@@ -302,7 +305,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                             loop=loop,
                         ),
                     },
@@ -349,7 +351,6 @@ class PregelRunner:
                             futures=weakref.ref(futures),
                             schedule_task=schedule_task,
                             submit=self.submit,
-                            reraise=reraise,
                             loop=loop,
                         ),
                     },
@@ -431,10 +432,11 @@ class PregelRunner:
                         writes.extend(resumes)
                     self.put_writes()(task.id, writes)  # type: ignore[misc]
             elif isinstance(exception, GraphBubbleUp):
-                raise exception
+                pass
             else:
                 # save error to checkpointer
-                self.put_writes()(task.id, [(ERROR, exception)])  # type: ignore[misc]
+                task.writes.append((ERROR, exception))
+                self.put_writes()(task.id, task.writes)  # type: ignore[misc]
         else:
             if self.node_finished and (
                 task.config is None or TAG_HIDDEN not in task.config.get("tags", [])
@@ -456,7 +458,7 @@ def _should_stop_others(
         if fut.cancelled():
             continue
         elif exc := fut.exception():
-            if not isinstance(exc, GraphBubbleUp):
+            if not isinstance(exc, GraphBubbleUp) and fut not in SKIP_RERAISE_SET:
                 return True
 
     return False
@@ -494,7 +496,8 @@ def _panic_or_proceed(
     interrupts: list[GraphInterrupt] = []
     while done:
         # if any task failed
-        if exc := _exception(done.pop()):
+        fut = done.pop()
+        if exc := _exception(fut):
             # cancel all pending tasks
             while inflight:
                 inflight.pop().cancel()
@@ -503,7 +506,7 @@ def _panic_or_proceed(
                 if isinstance(exc, GraphInterrupt):
                     # collect interrupts
                     interrupts.append(exc)
-                else:
+                elif fut not in SKIP_RERAISE_SET:
                     raise exc
     # raise combined interrupts
     if interrupts:
@@ -530,7 +533,6 @@ def _call(
         [PregelExecutableTask, int, Optional[Call]], Optional[PregelExecutableTask]
     ],
     submit: weakref.ref[Submit],
-    reraise: bool,
 ) -> concurrent.futures.Future[Any]:
     if asyncio.iscoroutinefunction(func):
         raise RuntimeError("In an sync context async tasks cannot be called")
@@ -582,14 +584,16 @@ def _call(
                         callbacks=callbacks,
                         schedule_task=schedule_task,
                         submit=submit,
-                        reraise=reraise,
                     ),
                 },
-                __reraise_on_exit__=reraise,
+                __reraise_on_exit__=False,
                 # starting a new task in the next tick ensures
                 # updates from this tick are committed/streamed first
                 __next_tick__=True,
             )
+            # exceptions for call() tasks are raised into the parent task
+            # so we should not re-raise at the end of the tick
+            SKIP_RERAISE_SET.add(fut)
             futures()[fut] = next_task  # type: ignore[index]
     fut = cast(Union[asyncio.Future, concurrent.futures.Future], fut)
     # return a chained future to ensure commit() callback is called
@@ -613,7 +617,6 @@ def _acall(
     ],
     submit: weakref.ref[Submit],
     loop: asyncio.AbstractEventLoop,
-    reraise: bool = False,
     stream: bool = False,
 ) -> Union[asyncio.Future[Any], concurrent.futures.Future[Any]]:
     # return a chained future to ensure commit() callback is called
@@ -643,7 +646,6 @@ def _acall(
             schedule_task=schedule_task,
             submit=submit,
             loop=loop,
-            reraise=reraise,
             stream=stream,
         ),
         loop,
@@ -669,7 +671,6 @@ async def _acall_impl(
     ],
     submit: weakref.ref[Submit],
     loop: asyncio.AbstractEventLoop,
-    reraise: bool = False,
     stream: bool = False,
 ) -> None:
     try:
@@ -726,17 +727,19 @@ async def _acall_impl(
                                 schedule_task=schedule_task,
                                 submit=submit,
                                 loop=loop,
-                                reraise=reraise,
                             ),
                         },
-                        __name__=task().name,  # type: ignore[union-attr]
+                        __name__=next_task.name,
                         __cancel_on_exit__=True,
-                        __reraise_on_exit__=reraise,
+                        __reraise_on_exit__=False,
                         # starting a new task in the next tick ensures
                         # updates from this tick are committed/streamed first
                         __next_tick__=True,
                     ),
                 )
+                # exceptions for call() tasks are raised into the parent task
+                # so we should not re-raise at the end of the tick
+                SKIP_RERAISE_SET.add(fut)
                 futures()[fut] = next_task  # type: ignore[index]
         if fut is not None:
             chain_future(fut, destination)

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.8"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"
@@ -15,7 +15,7 @@ dependencies = [
     "langchain-core>=0.1",
     "langgraph-checkpoint>=2.0.26",
     "langgraph-sdk>=0.1.42",
-    "langgraph-prebuilt>=0.1.8",
+    "langgraph-prebuilt>=0.2.0",
     "xxhash>=3.5.0",
     "pydantic>=2.7.4",
 ]

libs/langgraph/tests/test_messages_state.py

@@ -15,7 +15,7 @@ from pydantic import BaseModel
 from typing_extensions import TypedDict
 
 from langgraph.graph import add_messages
-from langgraph.graph.message import REMOVE_ALL_MESSAGES, MessagesState
+from langgraph.graph.message import REMOVE_ALL_MESSAGES, MessagesState, push_message
 from langgraph.graph.state import END, START, StateGraph
 from tests.messages import _AnyIdHumanMessage
 
@@ -332,3 +332,34 @@ def test_remove_all_messages():
     assert result == [
         _AnyIdHumanMessage(content="Updated hi there"),
     ]
+
+
+def test_push_messages_in_graph():
+    class MessagesState(TypedDict):
+        messages: Annotated[list[AnyMessage], add_messages]
+
+    def chat(_: MessagesState) -> MessagesState:
+        with pytest.raises(ValueError, match="Message ID is required"):
+            push_message(AIMessage(content="No ID"))
+
+        push_message(AIMessage(content="First", id="1"))
+        push_message(HumanMessage(content="Second", id="2"))
+        push_message(AIMessage(content="Third", id="3"))
+
+    builder = StateGraph(MessagesState)
+    builder.add_node(chat)
+    builder.add_edge(START, "chat")
+
+    graph = builder.compile()
+
+    messages, values = [], None
+    for event, chunk in graph.stream(
+        {"messages": []}, stream_mode=["messages", "values"]
+    ):
+        if event == "values":
+            values = chunk
+        elif event == "messages":
+            message, _ = chunk
+            messages.append(message)
+
+    assert values["messages"] == messages

libs/langgraph/tests/test_pregel.py

@@ -5514,8 +5514,11 @@ def test_runnable_passthrough_node_graph() -> None:
     assert graph.get_graph(xray=True).to_json() == graph.get_graph(xray=False).to_json()
 
 
+@pytest.mark.parametrize("subgraph_persist", [True, False])
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_parent_command(request: pytest.FixtureRequest, checkpointer_name: str) -> None:
+def test_parent_command(
+    request: pytest.FixtureRequest, checkpointer_name: str, subgraph_persist: bool
+) -> None:
     from langchain_core.messages import BaseMessage
     from langchain_core.tools import tool
 
@@ -5527,7 +5530,7 @@ def test_parent_command(request: pytest.FixtureRequest, checkpointer_name: str)
     subgraph_builder = StateGraph(MessagesState)
     subgraph_builder.add_node("tool", get_user_name)
     subgraph_builder.add_edge(START, "tool")
-    subgraph = subgraph_builder.compile()
+    subgraph = subgraph_builder.compile(checkpointer=subgraph_persist)
 
     class CustomParentState(TypedDict):
         messages: Annotated[list[BaseMessage], add_messages]
@@ -6873,7 +6876,7 @@ def test_sync_streaming_with_functional_api() -> None:
     should be greater than the time delay between the two tasks.
     """
 
-    time_delay = 0.01
+    time_delay = 0.05
 
     @task()
     def slow() -> dict:
@@ -8769,3 +8772,76 @@ def test_get_graph_root_channel(snapshot: SnapshotAssertion) -> None:
 
     assert json.dumps(graph.get_graph().to_json(), indent=2) == snapshot
     assert graph.get_graph().draw_mermaid(with_styles=False) == snapshot
+
+
+def test_imp_exception(
+    sync_checkpointer: BaseCheckpointSaver,
+) -> None:
+    @task()
+    def my_task(number: int):
+        time.sleep(0.1)
+        return number * 2
+
+    @task()
+    def task_with_exception(number: int):
+        time.sleep(0.1)
+        raise Exception("This is a test exception")
+
+    @entrypoint(checkpointer=sync_checkpointer)
+    def my_workflow(number: int):
+        my_task(number).result()
+        try:
+            task_with_exception(number).result()
+        except Exception as e:
+            print(f"Exception caught: {e}")
+        my_task(number).result()
+        return "done"
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert my_workflow.invoke(1, thread1) == "done"
+
+    assert [c for c in my_workflow.stream(1, thread1)] == [
+        {"my_task": 2},
+        {"my_task": 2},
+        {"my_workflow": "done"},
+    ]
+
+
+@pytest.mark.parametrize("subgraph_persist", [True, False])
+def test_parent_command_goto(
+    sync_checkpointer: BaseCheckpointSaver, subgraph_persist: bool
+) -> None:
+    class State(TypedDict):
+        dialog_state: Annotated[list[str], operator.add]
+
+    def node_a_child(state):
+        return {"dialog_state": ["a_child_state"]}
+
+    def node_b_child(state):
+        return Command(
+            graph=Command.PARENT,
+            goto="node_b_parent",
+            update={"dialog_state": ["b_child_state"]},
+        )
+
+    sub_builder = StateGraph(State)
+    sub_builder.add_node(node_a_child)
+    sub_builder.add_node(node_b_child)
+    sub_builder.add_edge(START, "node_a_child")
+    sub_builder.add_edge("node_a_child", "node_b_child")
+    sub_graph = sub_builder.compile(checkpointer=subgraph_persist)
+
+    def node_b_parent(state):
+        return {"dialog_state": ["node_b_parent"]}
+
+    main_builder = StateGraph(State)
+    main_builder.add_node(node_b_parent)
+    main_builder.add_edge(START, "subgraph_node")
+    main_builder.add_node("subgraph_node", sub_graph, destinations=("node_b_parent",))
+
+    main_graph = main_builder.compile(sync_checkpointer, name="parent")
+    config = {"configurable": {"thread_id": 1}}
+
+    assert main_graph.invoke(input={"dialog_state": ["init_state"]}, config=config) == {
+        "dialog_state": ["init_state", "b_child_state", "node_b_parent"]
+    }

libs/langgraph/tests/test_pregel_async.py

@@ -6772,8 +6772,9 @@ async def test_debug_nested_subgraphs(async_checkpointer: BaseCheckpointSaver):
                 assert stream_task.get("state") == history_task.state
 
 
+@pytest.mark.parametrize("subgraph_persist", [True, False])
 @pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_ASYNC)
-async def test_parent_command(checkpointer_name: str) -> None:
+async def test_parent_command(checkpointer_name: str, subgraph_persist: bool) -> None:
     from langchain_core.messages import BaseMessage
     from langchain_core.tools import tool
 
@@ -6785,7 +6786,7 @@ async def test_parent_command(checkpointer_name: str) -> None:
     subgraph_builder = StateGraph(MessagesState)
     subgraph_builder.add_node("tool", get_user_name)
     subgraph_builder.add_edge(START, "tool")
-    subgraph = subgraph_builder.compile()
+    subgraph = subgraph_builder.compile(checkpointer=subgraph_persist)
 
     class CustomParentState(TypedDict):
         messages: Annotated[list[BaseMessage], add_messages]
@@ -9148,3 +9149,341 @@ async def test_draw_invalid():
             {"source": "nothing", "target": "__end__"},
         ],
     }
+
+
+@NEEDS_CONTEXTVARS
+async def test_imp_exception(
+    async_checkpointer: BaseCheckpointSaver,
+) -> None:
+    @task()
+    async def my_task(number: int):
+        await asyncio.sleep(0.1)
+        return number * 2
+
+    @task()
+    async def task_with_exception(number: int):
+        await asyncio.sleep(0.1)
+        raise Exception("This is a test exception")
+
+    @entrypoint(checkpointer=async_checkpointer)
+    async def my_workflow(number: int):
+        await my_task(number)
+        try:
+            await task_with_exception(number)
+        except Exception as e:
+            print(f"Exception caught: {e}")
+        await my_task(number)
+        return "done"
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert await my_workflow.ainvoke(1, thread1) == "done"
+
+    assert [c async for c in my_workflow.astream(1, thread1)] == [
+        {"my_task": 2},
+        {"my_task": 2},
+        {"my_workflow": "done"},
+    ]
+
+    assert [c async for c in my_workflow.astream_events(1, thread1)] == [
+        {
+            "event": "on_chain_start",
+            "data": {"input": 1},
+            "name": "LangGraph",
+            "tags": [],
+            "run_id": AnyStr(),
+            "metadata": {"thread_id": "1"},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": 1},
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": 2},
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": 2, "input": {"number": 1}},
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_task": 2}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "task_with_exception",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_start",
+            "data": {"input": {"number": 1}},
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "run_id": AnyStr(),
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": 2},
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": 2, "input": {"number": 1}},
+            "run_id": AnyStr(),
+            "name": "my_task",
+            "tags": ["seq:step:1"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_task",
+                "langgraph_triggers": ("__pregel_push",),
+                "langgraph_path": (
+                    "__pregel_push",
+                    ("__pregel_pull", "my_workflow"),
+                    2,
+                    True,
+                ),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [
+                AnyStr(),
+                AnyStr(),
+            ],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "data": {"chunk": "done"},
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_task": 2}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": "done", "input": 1},
+            "run_id": AnyStr(),
+            "name": "my_workflow",
+            "tags": ["graph:step:4"],
+            "metadata": {
+                "thread_id": "1",
+                "langgraph_step": 4,
+                "langgraph_node": "my_workflow",
+                "langgraph_triggers": ("__start__",),
+                "langgraph_path": ("__pregel_pull", "my_workflow"),
+                "langgraph_checkpoint_ns": AnyStr(),
+            },
+            "parent_ids": [AnyStr()],
+        },
+        {
+            "event": "on_chain_stream",
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "data": {"chunk": {"my_workflow": "done"}},
+            "parent_ids": [],
+        },
+        {
+            "event": "on_chain_end",
+            "data": {"output": "done"},
+            "run_id": AnyStr(),
+            "name": "LangGraph",
+            "tags": [],
+            "metadata": {"thread_id": "1"},
+            "parent_ids": [],
+        },
+    ]
+
+
+@pytest.mark.parametrize("subgraph_persist", [True, False])
+async def test_parent_command_goto(
+    async_checkpointer: BaseCheckpointSaver, subgraph_persist: bool
+) -> None:
+    class State(TypedDict):
+        dialog_state: Annotated[list[str], operator.add]
+
+    async def node_a_child(state):
+        return {"dialog_state": ["a_child_state"]}
+
+    async def node_b_child(state):
+        return Command(
+            graph=Command.PARENT,
+            goto="node_b_parent",
+            update={"dialog_state": ["b_child_state"]},
+        )
+
+    sub_builder = StateGraph(State)
+    sub_builder.add_node(node_a_child)
+    sub_builder.add_node(node_b_child)
+    sub_builder.add_edge(START, "node_a_child")
+    sub_builder.add_edge("node_a_child", "node_b_child")
+    sub_graph = sub_builder.compile(checkpointer=subgraph_persist)
+
+    async def node_b_parent(state):
+        return {"dialog_state": ["node_b_parent"]}
+
+    main_builder = StateGraph(State)
+    main_builder.add_node(node_b_parent)
+    main_builder.add_edge(START, "subgraph_node")
+    main_builder.add_node("subgraph_node", sub_graph, destinations=("node_b_parent",))
+
+    main_graph = main_builder.compile(async_checkpointer, name="parent")
+    config = {"configurable": {"thread_id": 1}}
+
+    assert await main_graph.ainvoke(
+        input={"dialog_state": ["init_state"]}, config=config
+    ) == {"dialog_state": ["init_state", "b_child_state", "node_b_parent"]}

libs/prebuilt/langgraph/prebuilt/chat_agent_executor.py

@@ -240,7 +240,7 @@ def _validate_chat_history(
 
 def create_react_agent(
     model: Union[str, LanguageModelLike],
-    tools: Union[Sequence[Union[BaseTool, Callable]], ToolNode],
+    tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
     *,
     prompt: Optional[Prompt] = None,
     response_format: Optional[
@@ -420,12 +420,13 @@ def create_react_agent(
             else AgentState
         )
 
+    llm_builtin_tools: list[dict] = []
     if isinstance(tools, ToolNode):
         tool_classes = list(tools.tools_by_name.values())
         tool_node = tools
     else:
-        tool_node = ToolNode(tools)
-        # get the tool functions wrapped in a tool class from the ToolNode
+        llm_builtin_tools = [t for t in tools if isinstance(t, dict)]
+        tool_node = ToolNode([t for t in tools if not isinstance(t, dict)])
         tool_classes = list(tool_node.tools_by_name.values())
 
     if isinstance(model, str):
@@ -442,8 +443,12 @@ def create_react_agent(
 
     tool_calling_enabled = len(tool_classes) > 0
 
-    if _should_bind_tools(model, tool_classes) and tool_calling_enabled:
-        model = cast(BaseChatModel, model).bind_tools(tool_classes)
+    if (
+        _should_bind_tools(model, tool_classes)
+        and len(tool_classes) > 0
+        or (len(llm_builtin_tools) > 0)
+    ):
+        model = cast(BaseChatModel, model).bind_tools(tool_classes + llm_builtin_tools)  # type: ignore[operator]
 
     model_runnable = _get_prompt_runnable(prompt) | model
 
@@ -722,6 +727,10 @@ def create_react_agent(
             ]
 
             if pending_tool_calls:
+                pending_tool_calls = [
+                    tool_node.inject_tool_args(call, state, store)  # type: ignore[arg-type]
+                    for call in pending_tool_calls
+                ]
                 return [Send("tools", [tool_call]) for tool_call in pending_tool_calls]
             elif isinstance(messages[-1], ToolMessage):
                 return entrypoint

libs/prebuilt/langgraph/prebuilt/interrupt.py

@@ -1,12 +1,7 @@
-from copy import deepcopy
-from typing import Any, Literal, Optional, Union, cast
+from typing import Literal, Optional, Union
 
-from langchain_core.messages import ToolCall, ToolMessage
 from typing_extensions import TypedDict
 
-from langgraph.types import Command, interrupt
-from langgraph.utils.runnable import RunnableCallable
-
 
 class HumanInterruptConfig(TypedDict):
     """Configuration that defines what actions are allowed for a human interrupt.
@@ -93,159 +88,3 @@ class HumanResponse(TypedDict):
 
     type: Literal["accept", "ignore", "response", "edit"]
     args: Union[None, str, ActionRequest]
-
-
-class InterruptToolNode(RunnableCallable):
-    """Prebuilt post model hook node used to enable common patterns for tool interrupts.
-
-    For any tools with specified policies, an interrupt will be raised when the LLM returns
-    a tool call for said tool. The interrupt policy will be used to determine what sort of resume logic is allowed.
-    Any of the following resume patterns are supported:
-
-    * accept: the tool call is executed as planned
-    * edit: the args for the tool call are edited and then the tool call is executed
-    * response: text response/feedback is fed back into the LLM
-    * ignore: the current tool call is ignored / skipped
-
-    Args:
-        **interrupt_policy: a mapping of tool names to [`HumanInterruptConfig`][prebuilt.interrupt.HumanInterruptConfig] dictionaries
-            specifying which interrupt patterns to enable for said tool.
-
-        Example:
-        ```python
-        from langgraph.prebuilt import create_react_agent
-        from langgraph.checkpoint.memory import InMemorySaver
-        from langgraph.prebuilt.interrupt import HumanInterruptConfig, InterruptToolNode
-        from langgraph.types import Command
-
-
-        def book_hotel(hotel_name: str) -> str:
-            '''Book a room at the provided hotel.'''
-            # Some hotel API calls, a sensitive / expensive operation
-            return f"Booked a hotel at {hotel_name}."
-
-
-        agent = create_react_agent(
-            "openai:gpt-4.1",
-            tools=[book_hotel],
-            prompt="You are a hotel booking assistant.",
-            post_model_hook=InterruptToolNode(
-                book_hotel=HumanInterruptConfig(
-                    allow_accept=True,
-                    allow_edit=True,
-                    allow_ignore=True,
-                    allow_respond=True,
-                )
-            ),
-            checkpointer=InMemorySaver(),
-        )
-
-        config = {"configurable": {"thread_id": 1}}
-
-        response = agent.invoke(
-            {"messages": [{"role": "user", "content": "please book a hotel at the hilton inn in boston."}]},
-            config=config,
-        )
-
-        response = agent.invoke(Command(resume={"type": "accept"}), config=config)
-        ```
-    """
-
-    def __init__(self, **interrupt_policy: HumanInterruptConfig):
-        super().__init__(self._func, self._afunc)
-        self.interrupt_policy = interrupt_policy
-
-    def _interrupt(
-        self,
-        tool_call: ToolCall,
-        interrupt_config: HumanInterruptConfig,
-    ) -> Union[ToolCall, ToolMessage]:
-        """Interrupt before a tool call and ask for human input."""
-        call_id = tool_call["id"]
-        tool_name = tool_call["name"]
-
-        request = HumanInterrupt(
-            action_request=ActionRequest(
-                action=tool_name,
-                args=tool_call["args"],
-            ),
-            config=interrupt_config,
-            description=f"Please review tool call for `{tool_name}` before execution.",
-        )
-        response = interrupt([request])
-
-        # resume provided by agent inbox as a list
-        response = response[0] if isinstance(response, list) else response
-
-        try:
-            response_type = response.get("type")
-        except AttributeError:
-            raise TypeError(
-                f"Unexpected resume value: {response}."
-                f"Expected a dict with `'type'` key."
-            )
-
-        if response_type == "accept" and interrupt_config["allow_accept"]:
-            return tool_call
-        elif response_type == "edit" and interrupt_config["allow_edit"]:
-            return ToolCall(
-                args=cast(ActionRequest, response)["args"]["args"],
-                name=tool_name,
-                id=call_id,
-                type="tool_call",
-            )
-        elif response_type == "response" and interrupt_config["allow_respond"]:
-            return ToolMessage(
-                content=cast(str, response["args"]),
-                name=tool_name,
-                tool_call_id=call_id,
-                status="error",
-            )
-        elif response_type == "ignore" and interrupt_config["allow_ignore"]:
-            return ToolMessage(
-                content=f"User ignored the tool call for `{tool_name}` with id {call_id}",
-                name=tool_name,
-                tool_call_id=call_id,
-                status="success",
-            )
-
-        allowed_types = [
-            type_name
-            for type_name, is_allowed in {
-                "accept": interrupt_config["allow_accept"],
-                "edit": interrupt_config["allow_edit"],
-                "response": interrupt_config["allow_respond"],
-                "ignore": interrupt_config["allow_ignore"],
-            }.items()
-            if is_allowed
-        ]
-
-        raise ValueError(
-            f"Unexpected human response: {response}. "
-            f"Expected one with `'type'` in {allowed_types} based on {tool_name}'s interrupt configuration."
-        )
-
-    def _func(self, input: dict[str, Any]) -> Command:
-        ai_msg = input["messages"][-1]
-        tool_calls: list[ToolCall] = deepcopy(ai_msg.tool_calls) or []
-        tool_messages: list[ToolMessage] = []
-
-        for idx, tool_call in enumerate(tool_calls):
-            if interrupt_config := self.interrupt_policy.get(tool_call["name"]):
-                interrupt_result = self._interrupt(
-                    tool_call=tool_call, interrupt_config=interrupt_config
-                )
-
-                if isinstance(interrupt_result, ToolMessage):
-                    tool_messages.append(interrupt_result)
-                else:
-                    tool_calls[idx] = interrupt_result
-
-        updated_ai_msg = ai_msg.copy(update={"tool_calls": tool_calls})
-
-        # conditional routing logic for post_model_hook will direct to the tools node
-        # or agent node depending on if there are pending tool calls
-        return {"messages": [updated_ai_msg, *tool_messages]}
-
-    async def _afunc(self, input: dict[str, Any]) -> Command:
-        return self._func(input)

libs/prebuilt/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-prebuilt"
-version = "0.1.8"
+version = "0.2.2"
 description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
 authors = []
 requires-python = ">=3.9"

libs/prebuilt/tests/test_interrupt_tool_node.py

@@ -1,191 +0,0 @@
-import pytest
-from langchain_core.messages import ToolMessage
-from langchain_core.runnables import RunnableConfig
-
-from langgraph.checkpoint.base import BaseCheckpointSaver
-from langgraph.prebuilt import create_react_agent
-from langgraph.prebuilt.interrupt import HumanInterruptConfig, InterruptToolNode
-from langgraph.types import Command
-from tests.model import FakeToolCallingModel
-
-
-def hello_tool(name: str) -> str:
-    """Return a greeting for the provided person."""
-    return f"Hello, {name}!"
-
-
-post_model_hook = InterruptToolNode(
-    hello_tool=HumanInterruptConfig(
-        allow_accept=True,
-        allow_edit=True,
-        allow_ignore=True,
-        allow_respond=True,
-    )
-)
-
-default_model = FakeToolCallingModel(
-    tool_calls=[
-        [
-            {
-                "name": "hello_tool",
-                "args": {"name": "lady gaga"},
-                "id": "some-random-id",
-            }
-        ]
-    ]
-)
-
-
-def test_interrupt_surfaced(
-    request: pytest.FixtureRequest,
-    sync_checkpointer: BaseCheckpointSaver,
-) -> None:
-    agent = create_react_agent(
-        default_model,
-        [hello_tool],
-        checkpointer=sync_checkpointer,
-        post_model_hook=post_model_hook,
-    )
-    config: RunnableConfig = {"configurable": {"thread_id": "1"}}
-    result = agent.invoke({"messages": [("user", "Say hi to lady gaga!")]}, config)
-
-    interrupt_data = result["__interrupt__"]
-    assert interrupt_data[0].value == [
-        {
-            "action_request": {"action": "hello_tool", "args": {"name": "lady gaga"}},
-            "config": {
-                "allow_accept": True,
-                "allow_edit": True,
-                "allow_ignore": True,
-                "allow_respond": True,
-            },
-            "description": "Please review tool call for `hello_tool` before execution.",
-        }
-    ]
-
-    response = agent.invoke(Command(resume={"type": "accept"}), config=config)
-    tool_message: ToolMessage = response["messages"][-2]
-    assert tool_message.content == "Hello, lady gaga!"
-    assert tool_message.name == "hello_tool"
-
-
-@pytest.mark.parametrize(
-    "resume, expected_content",
-    [
-        ({"type": "accept"}, "Hello, lady gaga!"),
-        (
-            {"type": "ignore"},
-            "User ignored the tool call for `hello_tool` with id some-random-id",
-        ),
-        (
-            {
-                "type": "edit",
-                "args": {"action": "hello_tool", "args": {"name": "bruno mars"}},
-            },
-            "Hello, bruno mars!",
-        ),
-    ],
-)
-def test_interrupt_resume_variants(
-    request: pytest.FixtureRequest,
-    sync_checkpointer: BaseCheckpointSaver,
-    resume: dict,
-    expected_content: str,
-) -> None:
-    agent = create_react_agent(
-        default_model,
-        [hello_tool],
-        checkpointer=sync_checkpointer,
-        post_model_hook=post_model_hook,
-    )
-
-    config: RunnableConfig = {"configurable": {"thread_id": "1"}}
-    agent.invoke({"messages": [("user", "Say hi to lady gaga!")]}, config)
-
-    response = agent.invoke(Command(resume=resume), config=config)
-    tool_message: ToolMessage = response["messages"][-2]
-    assert tool_message.name == "hello_tool"
-    assert tool_message.content == expected_content
-
-    if resume["type"] == "edit":
-        ai_msg = response["messages"][-1]
-        assert ai_msg.tool_calls == [
-            {
-                "name": "hello_tool",
-                "args": {"name": "lady gaga"},
-                "id": "some-random-id",
-                "type": "tool_call",
-            }
-        ]
-
-
-def test_resume_with_response(
-    request: pytest.FixtureRequest,
-    sync_checkpointer: BaseCheckpointSaver,
-) -> None:
-    model = FakeToolCallingModel(
-        tool_calls=[
-            [
-                {
-                    "name": "hello_tool",
-                    "args": {"name": "lady gaga"},
-                    "id": "some-random-id",
-                }
-            ],
-            [
-                {
-                    "name": "hello_tool",
-                    "args": {"name": "bruno mars"},
-                    "id": "some-random-id-2",
-                }
-            ],
-        ]
-    )
-
-    agent = create_react_agent(
-        model,
-        [hello_tool],
-        checkpointer=sync_checkpointer,
-        post_model_hook=post_model_hook,
-    )
-
-    config: RunnableConfig = {"configurable": {"thread_id": "1"}}
-    agent.invoke({"messages": [("user", "Say hi to lady gaga!")]}, config)
-
-    # Provide user response
-    agent.invoke(
-        Command(
-            resume={
-                "type": "response",
-                "args": "actually, please say hello to bruno mars",
-            }
-        ),
-        config=config,
-    )
-
-    # Accept the updated call
-    response = agent.invoke(Command(resume={"type": "accept"}), config=config)
-
-    assert len(response["messages"]) == 6
-    tool_message: ToolMessage = response["messages"][-2]
-    assert tool_message.name == "hello_tool"
-    assert tool_message.content == "Hello, bruno mars!"
-
-
-def test_resume_with_type_not_allowed(sync_checkpointer: BaseCheckpointSaver) -> None:
-    agent = create_react_agent(
-        default_model,
-        [hello_tool],
-        checkpointer=sync_checkpointer,
-        post_model_hook=post_model_hook,
-    )
-    config: RunnableConfig = {"configurable": {"thread_id": "1"}}
-    agent.invoke({"messages": [("user", "Say hi to lady gaga!")]}, config)
-
-    with pytest.raises(ValueError) as exc_info:
-        agent.invoke(Command(resume={"type": "not-allowed"}), config=config)
-
-    assert (
-        str(exc_info.value)
-        == "Unexpected human response: {'type': 'not-allowed'}. Expected one with `'type'` in ['accept', 'edit', 'response', 'ignore'] based on hello_tool's interrupt configuration."
-    )

libs/prebuilt/tests/test_react_agent.py

@@ -1540,3 +1540,63 @@ def test_post_model_hook_with_structured_output() -> None:
             }
         },
     ]
+
+
+@pytest.mark.parametrize(
+    "state_schema", [AgentStateExtraKey, AgentStateExtraKeyPydantic]
+)
+def test_create_react_agent_inject_vars_with_post_model_hook(
+    state_schema: StateSchemaType,
+) -> None:
+    store = InMemoryStore()
+    namespace = ("test",)
+    store.put(namespace, "test_key", {"bar": 3})
+
+    if issubclass(state_schema, AgentStatePydantic):
+
+        def tool1(
+            some_val: int,
+            state: Annotated[AgentStateExtraKeyPydantic, InjectedState],
+            store: Annotated[BaseStore, InjectedStore()],
+        ) -> str:
+            """Tool 1 docstring."""
+            store_val = store.get(namespace, "test_key").value["bar"]
+            return some_val + state.foo + store_val
+    else:
+
+        def tool1(
+            some_val: int,
+            state: Annotated[dict, InjectedState],
+            store: Annotated[BaseStore, InjectedStore()],
+        ) -> str:
+            """Tool 1 docstring."""
+            store_val = store.get(namespace, "test_key").value["bar"]
+            return some_val + state["foo"] + store_val
+
+    tool_call = {
+        "name": "tool1",
+        "args": {"some_val": 1},
+        "id": "some 0",
+        "type": "tool_call",
+    }
+
+    def post_model_hook(state: dict) -> None:
+        return
+
+    model = FakeToolCallingModel(tool_calls=[[tool_call], []])
+    agent = create_react_agent(
+        model,
+        [tool1],
+        state_schema=state_schema,
+        store=store,
+        post_model_hook=post_model_hook,
+    )
+    input_message = HumanMessage("hi")
+    result = agent.invoke({"messages": [input_message], "foo": 2})
+    assert result["messages"] == [
+        input_message,
+        AIMessage(content="hi", tool_calls=[tool_call], id="0"),
+        _AnyIdToolMessage(content="6", name="tool1", tool_call_id="some 0"),
+        AIMessage("hi-hi-6", id="1"),
+    ]
+    assert result["foo"] == 2

libs/prebuilt/uv.lock

@@ -320,7 +320,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -461,7 +461,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.1.8"
+version = "0.2.2"
 source = { editable = "." }
 dependencies = [
     { name = "langchain-core" },
@@ -508,7 +508,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.69"
+version = "0.1.70"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/scheduler-kafka/uv.lock

@@ -447,7 +447,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "0.4.5"
+version = "0.4.7"
 source = { editable = "../langgraph" }
 dependencies = [
     { name = "langchain-core" },
@@ -558,7 +558,7 @@ dev = [
 
 [[package]]
 name = "langgraph-prebuilt"
-version = "0.1.8"
+version = "0.2.2"
 source = { editable = "../prebuilt" }
 dependencies = [
     { name = "langchain-core" },
@@ -636,7 +636,7 @@ dev = [
 
 [[package]]
 name = "langgraph-sdk"
-version = "0.1.69"
+version = "0.1.70"
 source = { editable = "../sdk-py" }
 dependencies = [
     { name = "httpx" },

libs/sdk-js/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/langgraph-sdk",
-  "version": "0.0.77",
+  "version": "0.0.78",
   "description": "Client library for interacting with the LangGraph API",
   "type": "module",
   "packageManager": "yarn@1.22.19",

libs/sdk-js/src/react/stream.tsx

@@ -457,6 +457,11 @@ export interface UseStreamOptions<
    */
   onFinish?: (state: ThreadState<StateType>) => void;
 
+  /**
+   * Callback that is called when a new stream is created.
+   */
+  onCreated?: (run: { run_id: string; thread_id: string }) => void;
+
   /**
    * Callback that is called when an update event is received.
    */
@@ -502,6 +507,15 @@ export interface UseStreamOptions<
    * Callback that is called when the thread ID is updated (ie when a new thread is created).
    */
   onThreadId?: (threadId: string) => void;
+
+  /** Will reconnect the stream on mount */
+  reconnectOnMount?: boolean | (() => RunMetadataStorage);
+}
+
+interface RunMetadataStorage {
+  getItem(key: `lg:stream:${string}`): string | null;
+  setItem(key: `lg:stream:${string}`, value: string): void;
+  removeItem(key: `lg:stream:${string}`): void;
 }
 
 export interface UseStream<
@@ -590,6 +604,11 @@ export interface UseStream<
    * The ID of the assistant to use.
    */
   assistantId: string;
+
+  /**
+   * Join an active stream.
+   */
+  joinStream: (runId: string) => Promise<void>;
 }
 
 type ConfigWithConfigurable<ConfigurableType extends Record<string, unknown>> =
@@ -619,6 +638,7 @@ interface SubmitOptions<
    * @default false
    */
   streamSubgraphs?: boolean;
+  streamResumable?: boolean;
 }
 
 export function useStream<
@@ -647,7 +667,17 @@ export function useStream<
     | ErrorStreamEvent
     | FeedbackStreamEvent;
 
-  let { assistantId, messagesKey, onError, onFinish } = options;
+  let { assistantId, messagesKey, onCreated, onError, onFinish } = options;
+
+  const reconnectOnMountRef = useRef(options.reconnectOnMount);
+  const runMetadataStorage = useMemo(() => {
+    if (typeof window === "undefined") return null;
+    const storage = reconnectOnMountRef.current;
+    if (storage === true) return window.sessionStorage;
+    if (typeof storage === "function") return storage();
+    return null;
+  }, []);
+
   messagesKey ??= "messages";
 
   const client = useMemo(
@@ -722,6 +752,7 @@ export function useStream<
 
   // TODO: this should be done on the server to avoid pagination
   // TODO: should we permit adapter? SWR / React Query?
+  // TODO: make this only when branching is expected
   const history = useThreadHistory<StateType>(
     threadId,
     client,
@@ -800,15 +831,23 @@ export function useStream<
     );
   })();
 
-  const stop = useCallback(() => {
+  const stop = () => {
     if (abortRef.current != null) abortRef.current.abort();
     abortRef.current = null;
-  }, []);
 
-  const submit = async (
-    values: UpdateType | null | undefined,
-    submitOptions?: SubmitOptions<StateType, ConfigurableType>,
-  ) => {
+    if (runMetadataStorage && threadId) {
+      const runId = runMetadataStorage.getItem(`lg:stream:${threadId}`);
+      if (runId) client.runs.cancel(threadId, runId);
+      runMetadataStorage.removeItem(`lg:stream:${threadId}`);
+    }
+  };
+
+  async function consumeStream(
+    action: (signal: AbortSignal) => Promise<{
+      onSuccess: () => Promise<ThreadState<StateType>[]>;
+      stream: AsyncGenerator<EventStreamEvent>;
+    }>,
+  ) {
     try {
       setIsLoading(true);
       setStreamError(undefined);
@@ -816,69 +855,10 @@ export function useStream<
       submittingRef.current = true;
       abortRef.current = new AbortController();
 
-      // Unbranch things
-      const newPath = submitOptions?.checkpoint?.checkpoint_id
-        ? branchByCheckpoint[submitOptions?.checkpoint?.checkpoint_id]?.branch
-        : undefined;
-
-      if (newPath != null) setBranch(newPath ?? "");
-
-      // Assumption: we're setting the initial value
-      // Used for instant feedback
-      setStreamValues(() => {
-        const values = { ...historyValues };
-
-        if (submitOptions?.optimisticValues != null) {
-          return {
-            ...values,
-            ...(typeof submitOptions.optimisticValues === "function"
-              ? submitOptions.optimisticValues(values)
-              : submitOptions.optimisticValues),
-          };
-        }
-
-        return values;
-      });
-
-      let usableThreadId = threadId;
-      if (!usableThreadId) {
-        const thread = await client.threads.create();
-        onThreadId(thread.thread_id);
-        usableThreadId = thread.thread_id;
-      }
-
-      const streamMode = unique([
-        ...(submitOptions?.streamMode ?? []),
-        ...trackStreamModeRef.current,
-        ...callbackStreamMode,
-      ]);
-
-      const checkpoint =
-        submitOptions?.checkpoint ?? threadHead?.checkpoint ?? undefined;
-      // @ts-expect-error
-      if (checkpoint != null) delete checkpoint.thread_id;
-
-      const run = client.runs.stream(usableThreadId, assistantId, {
-        input: values as Record<string, unknown>,
-        config: submitOptions?.config,
-        command: submitOptions?.command,
-
-        interruptBefore: submitOptions?.interruptBefore,
-        interruptAfter: submitOptions?.interruptAfter,
-        metadata: submitOptions?.metadata,
-        multitaskStrategy: submitOptions?.multitaskStrategy,
-        onCompletion: submitOptions?.onCompletion,
-        onDisconnect: submitOptions?.onDisconnect ?? "cancel",
-
-        signal: abortRef.current.signal,
-
-        checkpoint,
-        streamMode,
-        streamSubgraphs: submitOptions?.streamSubgraphs,
-      }) as AsyncGenerator<EventStreamEvent>;
+      const run = await action(abortRef.current.signal);
 
       let streamError: StreamError | undefined;
-      for await (const { event, data } of run) {
+      for await (const { event, data } of run.stream) {
         if (event === "error") {
           streamError = new StreamError(data);
           break;
@@ -930,9 +910,9 @@ export function useStream<
       }
 
       // TODO: stream created checkpoints to avoid an unnecessary network request
-      const result = await history.mutate(usableThreadId);
-      setStreamValues(null);
+      const result = await run.onSuccess();
 
+      setStreamValues(null);
       if (streamError != null) throw streamError;
 
       const lastHead = result.at(0);
@@ -956,8 +936,146 @@ export function useStream<
       submittingRef.current = false;
       abortRef.current = null;
     }
+  }
+
+  const joinStream = async (runId: string, lastEventId?: string) => {
+    lastEventId ??= "-1";
+    if (!threadId) return;
+    await consumeStream(async (signal: AbortSignal) => {
+      const stream = client.runs.joinStream(threadId, runId, {
+        signal,
+        lastEventId,
+      }) as AsyncGenerator<EventStreamEvent>;
+
+      return {
+        onSuccess: () => {
+          runMetadataStorage?.removeItem(`lg:stream:${threadId}`);
+          return history.mutate(threadId);
+        },
+        stream,
+      };
+    });
   };
 
+  const submit = async (
+    values: UpdateType | null | undefined,
+    submitOptions?: SubmitOptions<StateType, ConfigurableType>,
+  ) => {
+    await consumeStream(async (signal: AbortSignal) => {
+      // Unbranch things
+      const newPath = submitOptions?.checkpoint?.checkpoint_id
+        ? branchByCheckpoint[submitOptions?.checkpoint?.checkpoint_id]?.branch
+        : undefined;
+
+      if (newPath != null) setBranch(newPath ?? "");
+
+      // Assumption: we're setting the initial value
+      // Used for instant feedback
+      setStreamValues(() => {
+        const values = { ...historyValues };
+
+        if (submitOptions?.optimisticValues != null) {
+          return {
+            ...values,
+            ...(typeof submitOptions.optimisticValues === "function"
+              ? submitOptions.optimisticValues(values)
+              : submitOptions.optimisticValues),
+          };
+        }
+
+        return values;
+      });
+
+      let usableThreadId = threadId;
+      if (!usableThreadId) {
+        const thread = await client.threads.create();
+        onThreadId(thread.thread_id);
+        usableThreadId = thread.thread_id;
+      }
+
+      const streamMode = unique([
+        ...(submitOptions?.streamMode ?? []),
+        ...trackStreamModeRef.current,
+        ...callbackStreamMode,
+      ]);
+
+      const checkpoint =
+        submitOptions?.checkpoint ?? threadHead?.checkpoint ?? undefined;
+      // @ts-expect-error
+      if (checkpoint != null) delete checkpoint.thread_id;
+      let rejoinKey: `lg:stream:${string}` | undefined;
+
+      const stream = client.runs.stream(usableThreadId, assistantId, {
+        input: values as Record<string, unknown>,
+        config: submitOptions?.config,
+        command: submitOptions?.command,
+
+        interruptBefore: submitOptions?.interruptBefore,
+        interruptAfter: submitOptions?.interruptAfter,
+        metadata: submitOptions?.metadata,
+        multitaskStrategy: submitOptions?.multitaskStrategy,
+        onCompletion: submitOptions?.onCompletion,
+        onDisconnect:
+          submitOptions?.onDisconnect ??
+          (runMetadataStorage ? "continue" : "cancel"),
+
+        signal,
+
+        checkpoint,
+        streamMode,
+        streamSubgraphs: submitOptions?.streamSubgraphs,
+        streamResumable: submitOptions?.streamResumable ?? !!runMetadataStorage,
+        onRunCreated(params) {
+          const runParams = {
+            run_id: params.run_id,
+            thread_id: params.thread_id ?? usableThreadId,
+          };
+          if (runMetadataStorage) {
+            rejoinKey = `lg:stream:${runParams.thread_id}`;
+            runMetadataStorage.setItem(rejoinKey, runParams.run_id);
+          }
+          onCreated?.(runParams);
+        },
+      }) as AsyncGenerator<EventStreamEvent>;
+
+      return {
+        stream,
+        onSuccess: () => {
+          if (rejoinKey) runMetadataStorage?.removeItem(rejoinKey);
+          return history.mutate(usableThreadId);
+        },
+      };
+    });
+  };
+
+  const reconnectKey = useMemo(() => {
+    if (!runMetadataStorage || isLoading) return undefined;
+    if (typeof window === "undefined") return undefined;
+    const runId = runMetadataStorage?.getItem(`lg:stream:${threadId}`);
+    if (!runId) return undefined;
+    return { runId, threadId };
+  }, [runMetadataStorage, isLoading, threadId]);
+
+  const shouldReconnect = !!runMetadataStorage;
+  const reconnectRef = useRef({ threadId, shouldReconnect });
+
+  const joinStreamRef = useRef<typeof joinStream>(joinStream);
+  joinStreamRef.current = joinStream;
+
+  useEffect(() => {
+    // reset shouldReconnect when switching threads
+    if (reconnectRef.current.threadId !== threadId) {
+      reconnectRef.current = { threadId, shouldReconnect };
+    }
+  }, [threadId, shouldReconnect]);
+
+  useEffect(() => {
+    if (reconnectKey && reconnectRef.current.shouldReconnect) {
+      reconnectRef.current.shouldReconnect = false;
+      joinStreamRef.current?.(reconnectKey.runId);
+    }
+  }, [reconnectKey]);
+
   const error = streamError ?? historyError;
   const values = streamValues ?? historyValues;
 
@@ -976,6 +1094,8 @@ export function useStream<
     stop,
     submit,
 
+    joinStream,
+
     branch,
     setBranch,
 

libs/sdk-py/langgraph_sdk/client.py

@@ -13,10 +13,12 @@ import asyncio
 import functools
 import logging
 import os
+import re
 import sys
 from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import (
     Any,
+    Callable,
     Literal,
     Optional,
     Union,
@@ -49,6 +51,7 @@ from langgraph_sdk.schema import (
     OnConflictBehavior,
     Run,
     RunCreate,
+    RunCreateMetadata,
     RunStatus,
     SearchItemsResponse,
     SortOrder,
@@ -116,6 +119,27 @@ def _orjson_default(obj: Any) -> Any:
         raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
 
 
+# Compiled regex pattern for extracting run metadata from Content-Location header
+_RUN_METADATA_PATTERN = re.compile(
+    r"(\/threads\/(?P<thread_id>.+))?\/runs\/(?P<run_id>.+)"
+)
+
+
+def _get_run_metadata_from_response(
+    response: httpx.Response,
+) -> Optional[RunCreateMetadata]:
+    """Extract run metadata from the response headers."""
+    if (content_location := response.headers.get("Content-Location")) and (
+        match := _RUN_METADATA_PATTERN.search(content_location)
+    ):
+        return RunCreateMetadata(
+            run_id=match.group("run_id"),
+            thread_id=match.group("thread_id") or None,
+        )
+
+    return None
+
+
 def get_client(
     *,
     url: Optional[str] = None,
@@ -226,9 +250,12 @@ class HttpClient:
         *,
         params: Optional[QueryParamTypes] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a GET request."""
         r = await self.client.get(path, params=params, headers=headers)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -246,6 +273,7 @@ class HttpClient:
         *,
         json: Optional[dict],
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a POST request."""
         if json is not None:
@@ -256,6 +284,8 @@ class HttpClient:
         if headers:
             request_headers.update(headers)
         r = await self.client.post(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -268,13 +298,20 @@ class HttpClient:
         return await _adecode_json(r)
 
     async def put(
-        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+        self,
+        path: str,
+        *,
+        json: dict,
+        headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a PUT request."""
         request_headers, content = await _aencode_json(json)
         if headers:
             request_headers.update(headers)
         r = await self.client.put(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -287,13 +324,20 @@ class HttpClient:
         return await _adecode_json(r)
 
     async def patch(
-        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+        self,
+        path: str,
+        *,
+        json: dict,
+        headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a PATCH request."""
         request_headers, content = await _aencode_json(json)
         if headers:
             request_headers.update(headers)
         r = await self.client.patch(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -311,9 +355,12 @@ class HttpClient:
         *,
         json: Optional[Any] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> None:
         """Send a DELETE request."""
         r = await self.client.request("DELETE", path, json=json, headers=headers)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -332,6 +379,7 @@ class HttpClient:
         json: Optional[dict] = None,
         params: Optional[QueryParamTypes] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> AsyncIterator[StreamPart]:
         """Stream results using SSE."""
         request_headers, content = await _aencode_json(json)
@@ -344,6 +392,8 @@ class HttpClient:
         async with self.client.stream(
             method, path, headers=request_headers, content=content, params=params
         ) as res:
+            if on_response:
+                on_response(res)
             # check status
             try:
                 res.raise_for_status()
@@ -1455,6 +1505,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -1469,6 +1520,7 @@ class RunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> AsyncIterator[StreamPart]: ...
 
     @overload
@@ -1481,6 +1533,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint_during: Optional[bool] = None,
@@ -1493,6 +1546,7 @@ class RunsClient:
         webhook: Optional[str] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> AsyncIterator[StreamPart]: ...
 
     def stream(
@@ -1504,6 +1558,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -1519,6 +1574,7 @@ class RunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> AsyncIterator[StreamPart]:
         """Create a run and stream the results.
 
@@ -1531,6 +1587,8 @@ class RunsClient:
             command: A command to execute. Cannot be combined with input.
             stream_mode: The stream mode(s) to use.
             stream_subgraphs: Whether to stream output from subgraphs.
+            stream_resumable: Whether the stream is considered resumable.
+                If true, the stream can be resumed and replayed in its entirety even after disconnection.
             metadata: Metadata to assign to the run.
             config: The configuration for the assistant.
             checkpoint: The checkpoint to resume from.
@@ -1549,6 +1607,7 @@ class RunsClient:
                 Must be either 'reject' (raise error if missing), or 'create' (create new thread).
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
+            on_run_created: Callback when a run is created.
 
         Returns:
             AsyncIterator[StreamPart]: Asynchronous iterator of stream results.
@@ -1593,6 +1652,7 @@ class RunsClient:
             "metadata": metadata,
             "stream_mode": stream_mode,
             "stream_subgraphs": stream_subgraphs,
+            "stream_resumable": stream_resumable,
             "assistant_id": assistant_id,
             "interrupt_before": interrupt_before,
             "interrupt_after": interrupt_after,
@@ -1612,11 +1672,18 @@ class RunsClient:
             if thread_id is not None
             else "/runs/stream"
         )
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
         return self.http.stream(
             endpoint,
             "POST",
             json={k: v for k, v in payload.items() if v is not None},
             headers=headers,
+            on_response=on_response if on_run_created else None,
         )
 
     @overload
@@ -1629,6 +1696,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         checkpoint_during: Optional[bool] = None,
         config: Optional[Config] = None,
@@ -1639,6 +1707,7 @@ class RunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run: ...
 
     @overload
@@ -1651,6 +1720,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -1663,6 +1733,7 @@ class RunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run: ...
 
     async def create(
@@ -1674,6 +1745,7 @@ class RunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -1687,6 +1759,7 @@ class RunsClient:
         on_completion: Optional[OnCompletionBehavior] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run:
         """Create a background run.
 
@@ -1699,6 +1772,8 @@ class RunsClient:
             command: A command to execute. Cannot be combined with input.
             stream_mode: The stream mode(s) to use.
             stream_subgraphs: Whether to stream output from subgraphs.
+            stream_resumable: Whether the stream is considered resumable.
+                If true, the stream can be resumed and replayed in its entirety even after disconnection.
             metadata: Metadata to assign to the run.
             config: The configuration for the assistant.
             checkpoint: The checkpoint to resume from.
@@ -1715,6 +1790,7 @@ class RunsClient:
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
             headers: Optional custom headers to include with the request.
+            on_run_created: Optional callback to call when a run is created.
 
         Returns:
             Run: The created background run.
@@ -1794,6 +1870,7 @@ class RunsClient:
             ),
             "stream_mode": stream_mode,
             "stream_subgraphs": stream_subgraphs,
+            "stream_resumable": stream_resumable,
             "config": config,
             "metadata": metadata,
             "assistant_id": assistant_id,
@@ -1809,10 +1886,18 @@ class RunsClient:
             "after_seconds": after_seconds,
         }
         payload = {k: v for k, v in payload.items() if v is not None}
-        if thread_id:
-            return await self.http.post(f"/threads/{thread_id}/runs", json=payload)
-        else:
-            return await self.http.post("/runs", json=payload)
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
+        return await self.http.post(
+            f"/threads/{thread_id}/runs" if thread_id else "/runs",
+            json=payload,
+            headers=headers,
+            on_response=on_response if on_run_created else None,
+        )
 
     async def create_batch(self, payloads: list[RunCreate]) -> list[Run]:
         """Create a batch of stateless background runs."""
@@ -1845,6 +1930,7 @@ class RunsClient:
         after_seconds: Optional[int] = None,
         raise_error: bool = True,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]: ...
 
     @overload
@@ -1867,6 +1953,7 @@ class RunsClient:
         after_seconds: Optional[int] = None,
         raise_error: bool = True,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]: ...
 
     async def wait(
@@ -1891,6 +1978,7 @@ class RunsClient:
         after_seconds: Optional[int] = None,
         raise_error: bool = True,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]:
         """Create a run, wait until it finishes and return the final state.
 
@@ -1919,6 +2007,7 @@ class RunsClient:
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
             headers: Optional custom headers to include with the request.
+            on_run_created: Optional callback to call when a run is created.
 
         Returns:
             Union[list[dict], dict[str, Any]]: The output of the run.
@@ -1995,10 +2084,17 @@ class RunsClient:
         endpoint = (
             f"/threads/{thread_id}/runs/wait" if thread_id is not None else "/runs/wait"
         )
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
         response = await self.http.post(
             endpoint,
             json={k: v for k, v in payload.items() if v is not None},
             headers=headers,
+            on_response=on_response if on_run_created else None,
         )
         if (
             raise_error
@@ -2160,6 +2256,7 @@ class RunsClient:
         cancel_on_disconnect: bool = False,
         stream_mode: Optional[Union[StreamMode, Sequence[StreamMode]]] = None,
         headers: Optional[dict[str, str]] = None,
+        last_event_id: Optional[str] = None,
     ) -> AsyncIterator[StreamPart]:
         """Stream output from a run in real-time, until the run is done.
         Output is not buffered, so any output produced before this call will
@@ -2197,7 +2294,11 @@ class RunsClient:
                 "cancel_on_disconnect": cancel_on_disconnect,
                 "stream_mode": stream_mode,
             },
-            headers=headers,
+            headers={
+                **({"Last-Event-ID": last_event_id} if last_event_id else {}),
+                **(headers or {}),
+            }
+            or None,
         )
 
     async def delete(
@@ -2875,9 +2976,12 @@ class SyncHttpClient:
         *,
         params: Optional[QueryParamTypes] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a GET request."""
         r = self.client.get(path, params=params, headers=headers)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2895,6 +2999,7 @@ class SyncHttpClient:
         *,
         json: Optional[dict],
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a POST request."""
         if json is not None:
@@ -2904,6 +3009,8 @@ class SyncHttpClient:
         if headers:
             request_headers.update(headers)
         r = self.client.post(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2916,7 +3023,12 @@ class SyncHttpClient:
         return _decode_json(r)
 
     def put(
-        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+        self,
+        path: str,
+        *,
+        json: dict,
+        headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a PUT request."""
         request_headers, content = _encode_json(json)
@@ -2924,6 +3036,8 @@ class SyncHttpClient:
             request_headers.update(headers)
 
         r = self.client.put(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2936,13 +3050,20 @@ class SyncHttpClient:
         return _decode_json(r)
 
     def patch(
-        self, path: str, *, json: dict, headers: Optional[dict[str, str]] = None
+        self,
+        path: str,
+        *,
+        json: dict,
+        headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Any:
         """Send a PATCH request."""
         request_headers, content = _encode_json(json)
         if headers:
             request_headers.update(headers)
         r = self.client.patch(path, headers=request_headers, content=content)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2960,9 +3081,12 @@ class SyncHttpClient:
         *,
         json: Optional[Any] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> None:
         """Send a DELETE request."""
         r = self.client.request("DELETE", path, json=json, headers=headers)
+        if on_response:
+            on_response(r)
         try:
             r.raise_for_status()
         except httpx.HTTPStatusError as e:
@@ -2981,6 +3105,7 @@ class SyncHttpClient:
         json: Optional[dict] = None,
         params: Optional[QueryParamTypes] = None,
         headers: Optional[dict[str, str]] = None,
+        on_response: Optional[Callable[[httpx.Response], None]] = None,
     ) -> Iterator[StreamPart]:
         """Stream the results of a request using SSE."""
         request_headers, content = _encode_json(json)
@@ -2991,6 +3116,8 @@ class SyncHttpClient:
         with self.client.stream(
             method, path, headers=request_headers, content=content, params=params
         ) as res:
+            if on_response:
+                on_response(res)
             # check status
             try:
                 res.raise_for_status()
@@ -4086,6 +4213,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Iterator[StreamPart]: ...
 
     @overload
@@ -4098,6 +4226,7 @@ class SyncRunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint_during: Optional[bool] = None,
@@ -4110,6 +4239,7 @@ class SyncRunsClient:
         webhook: Optional[str] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Iterator[StreamPart]: ...
 
     def stream(
@@ -4121,6 +4251,7 @@ class SyncRunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -4136,6 +4267,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Iterator[StreamPart]:
         """Create a run and stream the results.
 
@@ -4148,6 +4280,8 @@ class SyncRunsClient:
             command: The command to execute.
             stream_mode: The stream mode(s) to use.
             stream_subgraphs: Whether to stream output from subgraphs.
+            stream_resumable: Whether the stream is considered resumable.
+                If true, the stream can be resumed and replayed in its entirety even after disconnection.
             metadata: Metadata to assign to the run.
             config: The configuration for the assistant.
             checkpoint: The checkpoint to resume from.
@@ -4167,6 +4301,7 @@ class SyncRunsClient:
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
             headers: Optional custom headers to include with the request.
+            on_run_created: Optional callback to call when a run is created.
 
         Returns:
             Iterator[StreamPart]: Iterator of stream results.
@@ -4208,6 +4343,7 @@ class SyncRunsClient:
             "metadata": metadata,
             "stream_mode": stream_mode,
             "stream_subgraphs": stream_subgraphs,
+            "stream_resumable": stream_resumable,
             "assistant_id": assistant_id,
             "interrupt_before": interrupt_before,
             "interrupt_after": interrupt_after,
@@ -4227,11 +4363,18 @@ class SyncRunsClient:
             if thread_id is not None
             else "/runs/stream"
         )
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
         return self.http.stream(
             endpoint,
             "POST",
             json={k: v for k, v in payload.items() if v is not None},
             headers=headers,
+            on_response=on_response if on_run_created else None,
         )
 
     @overload
@@ -4244,6 +4387,7 @@ class SyncRunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint_during: Optional[bool] = None,
@@ -4254,6 +4398,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run: ...
 
     @overload
@@ -4266,6 +4411,7 @@ class SyncRunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -4278,6 +4424,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run: ...
 
     def create(
@@ -4289,6 +4436,7 @@ class SyncRunsClient:
         command: Optional[Command] = None,
         stream_mode: Union[StreamMode, Sequence[StreamMode]] = "values",
         stream_subgraphs: bool = False,
+        stream_resumable: bool = False,
         metadata: Optional[dict] = None,
         config: Optional[Config] = None,
         checkpoint: Optional[Checkpoint] = None,
@@ -4302,6 +4450,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Run:
         """Create a background run.
 
@@ -4314,6 +4463,8 @@ class SyncRunsClient:
             command: The command to execute.
             stream_mode: The stream mode(s) to use.
             stream_subgraphs: Whether to stream output from subgraphs.
+            stream_resumable: Whether the stream is considered resumable.
+                If true, the stream can be resumed and replayed in its entirety even after disconnection.
             metadata: Metadata to assign to the run.
             config: The configuration for the assistant.
             checkpoint: The checkpoint to resume from.
@@ -4330,6 +4481,7 @@ class SyncRunsClient:
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
             headers: Optional custom headers to include with the request.
+            on_run_created: Optional callback to call when a run is created.
 
         Returns:
             Run: The created background run.
@@ -4409,6 +4561,7 @@ class SyncRunsClient:
             ),
             "stream_mode": stream_mode,
             "stream_subgraphs": stream_subgraphs,
+            "stream_resumable": stream_resumable,
             "config": config,
             "metadata": metadata,
             "assistant_id": assistant_id,
@@ -4424,12 +4577,18 @@ class SyncRunsClient:
             "after_seconds": after_seconds,
         }
         payload = {k: v for k, v in payload.items() if v is not None}
-        if thread_id:
-            return self.http.post(
-                f"/threads/{thread_id}/runs", json=payload, headers=headers
-            )
-        else:
-            return self.http.post("/runs", json=payload, headers=headers)
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
+        return self.http.post(
+            f"/threads/{thread_id}/runs" if thread_id else "/runs",
+            json=payload,
+            headers=headers,
+            on_response=on_response if on_run_created else None,
+        )
 
     def create_batch(
         self, payloads: list[RunCreate], *, headers: Optional[dict[str, str]] = None
@@ -4463,6 +4622,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]: ...
 
     @overload
@@ -4484,6 +4644,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]: ...
 
     def wait(
@@ -4507,6 +4668,7 @@ class SyncRunsClient:
         if_not_exists: Optional[IfNotExists] = None,
         after_seconds: Optional[int] = None,
         headers: Optional[dict[str, str]] = None,
+        on_run_created: Optional[Callable[[RunCreateMetadata], None]] = None,
     ) -> Union[list[dict], dict[str, Any]]:
         """Create a run, wait until it finishes and return the final state.
 
@@ -4535,6 +4697,7 @@ class SyncRunsClient:
             after_seconds: The number of seconds to wait before starting the run.
                 Use to schedule future runs.
             headers: Optional custom headers to include with the request.
+            on_run_created: Optional callback to call when a run is created.
 
         Returns:
             Union[list[dict], dict[str, Any]]: The output of the run.
@@ -4609,6 +4772,12 @@ class SyncRunsClient:
             "on_completion": on_completion,
             "after_seconds": after_seconds,
         }
+
+        def on_response(res: httpx.Response):
+            """Callback function to handle the response."""
+            if on_run_created and (metadata := _get_run_metadata_from_response(res)):
+                on_run_created(metadata)
+
         endpoint = (
             f"/threads/{thread_id}/runs/wait" if thread_id is not None else "/runs/wait"
         )
@@ -4616,6 +4785,7 @@ class SyncRunsClient:
             endpoint,
             json={k: v for k, v in payload.items() if v is not None},
             headers=headers,
+            on_response=on_response if on_run_created else None,
         )
 
     def list(
@@ -4764,6 +4934,7 @@ class SyncRunsClient:
         stream_mode: Optional[Union[StreamMode, Sequence[StreamMode]]] = None,
         cancel_on_disconnect: bool = False,
         headers: Optional[dict[str, str]] = None,
+        last_event_id: Optional[str] = None,
     ) -> Iterator[StreamPart]:
         """Stream output from a run in real-time, until the run is done.
         Output is not buffered, so any output produced before this call will
@@ -4800,7 +4971,11 @@ class SyncRunsClient:
                 "stream_mode": stream_mode,
                 "cancel_on_disconnect": cancel_on_disconnect,
             },
-            headers=headers,
+            headers={
+                **({"Last-Event-ID": last_event_id} if last_event_id else {}),
+                **(headers or {}),
+            }
+            or None,
         )
 
     def delete(
@@ -5392,7 +5567,9 @@ def get_asgi_transport() -> type[httpx.ASGITransport]:
 
 
 TimeoutTypes = Union[
-    Optional[float],
+    None,
+    float,
+    tuple[Optional[float], Optional[float]],
     tuple[Optional[float], Optional[float], Optional[float], Optional[float]],
     httpx.Timeout,
 ]

libs/sdk-py/langgraph_sdk/schema.py

@@ -435,3 +435,13 @@ class Command(TypedDict, total=False):
     """Value to resume execution with after an interruption.
        Used in conjunction with interrupt() to implement control flow.
     """
+
+
+class RunCreateMetadata(TypedDict):
+    """Metadata for a run creation request."""
+
+    run_id: str
+    """The ID of the run."""
+
+    thread_id: Optional[str]
+    """The ID of the thread."""

libs/sdk-py/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-sdk"
-version = "0.1.69"
+version = "0.1.70"
 description = "SDK for interacting with LangGraph API"
 authors = []
 requires-python = ">=3.9"

libs/sdk-py/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 2
 requires-python = ">=3.9"
 
 [[package]]
@@ -12,43 +12,43 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
+    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
 ]
 
 [[package]]
 name = "certifi"
 version = "2025.4.26"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705 }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
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