release(langgraph): v0.5.3 (#5498)

bump

.github/workflows/pr_lint.yml

@@ -38,6 +38,7 @@ jobs:
             prebuilt
             scheduler-kafka
             sdk-py
+            docs
           requireScope: false
           ignoreLabels: |
             ignore-lint-pr-title

README.md

@@ -74,6 +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/overview/): Guided examples on getting started with LangGraph.
+- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [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/copy_page_hooks.py

@@ -1,195 +0,0 @@
-"""
-Copy page functionality hooks for MkDocs.
-
-This module provides hooks to inject original markdown content into HTML pages
-for the copy page functionality, allowing users to copy clean markdown content
-optimized for LLMs.
-"""
-
-import json
-import re
-from pathlib import Path
-from typing import Optional
-
-from mkdocs.config.defaults import MkDocsConfig
-from mkdocs.structure.pages import Page
-
-
-def _process_includes(content: str, docs_dir: Path) -> str:
-    """Process MkDocs includes like {!../README.md!}."""
-    include_pattern = r'\{!([^!]+)!\}'
-    
-    def replace_include(match):
-        include_path = match.group(1)
-        # Resolve relative path
-        if include_path.startswith('../'):
-            # Go up from docs dir
-            include_file = docs_dir.parent / include_path[3:]
-        else:
-            include_file = docs_dir / include_path
-        
-        try:
-            with open(include_file, 'r', encoding='utf-8') as f:
-                included_content = f.read()
-                # Remove frontmatter from included content to avoid duplication
-                included_content = re.sub(r'^---\n.*?\n---\n', '', included_content, flags=re.DOTALL)
-                return included_content
-        except:
-            return f"[Content from {include_path}]"
-    
-    return re.sub(include_pattern, replace_include, content)
-
-
-def _clean_markdown(content: str) -> str:
-    """Clean up markdown content by removing MkDocs artifacts."""
-    # Remove frontmatter
-    content = re.sub(r'^---\n.*?\n---\n', '', content, flags=re.DOTALL)
-    
-    # Remove script tags (handle malformed closing tags)
-    content = re.sub(r'<script[^>]*>.*?</script[^>]*>', '', content, flags=re.DOTALL | re.IGNORECASE)
-    
-    # Remove style tags (handle spaces in closing tags)
-    content = re.sub(r'<style[^>]*>.*?</style\s*>', '', content, flags=re.DOTALL | re.IGNORECASE)
-    
-    # Remove HTML comments
-    content = re.sub(r'<!--.*?-->', '', content, flags=re.DOTALL)
-    
-    # Remove all HTML tags (more aggressive cleaning)
-    content = re.sub(r'<[^>]+>', '', content)
-    
-    # Remove markdown image references that might be logos
-    content = re.sub(r'!\[[^\]]*\]\([^)]*logo[^)]*\)', '', content, flags=re.IGNORECASE)
-    
-    # Clean up line by line
-    lines = content.split('\n')
-    cleaned_lines = []
-    
-    for line in lines:
-        line = line.strip()
-        # Skip completely empty lines for now
-        if line:
-            cleaned_lines.append(line)
-    
-    # Join lines and then clean up spacing
-    content = '\n'.join(cleaned_lines)
-    
-    # Add proper paragraph breaks by looking for markdown patterns
-    # Add double newline before headers
-    content = re.sub(r'\n(#{1,6}\s)', r'\n\n\1', content)
-    
-    # Add double newline before list items
-    content = re.sub(r'\n(\*\s|-\s|\d+\.\s)', r'\n\n\1', content)
-    
-    # Add double newline before code blocks
-    content = re.sub(r'\n(```)', r'\n\n\1', content)
-    
-    # Clean up any triple+ newlines
-    content = re.sub(r'\n{3,}', '\n\n', content)
-    
-    content = content.strip()
-    
-    return content
-
-
-def inject_markdown_content(html: str, page: Page, config: MkDocsConfig) -> str:
-    """
-    Inject the original markdown content into the HTML for copy page functionality.
-    
-    Args:
-        html: The HTML content to inject into
-        page: The MkDocs page object
-        config: The MkDocs configuration
-        
-    Returns:
-        Modified HTML with markdown content injected as JSON
-    """
-    if not hasattr(page, 'file') or not page.file:
-        return html
-    
-    # Get the original markdown file path
-    docs_dir = Path(config.get('docs_dir', 'docs'))
-    src_path = page.file.src_path
-    
-    # Handle different file types
-    if src_path.endswith('.ipynb'):
-        # For notebook files, we might want to use the converted markdown
-        # For now, just return the HTML as-is
-        return html
-    
-    markdown_file = docs_dir / src_path
-    
-    if not markdown_file.exists():
-        return html
-    
-    try:
-        # Read the original markdown content
-        with open(markdown_file, 'r', encoding='utf-8') as f:
-            markdown_content = f.read()
-        
-        # Special handling for index page - use relative path to the actual README.md
-        if src_path == 'index.md':
-            # Relative path to the repository README.md file (go up two levels from docs/docs)
-            readme_path = docs_dir.parent.parent / 'README.md'
-            
-            try:
-                with open(readme_path, 'r', encoding='utf-8') as f:
-                    readme_content = f.read()
-                # Remove frontmatter if present
-                processed_markdown = re.sub(r'^---\n.*?\n---\n', '', readme_content, flags=re.DOTALL)
-                processed_markdown = processed_markdown.strip()
-            except Exception as e:
-                # If we can't read the README, fallback to original behavior
-                processed_markdown = _process_includes(markdown_content, docs_dir)
-                processed_markdown = re.sub(r'^---\n.*?\n---\n', '', processed_markdown, flags=re.DOTALL)
-                processed_markdown = processed_markdown.strip()
-        else:
-            # Process any includes in the markdown to get the full content
-            processed_markdown = _process_includes(markdown_content, docs_dir)
-            # Clean up the processed markdown normally for other pages
-            processed_markdown = _clean_markdown(processed_markdown)
-        
-        # Create the JSON data
-        markdown_data = {
-            'markdown': processed_markdown,
-            'title': page.title or 'Page Content',
-            'url': page.url or ''
-        }
-        
-        # Properly escape the JSON for HTML
-        json_content = json.dumps(markdown_data, ensure_ascii=False)
-        json_content = json_content.replace('</', '\\u003c/')
-        json_content = json_content.replace('<script', '\\u003cscript')
-        json_content = json_content.replace('</script', '\\u003c/script')
-        
-        script_content = f'<script id="page-markdown-content" type="application/json">{json_content}</script>'
-        
-        # Insert before </head> if it exists, otherwise before </body>
-        if '</head>' in html:
-            html = html.replace('</head>', f'{script_content}</head>')
-        elif '</body>' in html:
-            html = html.replace('</body>', f'{script_content}</body>')
-    
-    except Exception as e:
-        # If anything goes wrong, just return the original HTML
-        # Could log the error here if needed
-        pass
-    
-    return html
-
-
-def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
-    """
-    MkDocs hook to inject markdown content into HTML pages.
-    
-    This hook is called after each page is rendered and injects the original
-    markdown content as JSON for the copy page functionality.
-    
-    Args:
-        output: The HTML output of the page
-        page: The MkDocs page object
-        config: The MkDocs configuration
-        
-    Returns:
-        Modified HTML with markdown content injected
-    """
-    return inject_markdown_content(output, page, config)

docs/_scripts/notebook_hooks.py

@@ -3,6 +3,7 @@
 Lifecycle events: https://www.mkdocs.org/dev-guide/plugins/#events
 """
 
+import json
 import logging
 import os
 import posixpath
@@ -15,8 +16,8 @@ from mkdocs.structure.files import Files, File
 from mkdocs.structure.pages import Page
 
 from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
 from _scripts.link_map import JS_LINK_MAP
+from _scripts.notebook_convert import convert_notebook
 
 logger = logging.getLogger(__name__)
 logging.basicConfig()
@@ -100,10 +101,6 @@ REDIRECT_MAP = {
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
     "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
-    # Time-travel
-    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
-    # breakpoints
-    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
     # misc
     "prebuilt.md": "agents/prebuilt.md",
     "reference/prebuilt.md": "reference/agents.md",
@@ -125,6 +122,11 @@ REDIRECT_MAP = {
     "how-tos/review-tool-calls-functional.ipynb": "how-tos/use-functional-api.md",
     "how-tos/create-react-agent-hitl.ipynb": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
     "agents/human-in-the-loop.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.md",
+    "concepts/breakpoints.md": "concepts/human_in_the_loop.md",
+    "how-tos/human_in_the_loop/breakpoints.md": "how-tos/human_in_the_loop/add-human-in-the-loop.md",
+    "cloud/how-tos/human_in_the_loop_breakpoint.md": "cloud/how-tos/add-human-in-the-loop.md",
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.md",
 }
 
 
@@ -356,12 +358,16 @@ def _on_page_markdown_with_config(
 
 
 def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
+    finalized_markdown = (
+        _on_page_markdown_with_config(
+            markdown,
+            page,
+            add_api_references=True,
+            **kwargs,
+        )
     )
+    page.meta["original_markdown"] = finalized_markdown
+    return finalized_markdown
 
 
 # redirects
@@ -431,20 +437,51 @@ height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
     else:
         return html  # fallback if no <body> found
 
+def _inject_markdown_into_html(html: str, page: Page) -> str:
+    """Inject the original markdown content into the HTML page as JSON."""
+    original_markdown = page.meta.get("original_markdown", "")
+    if not original_markdown:
+        return html
+    markdown_data = {
+        "markdown": original_markdown,
+        "title": page.title or "Page Content",
+        "url": page.url or "",
+    }
 
-def on_post_page(output: str, page: Page, config: MkDocsConfig) -> str:
+    # Properly escape the JSON for HTML
+    json_content = json.dumps(markdown_data, ensure_ascii=False)
+
+    json_content = (
+        json_content.replace("</", "\\u003c/")
+        .replace("<script", "\\u003cscript")
+        .replace("</script", "\\u003c/script")
+    )
+
+    script_content = (
+        f'<script id="page-markdown-content" '
+        f'type="application/json">{json_content}</script>'
+    )
+
+    # Insert before </head> if it exists, otherwise before </body>
+    if "</head>" not in html:
+        raise ValueError(
+            "HTML does not contain </head> tag. Cannot inject markdown content."
+        )
+    return html.replace("</head>", f"{script_content}</head>")
+
+def on_post_page(html: str, page: Page, config: MkDocsConfig) -> str:
     """Inject Google Tag Manager noscript tag immediately after <body>.
 
     Args:
-        output: The HTML output of the page.
+        html: 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)
-
+    html = _inject_markdown_into_html(html, page)
+    return _inject_gtm(html)
 
 # Create HTML files for redirects after site dir has been built
 def on_post_build(config):

docs/docs/agents/evals.md

@@ -15,7 +15,7 @@ To evaluate your agent's performance you can use `LangSmith` [evaluations](https
 def evaluator(*, outputs: dict, reference_outputs: dict):
     # compare agent outputs against reference outputs
     output_messages = outputs["messages"]
-    reference_messages = reference["messages"]
+    reference_messages = reference_outputs["messages"]
     score = compare_messages(output_messages, reference_messages)
     return {"key": "evaluator_score", "score": score}
 ```

docs/docs/agents/models.md

@@ -7,7 +7,7 @@ LangGraph provides built-in support for [LLMs (language models)](https://python.
 
 Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../snippets/chat_model_tabs.md" %}
 
 ### Instantiate a model directly
 

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -35,7 +35,6 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
 1. Configure your `langgraph-dataplane-values.yaml` file.
 
         config:
-          langgraphPlatformLicenseKey: "" # Your LangGraph Platform license key
           langsmithApiKey: "" # API Key of your Workspace
           langsmithWorkspaceId: "" # Workspace ID
           hostBackendUrl: "https://api.host.langchain.com" # Only override this if on EU

docs/docs/cloud/how-tos/add-human-in-the-loop.md

@@ -2,7 +2,7 @@
 
 To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
 
-## LangGraph API invoke & resume
+## Dynamic interrupts
 
 === "Python"
 
@@ -305,6 +305,185 @@ To review, edit, and approve tool calls in an agent or workflow, use LangGraph's
         }"
         ```
 
+## Static interrupts
+
+Static interrupts (also known as static breakpoints) are triggered either before or after a node executes. 
+
+!!! warning
+
+    Static interrupts are **not** recommended for human-in-the-loop workflows. They are best used for debugging and testing.
+
+You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time:
+
+```python
+# highlight-next-line
+graph = graph_builder.compile( # (1)!
+    # highlight-next-line
+    interrupt_before=["node_a"], # (2)!
+    # highlight-next-line
+    interrupt_after=["node_b", "node_c"], # (3)!
+)
+```
+
+1. The breakpoints are set during `compile` time.
+2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+Alternatively, you can set static interrupts at run time:
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    await client.runs.wait( # (1)!
+        thread_id,
+        assistant_id,
+        inputs=inputs,
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"] # (3)!
+    )
+    ```
+
+    1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+
+=== "JavaScript"
+
+    ```js
+    // highlight-next-line
+    await client.runs.wait( // (1)!
+        threadID,
+        assistantID,
+        {
+        input: input,
+        // highlight-next-line
+        interruptBefore: ["node_a"], // (2)!
+        // highlight-next-line
+        interruptAfter: ["node_b", "node_c"] // (3)!
+        }
+    )
+    ```
+
+    1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
+    3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+        \"assistant_id\": \"agent\",
+        \"interrupt_before\": [\"node_a\"],
+        \"interrupt_after\": [\"node_b\", \"node_c\"],
+        \"input\": <INPUT>
+    }"
+    ```
+
+The following example shows how to add static interrupts:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>)
+
+    # Using the graph deployed with the name "agent"
+    assistant_id = "agent"
+
+    # create a thread
+    thread = await client.threads.create()
+    thread_id = thread["thread_id"]
+
+    # Run the graph until the breakpoint
+    result = await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=inputs   # (1)!
+    )
+
+    # Resume the graph
+    await client.runs.wait(
+        thread_id,
+        assistant_id,
+        input=None   # (2)!
+    )
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
+
+    // Using the graph deployed with the name "agent"
+    const assistantID = "agent";
+
+    // create a thread
+    const thread = await client.threads.create();
+    const threadID = thread["thread_id"];
+
+    // Run the graph until the breakpoint
+    const result = await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: input }   // (1)!
+    );
+
+    // Resume the graph
+    await client.runs.wait(
+      threadID,
+      assistantID,
+      { input: null }   // (2)!
+    );
+    ```
+
+    1. The graph is run until the first breakpoint is hit.
+    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "cURL"
+
+    Create a thread:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads \
+    --header 'Content-Type: application/json' \
+    --data '{}'
+    ```
+
+    Run the graph until the breakpoint:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <INPUT>
+    }"
+    ```
+
+    Resume the graph:
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
+    --header 'Content-Type: application/json' \
+    --data "{
+      \"assistant_id\": \"agent\"
+    }"
+    ```
+
+
 ## Learn more
 
 - [Human-in-the-loop conceptual guide](../../concepts/human_in_the_loop.md): learn more about LangGraph human-in-the-loop features. 

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

@@ -1,185 +0,0 @@
-# Set breakpoints using Server API
-
-[Breakpoints](../../concepts/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](../../concepts/persistence.md), which saves the graph state after each step.
-
-With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses indefinitely until you resume, as the checkpointer preserves the state.
-
-!!! tip
-
-    For conceptual information on breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
-
-## Set static breakpoints
-
-Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
-
-=== "Compile time"
-
-    ```python
-    # highlight-next-line
-    graph = graph_builder.compile( # (1)!
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"], # (3)!
-    )
-    ```
-
-    1. The breakpoints are set during `compile` time.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-=== "Run time"
-
-    === "Python"
-
-        ```python
-        # highlight-next-line
-        await client.runs.wait( # (1)!
-            thread_id,
-            assistant_id,
-            inputs=inputs,
-            # highlight-next-line
-            interrupt_before=["node_a"], # (2)!
-            # highlight-next-line
-            interrupt_after=["node_b", "node_c"] # (3)!
-        )
-        ```
-
-        1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
-        2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-        3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-
-    === "JavaScript"
-
-        ```js
-        // highlight-next-line
-        await client.runs.wait( // (1)!
-          threadID,
-          assistantID,
-          {
-            input: input,
-            // highlight-next-line
-            interruptBefore: ["node_a"], // (2)!
-            // highlight-next-line
-            interruptAfter: ["node_b", "node_c"] // (3)!
-          }
-        )
-        ```
-
-        1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
-        2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
-        3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
-
-    === "cURL"
-
-        ```bash
-        curl --request POST \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-        --header 'Content-Type: application/json' \
-        --data "{
-          \"assistant_id\": \"agent\",
-          \"interrupt_before\": [\"node_a\"],
-          \"interrupt_after\": [\"node_b\", \"node_c\"],
-          \"input\": <INPUT>
-        }"
-        ```
-
-## Example
-
-This example shows how to add **static** breakpoints. See [Use breakpoints](../../how-tos/human_in_the_loop/breakpoints.md) for more options on adding breakpoints.
-
-=== "Python"
-
-    ```python
-    from langgraph_sdk import get_client
-    client = get_client(url=<DEPLOYMENT_URL>)
-
-    # Using the graph deployed with the name "agent"
-    assistant_id = "agent"
-
-    # create a thread
-    thread = await client.threads.create()
-    thread_id = thread["thread_id"]
-
-    # Run the graph until the breakpoint
-    result = await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=inputs   # (1)!
-    )
-
-    # Resume the graph
-    await client.runs.wait(
-        thread_id,
-        assistant_id,
-        input=None   # (2)!
-    )
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "JavaScript"
-
-    ```js
-    import { Client } from "@langchain/langgraph-sdk";
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
-
-    // Using the graph deployed with the name "agent"
-    const assistantID = "agent";
-
-    // create a thread
-    const thread = await client.threads.create();
-    const threadID = thread["thread_id"];
-
-    // Run the graph until the breakpoint
-    const result = await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: input }   // (1)!
-    );
-
-    // Resume the graph
-    await client.runs.wait(
-      threadID,
-      assistantID,
-      { input: null }   // (2)!
-    );
-    ```
-
-    1. The graph is run until the first breakpoint is hit.
-    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "cURL"
-
-    Create a thread:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads \
-    --header 'Content-Type: application/json' \
-    --data '{}'
-    ```
-
-    Run the graph until the breakpoint:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\",
-      \"input\": <INPUT>
-    }"
-    ```
-
-    Resume the graph:
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
-    --header 'Content-Type: application/json' \
-    --data "{
-      \"assistant_id\": \"agent\"
-    }"
-    ```

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

@@ -29,7 +29,7 @@ Click the dropdown next to "Submit" and click the toggle to enable/disable strea
 To run your graph with breakpoints, click the "Interrupt" button. Select a node and whether to pause before and/or after that node has executed. Click "Continue" in the thread log to resume execution.
 
 
-For more information on breakpoints see [here](../../concepts/breakpoints.md).
+For more information on breakpoints see [here](../../concepts/human_in_the_loop.md).
 
 ### Submit run
 

docs/docs/cloud/reference/cli.md

@@ -409,6 +409,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                       | Default                   | Description                                                                                                             |
     | ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+| `--base-image TEXT`          | `langchain/langgraph-api`  | Base image to use for the LangGraph API server. Pin to specific versions using version tags.                            |
+| `--image TEXT`               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly.           |
     | `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
     | `--watch`                    |                           | Restart on file changes                                                                                                 |
     | `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
@@ -436,6 +438,8 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
     | Option                                                                 | Default                   | Description                                                                                                             |
     | ---------------------------------------------------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
     | <span style="white-space: nowrap;">`--wait`</span>                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+| <span style="white-space: nowrap;">`--base-image TEXT`</span>          | <span style="white-space: nowrap;">`langchain/langgraph-api`</span> | Base image to use for the LangGraph API server. Pin to specific versions using version tags. |
+| <span style="white-space: nowrap;">`--image TEXT`</span>               |                           | Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly. |
     | <span style="white-space: nowrap;">`--postgres-uri TEXT`</span>        | Local database            | Postgres URI to use for the database.                                                                                   |
     | <span style="white-space: nowrap;">`--watch`</span>                    |                           | Restart on file changes                                                                                                 |
     | <span style="white-space: nowrap;">`-c, --config FILE`</span>          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |

docs/docs/cloud/reference/langgraph_server_changelog.md

@@ -0,0 +1,151 @@
+# LangGraph Server Changelog
+
+[LangGraph Server](../../concepts/langgraph_server.md) is an API platform for creating and managing agent-based applications. It provides built-in persistence, a task queue, and supports deploying, configuring, and running assistants (agentic workflows) at scale. This changelog documents all notable updates, features, and fixes to LangGraph Server releases.
+
+---
+
+## v0.2.86 (2025-07-11)
+- Honored tool descriptions in the `/mcp` endpoint to align with expected functionality.
+
+## v0.2.85 (2025-07-10)
+- Added support for the `on_disconnect` field to `runs/wait` and included disconnect logs for better debugging.
+
+## v0.2.84 (2025-07-09)
+- Removed unnecessary status updates to streamline thread handling and updated version to 0.2.84.
+
+## v0.2.83 (2025-07-09)
+- Reduced the default time-to-live for resumable streams to 2 minutes.
+- Enhanced data submission logic to send data to both Beacon and LangSmith instance based on license configuration.
+- Enabled submission of self-hosted data to a Langsmith instance when the endpoint is configured.
+
+## v0.2.82 (2025-07-03)
+- Addressed a race condition in background runs by implementing a lock using join, ensuring reliable execution across CTEs.
+
+## v0.2.81 (2025-07-03)
+- Optimized run streams by reducing initial wait time to improve responsiveness for older or non-existent runs.
+
+## v0.2.80 (2025-07-03)
+- Corrected parameter passing in the `logger.ainfo()` API call to resolve a TypeError.
+
+## v0.2.79 (2025-07-02)
+- Fixed a JsonDecodeError in checkpointing with remote graph by correcting JSON serialization to handle trailing slashes properly.
+- Introduced a configuration flag to disable webhooks globally across all routes.
+
+## v0.2.78 (2025-07-02)
+- Added timeout retries to webhook calls to improve reliability.
+- Added HTTP request metrics, including a request count and latency histogram, for enhanced monitoring capabilities.
+
+## v0.2.77 (2025-07-02)
+- Added HTTP metrics to improve performance monitoring.
+- Changed the Redis cache delimiter to reduce conflicts with subgraph message names and updated caching behavior.
+
+## v0.2.76 (2025-07-01)
+- Updated Redis cache delimiter to prevent conflicts with subgraph messages.
+
+## v0.2.74 (2025-06-30)
+- Scheduled webhooks in an isolated loop to ensure thread-safe operations and prevent errors with PYTHONASYNCIODEBUG=1.
+
+## v0.2.73 (2025-06-27)
+- Fixed an infinite frame loop issue and removed the dict_parser due to structlog's unexpected behavior.
+- Throw a 409 error on deadlock occurrence during run cancellations to handle lock conflicts gracefully.
+
+## v0.2.72 (2025-06-27)
+- Ensured compatibility with future langgraph versions.
+- Implemented a 409 response status to handle deadlock issues during cancellation.
+
+## v0.2.71 (2025-06-26)
+- Improved logging for better clarity and detail regarding log types.
+
+## v0.2.70 (2025-06-26)
+- Improved error handling to better distinguish and log TimeoutErrors caused by users from internal run timeouts.
+
+## v0.2.69 (2025-06-26)
+- Added sorting and pagination to the crons API and updated schema definitions for improved accuracy.
+
+## v0.2.66 (2025-06-26)
+- Fixed a 404 error when creating multiple runs with the same thread_id using `on_not_exist="create"`.
+
+## v0.2.65 (2025-06-25)
+- Ensured that only fields from `assistant_versions` are returned when necessary.
+- Ensured consistent data types for in-memory and PostgreSQL users, improving internal authentication handling.
+
+## v0.2.64 (2025-06-24)
+- Added descriptions to version entries for better clarity.
+
+## v0.2.62 (2025-06-23)
+- Improved user handling for custom authentication in the JS Studio.
+- Added Prometheus-format run statistics to the metrics endpoint for better monitoring.
+- Added run statistics in Prometheus format to the metrics endpoint.
+
+## v0.2.61 (2025-06-20)
+- Set a maximum idle time for Redis connections to prevent unnecessary open connections.
+
+## v0.2.60 (2025-06-20)
+- Enhanced error logging to include traceback details for dictionary operations.
+- Added a `/metrics` endpoint to expose queue worker metrics for monitoring.
+
+## v0.2.57 (2025-06-18)
+- Removed CancelledError from retriable exceptions to allow local interrupts while maintaining retriability for workers.
+- Introduced middleware to gracefully shut down the server after completing in-flight requests upon receiving a SIGINT.
+- Reduced metadata stored in checkpoint to only include necessary information.
+- Improved error handling in join runs to return error details when present.
+
+## v0.2.56 (2025-06-17)
+- Improved application stability by adding a handler for SIGTERM signals.
+
+## v0.2.55 (2025-06-17)
+- Improved the handling of cancellations in the queue entrypoint.
+- Improved cancellation handling in the queue entry point.
+
+## v0.2.54 (2025-06-16)
+- Enhanced error message for LuaLock timeout during license validation.
+- Fixed the $contains filter in custom auth by requiring an explicit ::text cast and updated tests accordingly.
+- Ensured project and tenant IDs are formatted as UUIDs for consistency.
+
+## v0.2.53 (2025-06-13)
+- Resolved a timing issue to ensure the queue starts only after the graph is registered.
+- Improved performance by setting thread and run status in a single query and enhanced error handling during checkpoint writes.
+- Reduced the default background grace period to 3 minutes.
+
+## v0.2.52 (2025-06-12)
+- Now logging expected graphs when one is omitted to improve traceability.
+- Implemented a time-to-live (TTL) feature for resumable streams.
+- Improved query efficiency and consistency by adding a unique index and optimizing row locking.
+
+## v0.2.51 (2025-06-12)
+- Handled `CancelledError` by marking tasks as ready to retry, improving error management in worker processes.
+- Added LG API version and request ID to metadata and logs for better tracking.
+- Added LG API version and request ID to metadata and logs to improve traceability.
+- Improved database performance by creating indexes concurrently.
+- Ensured postgres write is committed only after the Redis running marker is set to prevent race conditions.
+- Enhanced query efficiency and reliability by adding a unique index on thread_id/running, optimizing row locks, and ensuring deterministic run selection.
+- Resolved a race condition by ensuring Postgres updates only occur after the Redis running marker is set.
+
+## v0.2.46 (2025-06-07)
+- Introduced a new connection for each operation while preserving transaction characteristics in Threads state `update()` and `bulk()` commands.
+
+## v0.2.45 (2025-06-05)
+- Enhanced streaming feature by incorporating tracing contexts.
+- Removed an unnecessary query from the Crons.search function.
+- Resolved connection reuse issue when scheduling next run for multiple cron jobs.
+- Removed an unnecessary query in the Crons.search function to improve efficiency.
+- Resolved an issue with scheduling the next cron run by improving connection reuse.
+
+## v0.2.44 (2025-06-04)
+- Enhanced the worker logic to exit the pipeline before continuing when the Redis message limit is reached.
+- Introduced a ceiling for Redis message size with an option to skip messages larger than 128 MB for improved performance.
+- Ensured the pipeline always closes properly to prevent resource leaks.
+
+## v0.2.43 (2025-06-04)
+- Improved performance by omitting logs in metadata calls and ensuring output schema compliance in value streaming.
+- Ensured the connection is properly closed after use.
+- Aligned output format to strictly adhere to the specified schema.
+- Stopped sending internal logs in metadata requests to improve privacy.
+
+## v0.2.42 (2025-06-04)
+- Added timestamps to track the start and end of a request's run.
+- Added tracer information to the configuration settings.
+- Added support for streaming with tracing contexts.
+
+## v0.2.41 (2025-06-03)
+- Added locking mechanism to prevent errors in pipelined executions.

docs/docs/concepts/breakpoints.md

@@ -1,18 +0,0 @@
----
-search:
-  boost: 2
----
-
-# Breakpoints
-
-[Breakpoints](../how-tos/human_in_the_loop/breakpoints.md) pause graph execution at defined points and let you step through each stage. They use LangGraph's [**persistence layer**](./persistence.md), which saves the graph state after each step.
-
-With breakpoints, you can inspect the graph's state and node inputs at any point. Execution pauses **indefinitely** until you resume, as the checkpointer preserves the state.
-
-<figure markdown="1">
-![image](img/breakpoints.png){: style="max-height:400px"}
-<figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
-
-!!! tip
-
-    For information on how to use breakpoints, see [Set breakpoints](../how-tos/human_in_the_loop/breakpoints.md) and [Set breakpoints using Server API](../cloud/how-tos/human_in_the_loop_breakpoint.md).

docs/docs/concepts/human_in_the_loop.md

@@ -23,9 +23,18 @@ To review, edit, and approve tool calls in an agent or workflow, [use LangGraph'
 
 ## Key capabilities
 
-* **Persistent execution state**: LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
+* **Persistent execution state**: Interrupts use LangGraph's [persistence](../../concepts/persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
 
-* **Flexible integration points**: HIL logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
+    There are two ways to pause a graph:
+
+    - [Dynamic interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt): Use `interrupt` to pause a graph from inside a specific node, based on the current state of the graph.
+    - [Static interrupts](../how-tos/human_in_the_loop/add-human-in-the-loop.md#debug-with-interrupts): Use `interrupt_before` and `interrupt_after` to pause the graph at defined points, either before or after a node executes.
+
+    <figure markdown="1">
+    ![image](./img/breakpoints.png){: style="max-height:400px"}
+    <figcaption>An example graph consisting of 3 sequential steps with a breakpoint before step_3. </figcaption> </figure>
+
+* **Flexible integration points**: Human-in-the-loop logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
 
 ## Patterns
 

docs/docs/concepts/persistence.md

@@ -33,7 +33,7 @@ The state of a thread at a particular point in time is called a checkpoint. Chec
 - `metadata`: Metadata associated with this checkpoint.
 - `values`: Values of the state channels at this point in time.
 - `next` A tuple of the node names to execute next in the graph.
-- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/breakpoints.md#dynamic-breakpoints) from within a node, tasks will contain additional data associated with interrupts.
+- `tasks`: A tuple of `PregelTask` objects that contain information about next tasks to be executed. If the step was previously attempted, it will include error information. If a graph was interrupted [dynamically](../how-tos/human_in_the_loop/add-human-in-the-loop.md#pause-using-interrupt) from within a node, tasks will contain additional data associated with interrupts.
 
 Checkpoints are persisted and can be used to restore the state of a thread at a later time.
 
@@ -525,7 +525,7 @@ When running on LangGraph Platform, encryption is automatically enabled whenever
 
 ### Human-in-the-loop
 
-First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [these how-to guides](../how-tos/human_in_the_loop/breakpoints.md) for concrete examples.
+First, checkpointers facilitate [human-in-the-loop workflows](agentic_concepts.md#human-in-the-loop) workflows by allowing humans to inspect, interrupt, and approve graph steps. Checkpointers are needed for these workflows as the human has to be able to view the state of a graph at any point in time, and the graph has to be to resume execution after the human has made any updates to the state. See [the how-to guides](../how-tos/human_in_the_loop/add-human-in-the-loop.md) for examples.
 
 ### Memory
 

docs/docs/examples/index.md

@@ -5,17 +5,17 @@ The pages in this section provide end-to-end examples for the following topics:
 ## General 
 
 - [Template Applications](../concepts/template_applications.md): Create a LangGraph application from a template.
-- [Agentic RAG](./rag/langgraph_agentic_rag.md): Build a retrieval agent that can decide when to use a retriever tool.
-- [Agent Supervisor](./multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents.
-- [SQL agent](./sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results.
+- [Agentic RAG](../tutorials/rag/langgraph_agentic_rag.md): Build a retrieval agent that can decide when to use a retriever tool.
+- [Agent Supervisor](../tutorials/multi_agent/agent_supervisor.md): Build a supervisor agent that can manage a team of agents.
+- [SQL agent](../tutorials/sql/sql-agent.md): Build a SQL agent that can execute SQL queries and return the results.
 - [Prebuilt chat UI](../agents/ui.md): Use a prebuilt chat UI to interact with any LangGraph agent.
 - [Graph runs in LangSmith](../how-tos/run-id-langsmith.md): Use LangSmith to track and analyze graph runs.
 
 ## LangGraph Platform
 
-- [Set up custom authentication](./auth/getting_started.md): Set up custom authentication for your LangGraph application.
-- [Make conversations private](./auth/resource_auth.md): Make conversations private by using resource-based authentication.
-- [Connect an authentication provider](./auth/add_auth_server.md): Connect an authentication provider to your LangGraph application.
+- [Set up custom authentication](../tutorials/auth/getting_started.md): Set up custom authentication for your LangGraph application.
+- [Make conversations private](../tutorials/auth/resource_auth.md): Make conversations private by using resource-based authentication.
+- [Connect an authentication provider](../tutorials/auth/add_auth_server.md): Connect an authentication provider to your LangGraph application.
 - [Rebuild graph at runtime](../cloud/deployment/graph_rebuild.md): Rebuild a graph at runtime.
 - [Use RemoteGraph](../how-tos/use-remote-graph.md): Use RemoteGraph to deploy your LangGraph application to a remote server.
 - [Deploy CrewAI, AutoGen, and other frameworks](../how-tos/autogen-integration.md): Deploy CrewAI, AutoGen, and other frameworks with LangGraph.

docs/docs/guides/index.md

@@ -19,8 +19,7 @@ These capabilities are available in both LangGraph OSS and the LangGraph Platfor
 - [Context](../agents/context.md): Pass outside data to a LangGraph graph to provide context for the graph execution.
 - [Models](../agents/models.md): Integrate various LLMs into your LangGraph application.
 - [Tools](../concepts/tools.md): Interface directly with external systems.
-- [Human-in-the-loop](../concepts/human_in_the_loop.md): Enable human intervention at any point in a workflow.
-- [Breakpoints](../concepts/breakpoints.md): Pause the execution of a LangGraph graph at a specific point.
+- [Human-in-the-loop](../concepts/human_in_the_loop.md): Pause a graph and wait for human input at any point in a workflow.
 - [Time travel](../concepts/time-travel.md): Travel back in time to a specific point in the execution of a LangGraph graph.
 - [Subgraphs](../concepts/subgraphs.md): Build modular graphs.
 - [Multi-agent](../concepts/multi_agent.md): Break down a complex workflow into multiple agents.
@@ -31,11 +30,11 @@ These capabilities are available in both LangGraph OSS and the LangGraph Platfor
 
 These capabilities are only available in [LangGraph Platform](../concepts/langgraph_platform.md).
 
-- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a Langraph graph.
+- [Authentication and access control](../concepts/auth.md): Authenticate and authorize users to access a LangGraph graph.
 - [Assistants](../concepts/assistants.md): Build assistants that can be used to interact with a LangGraph graph.
 - [Double-texting](../concepts/double_texting.md): Handle double-texting (consecutive messages before a first response is returned) in a LangGraph graph.
 - [Webhooks](../cloud/concepts/webhooks.md): Send webhooks to a LangGraph graph.
 - [Cron jobs](../cloud/concepts/cron_jobs.md): Schedule jobs to run at a specific time.
 - [Server customization](../how-tos/http/custom_lifespan.md): Customize the server that runs a LangGraph graph.
 - [Data management](../cloud/concepts/data_storage_and_privacy.md): Manage data in a LangGraph graph.
-- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.
+- [Deployment](../concepts/deployment_options.md): Deploy a LangGraph graph to a server.

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

@@ -1149,7 +1149,8 @@ Adding "C" to ['A']
 LangGraph supports map-reduce and other advanced branching patterns using the Send API. Here is an example of how to use it:
 
 ```python
-from langgraph.graph import StateGraph, START, END, Send
+from langgraph.graph import StateGraph, START, END
+from langgraph.types import Send
 from typing_extensions import TypedDict
 
 class OverallState(TypedDict):
@@ -1507,7 +1508,7 @@ Because many LangChain objects implement the [Runnable Protocol](https://python.
 
 See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../snippets/chat_model_tabs.md" %}
 
 ```python
 from langchain.chat_models import init_chat_model

docs/docs/how-tos/human_in_the_loop/add-human-in-the-loop.md

@@ -11,11 +11,19 @@ hide:
 
 # Enable human intervention
 
-To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](../../concepts/human_in_the_loop.md) features.
+To review, edit, and approve tool calls in an agent or workflow, use interrupts to pause a graph and wait for human input. Interrupts use LangGraph's [persistence](../../concepts/persistence.md) layer, which saves the graph state, to indefinitely pause graph execution until you resume.
+
+!!! info
+
+    For more information about human-in-the-loop workflows, see the [Human-in-the-Loop](../../concepts/human_in_the_loop.md) conceptual guide.
 
 ## Pause using `interrupt`
 
-The [`interrupt` function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+[Dynamic interrupts](../../concepts/human_in_the_loop.md#key-capabilities) (also known as dynamic breakpoints) are triggered based on the current state of the graph. You can set dynamic interrupts by calling [`interrupt` function][langgraph.types.interrupt] in the appropriate place. The graph will pause, which allows for human intervention, and then resumes the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.
+
+!!! note
+
+    As of v1.0, `interrupt` is the recommended way to pause a graph. `NodeInterrupt` is deprecated and will be removed in v2.0.
 
 To use `interrupt` in your graph, you need to:
 
@@ -138,15 +146,10 @@ print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
 
 !!! warning
 
-      Interrupts are both powerful and ergonomic. However, while they may resemble Python's input() function in terms of developer experience, it's important to note that they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node.
-
+      Interrupts resemble Python's input() function in terms of developer experience, but they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node.
 
 ## Resume using the `Command` primitive
 
-!!! warning
-
-    Resuming from an `interrupt` is different from Python's `input()` function, where execution resumes from the exact point where the `input()` function was called.
-
 When the `interrupt` function is used within a graph, execution pauses at that point and awaits user input.
 
 To resume execution, use the [`Command`][langgraph.types.Command] primitive, which can be supplied via the `invoke`, `ainvoke`, `stream`, or `astream` methods. The graph resumes execution from the beginning of the node where `interrupt(...)` was initially called. This time, the `interrupt` function will return the value provided in `Command(resume=value)` rather than pausing again. All code from the beginning of the node to the `interrupt` will be re-executed.
@@ -712,6 +715,162 @@ def human_node(state: State):
     print(final_result)  # Should include the valid age
     ```
 
+## Debug with interrupts
+
+To debug and test a graph, use [static interrupts](../../concepts/human_in_the_loop.md#key-capabilities) (also known as static breakpoints) to step through the graph execution one node at a time or to pause the graph execution at specific nodes. Static interrupts are triggered at defined points either before or after a node executes. You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
+
+!!! warning
+
+    Static interrupts are **not** recommended for human-in-the-loop workflows. Use [dynamic interrupts](#pause-using-interrupt) instead.
+
+=== "Compile time"
+
+    ```python
+    # highlight-next-line
+    graph = graph_builder.compile( # (1)!
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"], # (3)!
+        checkpointer=checkpointer, # (4)!
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config) # (5)!
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config) # (6)!
+    ```
+
+    1. The breakpoints are set during `compile` time.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. A checkpointer is required to enable breakpoints.
+    5. The graph is run until the first breakpoint is hit.
+    6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+=== "Run time"
+
+    ```python
+    # highlight-next-line
+    graph.invoke( # (1)!
+        inputs, 
+        # highlight-next-line
+        interrupt_before=["node_a"], # (2)!
+        # highlight-next-line
+        interrupt_after=["node_b", "node_c"] # (3)!
+        config={
+            "configurable": {"thread_id": "some_thread"}
+        }, 
+    )
+
+    config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=config) # (4)!
+
+    # Resume the graph
+    graph.invoke(None, config=config) # (5)!
+    ```
+
+    1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
+    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
+    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
+    4. The graph is run until the first breakpoint is hit.
+    5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
+
+    !!! note
+
+        You cannot set static breakpoints at runtime for **sub-graphs**.
+        If you have a sub-graph, you must set the breakpoints at compilation time.
+
+??? example "Setting static breakpoints"
+
+    ```python
+    from IPython.display import Image, display
+    from typing_extensions import TypedDict
+    
+    from langgraph.checkpoint.memory import InMemorySaver 
+    from langgraph.graph import StateGraph, START, END
+    
+    
+    class State(TypedDict):
+        input: str
+    
+    
+    def step_1(state):
+        print("---Step 1---")
+        pass
+    
+    
+    def step_2(state):
+        print("---Step 2---")
+        pass
+    
+    
+    def step_3(state):
+        print("---Step 3---")
+        pass
+    
+    
+    builder = StateGraph(State)
+    builder.add_node("step_1", step_1)
+    builder.add_node("step_2", step_2)
+    builder.add_node("step_3", step_3)
+    builder.add_edge(START, "step_1")
+    builder.add_edge("step_1", "step_2")
+    builder.add_edge("step_2", "step_3")
+    builder.add_edge("step_3", END)
+    
+    # Set up a checkpointer 
+    checkpointer = InMemorySaver() # (1)!
+    
+    graph = builder.compile(
+        checkpointer=checkpointer, # (2)!
+        interrupt_before=["step_3"] # (3)!
+    )
+    
+    # View
+    display(Image(graph.get_graph().draw_mermaid_png()))
+    
+    
+    # Input
+    initial_input = {"input": "hello world"}
+    
+    # Thread
+    thread = {"configurable": {"thread_id": "1"}}
+    
+    # Run the graph until the first interruption
+    for event in graph.stream(initial_input, thread, stream_mode="values"):
+        print(event)
+        
+    # This will run until the breakpoint
+    # You can get the state of the graph at this point
+    print(graph.get_state(config))
+    
+    # You can continue the graph execution by passing in `None` for the input
+    for event in graph.stream(None, thread, stream_mode="values"):
+        print(event)
+    ```
+
+### Use static interrupts in LangGraph Studio
+
+You can use [LangGraph Studio](../../concepts/langgraph_studio.md) to debug your graph. You can set static breakpoints in the UI and then run the graph. You can also use the UI to inspect the graph state at any point in the execution.
+
+![image](../../concepts/img/human_in_the_loop/static-interrupt.png){: style="max-height:400px"}
+
+LangGraph Studio is free with [locally deployed applications](../../tutorials/langgraph-platform/local-server.md) using `langgraph dev`. 
+
 ## Considerations
 
 When using human-in-the-loop, there are some considerations to keep in mind.
@@ -953,4 +1112,3 @@ To avoid issues, refrain from dynamically changing the node's structure between
     Name: N/A. Age: John
     {'human_node': {'age': 'John', 'name': 'N/A'}}
     ```
-

docs/docs/how-tos/human_in_the_loop/breakpoints.md

@@ -1,342 +0,0 @@
-# Set breakpoints
-
-There are two places where you can set breakpoints:
-
-1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
-2. **Inside** a node using the `NodeInterrupt` exception. We call these [**dynamic breakpoints**](#dynamic-breakpoints).
-
-To use breakpoints, you will need to:
-
-1. [**Specify a checkpointer**](../../concepts/persistence.md#checkpoints) to save the graph state after each step.
-2. **Set breakpoints** to specify where execution should pause.
-3. **Run the graph** with a [**thread ID**](../../concepts/persistence.md#threads) to pause execution at the breakpoint.
-4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` passing a `None` as the argument for the inputs.
-
-!!! tip
-
-    For a conceptual overview of breakpoints, see [Breakpoints](../../concepts/breakpoints.md).
-
-## Static breakpoints
-
-Static breakpoints are triggered either before or after a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at compile time or run time.
-
-Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
-node at a time or if you want to pause the graph execution at specific nodes.
-
-=== "Compile time"
-
-    ```python
-    # highlight-next-line
-    graph = graph_builder.compile( # (1)!
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"], # (3)!
-        checkpointer=checkpointer, # (4)!
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "some_thread"
-        }
-    }
-
-    # Run the graph until the breakpoint
-    graph.invoke(inputs, config=thread_config) # (5)!
-
-    # Resume the graph
-    graph.invoke(None, config=thread_config) # (6)!
-    ```
-
-    1. The breakpoints are set during `compile` time.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-    4. A checkpointer is required to enable breakpoints.
-    5. The graph is run until the first breakpoint is hit.
-    6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-=== "Run time"
-
-    ```python
-    # highlight-next-line
-    graph.invoke( # (1)!
-        inputs, 
-        # highlight-next-line
-        interrupt_before=["node_a"], # (2)!
-        # highlight-next-line
-        interrupt_after=["node_b", "node_c"] # (3)!
-        config={
-            "configurable": {"thread_id": "some_thread"}
-        }, 
-    )
-
-    config = {
-        "configurable": {
-            "thread_id": "some_thread"
-        }
-    }
-
-    # Run the graph until the breakpoint
-    graph.invoke(inputs, config=config) # (4)!
-
-    # Resume the graph
-    graph.invoke(None, config=config) # (5)!
-    ```
-
-    1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
-    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
-    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
-    4. The graph is run until the first breakpoint is hit.
-    5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
-
-    !!! note
-
-        You cannot set static breakpoints at runtime for **sub-graphs**.
-        If you have a sub-graph, you must set the breakpoints at compilation time.
-
-??? example "Setting static breakpoints"
-
-    ```python
-    from IPython.display import Image, display
-    from typing_extensions import TypedDict
-    
-    from langgraph.checkpoint.memory import InMemorySaver 
-    from langgraph.graph import StateGraph, START, END
-    
-    
-    class State(TypedDict):
-        input: str
-    
-    
-    def step_1(state):
-        print("---Step 1---")
-        pass
-    
-    
-    def step_2(state):
-        print("---Step 2---")
-        pass
-    
-    
-    def step_3(state):
-        print("---Step 3---")
-        pass
-    
-    
-    builder = StateGraph(State)
-    builder.add_node("step_1", step_1)
-    builder.add_node("step_2", step_2)
-    builder.add_node("step_3", step_3)
-    builder.add_edge(START, "step_1")
-    builder.add_edge("step_1", "step_2")
-    builder.add_edge("step_2", "step_3")
-    builder.add_edge("step_3", END)
-    
-    # Set up a checkpointer 
-    checkpointer = InMemorySaver() # (1)!
-    
-    graph = builder.compile(
-        checkpointer=checkpointer, # (2)!
-        interrupt_before=["step_3"] # (3)!
-    )
-    
-    # View
-    display(Image(graph.get_graph().draw_mermaid_png()))
-    
-    
-    # Input
-    initial_input = {"input": "hello world"}
-    
-    # Thread
-    thread = {"configurable": {"thread_id": "1"}}
-    
-    # Run the graph until the first interruption
-    for event in graph.stream(initial_input, thread, stream_mode="values"):
-        print(event)
-        
-    # This will run until the breakpoint
-    # You can get the state of the graph at this point
-    print(graph.get_state(config))
-    
-    # You can continue the graph execution by passing in `None` for the input
-    for event in graph.stream(None, thread, stream_mode="values"):
-        print(event)
-    ```
-
-## Dynamic breakpoints
-
-Use dynamic breakpoints if you need to interrupt the graph from inside a given node based on a condition.
-
-```python
-from langgraph.errors import NodeInterrupt
-
-def step_2(state: State) -> State:
-    # highlight-next-line
-    if len(state["input"]) > 5:
-        # highlight-next-line
-        raise NodeInterrupt( # (1)!
-            f"Received input that is longer than 5 characters: {state['foo']}"
-        )
-    return state
-```
-
-1. raise NodeInterrupt exception based on a some condition. In this example, we create a dynamic breakpoint if the length of the attribute `input` is longer than 5 characters.
-
-<details class="example"><summary>Using dynamic breakpoints</summary>
-
-```python
-from typing_extensions import TypedDict
-from IPython.display import Image, display
-
-from langgraph.graph import StateGraph, START, END
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.errors import NodeInterrupt
-
-
-class State(TypedDict):
-    input: str
-
-
-def step_1(state: State) -> State:
-    print("---Step 1---")
-    return state
-
-
-def step_2(state: State) -> State:
-    # Let's optionally raise a NodeInterrupt
-    # if the length of the input is longer than 5 characters
-    if len(state["input"]) > 5:
-        raise NodeInterrupt(
-            f"Received input that is longer than 5 characters: {state['input']}"
-        )
-    print("---Step 2---")
-    return state
-
-
-def step_3(state: State) -> State:
-    print("---Step 3---")
-    return state
-
-
-builder = StateGraph(State)
-builder.add_node("step_1", step_1)
-builder.add_node("step_2", step_2)
-builder.add_node("step_3", step_3)
-builder.add_edge(START, "step_1")
-builder.add_edge("step_1", "step_2")
-builder.add_edge("step_2", "step_3")
-builder.add_edge("step_3", END)
-
-# Set up memory
-memory = MemorySaver()
-
-# Compile the graph with memory
-graph = builder.compile(checkpointer=memory)
-
-# View
-display(Image(graph.get_graph().draw_mermaid_png()))
-```
-
-First, let's run the graph with an input that <= 5 characters long. This should safely ignore the interrupt condition we defined and return the original input at the end of the graph execution.
-
-```python
-initial_input = {"input": "hello"}
-thread_config = {"configurable": {"thread_id": "1"}}
-
-for event in graph.stream(initial_input, thread_config, stream_mode="values"):
-    print(event)
-```
-
-If we inspect the graph at this point, we can see that there are no more tasks left to run and that the graph indeed finished execution.
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-Now, let's run the graph with an input that's longer than 5 characters. This should trigger the dynamic interrupt we defined via raising a `NodeInterrupt` error inside the `step_2` node.
-
-```python
-initial_input = {"input": "hello world"}
-thread_config = {"configurable": {"thread_id": "2"}}
-
-# Run the graph until the first interruption
-for event in graph.stream(initial_input, thread_config, stream_mode="values"):
-    print(event)
-```
-
-We can see that the graph now stopped while executing `step_2`. If we inspect the graph state at this point, we can see the information on what node is set to execute next (`step_2`), as well as what node raised the interrupt (also `step_2`), and additional information about the interrupt.
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-If we try to resume the graph from the breakpoint, we will simply interrupt again as our inputs & graph state haven't changed.
-
-```python
-# NOTE: to resume the graph from a dynamic interrupt we use the same syntax as with regular interrupts -- we pass None as the input
-for event in graph.stream(None, thread_config, stream_mode="values"):
-    print(event)
-```
-
-```python
-state = graph.get_state(thread_config)
-print(state.next)
-print(state.tasks)
-```
-
-</details>
-
-## Use with subgraphs
-
-To add breakpoints to subgraph either:
-
-* Define [static breakpoints](#static-breakpoints) by specifying them when **compiling** the subgraph.
-* Define [dynamic breakpoints](#dynamic-breakpoints).
-
-<details class="example"><summary>Add breakpoints to subgraphs</summary>
-
-```python
-from typing_extensions import TypedDict
-
-from langgraph.graph import START, StateGraph
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.types import interrupt
-
-
-class State(TypedDict):
-    foo: str
-
-
-def subgraph_node_1(state: State):
-    return {"foo": state["foo"]}
-
-
-subgraph_builder = StateGraph(State)
-subgraph_builder.add_node(subgraph_node_1)
-subgraph_builder.add_edge(START, "subgraph_node_1")
-
-subgraph = subgraph_builder.compile(interrupt_before=["subgraph_node_1"])
-
-builder = StateGraph(State)
-builder.add_node("node_1", subgraph)  # directly include subgraph as a node
-builder.add_edge(START, "node_1")
-
-checkpointer = InMemorySaver()
-graph = builder.compile(checkpointer=checkpointer)
-
-config = {"configurable": {"thread_id": "1"}}
-
-graph.invoke({"foo": ""}, config)
-
-# Fetch state including subgraph state.
-print(graph.get_state(config, subgraphs=True).tasks[0].state)
-
-# resume the subgraph
-graph.invoke(None, config)
-```
-
-</details> 

docs/docs/how-tos/human_in_the_loop/time-travel.md

@@ -4,7 +4,7 @@ To use [time-travel](../../concepts/time-travel.md) in LangGraph:
 
 1. [Run the graph](#1-run-the-graph) with initial inputs using [`invoke`][langgraph.graph.state.CompiledStateGraph.invoke] or [`stream`][langgraph.graph.state.CompiledStateGraph.stream] methods.
 2. [Identify a checkpoint in an existing thread](#2-identify-a-checkpoint): Use the [`get_state_history()`][langgraph.graph.state.CompiledStateGraph.get_state_history] method to retrieve the execution history for a specific `thread_id` and locate the desired `checkpoint_id`.  
-   Alternatively, set a [breakpoint](../../concepts/breakpoints.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that breakpoint.
+   Alternatively, set an [interrupt](../../how-tos/human_in_the_loop/add-human-in-the-loop.md) before the node(s) where you want execution to pause. You can then find the most recent checkpoint recorded up to that interrupt.
 3. [Update the graph state (optional)](#3-update-the-state-optional): Use the [`update_state`][langgraph.graph.state.CompiledStateGraph.update_state] method to modify the graph's state at the checkpoint and resume execution from alternative state.
 4. [Resume execution from the checkpoint](#4-resume-execution-from-the-checkpoint): Use the `invoke` or `stream` methods with an input of `None` and a configuration containing the appropriate `thread_id` and `checkpoint_id`.
 

docs/docs/how-tos/memory/semantic-search.ipynb

@@ -125,7 +125,7 @@
     "memories = store.search((\"user_123\", \"memories\"), query=\"I like food?\", limit=5)\n",
     "\n",
     "for memory in memories:\n",
-    "    print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
+    "    print(f\"Memory: {memory.value['text']} (similarity: {memory.score})\")"
    ]
   },
   {

docs/docs/index.md

@@ -28,4 +28,4 @@ title: LangGraph
 }
 </style>
 
-{!../README.md!}
+{% include-markdown "../../README.md" %}

docs/docs/snippets/chat_model_tabs.md

@@ -0,0 +1,87 @@
+=== "OpenAI"
+
+    ```shell
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["OPENAI_API_KEY"] = "sk-..."
+
+    llm = init_chat_model("openai:gpt-4.1")
+    ```
+
+    👉 Read the [OpenAI integration docs](https://python.langchain.com/docs/integrations/chat/openai/)
+
+=== "Anthropic"
+
+    ```shell
+    pip install -U "langchain[anthropic]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["ANTHROPIC_API_KEY"] = "sk-..."
+
+    llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
+    ```
+
+    👉 Read the [Anthropic integration docs](https://python.langchain.com/docs/integrations/chat/anthropic/)
+
+=== "Azure"
+
+    ```shell
+    pip install -U "langchain[openai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["AZURE_OPENAI_API_KEY"] = "..."
+    os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
+    os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
+
+    llm = init_chat_model(
+        "azure_openai:gpt-4.1",
+        azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+    )
+    ```
+ 
+    👉 Read the [Azure integration docs](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/)
+
+=== "Google Gemini"
+
+    ```shell
+    pip install -U "langchain[google-genai]"
+    ```
+    ```python
+    import os
+    from langchain.chat_models import init_chat_model
+
+    os.environ["GOOGLE_API_KEY"] = "..."
+
+    llm = init_chat_model("google_genai:gemini-2.0-flash")
+    ```
+
+    👉 Read the [Google GenAI integration docs](https://python.langchain.com/docs/integrations/chat/google_generative_ai/)
+
+=== "AWS Bedrock"
+
+    ```shell
+    pip install -U "langchain[aws]"
+    ```
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    # Follow the steps here to configure your credentials:
+    # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
+
+    llm = init_chat_model(
+        "anthropic.claude-3-5-sonnet-20240620-v1:0",
+        model_provider="bedrock_converse",
+    )
+    ```
+
+    👉 Read the [AWS Bedrock integration docs](https://python.langchain.com/docs/integrations/chat/bedrock/)

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

@@ -63,7 +63,7 @@ Next, add a "`chatbot`" node. **Nodes** represent units of work and are typicall
 
 Let's first select a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

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

@@ -73,7 +73,7 @@ For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot
 
 Let's first select our LLM:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python
@@ -286,7 +286,7 @@ For ease of use, adjust your code to replace the following with LangGraph prebui
 - `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
 - `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 
 ```python hl_lines="25 30"

docs/docs/tutorials/get-started/3-add-memory.md

@@ -154,7 +154,7 @@ The snapshot above contains the current state values, corresponding config, and
   
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

docs/docs/tutorials/get-started/4-human-in-the-loop.md

@@ -14,7 +14,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
 
 Let's first select a chat model:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python
@@ -221,7 +221,7 @@ The input has been received and processed as a tool message. Review this call's
 
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 ```python
 from typing import Annotated

docs/docs/tutorials/get-started/5-customize-state.md

@@ -221,7 +221,7 @@ Manual state updates will [generate a trace](https://smith.langchain.com/public/
 
 Check out the code snippet below to review the graph from this tutorial:
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

docs/docs/tutorials/get-started/6-time-travel.md

@@ -14,7 +14,7 @@ You can create these types of experiences using LangGraph's built-in **time trav
 
 Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
 
-{!snippets/chat_model_tabs.md!}
+{% include-markdown "../../../snippets/chat_model_tabs.md" %}
 
 <!---
 ```python

docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb

@@ -540,11 +540,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "================================\u001B[1m System Message \u001B[0m================================\n",
+      "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
-      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m types:\n",
-      "\u001B[33;1m\u001B[1;3m{tool_descriptions}\u001B[0m\n",
-      "\u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m. join(): Collects and combines results from prior actions.\n",
+      "Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
+      "\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
       "\n",
       " - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
       " - join should always be the last action in the plan, and will be called in two scenarios:\n",
@@ -561,11 +561,11 @@
       " - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
       " - Never introduce new actions other than the ones provided.\n",
       "\n",
-      "=============================\u001B[1m Messages Placeholder \u001B[0m=============================\n",
+      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
       "\n",
-      "\u001B[33;1m\u001B[1;3m{messages}\u001B[0m\n",
+      "\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
       "\n",
-      "================================\u001B[1m System Message \u001B[0m================================\n",
+      "================================\u001b[1m System Message \u001b[0m================================\n",
       "\n",
       "Remember, ONLY respond with the task list in the correct format! E.g.:\n",
       "idx. tool(arg_name=args)\n",
@@ -605,7 +605,7 @@
     "    llm: BaseChatModel, tools: Sequence[BaseTool], base_prompt: ChatPromptTemplate\n",
     "):\n",
     "    tool_descriptions = \"\\n\".join(\n",
-    "        f\"{i+1}. {tool.description}\\n\"\n",
+    "        f\"{i + 1}. {tool.description}\\n\"\n",
     "        for i, tool in enumerate(\n",
     "            tools\n",
     "        )  # +1 to offset the 0 starting index, we want it count normally from 1.\n",

docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb

@@ -378,7 +378,7 @@
     "\n",
     "async def execute_step(state: PlanExecute):\n",
     "    plan = state[\"plan\"]\n",
-    "    plan_str = \"\\n\".join(f\"{i+1}. {step}\" for i, step in enumerate(plan))\n",
+    "    plan_str = \"\\n\".join(f\"{i + 1}. {step}\" for i, step in enumerate(plan))\n",
     "    task = plan[0]\n",
     "    task_formatted = f\"\"\"For the following plan:\n",
     "{plan_str}\\n\\nYou are tasked with executing step {1}, {task}.\"\"\"\n",

docs/docs/tutorials/tnt-llm/tnt-llm.ipynb

@@ -302,7 +302,7 @@
     "def format_docs(docs: List[Doc]) -> str:\n",
     "    xml_table = \"<conversations>\\n\"\n",
     "    for doc in docs:\n",
-    "        xml_table += f'<conv_summ id={doc[\"id\"]}>{doc[\"summary\"]}</conv_summ>\\n'\n",
+    "        xml_table += f\"<conv_summ id={doc['id']}>{doc['summary']}</conv_summ>\\n\"\n",
     "    xml_table += \"</conversations>\"\n",
     "    return xml_table\n",
     "\n",
@@ -311,9 +311,9 @@
     "    xml = \"<cluster_table>\\n\"\n",
     "    for label in clusters:\n",
     "        xml += \"  <cluster>\\n\"\n",
-    "        xml += f'    <id>{label[\"id\"]}</id>\\n'\n",
-    "        xml += f'    <name>{label[\"name\"]}</name>\\n'\n",
-    "        xml += f'    <description>{label[\"description\"]}</description>\\n'\n",
+    "        xml += f\"    <id>{label['id']}</id>\\n\"\n",
+    "        xml += f\"    <name>{label['name']}</name>\\n\"\n",
+    "        xml += f\"    <description>{label['description']}</description>\\n\"\n",
     "        xml += \"  </cluster>\\n\"\n",
     "    xml += \"</cluster_table>\"\n",
     "    return xml\n",
@@ -600,13 +600,13 @@
     "    turns.append(\n",
     "        f\"\"\"\n",
     "<human idx={idx}>\n",
-    "{run.inputs['question']}\n",
+    "{run.inputs[\"question\"]}\n",
     "</human>\"\"\"\n",
     "    )\n",
     "    if run.outputs and run.outputs[\"output\"]:\n",
     "        turns.append(\n",
-    "            f\"\"\"<ai idx={idx+1}>\n",
-    "{run.outputs['output']}\n",
+    "            f\"\"\"<ai idx={idx + 1}>\n",
+    "{run.outputs[\"output\"]}\n",
     "</ai>\"\"\"\n",
     "        )\n",
     "    return {\n",

docs/mkdocs.yml

@@ -54,6 +54,7 @@ plugins:
       separator: '[\s\u200b\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;'
   - autorefs
   - tags
+  - include-markdown
   - mkdocstrings:
       custom_templates: templates
       handlers:
@@ -141,10 +142,6 @@ nav:
           - Overview: concepts/human_in_the_loop.md
           - Add human intervention: how-tos/human_in_the_loop/add-human-in-the-loop.md
           - Use Server API: cloud/how-tos/add-human-in-the-loop.md
-      - Breakpoints:
-          - Overview: concepts/breakpoints.md
-          - Set breakpoints: how-tos/human_in_the_loop/breakpoints.md
-          - Use Server API: cloud/how-tos/human_in_the_loop_breakpoint.md
       - Time travel:
           - Overview: concepts/time-travel.md
           - Use time travel: how-tos/human_in_the_loop/time-travel.md
@@ -261,6 +258,7 @@ nav:
       - MCP Adapters: reference/mcp.md
     - LangGraph Platform:
       - Server API: cloud/reference/api/api_ref.md
+      - Server changelog: cloud/reference/langgraph_server_changelog.md
       - Control Plane API: cloud/reference/api/api_ref_control_plane.md
       - CLI: cloud/reference/cli.md
       - SDK (Python): cloud/reference/sdk/python_sdk_ref.md
@@ -356,12 +354,9 @@ markdown_extensions:
       combine_header_slug: true
   - pymdownx.tasklist:
       custom_checkbox: true
-  - markdown_include.include:
-      base_path: ./
   - github-callouts
 hooks:
   - _scripts/notebook_hooks.py
-  - _scripts/copy_page_hooks.py
 extra:
   social:
     - icon: fontawesome/brands/js

docs/overrides/main.html

@@ -25,15 +25,19 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
         try {
             let rawContent = markdownScript.textContent;
             
-            // Import a library for decoding HTML entities
-            import { decode } from 'he';
+            // Safe HTML entity decoding function
+            function decodeHtmlEntities(text) {
+                const parser = new DOMParser();
+                const doc = parser.parseFromString(text, 'text/html');
+                return doc.documentElement.textContent || '';
+            }
             
-            // Always decode HTML entities using a safe library
-            rawContent = decode(rawContent);
+            // Always decode HTML entities since the browser might encode them
+            rawContent = decodeHtmlEntities(rawContent);
             
             
             const data = JSON.parse(rawContent);
-            const content = `# ${data.title}\n\nSource: ${window.location.href}\n\n${data.markdown}`;
+            const content = `Source: ${window.location.href}\n\n${data.markdown}`;
             
             navigator.clipboard.writeText(content).then(() => {
                 // Simple notification
@@ -79,7 +83,12 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
             };
             option1.onmouseout = function() { this.style.background = 'transparent'; };
             option1.onclick = function() {
-                copyPageAsMarkdown();
+                // Check if we're on a reference page
+                if (window.location.pathname.includes('/reference/')) {
+                    alert('Copy Page not yet available in API reference pages.');
+                } else {
+                    copyPageAsMarkdown();
+                }
                 dropdown.style.display = 'none';
             };
             
@@ -92,7 +101,7 @@ j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src=
             };
             option2.onmouseout = function() { this.style.background = 'transparent'; };
             option2.onclick = function() {
-                window.open('/llms-txt-overview/', '_blank');
+                window.open('/langgraph/llms-txt-overview/', '_blank');
                 dropdown.style.display = 'none';
             };
             

docs/pyproject.toml

@@ -7,7 +7,7 @@ name = "langgraph-docs"
 version = "0.0.1"
 description = "LangGraph docs"
 authors = []
-requires-python = "~=3.10"
+requires-python = "~=3.11"
 readme = "README.md"
 license = "MIT"
 dependencies = [
@@ -48,6 +48,7 @@ docs = [
     "ruff",
     "jupyter",
     "langchain-cohere",
+    "mkdocs-include-markdown-plugin>=7.1.6",
 ]
 test = [
     "langchain",

libs/checkpoint-postgres/langgraph/store/postgres/base.py

@@ -557,7 +557,7 @@ class BasePostgresStore(Generic[C]):
     ) -> list[tuple[str, Sequence]]:
         queries: list[tuple[str, Sequence]] = []
         for _, op in list_ops:
-            query = """
+            query = r"""
                 SELECT DISTINCT ON (truncated_prefix) truncated_prefix, prefix
                 FROM (
                     SELECT
@@ -756,6 +756,7 @@ class PostgresStore(BaseStore, BasePostgresStore[_pg_internal.Conn]):
                 If provided, will create a connection pool and use it instead of a single connection.
                 This overrides the `pipeline` argument.
             index: The index configuration for the store.
+            ttl: The TTL configuration for the store.
 
         Returns:
             PostgresStore: A new PostgresStore instance.

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.21"
+version = "2.0.22"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.9"

libs/checkpoint-postgres/uv.lock

@@ -28,11 +28,11 @@ wheels = [
 
 [[package]]
 name = "certifi"
-version = "2025.6.15"
+version = "2025.7.9"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/73/f7/f14b46d4bcd21092d7d3ccef689615220d8a08fb25e564b65d20738e672e/certifi-2025.6.15.tar.gz", hash = "sha256:d747aa5a8b9bbbb1bb8c22bb13e22bd1f18e9796defa16bab421f7f7a317323b", size = 158753, upload-time = "2025-06-15T02:45:51.329Z" }
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 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/66/f3/80a3f974c8b535d394ff960a11ac20368e06b736da395b551a49ce950cce/certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39", size = 159230, upload-time = "2025-07-09T02:13:57.007Z" },
 ]
 
 [[package]]
@@ -334,7 +334,7 @@ dev = [
 
 [[package]]
 name = "langgraph-checkpoint-postgres"
-version = "2.0.21"
+version = "2.0.22"
 source = { editable = "." }
 dependencies = [
     { name = "langgraph-checkpoint" },
@@ -381,7 +381,7 @@ dev = [
 
 [[package]]
 name = "langsmith"
-version = "0.4.4"
+version = "0.4.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
@@ -392,9 +392,9 @@ dependencies = [
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     { name = "zstandard" },
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 [[package]]

libs/langgraph/README.md

@@ -74,6 +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/overview/): Guided examples on getting started with LangGraph.
+- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
 - [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/state.py

@@ -794,7 +794,7 @@ class StateGraph(Generic[StateT, InputT, OutputT]):
         interrupt_after: All | list[str] | None = None,
         debug: bool = False,
         name: str | None = None,
-    ) -> CompiledStateGraph[StateT, InputT]:
+    ) -> CompiledStateGraph[StateT, InputT, OutputT]:
         """Compiles the state graph into a `CompiledStateGraph` object.
 
         The compiled graph implements the `Runnable` interface and can be invoked,
@@ -996,7 +996,7 @@ class CompiledStateGraph(
                 writers=[ChannelWrite(write_entries)],
             )
         elif node is not None:
-            input_schema = node.input if node else self.builder._state_schema
+            input_schema = node.input if node else self.builder.state_schema
             input_channels = list(self.builder.schemas[input_schema])
             is_single_input = len(input_channels) == 1 and "__root__" in input_channels
             if input_schema in self.schema_to_mapper:

libs/langgraph/langgraph/pregel/protocol.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
+from abc import abstractmethod
 from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import Any, Generic
 
@@ -14,7 +14,7 @@ from langgraph.typing import InputT, OutputT, StateT
 
 
 # TODO: remove Runnable inheritance here!
-class PregelProtocol(Runnable[InputT, Any], Generic[StateT, InputT, OutputT], ABC):
+class PregelProtocol(Runnable[InputT, Any], Generic[StateT, InputT, OutputT]):
     @abstractmethod
     def with_config(
         self, config: RunnableConfig | None = None, **kwargs: Any

libs/langgraph/langgraph/pregel/runner.py

@@ -190,7 +190,7 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` schuduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
                 tasks = ()  # don't reschedule this task
@@ -326,7 +326,7 @@ class PregelRunner:
                             tb = tb.tb_next
                         exc.__traceback__ = tb
                     raise
-            if not futures:  # maybe `t` schuduled another task
+            if not futures:  # maybe `t` scheduled another task
                 return
             else:
                 tasks = ()  # don't reschedule this task

libs/langgraph/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph"
-version = "0.5.1"
+version = "0.5.3"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 requires-python = ">=3.9"

libs/langgraph/uv.lock

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libs/prebuilt/langgraph/prebuilt/interrupt.py

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             - "edit": Modifies the current state/content
-        arg: The response payload:
+        args: The response payload:
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             - str: For text responses
             - ActionRequest: For edit actions with updated content

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