checkpointer: Enter stack for blobs dict in InMemorySaver (#4419 )

docs(cloud): update human-in-the-loop guides to use interrupt() (#4418 )
Improve logic for drawing virtual end node (#4409 )
2026-08-28 10:49:56 +02:00 · 2025-04-26 13:57:36 -07:00 · 2025-04-26 13:37:06 -04:00 · 2025-04-25 15:45:12 -07:00 · 2025-04-25 15:38:41 -07:00 · 2025-04-25 15:23:46 -07:00
142 changed files with 5482 additions and 9869 deletions
@@ -88,10 +88,11 @@ def has_blocklisted_command(code: str, metadata: dict) -> bool:
            return True
    return False

-def add_mermaid_retries(code: str) -> str:
+def remove_mermaid(code: str) -> str:
    return code.replace(
-        "draw_mermaid_png()",
-        "draw_mermaid_png(max_retries=10, retry_delay=2.0)"
+        "display(Image(graph.get_graph().draw_mermaid_png()))",
+        # replace with a dummy statement
+        "print()"
    )


@@ -188,12 +189,12 @@ def add_vcr_to_notebook(
    return notebook


-def add_mermaid_retries_to_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
+def remove_mermaid_from_notebook(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
    for cell in notebook.cells:
        if cell.cell_type != "code":
            continue

-        cell.source = add_mermaid_retries(cell.source)
+        cell.source = remove_mermaid(cell.source)
    return notebook


@@ -218,7 +219,7 @@ def process_notebooks(should_comment_install_cells: bool) -> None:
                            notebook, cassette_prefix=cassette_prefix
                        )

-                    notebook = add_mermaid_retries_to_notebook(notebook)
+                    notebook = remove_mermaid_from_notebook(notebook)

                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
                        # Add a cell at the beginning to indicate that this notebook should not be executed
@@ -1 +0,0 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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@@ -1 +1 @@
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
@@ -29,7 +29,9 @@ agent = create_react_agent(
 )

 # Run the agent
-agent.invoke({"messages": "what is the weather in sf"})
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)
 ```

 1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the [tools](./tools.md) page.
@@ -85,7 +87,7 @@ agent = create_react_agent(
 )

 agent.invoke(
-    {"messages": "what is the weather in sf"},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
 )
 ```

@@ -113,7 +115,7 @@ agent = create_react_agent(
 )

 agent.invoke(
-    {"messages": "what is the weather in sf"},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    config={"configurable": {"user_name": "John Smith"}}
 )
@@ -150,12 +152,12 @@ agent = create_react_agent(
 # highlight-next-line
 config = {"configurable": {"thread_id": "1"}}
 sf_response = agent.invoke(
-    {"messages": "what is the weather in sf"},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    config  # (2)!
 )
 ny_response = agent.invoke(
-    {"messages": "what about new york?"},
+    {"messages": [{"role": "user", "content": "what about new york?"}]},
    # highlight-next-line
    config
 )
@@ -189,7 +191,9 @@ agent = create_react_agent(
    response_format=WeatherResponse  # (1)!
 )

-response = agent.invoke({"messages": "what is the weather in sf"})
+response = agent.invoke(
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+)

 # highlight-next-line
 response["structured_response"]
@@ -36,7 +36,7 @@ for this purpose:

 ```python
 agent.invoke(
-    {"messages": "hi!"},
+    {"messages": [{"role": "user", "content": "hi!"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
 )
@@ -183,7 +183,7 @@ Tools can access context through special parameter **annotations**.
    )

    agent.invoke(
-        {"messages": "look up user information"},
+        {"messages": [{"role": "user", "content": "look up user information"}]},
        # highlight-next-line
        config={"configurable": {"user_id": "user_123"}}
    )
@@ -278,7 +278,7 @@ agent = create_react_agent(
 )

 agent.invoke(
-    {"messages": "greet the user"},
+    {"messages": [{"role": "user", "content": "greet the user"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
 )
@@ -70,7 +70,7 @@ config = {
 }

 for chunk in agent.stream(
-    {"messages": "book a stay at McKittrick hotel"},
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
    # highlight-next-line
    config
 ):
@@ -194,7 +194,7 @@ config = {"configurable": {"thread_id": "1"}}

 # Run the agent
 for chunk in agent.stream(
-    {"messages": "book a stay at McKittrick hotel"},
+    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
    # highlight-next-line
    config
 ):
@@ -40,8 +40,12 @@ async with MultiServerMCPClient(
        # highlight-next-line
        client.get_tools()
    )
-    math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
-    weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
+    math_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
+    )
+    weather_response = await agent.ainvoke(
+        {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
+    )
 ```

 ## Custom MCP servers
@@ -59,14 +59,14 @@ config = {
 }

 sf_response = agent.invoke(
-    {"messages": "what is the weather in sf"},
+    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    config
 )

 # Continue the conversation using the same thread_id
 ny_response = agent.invoke(
-    {"messages": "what about new york?"},
+    {"messages": [{"role": "user", "content": "what about new york?"}]},
    # highlight-next-line
    config # (4)!
 )
@@ -188,13 +188,13 @@ agent = create_react_agent(

 # Run the agent
 agent.invoke(
-    {"messages": "look up user information"},
+    {"messages": [{"role": "user", "content": "look up user information"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}}
 )
 ```

-1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
 2. For this example, we write some sample data to the store using the `put` method. Please see the [BaseStore.put][langgraph.store.base.BaseStore.put] API reference for more details.
 3. The first argument is the namespace. This is used to group related data together. In this case, we are using the `users` namespace to group user data.
 4. A key within the namespace. This example uses a user ID for the key.
@@ -206,7 +206,7 @@ agent.invoke(
 ### Writing

 ```python title="Example of a tool that updates user information"
-from typing import TypedDict
+from typing_extensions import TypedDict

 from langgraph.config import get_store
 from langgraph.prebuilt import create_react_agent
@@ -236,7 +236,7 @@ agent = create_react_agent(

 # Run the agent
 agent.invoke(
-    {"messages": "My name is John Smith"},
+    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
    # highlight-next-line
    config={"configurable": {"user_id": "user_123"}} # (6)!
 )
@@ -245,7 +245,7 @@ agent.invoke(
 store.get(("users",), "user_123").value
 ```

-1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/stores.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
+1. The `InMemoryStore` is a store that stores data in memory. In a production setting, you would typically use a database or other persistent storage. Please review the [store documentation](../reference/store.md) for more options. If you're deploying with **LangGraph Platform**, the platform will provide a production-ready store for you.
 2. The `UserInfo` class is a `TypedDict` that defines the structure of the user information. The LLM will use this to format the response according to the schema.
 3. The `save_user_info` function is a tool that allows an agent to update user information. This could be useful for a chat application where the user wants to update their profile information.
 4. The `get_store` function is used to access the store. You can call it from anywhere in your code, including tools and prompts. This function returns the store that was passed to the agent when it was created.
@@ -53,12 +53,22 @@ hotel_assistant = create_react_agent(
 supervisor = create_supervisor(
    agents=[flight_assistant, hotel_assistant],
    model=ChatOpenAI(model="gpt-4o"),
-    prompt="You manage a hotel booking assistant and a flight booking assistant. Assign work to them."
+    prompt=(
+        "You manage a hotel booking assistant and a"
+        "flight booking assistant. Assign work to them."
+    )
 ).compile()

-for chunk in supervisor.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
+for chunk in supervisor.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
    print(chunk)
    print("\n")
 ```
@@ -110,9 +120,16 @@ swarm = create_swarm(
    default_active_agent="flight_assistant"
 ).compile()

-for chunk in supervisor.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
+for chunk in swarm.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
    print(chunk)
    print("\n")
 ```
@@ -253,9 +270,16 @@ multi_agent_graph = (
 )

 # Run the multi-agent graph
-for chunk in multi_agent_graph.stream({
-    "messages": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
-}):
+for chunk in multi_agent_graph.stream(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "book a flight from BOS to JFK and a stay at McKittrick Hotel"
+            }
+        ]
+    }
+):
    print(chunk)
    print("\n")
 ```
@@ -1,7 +1,7 @@
 # Running agents


-Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](#streaming) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.
+Agents support both synchronous and asynchronous execution using either `.invoke()` / `await .invoke()` for full responses, or `.stream()` / `.astream()` for **incremental** [streaming](streaming.md) output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.


 ## Basic usage
@@ -18,7 +18,7 @@ Agents can be executed in two primary modes:
    agent = create_react_agent(...)

    # highlight-next-line
-    response = agent.invoke({"messages": "what is the weather in sf"})
+    response = agent.invoke({"messages": [{"role": "user", "content": "what is the weather in sf"}]})
    ```

 === "Async invocation"
@@ -27,7 +27,7 @@ Agents can be executed in two primary modes:

    agent = create_react_agent(...)
    # highlight-next-line
-    response = await agent.ainvoke({"messages": "what is the weather in sf"})
+    response = await agent.ainvoke({"messages": [{"role": "user", "content": "what is the weather in sf"}]})
    ```

 ## Inputs and outputs
@@ -82,7 +82,7 @@ Streaming is available in both sync and async modes:

    ```python
    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        stream_mode="updates"
    ):
        print(chunk)
@@ -92,7 +92,7 @@ Streaming is available in both sync and async modes:

    ```python
    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        stream_mode="updates"
    ):
        print(chunk)
@@ -122,7 +122,7 @@ To control agent execution and avoid infinite loops, set a recursion limit. This

    try:
        response = agent.invoke(
-            {"messages": "what's the weather in sf"},
+            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
            # highlight-next-line
            {"recursion_limit": recursion_limit},
        )
@@ -148,7 +148,7 @@ To control agent execution and avoid infinite loops, set a recursion limit. This

    try:
        response = agent_with_recursion_limit.invoke(
-            {"messages": "what's the weather in sf"},
+            {"messages": [{"role": "user", "content": "what's the weather in sf"}]},
        )
    except GraphRecursionError:
        print("Agent stopped due to max iterations.")
@@ -35,7 +35,7 @@ For example, if you have an agent that calls a tool once, you should see the fol
    )
    # highlight-next-line
    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="updates"
    ):
@@ -52,7 +52,7 @@ For example, if you have an agent that calls a tool once, you should see the fol
    )
    # highlight-next-line
    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="updates"
    ):
@@ -73,7 +73,7 @@ To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
    )
    # highlight-next-line
    for token, metadata in agent.stream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="messages"
    ):
@@ -91,7 +91,7 @@ To stream tokens as they are produced by the LLM, use `stream_mode="messages"`:
    )
    # highlight-next-line
    async for token, metadata in agent.astream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="messages"
    ):
@@ -125,7 +125,7 @@ To stream updates from tools as they are executed, you can use [get_stream_write
    )

    for chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="custom"
    ):
@@ -154,7 +154,7 @@ To stream updates from tools as they are executed, you can use [get_stream_write
    )

    async for chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode="custom"
    ):
@@ -178,7 +178,7 @@ You can specify multiple streaming modes by passing stream mode as a list: `stre
    )

    for stream_mode, chunk in agent.stream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode=["updates", "messages", "custom"]
    ):
@@ -195,7 +195,7 @@ You can specify multiple streaming modes by passing stream mode as a list: `stre
    )

    async for stream_mode, chunk in agent.astream(
-        {"messages": "what is the weather in sf"},
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
        # highlight-next-line
        stream_mode=["updates", "messages", "custom"]
    ):
@@ -116,7 +116,9 @@ agent = create_react_agent(
    tools=tools
 )

-agent.invoke({"messages": "what's 3 + 5 and 4 * 7? make both calculations in parallel"})
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5 and 4 * 7?"}]}
+)
 ```

 ## Return tool results directly
@@ -137,7 +139,9 @@ agent = create_react_agent(
    tools=[add]
 )

-agent.invoke({"messages": "what's 3 + 5?"})
+agent.invoke(
+    {"messages": [{"role": "user", "content": "what's 3 + 5?"}]}
+)
 ```

 ## Force tool use
@@ -161,7 +165,9 @@ agent = create_react_agent(
    tools=tools
 )

-agent.invoke({"messages": "Hi, I am Bob"})
+agent.invoke(
+    {"messages": [{"role": "user", "content": "Hi, I am Bob"}]}
+)
 ```

 !!! Warning "Avoid infinite loops"
@@ -191,7 +197,9 @@ By default, the agent will catch all exceptions raised during tool calls and wil
        model="anthropic:claude-3-7-sonnet-latest",
        tools=[multiply]
    )
-    agent.invoke({"messages": "what's 42 x 7?"})
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
    ```

 === "Disable error handling"
@@ -215,7 +223,9 @@ By default, the agent will catch all exceptions raised during tool calls and wil
        model="anthropic:claude-3-7-sonnet-latest",
        tools=tool_node
    )
-    agent_no_error_handling.invoke({"messages": "what's 42 x 7?"})
+    agent_no_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
    ```

    1. This disables error handling (enabled by default). See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
@@ -243,7 +253,9 @@ By default, the agent will catch all exceptions raised during tool calls and wil
        model="anthropic:claude-3-7-sonnet-latest",
        tools=tool_node
    )
-    agent_custom_error_handling.invoke({"messages": "what's 42 x 7?"})
+    agent_custom_error_handling.invoke(
+        {"messages": [{"role": "user", "content": "what's 42 x 7?"}]}
+    )
    ```

    1. This provides a custom message to send to the LLM in case of an exception. See all available strategies in the [API reference][langgraph.prebuilt.tool_node.ToolNode].
@@ -40,6 +40,14 @@ Example `package.json` file:
 }
 ```

+When deploying your app, the dependencies will be installed using the package manager of your choice, provided they adhere to the compatible version ranges listed below:
+
+```
+"@langchain/core": "^0.3.42",
+"@langchain/langgraph": "^0.2.57",
+"@langchain/langgraph-checkpoint": "~0.0.16",
+```
+
 Example file directory:

 ```bash
@@ -82,14 +90,10 @@ import { ChatOpenAI } from "@langchain/openai";
 import { MessagesAnnotation, StateGraph } from "@langchain/langgraph";
 import { ToolNode } from "@langchain/langgraph/prebuilt";

-const tools = [
-  new TavilySearchResults({ maxResults: 3, }),
-];
+const tools = [new TavilySearchResults({ maxResults: 3 })];

 // Define the function that calls the model
-async function callModel(
-  state: typeof MessagesAnnotation.State,
-) {
+async function callModel(state: typeof MessagesAnnotation.State) {
  /**
   * Call the LLM powering our agent.
   * Feel free to customize the prompt, model, and other logic!
@@ -101,9 +105,9 @@ async function callModel(
  const response = await model.invoke([
    {
      role: "system",
-      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`
+      content: `You are a helpful assistant. The current date is ${new Date().getTime()}.`,
    },
-    ...state.messages
+    ...state.messages,
  ]);

  // MessagesAnnotation supports returning a single message or array of messages
@@ -141,10 +145,7 @@ const workflow = new StateGraph(MessagesAnnotation)
    routeModelOutput,
    // List of the possible destinations the conditional edge can route to.
    // Required for conditional edges to properly render the graph in Studio
-    [
-      "tools",
-      "__end__"
-    ],
+    ["tools", "__end__"]
  )
  // This means that after `tools` is called, `callModel` node is called next.
  .addEdge("tools", "callModel");
@@ -155,6 +156,7 @@ export const graph = workflow.compile();
 ```

 !!! info "Assign `CompiledGraph` to Variable"
+
    The build process for LangGraph Cloud requires that the `CompiledGraph` object be assigned to a variable at the top-level of a JavaScript module (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).

 Example file directory:
@@ -193,6 +195,7 @@ Example `langgraph.json` file:
 Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).

 !!! info "Configuration Location"
+
    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the TypeScript files that contain compiled graphs and associated dependencies.

 ## Next
@@ -22,7 +22,7 @@ To support this, LangGraph Studio, in combination with LangSmith, allows you to

 First navigate to the LangSmith trace. Here you should see a button to "Run in Studio".

-![Run in Studio](../img/run_in_studio.png){width=1200}
+![Run in Studio](img/run_in_studio.png){width=1200}

 This will prompt you to enter the url that your locally running agent is accessible at. Once provided, select "Clone thread locally". If you have multiple graphs in your agent, you will also be prompted to select a graph to clone this thread under.

@@ -1,12 +1,16 @@
-# How to Add Breakpoints
+# How to add static breakpoints

-When creating LangGraph agents, it is often nice to add a human-in-the-loop component.
-This can be helpful when giving them access to tools.
-Often in these situations you may want to manually approve an action before taking.
+!!! tip "Prerequisites"

-This can be in several ways, but the primary supported way is to add an "interrupt" before a node is executed.
-This interrupts execution at that node.
-You can then resume from that spot to continue.  
+    This guide assumes familiarity with the following concepts:
+
+    * [Breakpoints](../../concepts/breakpoints.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+    
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). [Breakpoints](../../concepts/low_level.md#breakpoints) are a common HIL interaction pattern, allowing the graph to stop at specific steps and seek human approval before proceeding (e.g., for sensitive actions).
+
+Breakpoints are built on top of LangGraph [checkpoints](../../concepts/low_level.md#persistence), which save the graph's state after each node execution. Checkpoints are saved in [threads](../../concepts/low_level.md#threads) that preserve graph state and can be accessed after a graph has finished execution. This allows for graph execution to pause at specific points, await human approval, and then resume execution from the last checkpoint.

 ## Setup

@@ -1,6 +1,14 @@
-# Review Tool Calls
+# How to review tool calls

-Human-in-the-loop (HIL) interactions are crucial for [agentic systems](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#human-in-the-loop). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:
+!!! tip "Prerequisites"
+
+    This guide assumes familiarity with the following concepts:
+
+    * [Tool calling](https://python.langchain.com/docs/concepts/tool_calling/)
+    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+
+Human-in-the-loop (HIL) interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md). A common pattern is to add some human in the loop step after certain tool calls. These tool calls often lead to either a function call or saving of some information. Examples include:

 - A tool call to execute SQL, which will then be run by the tool
 - A tool call to generate a summary, which will then be saved to the State of the graph
@@ -11,13 +19,46 @@ There are typically a few different interactions you may want to do here:

 1. Approve the tool call and continue
 2. Modify the tool call manually and then continue
-3. Give natural language feedback, and then pass that back to the agent instead of continuing
+3. Give natural language feedback, and then pass that back to the agent

-We can implement this in LangGraph using a [breakpoint](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/): breakpoints allow us to interrupt graph execution before a specific step. At this breakpoint, we can manually update the graph state taking one of the three options above
+
+We can implement these in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input:
+
+
+```python
+def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
+    # this is the value we'll be providing via Command(resume=<human_review>)
+    human_review = interrupt(
+        {
+            "question": "Is this correct?",
+            # Surface tool calls for review
+            "tool_call": tool_call
+        }
+    )
+
+    review_action, review_data = human_review
+
+    # Approve the tool call and continue
+    if review_action == "continue":
+        return Command(goto="run_tool")
+
+    # Modify the tool call manually and then continue
+    elif review_action == "update":
+        ...
+        updated_msg = get_updated_msg(review_data)
+        return Command(goto="run_tool", update={"messages": [updated_message]})
+
+    # Give natural language feedback, and then pass that back to the agent
+    elif review_action == "feedback":
+        ...
+        feedback_msg = get_feedback_msg(review_data)
+        return Command(goto="call_llm", update={"messages": [feedback_msg]})
+
+```

 ## Setup

-We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage) if you want to. Once this graph is hosted, we are ready to invoke it and wait for user input. 
+We are not going to show the full code for the graph we are hosting, but you can see it [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb). Once this graph is hosted, we are ready to invoke it and wait for user input. 

 ### SDK initialization

@@ -54,122 +95,9 @@ First, we need to setup our client so that we can communicate with our hosted gr
      --data '{}'
    ```

-## Example with no review
-
-Let's look at an example when no review is required (because no tools are called)
-
-=== "Python"
-
-    ```python
-    input = { 'messages':[{ "role":"user", "content":"hi!" }] }
-
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=input,
-        stream_mode="updates",
-        interrupt_before=["action"],
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const input = { "messages": [{ "role": "user", "content": "hi!" }] };
-
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: input,
-        streamMode: "updates",
-        interruptBefore: ["action"],
-      }
-    );
-
-    for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"hi!\"}]},
-       \"stream_mode\": [
-         \"updates\"
-       ],
-       \"interrupt_before\": [\"action\"]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
-    ```
-
-Output:
-
-    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}]}
-    {'messages': [{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '39c51f14-2d5c-4690-883a-d940854b1845', 'example': False}, {'content': [{'text': "Hello! Welcome. How can I assist you today? Is there anything specific you'd like to know or any information you're looking for?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d65e07fb-43ff-4d98-ab6b-6316191b9c8b', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 355, 'output_tokens': 31, 'total_tokens': 386}}]}
-
-
-If we check the state, we can see that it is finished
-
-=== "Python"
-
-    ```python
-    state = await client.threads.get_state(thread["thread_id"])
-
-    print(state['next'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-
-    console.log(state.next);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-        --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
-    ```
-
-Output:
-
-    []
-
 ## Example of approving tool

-Let's now look at what it looks like to approve a tool call. Note that we don't need to pass an interrupt to our streaming calls because the graph (defined [here](../../how-tos/human_in_the_loop/review-tool-calls.ipynb#simple-usage)) was already compiled with an interrupt before the `human_review_node`.
+First, let's run the agent with an input that requires tool calls with approval:

 === "Python"

@@ -180,6 +108,7 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
        thread["thread_id"],
        assistant_id,
        input=input,
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -195,6 +124,7 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
      assistantId,
      {
        input: input,
+        streamMode: "updates"
      }
    );

@@ -213,75 +143,32 @@ Let's now look at what it looks like to approve a tool call. Note that we don't
     --header 'Content-Type: application/json' \
     --data "{
       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

 Output:

-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}]}
+    {'call_llm': {'messages': [{'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01Tdfufy4nZYXMbVZvgyNbhc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 379, 'output_tokens': 66}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-a33434b2-f5ca-40c6-98e2-6288d349d4ce-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 379, 'output_tokens': 66, 'total_tokens': 445, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01142G3woscA8JjFTLdqymtn', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:9caf42cf-1371-7213-a331-e6fe5d026be8'], 'when': 'during'}]}

-
-If we now check, we can see that it is waiting on human review:
+To approve the tool call, we need to let `human_review_node` know what value to use for the `human_review` variable we defined inside the node. We can provide this value by invoking the graph with a `Command(resume=<human_review>)` input.  Since we're approving the tool call, we'll provide `resume` value of `{"action": "continue"}` to navigate to `run_tool` node:

 === "Python"

    ```python
-    state = await client.threads.get_state(thread["thread_id"])
+    # highlight-next-line
+    from langgraph_sdk.schema import Command

-    print(state['next'])
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-
-    console.log(state.next);
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-        --url <DELPOYMENT_URL>/threads/<THREAD_ID>/state | jq -c '.next'
-    ```
-
-Output:
-
-    ['human_review_node']
-
-To approve the tool call, we can just continue the thread with no edits. To do this, we just create a new run with no inputs.
-
-=== "Python"
-
-    ```python
    async for chunk in client.runs.stream(
        thread["thread_id"],
        assistant_id,
-        input=None,
-        stream_mode="values",
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -294,8 +181,9 @@ To approve the tool call, we can just continue the thread with no edits. To do t
      thread["thread_id"],
      assistantId,
      {
-        input: null,
-        streamMode: "values",
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
      }
    );

@@ -313,34 +201,21 @@ To approve the tool call, we can just continue the thread with no edits. To do t
     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
     --header 'Content-Type: application/json' \
     --data "{
-       \"assistant_id\": \"agent\"
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

 Output:

-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '54e19d6e-89fa-44fb-b92c-12e7dd4ddf08', 'example': False}, {'content': [{'text': "Certainly! I can help you check the weather in San Francisco. To get this information, I'll use the weather search function. Let me do that for you right away.", 'type': 'text', 'index': 0}, {'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-45a6b6c3-ac69-42a4-8957-d982203d6392', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 90, 'total_tokens': 450}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '826cd0f2-9cc6-46f0-b7df-daa6a05d13d2', 'tool_call_id': 'toolu_015yrR3GMDXe6X8m2p9CsEDN', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5d5fd0f1-a939-447e-801a-9aaa812322d3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 464, 'output_tokens': 50, 'total_tokens': 514}}]}
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01142G3woscA8JjFTLdqymtn'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01JJE9AtT4a9Lob91RRiW9rU', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 458, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-5e8d80b5-c46a-4aad-af37-b01f8bb15963-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 458, 'output_tokens': 18, 'total_tokens': 476, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}

 ## Edit Tool Call

@@ -355,7 +230,7 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
        thread["thread_id"],
        assistant_id,
        input=input,
-        stream_mode="values",
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -371,7 +246,7 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
      assistantId,
      {
        input: input,
-        streamMode: "values",
+        streamMode: "updates",
      }
    );

@@ -390,84 +265,35 @@ Let's now say we want to edit the tool call. E.g. change some of the parameters
     --header 'Content-Type: application/json' \
     --data "{
       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

-Output:
+To do this, we will use `Command` with a different resume value of `{"action": "update", "data": <tool call args>}`. This will do the following:

-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'cec11391-84da-464b-bd2a-bd4f0d93b9ee', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6326da9f-6061-4e12-8586-482e32ab4cab', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01SunSpDurNfcnXppWLPrtjC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-
-To do this, we first need to update the state. We can do this by passing a message in with the **same** id of the message we want to overwrite. This will have the effect of **replacing** that old message. Note that this is only possible because of the **reducer** we are using that replaces messages with the same ID - read more about that [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#working-with-messages-in-graph-state).
+* combine existing tool call with user-provided tool call arguments and update the existing AI message with the new tool call
+* navigate to `run_tool` node with the updated AI message and continue execution


 === "Python"

    ```python
-    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
-    state = await client.threads.get_state(thread['thread_id'])
-    print("Current State:")
-    print(state['values'])
-    print("\nCurrent Tool Call ID:")
-    current_content = state['values']['messages'][-1]['content']
-    current_id = state['values']['messages'][-1]['id']
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
-    print(tool_call_id)
+    # highlight-next-line
+    from langgraph_sdk.schema import Command

-    # We now need to construct a replacement tool call.
-    # We will change the argument to be `San Francisco, USA`
-    # Note that we could change any number of arguments or tool names - it just has to be a valid one
-    new_message = {
-        "role": "assistant", 
-        "content": current_content,
-        "tool_calls": [
-            {
-                "id": tool_call_id,
-                "name": "weather_search",
-                "args": {"city": "San Francisco, USA"}
-            }
-        ],
-        # This is important - this needs to be the same as the message you replacing!
-        # Otherwise, it will show up as a separate message
-        "id": current_id
-    }
-    await client.threads.update_state(
-        # This is the config which represents this thread
-        thread['thread_id'], 
-        # This is the updated value we want to push
-        {"messages": [new_message]}, 
-        # We push this update acting as our human_review_node
-        as_node="human_review_node"
-    )
-
-    print("\nResuming Execution")
-    # Let's now continue executing from here
    async for chunk in client.runs.stream(
        thread["thread_id"],
        assistant_id,
-        input=None,
+        # highlight-next-line
+        command=Command(
+            # highlight-next-line
+            resume={"action": "update", "data": {"city": "San Francisco, USA"}}
+            # highlight-next-line
+        ),
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -476,51 +302,21 @@ To do this, we first need to update the state. We can do this by passing a messa
 === "Javascript"

    ```js
-    const state = await client.threads.getState(thread.thread_id);
-    console.log("Current State:");
-    console.log(state.values);
-
-    console.log("\nCurrent Tool Call ID:");
-    const lastMessage = state.values.messages[state.values.messages.length - 1];
-    const currentContent = lastMessage.content;
-    const currentId = lastMessage.id;
-    const toolCallId = lastMessage.tool_calls[0].id;
-    console.log(toolCallId);
-
-    // Construct a replacement tool call
-    const newMessage = {
-      role: "assistant",
-      content: currentContent,
-      tool_calls: [
-        {
-          id: toolCallId,
-          name: "weather_search",
-          args: { city: "San Francisco, USA" }
-        }
-      ],
-      // Ensure the ID is the same as the message you're replacing
-      id: currentId
-    };
-
-    await client.threads.updateState(
-      thread.thread_id,  // Thread ID
-      {
-        values: { "messages": [newMessage] },  // Updated message
-        asNode: "human_review_node"
-      }  // Acting as human_review_node
-    );
-
-    console.log("\nResuming Execution");
-    // Continue executing from here
-    const streamResponseResumed = client.runs.stream(
+    const streamResponse = client.runs.stream(
      thread["thread_id"],
      assistantId,
      {
-        input: null,
+        // highlight-next-line
+        command: {
+          // highlight-next-line
+          resume: { "action": "update", "data": { "city": "San Francisco, USA" } }
+          // highlight-next-line
+        },
+        streamMode: "updates"
      }
    );

-    for await (const chunk of streamResponseResumed) {
+    for await (const chunk of streamResponse) {
      if (chunk.data && chunk.event !== "metadata") {
        console.log(chunk.data);
      }
@@ -531,76 +327,37 @@ To do this, we first need to update the state. We can do this by passing a messa

    ```bash
    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-    --header 'Content-Type: application/json' \
-    --data "{
-        \"values\": { \"messages\": [$(curl --request GET \
-            --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
-            jq -c '{
-            role: "assistant",
-            content: .values.messages[-1].content,
-            tool_calls: [
-                {
-                id: .values.messages[-1].tool_calls[0].id,
-                name: "weather_search",
-                args: { city: "San Francisco, USA" }
-                }
-            ],
-            id: .values.messages[-1].id
-            }')
-        ]},
-        \"as_node\": \"human_review_node\"
-    }" && echo "Resuming Execution" && curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "assistant_id": "agent"
-    }' | \
-    sed 's/\r$//' | \
-    awk '
-    /^event:/ {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-        sub(/^event: /, "", $0)
-        event_type = $0
-        data_content = ""
-    }
-    /^data:/ {
-        sub(/^data: /, "", $0)
-        data_content = $0
-    }
-    END {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-    }
-    '
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"update\", \"data\": { \"city\": \"San Francisco, USA\" } }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

 Output:

-    Current State:
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-    Current Tool Call ID:
-    toolu_01VzagzsUGZsNMwW1wHkcw7h
-
-    Resuming Execution
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '8713d1fa-9b26-4eab-b768-dafdaac70590', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-ede13f26-daf5-4d8f-817a-7611075bbcf1', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '7fc7d463-66bf-4555-9929-6af483de169b', 'tool_call_id': 'toolu_01VzagzsUGZsNMwW1wHkcw7h', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nBased on the search result, the weather in San Francisco is sunny! It's a beautiful day in the city by the bay. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-d90ce97a-39f9-4330-985e-67c5f351a0c5', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 455, 'output_tokens': 52, 'total_tokens': 507}}]}
+    {'human_review_node': {'messages': [{'role': 'ai', 'content': [{'text': "I'll help you check the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'input': {'city': 'San Francisco'}, 'name': 'weather_search', 'type': 'tool_use'}], 'tool_calls': [{'id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa', 'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}}], 'id': 'run-b07f0c35-4e93-43a5-9b48-363767ada3ca-0'}]}}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_016L4EDPcaQRzzZxiB4Wq2wa'}]}}
+    {'call_llm': {'messages': [{'content': "According to the search, it's sunny in San Francisco right now!", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01De5HurjNUMwMUpfRtMLbX1', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 460, 'output_tokens': 18}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-85e2aaaa-6f61-4fa0-b594-b6e57129d7e7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 460, 'output_tokens': 18, 'total_tokens': 478, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}

 ## Give feedback to a tool call

-Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert these feedback as a mock **RESULT** of the tool call.
+Sometimes, you may not want to execute a tool call, but you also may not want to ask the user to manually modify the tool call. In that case it may be better to get natural language feedback from the user. You can then insert this feedback as a mock **RESULT** of the tool call.

 There are multiple ways to do this:

-You could add a new message to the state (representing the "result" of a tool call)
-You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback
-Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_node` and how it handles different types of messages.
+1. You could add a new message to the state (representing the "result" of a tool call)
+2. You could add TWO new messages to the state - one representing an "error" from the tool call, other HumanMessage representing the feedback

-For this example we will just add a single tool call representing the feedback. Let's see this in action!
+Both are similar in that they involve adding messages to the state. The main difference lies in the logic AFTER the `human_review_node` and how it handles different types of messages.
+
+For this example we will just add a single tool call representing the feedback (see `human_review_node` implementation). Let's see this in action!

 === "Python"

@@ -611,6 +368,7 @@ For this example we will just add a single tool call representing the feedback.
        thread["thread_id"],
        assistant_id,
        input=input,
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -626,6 +384,7 @@ For this example we will just add a single tool call representing the feedback.
      assistantId,
      {
        input: input,
+        streamMode: "updates"
      }
    );

@@ -644,74 +403,35 @@ For this example we will just add a single tool call representing the feedback.
     --header 'Content-Type: application/json' \
     --data "{
       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]}
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

-Output:
+To do this, we will use `Command` with a different resume value of `{"action": "feedback", "data": <feedback string>}`. This will do the following:

-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'c80f13d0-674d-4233-b6a0-3940509d3cf3', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-4911ac27-3d7c-4edf-a3ca-c2908e3922eb', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_016XyTdFA8NuPWeLyZPSzoM3', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-To do this, we first need to update the state. We can do this by passing a message in with the same **tool call id** of the tool call we want to respond to. Note that this is a **different*** ID from above
+* create a new tool message that combines existing tool call from LLM with the with user-provided feedback as content
+* navigate to `call_llm` node with the updated tool message and continue execution

 === "Python"

    ```python
-    # To get the ID of the message we want to replace, we need to fetch the current state and find it there.
-    state = await client.threads.get_state(thread['thread_id'])
-    print("Current State:")
-    print(state['values'])
-    print("\nCurrent Tool Call ID:")
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
-    print(tool_call_id)
+    # highlight-next-line
+    from langgraph_sdk.schema import Command

-    # We now need to construct a replacement tool call.
-    # We will change the argument to be `San Francisco, USA`
-    # Note that we could change any number of arguments or tool names - it just has to be a valid one
-    new_message = {
-        "role": "tool", 
-        # This is our natural language feedback
-        "content": "User requested changes: pass in the country as well",
-        "name": "weather_search",
-        "tool_call_id": tool_call_id
-    }
-    await client.threads.update_state(
-        # This is the config which represents this thread
-        thread['thread_id'], 
-        # This is the updated value we want to push
-        {"messages": [new_message]}, 
-        # We push this update acting as our human_review_node
-        as_node="human_review_node"
-    )
-
-    print("\nResuming execution")
-    # Let's now continue executing from here
    async for chunk in client.runs.stream(
        thread["thread_id"],
        assistant_id,
-        input=None,
-        stream_mode="values",
+        # highlight-next-line
+        command=Command(
+            resume={
+                "action": "feedback", 
+                "data": "User requested changes: use <city, country> format for location"
+            }
+        ),
+        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -720,133 +440,22 @@ To do this, we first need to update the state. We can do this by passing a messa
 === "Javascript"

    ```js
-    const state = await client.threads.getState(thread.thread_id);
-    console.log("Current State:");
-    console.log(state.values);
-
-    console.log("\nCurrent Tool Call ID:");
-    const lastMessage = state.values.messages[state.values.messages.length - 1];
-    const toolCallId = lastMessage.tool_calls[0].id;
-    console.log(toolCallId);
-
-    // Construct a replacement tool call
-    const newMessage = {
-      role: "tool",
-      content: "User requested changes: pass in the country as well",
-      name: "weather_search",
-      tool_call_id: toolCallId,
-    };
-
-    await client.threads.updateState(
-      thread.thread_id,  // Thread ID
-      {
-        values: { "messages": [newMessage] },  // Updated message
-        asNode: "human_review_node"
-      }  // Acting as human_review_node
-    );
-
-    console.log("\nResuming Execution");
-    // Continue executing from here
-    const streamResponseEdited = client.runs.stream(
+    const streamResponse = client.runs.stream(
      thread["thread_id"],
      assistantId,
      {
-        input: null,
-        streamMode: "values",
-        interruptBefore: ["action"],
+        // highlight-next-line
+        command: {
+          resume: {
+            "action": "feedback", 
+            "data": "User requested changes: use <city, country> format for location"
+          }
+        },
+        streamMode: "updates"
      }
    );

-    for await (const chunk of streamResponseEdited) {
-      if (chunk.data && chunk.event !== "metadata") {
-        console.log(chunk.data);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-    --header 'Content-Type: application/json' \
-    --data "{
-        \"values\": { \"messages\": [$(curl --request GET \
-            --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state |
-            jq -c '{
-            role: "tool",
-            content: "User requested changes: pass in the country as well",
-            name: "get_weather",
-            tool_call_id: .values.messages[-1].id.tool_calls[0].id
-            }')
-        ]},
-        \"as_node\": \"human_review_node\"
-    }" && echo "Resuming Execution" && curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data '{
-    "assistant_id": "agent"
-    }' | \
-    sed 's/\r$//' | \
-    awk '
-    /^event:/ {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-        sub(/^event: /, "", $0)
-        event_type = $0
-        data_content = ""
-    }
-    /^data:/ {
-        sub(/^data: /, "", $0)
-        data_content = $0
-    }
-    END {
-        if (data_content != "" && event_type != "metadata") {
-            print data_content "\n"
-        }
-    }
-    '
-    ```
-
-
-Output:
-
-    Current State:
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}]}
-
-    Current Tool Call ID:
-    toolu_01NNw18j57GEGPZvsa9f1wvX
-
-    Resuming execution
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}]}
-
-We can see that we now get to another breakpoint - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue
-
-=== "Python"
-
-    ```python
-    async for chunk in client.runs.stream(
-        thread["thread_id"],
-        assistant_id,
-        input=None,
-    ):
-        if chunk.data and chunk.event != "metadata": 
-            print(chunk.data)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const streamResponseResumed = client.runs.stream(
-      thread["thread_id"],
-      assistantId,
-      {
-        input: null,
-      }
-    );
-
-    for await (const chunk of streamResponseResumed) {
+    for await (const chunk of streamResponse) {
      if (chunk.data && chunk.event !== "metadata") {
        console.log(chunk.data);
      }
@@ -860,31 +469,81 @@ We can see that we now get to another breakpoint - because it went back to the m
     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
     --header 'Content-Type: application/json' \
     --data "{
-       \"assistant_id\": \"agent\"
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"feedback\", \"data\": \"User requested changes: use <city, country> format for location\" }
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
+    ```
+
+
+Output:
+
+    {'human_review_node': {'messages': [{'role': 'tool', 'content': 'User requested changes: use <city, country> format for location', 'name': 'weather_search', 'tool_call_id': 'toolu_01RkPHCjpfoUvPAktaq4Cqhm'}]}}
+    {'call_llm': {'messages': [{'content': [{'text': 'Let me try that again with the correct format:', 'type': 'text'}, {'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'input': {'city': 'San Francisco, USA'}, 'name': 'weather_search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01EBan969yY5f6iGk6sPgKcj', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 469, 'output_tokens': 68}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-64bbc255-d126-4db0-8ae5-3197cf29bed1-0', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 469, 'output_tokens': 68, 'total_tokens': 537, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': {'question': 'Is this correct?', 'tool_call': {'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01Rdrag6cVufHZG26BwVaiE7', 'type': 'tool_call'}}, 'resumable': True, 'ns': ['human_review_node:e9856878-e28c-5dd1-d353-4d83aa1a3a2b'], 'when': 'during'}]}
+
+We can see that we now get to another interrupt - because it went back to the model and got an entirely new prediction of what to call. Let's now approve this one and continue.
+
+=== "Python"
+
+    ```python
+    # highlight-next-line
+    from langgraph_sdk.schema import Command
+
+    async for chunk in client.runs.stream(
+        thread["thread_id"],
+        assistant_id,
+        # highlight-next-line
+        command=Command(resume={"action": "continue"}),
+        stream_mode="updates",
+    ):
+        if chunk.data and chunk.event != "metadata": 
+            print(chunk.data)
+    ```
+
+=== "Javascript"
+
+    ```js
+    const streamResponse = client.runs.stream(
+      thread["thread_id"],
+      assistantId,
+      {
+        // highlight-next-line
+        command: { resume: { "action": "continue" } },
+        streamMode: "updates"
+      }
+    );
+
+    for await (const chunk of streamResponse) {
+      if (chunk.data && chunk.event !== "metadata") {
+        console.log(chunk.data);
+      }
+    }
+    ```
+
+=== "CURL"
+
+    ```bash
+    curl --request POST \
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": { \"action\": \"continue\"}
+       },
+       \"stream_mode\": [
+         \"updates\"
+       ]
+     }"
    ```

 Output:

-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}]}
-    {'messages': [{'content': "what's the weather in sf?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '3b2bbc38-d11b-49eb-80c0-c24a40dab5a8', 'example': False}, {'content': [{'text': 'To get the weather information for San Francisco, I can use the weather_search function. Let me do that for you.', 'type': 'text', 'index': 0}, {'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-c5a50900-abf5-4885-9cdb-da2bf0d892ac', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco'}, 'id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 360, 'output_tokens': 80, 'total_tokens': 440}}, {'content': 'User requested changes: pass in the country as well', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '787288be-213c-4fd3-8503-4a009bdb1b00', 'tool_call_id': 'toolu_01NNw18j57GEGPZvsa9f1wvX', 'artifact': None, 'status': 'success'}, {'content': [{'text': '\n\nI apologize for the oversight. It seems the function requires additional information. Let me try again with a more specific request.', 'type': 'text', 'index': 0}, {'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'input': {}, 'name': 'weather_search', 'type': 'tool_use', 'index': 1, 'partial_json': '{"city": "San Francisco, USA"}'}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'tool_use', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-5c355a56-cfe3-4046-b49f-f5b09fc397ef', 'example': False, 'tool_calls': [{'name': 'weather_search', 'args': {'city': 'San Francisco, USA'}, 'id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 461, 'output_tokens': 83, 'total_tokens': 544}}, {'content': 'Sunny!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'weather_search', 'id': '3b857482-bca2-4a73-a9ab-1f35a3e43e5f', 'tool_call_id': 'toolu_01YAbLBoKozJyRQnB8LUMpXC', 'artifact': None, 'status': 'success'}, {'content': [{'text': "\n\nGreat news! The weather in San Francisco is sunny today. Is there anything else you'd like to know about the weather or any other information I can help you with?", 'type': 'text', 'index': 0}], 'additional_kwargs': {}, 'response_metadata': {'stop_reason': 'end_turn', 'stop_sequence': None}, 'type': 'ai', 'name': None, 'id': 'run-6a857bb1-f65b-4b86-93d6-c025e003c777', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 557, 'output_tokens': 38, 'total_tokens': 595}}]}
+    {'human_review_node': None}
+    {'run_tool': {'messages': [{'role': 'tool', 'name': 'weather_search', 'content': 'Sunny!', 'tool_call_id': 'toolu_01Rdrag6cVufHZG26BwVaiE7'}]}}
+    {'call_llm': {'messages': [{'content': 'The weather in San Francisco is sunny!', 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_013WTDHhbg8WiYLiQ9n2CaTk', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 550, 'output_tokens': 12}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-b6c815f0-989a-47cf-b150-33e3bbc4eab7-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 550, 'output_tokens': 12, 'total_tokens': 562, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
@@ -1,16 +1,16 @@
-# How to Wait for User Input
+# How to wait for user input using `interrupt`

-One of the main human-in-the-loop interaction patterns is waiting for human input. A key use case involves asking the user clarifying questions. One way to accomplish this is simply go to the `END` node and exit the graph. Then, any user response comes back in as fresh invocation of the graph. This is basically just creating a chatbot architecture.
+!!! tip "Prerequisites"

-The issue with this is it is tough to resume back in a particular point in the graph. Often times the agent is halfway through some process, and just needs a bit of a user input. Although it is possible to design your graph in such a way where you have a `conditional_entry_point` to route user messages back to the right place, that is not super scalable (as it essentially involves having a routing function that can end up almost anywhere).
+    This guide assumes familiarity with the following concepts:

-A separate way to do this is to have a node explicitly for getting user input. This is easy to implement in a notebook setting - you just put an `input()` call in the node. But that isn't exactly production ready.
+    * [Human-in-the-loop](../../concepts/human_in_the_loop.md)
+    * [LangGraph Glossary](../../concepts/low_level.md)
+    

-Luckily, LangGraph makes it possible to do similar things in a production way. The basic idea is:
+**Human-in-the-loop (HIL)** interactions are crucial for [agentic systems](../../concepts/agentic_concepts.md#human-in-the-loop). Waiting for human input is a common HIL interaction pattern, allowing the agent to ask the user clarifying questions and await input before proceeding.

- Set up a node that represents human input. This can have specific incoming/outgoing edges (as you desire). There shouldn't actually be any logic inside this node.
- Add a breakpoint before the node. This will stop the graph before this node executes (which is good, because there's no real logic in it anyways)
- Use `.update_state` to update the state of the graph. Pass in whatever human response you get. The key here is to use the `as_node` parameter to apply this update **as if you were that node**. This will have the effect of making it so that when you resume execution next it resumes as if that node just acted, and not from the beginning.
+We can implement this in LangGraph using the [`interrupt()`][langgraph.types.interrupt] function. `interrupt` allows us to stop graph execution to collect input from a user and continue execution with collected input.

 ## Setup

@@ -54,7 +54,7 @@ First, we need to setup our client so that we can communicate with our hosted gr

 ### Initial invocation

-Now, let's invoke our graph by interrupting before `ask_human` node:
+Now, let's invoke our graph.

 === "Python"

@@ -73,7 +73,6 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
        assistant_id,
        input=input,
        stream_mode="updates",
-        interrupt_before=["ask_human"],
    ):
        if chunk.data and chunk.event != "metadata": 
            print(chunk.data)
@@ -95,7 +94,6 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
      {
        input: input,
        streamMode: "updates",
-        interruptBefore: ["ask_human"]
      }
    );

@@ -115,117 +113,34 @@ Now, let's invoke our graph by interrupting before `ask_human` node:
     --data "{
       \"assistant_id\": \"agent\",
       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Ask the user where they are, then look up the weather there\"}]},
-       \"interrupt_before\": [\"ask_human\"],
       \"stream_mode\": [
         \"updates\"
       ]
-     }" | \
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+     }"
    ```
    
 Output:

-    {'agent': {'messages': [{'content': [{'text': "Certainly! I'll use the AskHuman function to ask the user about their location, and then I'll use the search function to look up the weather for that location. Let's start by asking the user where they are.", 'type': 'text'}, {'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR', 'input': {'question': 'Where are you currently located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-a8422215-71d3-4093-afb4-9db141c94ddb', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you currently located?'}, 'id': 'toolu_01RFahzYPvnPWTb2USk2RdKR'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
+    {'agent': {'messages': [{'content': [{'text': "I'll help you ask the user about their location and then search for weather information.", 'type': 'text'}, {'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01UBEdS6UvuFMetdokNsykVG', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 438, 'output_tokens': 76}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-1b1210d8-39e0-4607-9f0e-0ea932d28d5c-0', 'example': False, 'tool_calls': [{'name': 'AskHuman', 'args': {'question': 'Where are you located?'}, 'id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 438, 'output_tokens': 76, 'total_tokens': 514, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'__interrupt__': [{'value': 'Where are you located?', 'resumable': True, 'ns': ['ask_human:2d41f894-f297-211e-9bfe-1d162ecba54a'], 'when': 'during'}]}

+You can see that our graph got interrupted inside the `ask_human` node, which is now waiting for a `location` to be provided.

-### Adding user input to state
-
-We now want to update this thread with a response from the user. We then can kick off another run.
-
-Because we are treating this as a tool call, we will need to update the state as if it is a response from a tool call. In order to do this, we will need to check the state to get the ID of the tool call.
+### Providing human input

+We can provide human input (`location`) by invoking the graph with a `Command(resume="<location>")`:

 === "Python"

    ```python
-    state = await client.threads.get_state(thread['thread_id'])
-    tool_call_id = state['values']['messages'][-1]['tool_calls'][0]['id']
+    # highlight-next-line
+    from langgraph_sdk.schema import Command

-    # We now create the tool call with the id and the response we want
-    tool_message = [{"tool_call_id": tool_call_id, "type": "tool", "content": "san francisco"}]
-
-    await client.threads.update_state(thread['thread_id'], {"messages": tool_message}, as_node="ask_human")
-    ```
-
-=== "Javascript"
-
-    ```js
-    const state = await client.threads.getState(thread["thread_id"]);
-    const toolCallId = state.values.messages[state.values.messages.length - 1].tool_calls[0].id;
-
-    // We now create the tool call with the id and the response we want
-    const toolMessage = [
-      {
-        tool_call_id: toolCallId,
-        type: "tool",
-        content: "san francisco"
-      }
-    ];
-
-    await client.threads.updateState(
-      thread["thread_id"],
-      { values: { messages: toolMessage } },
-      { asNode: "ask_human" }
-    );
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request GET \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-     | jq -r '.values.messages[-1].tool_calls[0].id' \
-     | sh -c '
-         TOOL_CALL_ID="$1"
-         
-         # Construct the JSON payload
-         JSON_PAYLOAD=$(printf "{\"messages\": [{\"tool_call_id\": \"%s\", \"type\": \"tool\", \"content\": \"san francisco\"}], \"as_node\": \"ask_human\"}" "$TOOL_CALL_ID")
-         
-         # Send the updated state
-         curl --request POST \
-              --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/state \
-              --header "Content-Type: application/json" \
-              --data "${JSON_PAYLOAD}"
-     ' _ 
-    ```
-
-Output:
-
-    {'configurable': {'thread_id': 'a9f322ae-4ed1-41ec-942b-38cb3d342c3a',
-    'checkpoint_ns': '',
-    'checkpoint_id': '1ef58e97-a623-63dd-8002-39a9a9b20be3'}}
-
-
-### Invoking after receiving human input
-
-We can now tell the agent to continue. We can just pass in None as the input to the graph, since no additional input is needed:
-
-=== "Python"
-
-    ```python
    async for chunk in client.runs.stream(
        thread["thread_id"],
        assistant_id,
-        input=None,
+        # highlight-next-line
+        command=Command(resume="san francisco"),
        stream_mode="updates",
    ):
        if chunk.data and chunk.event != "metadata": 
@@ -238,7 +153,8 @@ We can now tell the agent to continue. We can just pass in None as the input to
      thread["thread_id"],
      assistantId,
      {
-        input: null,
+        // highlight-next-line
+        command: { resume: "san francisco" },
        streamMode: "updates"
      }
    );
@@ -253,40 +169,23 @@ We can now tell the agent to continue. We can just pass in None as the input to
 === "CURL"

    ```bash
-    curl --request POST \                                                                             
+    curl --request POST \
     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
     --header 'Content-Type: application/json' \
     --data "{
       \"assistant_id\": \"agent\",
+       \"command\": {
+         \"resume\": \"san francisco\"
+       },
       \"stream_mode\": [
         \"updates\"
       ]
-     }"| \ 
-     sed 's/\r$//' | \
-     awk '
-     /^event:/ {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-         sub(/^event: /, "", $0)
-         event_type = $0
-         data_content = ""
-     }
-     /^data:/ {
-         sub(/^data: /, "", $0)
-         data_content = $0
-     }
-     END {
-         if (data_content != "" && event_type != "metadata") {
-             print data_content "\n"
-         }
-     }
-     '
+     }"
    ```

 Output:

-    {'agent': {'messages': [{'content': [{'text': "Thank you for letting me know that you're in San Francisco. Now, I'll use the search function to look up the weather in San Francisco.", 'type': 'text'}, {'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7', 'input': {'query': 'current weather in San Francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-241baed7-db5e-44ce-ac3c-56431705c22b', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in San Francisco'}, 'id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-    {'action': {'messages': [{'content': '["I looked up: current weather in San Francisco. Result: It\'s sunny in San Francisco, but you better look out if you\'re a Gemini 😈."]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': '8b699b95-8546-4557-8e66-14ea71a15ed8', 'tool_call_id': 'toolu_01K57ofmgG2wyJ8tYJjbq5k7'}]}}
-    {'agent': {'messages': [{'content': "Based on the search results, I can provide you with information about the current weather in San Francisco:\n\nThe weather in San Francisco is currently sunny. It's a beautiful day in the city! \n\nHowever, I should note that the search result included an unusual comment about Gemini zodiac signs. This appears to be either a joke or potentially irrelevant information added by the search engine. For accurate and detailed weather information, you might want to check a reliable weather service or app for San Francisco.\n\nIs there anything else you'd like to know about the weather or San Francisco?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-b4d7309f-f849-46aa-b6ef-475bcabd2be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
-
+    {'ask_human': {'messages': [{'tool_call_id': 'toolu_012JeNEvyePZFWK39d52Wdwi', 'type': 'tool', 'content': 'san francisco'}]}}
+    {'agent': {'messages': [{'content': [{'text': 'Let me search for the weather in San Francisco.', 'type': 'text'}, {'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'input': {'query': 'current weather in san francisco'}, 'name': 'search', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_0152YFm7DtnzfZQuiMUzaSsw', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 527, 'output_tokens': 67}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f509b5b2-eb30-4200-a8da-fa79ed68812a-0', 'example': False, 'tool_calls': [{'name': 'search', 'args': {'query': 'current weather in san francisco'}, 'id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 527, 'output_tokens': 67, 'total_tokens': 594, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
+    {'action': {'messages': [{'content': "I looked up: current weather in san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'search', 'id': 'cbd0f623-cc12-48a2-8c18-3cbb943e46e0', 'tool_call_id': 'toolu_019f9Y7ST6rNeDQkDjFCHk6C', 'artifact': None, 'status': 'success'}]}}
+    {'agent': {'messages': [{'content': "Based on the search results, it's currently sunny in San Francisco. Would you like any specific details about the weather forecast?", 'additional_kwargs': {}, 'response_metadata': {'id': 'msg_01FhzXj72CehBYkJGX69vsBc', 'model': 'claude-3-5-sonnet-20241022', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 0, 'input_tokens': 639, 'output_tokens': 29}, 'model_name': 'claude-3-5-sonnet-20241022'}, 'type': 'ai', 'name': None, 'id': 'run-f48e818e-dd88-415e-9a0b-4a958498b553-0', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 639, 'output_tokens': 29, 'total_tokens': 668, 'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}]}}
@@ -119,11 +119,11 @@ With this set up, running your graph and viewing in LangGraph Studio will result

 **Note the configuration icon in the top right corner of the `call_model` node**:

-![Graph in Studio](../img/studio_graph_with_configuration.png){width=1200}
+![Graph in Studio](img/studio_graph_with_configuration.png){width=1200}

 Clicking this icon will open a modal where you can edit the configuration for all of the fields associated with the `call_model` node. From here, you can save your changes and apply them to the graph. Note that these values reflect the currently active assistant, and saving will update the assistant with the new values.

-![Configuration modal](../img/studio_node_configuration.png){width=1200}
+![Configuration modal](img/studio_node_configuration.png){width=1200}

 ### Playground

@@ -133,7 +133,7 @@ LangGraph Studio also supports prompt engineering through an integration with th
 2. Within the thread log, any nodes that have made an LLM call will have a "View LLM Runs" button. Clicking this will open a popover with the LLM runs for that node.
 3. Select the LLM run you want to edit. This will open the LangSmith Playground with the selected LLM run.

-![Playground in Studio](../img/studio_playground.png){width=1200}
+![Playground in Studio](img/studio_playground.png){width=1200}

 From here you can edit the prompt, test different model configurations and re-run just this LLM call without having to re-run the entire graph. When you are happy with your changes, you can copy the updated prompt back into your graph.

@@ -1,7 +1,7 @@
 # How to stream events

 !!! info "Prerequisites"
-    * [Streaming](../../concepts/streaming.md#streaming-llm-tokens-and-events-astream_events)
+    * [Streaming](../../concepts/streaming.md#streaming-graph-outputs-stream-and-astream)

 This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently.

@@ -1,7 +1,7 @@
 # LangGraph Studio With Local Deployment

 !!! warning "Browser Compatibility"
-    Viewing the studio page of a local LangGraph deployment does not work in Safari. Use Chrome instead.
+    Safari blocks `localhost` connections to Studio. To work around this, start the server with `--tunnel` and you’ll be able to access Studio from Safari via a secure tunnel.

 ## Setup

@@ -476,7 +476,7 @@ The `useStream()` hook provides several callback options to help you respond to
 - `onError`: Called when an error occurs.
 - `onFinish`: Called when the stream is finished.
 - `onUpdateEvent`: Called when an update event is received.
- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../concepts/streaming.md#custom) to learn how to stream custom events.
+- `onCustomEvent`: Called when a custom event is received. See [Custom events](../../how-tos/streaming.ipynb#custom) to learn how to stream custom events.
 - `onMetadataEvent`: Called when a metadata event is received, which contains the Run ID and Thread ID.

 ## Learn More
@@ -3,7 +3,7 @@
  "info": {
    "title": "LangGraph Control Plane API (Beta)",
    "version": "0.0.1",
-    "description": "The LangGraph Control Plane API is used to programmatically create and manage LangGraph Server deployments. For example, the APIs can be orchestrated to create custom CI/CD workflows.\n\n### Beta\nThis API is currently in beta and may change or break without notice. This API documentation may not be up-to-date with actual API functionality.\n### Host\nhttps://api.host.langchain.com/\n\n### Authentication\nTo authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key for each request.\n\n### Versioning\nEach endpoint path is prefixed with a version (e.g. `v1`).\n\n### Quick Start\n\n1. Call `GET /{version}/projects` to retrieve the `Project` `id`. The `Project` `id` is needed in subsequent API calls.\n2. Call `POST /{version}/projects/{project_id}/revisions` to create a new `Revision` for the `Project`.\n3. Call `GET /{version}/projects/{project_id}/revisions` to get the latest `Revision` (first element in returned list). Get the `Revision` `id`.\n4. Poll for `Revision` `status` until `status` is `DEPLOYED` by calling `GET /{version}/projects/{project_id}/revisions/{revision_id}`."
+    "description": "The LangGraph Control Plane API is used to programmatically create and manage LangGraph Server deployments. For example, the APIs can be orchestrated to create custom CI/CD workflows.\n\n### Beta\nThis API is currently in beta and may change or break without notice. This API documentation may not be up-to-date with actual API functionality.\n### Host\nhttps://api.host.langchain.com/\n\n### Authentication\nTo authenticate with the LangGraph Control Plane API, set the `X-Api-Key` header to a valid LangSmith API key for each request.\n\n### Versioning\nEach endpoint path is prefixed with a version (e.g. `v1`).\n\n### Quick Start\n\n1. Call `POST /{version}/projects` to create a new `Project`.\n2. Call `GET /{version}/projects` to retrieve the `Project` `id`. The `Project` `id` is needed in subsequent API calls.\n3. Call `POST /{version}/projects/{project_id}/revisions` to create a new `Revision` for the `Project`.\n4. Call `GET /{version}/projects/{project_id}/revisions` to get the latest `Revision` (first element in returned list). Get the `Revision` `id`.\n5. Poll for `Revision` `status` until `status` is `DEPLOYED` by calling `GET /{version}/projects/{project_id}/revisions/{revision_id}`."
  },
  "servers": [
    {
@@ -22,6 +22,34 @@
  ],
  "paths": {
    "/v1/projects": {
+      "post": {
+        "tags": ["Projects (v1)"],
+        "summary": "Create Project",
+        "description": "Create a new project.",
+        "operationId": "create_project_projects_post",
+        "requestBody": {
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/CreateProjectRequest"
+              }
+            }
+          },
+          "required": true
+        },
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/Project"
+                }
+              }
+            }
+          }
+        }
+      },
      "get": {
        "tags": ["Projects (v1)"],
        "summary": "List Projects",
@@ -397,6 +425,83 @@
          }
        }
      },
+      "CreateProjectRequest":{
+        "type": "object",
+        "description": "Object for creating a new project.",
+        "properties": {
+          "name": {
+            "type": "string",
+            "description": "Name of the project.",
+            "required": true
+          },
+          "lc_hosted": {
+            "type": "boolean",
+            "description": "Whether the project is hosted on LangChain's cloud (i.e. Cloud SaaS deployment option). Set to `false` for Self-Hosted Data Plane and Self-Hosted Control Plane deployment options.",
+            "default": true
+          },
+          "repo_url": {
+            "type": ["string", "null"],
+            "description": "URL of the GitHub repository to use for the project. Omit this field if creating a new project from a Docker image.",
+            "default": "null"
+          },
+          "repo_path": {
+            "type": ["string", "null"],
+            "description": "Path to `langgraph.json` configuration file. For example, `langgraph.json` or `src/langgraph.json`.\n\nIf this field is omitted or set to `null`, the previous revision's `repo_path` value is used. Set this field for deployments from a GitHub repository. Omit this field if creating a new revision from a Docker image.",
+            "default": "null"
+          },
+          "repo_commit": {
+            "type": ["string", "null"],
+            "description": "Git branch name of deployment.\n\nThis field only applies to deployments from a GitHub repository.",
+            "default": "null"
+          },
+          "env_vars": {
+            "type": "array",
+            "description": "List of environment variables or secrets.\n\nIf this field is omitted or set to `null`, the previous revision's `env_vars` value is used.",
+            "items": {
+              "$ref": "#/components/schemas/EnvVar"
+            },
+            "default": "null"
+          },
+          "host_integration_id": {
+            "type": ["string", "null"],
+            "format": "uuid",
+            "description": "Do not use."
+          },
+          "deployment_type": {
+            "type": "string",
+            "description": "Development (`dev`) or Production (`prod`) type deployment.",
+            "enum": [
+              "dev",
+              "prod"
+            ]
+          },
+          "shareable": {
+            "type": ["boolean", "null"],
+            "description": "Boolean flag to configure if a deployment is shareable through LangGraph Studio.\n\nIf this field is omitted or set to `null`, the previous revision's `shareable` value is used. This field does not apply to BYOC deployments.",
+            "default": "null"
+          },
+          "platform": {
+            "type": "object",
+            "description": "Do not use.",
+            "default": "null"
+          },
+          "image_path": {
+            "type": ["string", "null"],
+            "description": "URI of the Docker image to deploy.\n\nIf this field is omitted or set to `null`, the previous revision's `image_path` value is used. Set this field for BYOC deployments. Omit this field if creating a new revision from a GitHub repository.",
+            "default": "null"
+          },
+          "build_on_push": {
+            "type": "boolean",
+            "description": "Boolean flag to indicate if a new revision is automatically created on push to GitHub branch (`repo_branch`).\n\nThis field does not apply for BYOC deployments.",
+            "default": false
+          },
+          "container_spec": {
+            "description": "If this field is omitted or set to `null`, the previous revision's `container_spec` value is used.",
+            "$ref": "#/components/schemas/ContainerSpec",
+            "default": "null"
+          }
+        }
+      },
      "CreateRevisionRequest": {
        "type": "object",
        "description": "Object for creating a new revision.",
@@ -10,9 +10,6 @@ The LangGraph command line interface includes commands to build and run a LangGr
    === "Python"
        ```bash
        pip install langgraph-cli
-
-        # Install via Homebrew
-        brew install langgraph-cli
        ```

    === "JS"
@@ -298,6 +295,11 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--wait-for-client`           | `False`          | Wait for a debugger client to connect to the debug port before starting the server   |
+    | `--no-browser`                |                  | Skip automatically opening the browser when the server starts                       |
+    | `--studio-url TEXT`           |                  | URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com |
+    | `--allow-blocking`            | `False`          | Do not raise errors for synchronous I/O blocking operations in your code (added in `0.2.6`)           |
+    | `--tunnel`                    | `False`          | Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers like Safari or networks blocking localhost connections        |
    | `--help`                      |                  | Display command documentation                                                       |


@@ -321,6 +323,11 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | `--no-reload`                 |                  | Disable auto-reload                                                                 |
    | `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
    | `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+    | `--wait-for-client`           | `False`          | Wait for a debugger client to connect to the debug port before starting the server   |
+    | `--no-browser`                |                  | Skip automatically opening the browser when the server starts                       |
+    | `--studio-url TEXT`           |                  | URL of the LangGraph Studio instance to connect to. Defaults to https://smith.langchain.com |
+    | `--allow-blocking`            | `False`          | Do not raise errors for synchronous I/O blocking operations in your code            |
+    | `--tunnel`                    | `False`          | Expose the local server via a public tunnel (Cloudflare) for remote frontend access. This avoids issues with browsers or networks blocking localhost connections        |
    | `--help`                      |                  | Display command documentation                                                       |

 ### `build`
@@ -97,3 +97,21 @@ Database Connectivity:
    Custom Redis instances are only available for [Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) and [Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployments.

 Specify `REDIS_URI_CUSTOM` to use a custom Redis instance. The value of `REDIS_URI_CUSTOM` must be a valid [Redis connection URI](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url).
+
+## `REDIS_KEY_PREFIX`
+
+!!! info "Available in API Server version 0.1.9+"
+    This environment variable is supported in API Server version 0.1.9 and above.
+
+Specify a prefix for Redis keys. This allows multiple LangGraph Server instances to share the same Redis instance by using different key prefixes. 
+
+Defaults to `''`.
+
+## `REDIS_CLUSTER`
+
+!!! info "Only Allowed in Self-Hosted Deployments"
+    Redis Cluster mode is only available in Self-Hosted Deployment models, LangGraph Cloud SaaS will provision a redis instance for you by default.
+
+Set `REDIS_CLUSTER` to `True` to enable Redis Cluster mode. When enabled, the system will connect to Redis using cluster mode. This is useful when connecting to a Redis Cluster deployment.
+
+Defaults to `False`.
@@ -15,7 +15,7 @@ This guide shows a typical structure for a LangGraph application and shows how t

 To deploy using the LangGraph Platform, the following information should be provided:

-1. A [LangGraph API Configuration file](#configuration-file) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
+1. A [LangGraph API Configuration file](#configuration-file-concepts) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
 2. The [graphs](#graphs) that implement the logic of the application.
 3. A file that specifies [dependencies](#dependencies) required to run the application.
 4. [Environment variable](#environment-variables) that are required for the application to run.
@@ -77,7 +77,7 @@ Below are examples of directory structures for Python and JavaScript application
    The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.


-## Configuration File
+## Configuration File {#configuration-file-concepts}

 The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.

@@ -145,18 +145,18 @@ A LangGraph application may depend on other Python packages or JavaScript librar
 You will generally need to specify the following information for dependencies to be set up correctly:

 1. A file in the directory that specifies the dependencies (e.g., `requirements.txt`, `pyproject.toml`, or `package.json`).
-2. A `dependencies` key in the [LangGraph configuration file](#configuration-file) that specifies the dependencies required to run the LangGraph application.
-3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file).
+2. A `dependencies` key in the [LangGraph configuration file](#configuration-file-concepts) that specifies the dependencies required to run the LangGraph application.
+3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file-concepts).

 ## Graphs

-Use the `graphs` key in the [LangGraph configuration file](#configuration-file) to specify which graphs will be available in the deployed LangGraph application.
+Use the `graphs` key in the [LangGraph configuration file](#configuration-file-concepts) to specify which graphs will be available in the deployed LangGraph application.

 You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.

 ## Environment Variables

-If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file).
+If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file-concepts).

 For a production deployment, you will typically want to configure the environment variables in the deployment environment.

@@ -409,39 +409,6 @@ The `Command` primitive provides several options to control and modify the graph

 By leveraging `Command`, you can resume graph execution, handle user inputs, and dynamically adjust the graph's state.

-## Using with `invoke` and `ainvoke`
-
-When you use `stream` or `astream` to run the graph, you will receive an `Interrupt` event that let you know the `interrupt` was triggered. 
-
-`invoke` and `ainvoke` do not return the interrupt information. To access this information, you must use the [get_state](../reference/graphs.md#langgraph.graph.graph.CompiledGraph.get_state) method to retrieve the graph state after calling `invoke` or `ainvoke`.
-
-```python
-# Run the graph up to the interrupt 
-result = graph.invoke(inputs, thread_config)
-# Get the graph state to get interrupt information.
-state = graph.get_state(thread_config)
-# Print the state values
-print(state.values)
-# Print the pending tasks
-print(state.tasks)
-# Resume the graph with the user's input.
-graph.invoke(Command(resume={"age": "25"}), thread_config)
-```
-
-```pycon
-{'foo': 'bar'} # State values
-(
-    PregelTask(
-        id='5d8ffc92-8011-0c9b-8b59-9d3545b7e553', 
-        name='node_foo', 
-        path=('__pregel_pull', 'node_foo'), 
-        error=None, 
-        interrupts=(Interrupt(value='value_in_interrupt', resumable=True, ns=['node_foo:5d8ffc92-8011-0c9b-8b59-9d3545b7e553'], when='during'),), state=None, 
-        result=None
-    ),
-) # Pending tasks. interrupts 
-```
-
 ## How does resuming from an interrupt work?

 !!! warning
@@ -20,7 +20,7 @@ The LangGraph Platform incorporates best practices for agent deployment, so you
 * **Double texting support**: Many times users might interact with your graph in unintended ways. For instance, a user may send one message and before the graph has finished running send a second message. We call this ["double texting"](double_texting.md) and have added four different ways to handle this.
 * **Optimized checkpointer**: LangGraph Platform comes with a built-in [checkpointer](./persistence.md#checkpoints) optimized for LangGraph applications.
 * **Human-in-the-loop endpoints**: We've exposed all endpoints needed to support [human-in-the-loop](human_in_the_loop.md) features.
-* **Memory**: In addition to thread-level persistence (covered above by [checkpointers]l(./persistence.md#checkpoints)), LangGraph Platform also comes with a built-in [memory store](persistence.md#memory-store).
+* **Memory**: In addition to thread-level persistence (covered above by [checkpointers](./persistence.md#checkpoints)), LangGraph Platform also comes with a built-in [memory store](persistence.md#memory-store).
 * **Cron jobs**: Built-in support for scheduling tasks, enabling you to automate regular actions like data clean-up or batch processing within your applications.
 * **Webhooks**: Allows your application to send real-time notifications and data updates to external systems, making it easy to integrate with third-party services and trigger actions based on specific events.
 * **Monitoring**: LangGraph Server integrates seamlessly with the [LangSmith](https://docs.smith.langchain.com/) monitoring platform, providing real-time insights into your application's performance and health.
@@ -7,19 +7,17 @@

 ## Versions

-There are two versions of the self-hosted deployment: [Self-Hosted Enterprise](./deployment_options.md#self-hosted-enterprise) and [Self-Hosted Lite](./deployment_options.md#self-hosted-lite).
+There are two versions of the self-hosted deployment: [Self-Hosted Data Plane](./deployment_options.md#self-hosted-data-plane) and [Self-Hosted Control Plane](./deployment_options.md#self-hosted-control-plane).

-### Self-Hosted Lite
+### Self-Hosted Data Plane

-The Self-Hosted Lite version is a limited version of LangGraph Platform that you can run locally or in a self-hosted manner (up to 1 million nodes executed per year).
+The [Self-Hosted Data Plane](./langgraph_self_hosted_data_plane.md) deployment option is a "hybrid" model for deployment where we manage the [control plane](./langgraph_control_plane.md) in our cloud and you manage the [data plane](./langgraph_data_plane.md) in your cloud. This option provides a way to securely manage your data plane infrastructure, while offloading control plane management to us.

-When using the Self-Hosted Lite version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.
+When using the Self-Hosted Data Plane version, you authenticate with a [LangSmith](https://smith.langchain.com/) API key.

-### Self-Hosted Enterprise
+### Self-Hosted Control Plane

-The Self-Hosted Enterprise version is the full version of LangGraph Platform.
-
-To use the Self-Hosted Enterprise version, you must acquire a license key that you will need to pass in when running the Docker image. To acquire a license key, please email sales@langchain.dev.
+The [Self-Hosted Control Plane](./langgraph_self_hosted_control_plane.md) deployment option is a fully self-hosted model for deployment where you manage the [control plane](./langgraph_control_plane.md) and [data plane](./langgraph_data_plane.md) in your cloud. This option give you full control and responsibility of the control plane and data plane infrastructure.

 ## Requirements

@@ -39,7 +39,7 @@ You will eventually need to pass in the following environment variables to the L

        `<database_name_1>` and `database_name_2` are different databases within the same instance, but `<hostname_1>` is shared. **The same database cannot be used for separate deployments**.

- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
+- `LANGSMITH_API_KEY`: (If using [Self-Hosted Data Plane](../concepts/deployment_options.md#self-hosted-data-plane)) LangSmith API key. This will be used to authenticate ONCE at server start up.
 - `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
 - `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.

@@ -249,7 +249,7 @@ LangGraph Platform supports multiple types of runs besides streaming runs.
 - [How to create cron jobs](../cloud/how-tos/cron_jobs.md)
 - [How to create stateless runs](../cloud/how-tos/stateless_runs.md)

-### Streaming
+### Streaming {#streaming_1}

 Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:

@@ -267,15 +267,21 @@ With LangGraph Platform you can integrate LangGraph agents into your React appli
 - [How to integrate LangGraph into your React application](../cloud/how-tos/use_stream_react.md)
 - [How to implement Generative User Interfaces with LangGraph](../cloud/how-tos/generative_ui_react.md)

-### Human-in-the-loop
+### Human-in-the-loop {#human-in-the-loop-1}

-When designing complex graphs, relying entirely on the LLM for decision-making can be risky, particularly when it involves tools that interact with files, APIs, or databases. These interactions may lead to unintended data access or modifications, depending on the use case. To mitigate these risks, LangGraph allows you to integrate human-in-the-loop behavior, ensuring your LLM applications operate as intended without undesirable outcomes.
+[Human-in-the-loop](../concepts/human_in_the_loop.md) functionality allows
+you to involve humans in the decision-making process of your graph. These how-to guides show how to implement human-in-the-loop workflows in your graph.

- [How to add a breakpoint](../cloud/how-tos/human_in_the_loop_breakpoint.md)
 - [How to wait for user input](../cloud/how-tos/human_in_the_loop_user_input.md)
+- [How to review tool calls](../cloud/how-tos/human_in_the_loop_review_tool_calls.md)
+- [How to add static breakpoints](../cloud/how-tos/human_in_the_loop_breakpoint.md)
+
+### Time Travel
+
+[Time travel](../concepts/time-travel.md) allows you to replay past actions in your LangGraph application to explore alternative paths and debug issues. These how-to guides show how to use time travel in your graph.
+
 - [How to edit graph state](../cloud/how-tos/human_in_the_loop_edit_state.md)
 - [How to replay and branch from prior states](../cloud/how-tos/human_in_the_loop_time_travel.md)
- [How to review tool calls](../cloud/how-tos/human_in_the_loop_review_tool_calls.md)

 ### Double-texting

@@ -193,7 +193,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Using in `create_react_agent`\n",
+    "## Using in `create_react_agent` {#using-in-create-react-agent}\n",
    "\n",
    "Add semantic search to your tool calling agent by injecting the store in the `prompt` function. You can also use the store in a tool to let your agent manually store or search for memories."
   ]
@@ -0,0 +1,3 @@
+.safari {
+  color: #0070C9;
+}
@@ -14,3 +14,4 @@ Errors referenced below will have an `lc_error_code` property corresponding to o
 These guides provide troubleshooting information for errors that are specific to the LangGraph Platform.

 - [INVALID_LICENSE](./INVALID_LICENSE.md)
+- [Studio Errors](../studio.md)
@@ -0,0 +1,45 @@
+# Troubleshooting LangGraph Studio
+
+## :fontawesome-brands-safari:{ .safari } Safari connection error with local dev server
+
+Safari blocks plain‑HTTP traffic on localhost. If you start Studio with a vanilla  
+`langgraph dev`, the page may report a "Failed to load assistants" error (or something similar) and the browser DevTools will show network errors.
+
+#### Quick fix — run Studio through a secure Cloudflare tunnel
+
+=== "Python"
+
+    ```shell
+    pip install -U langgraph-cli>=0.2.6            # Python
+    langgraph dev --tunnel
+    ```
+=== "JS"
+
+    ```shell
+    # Requires @langchain/langgraph-cli>=0.0.26
+    npx @langchain/langgraph-cli dev
+    ```
+
+The command prints a URL like:
+
+```shell
+https://smith.langchain.com/studio/?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
+```
+where
+```shell
+?baseUrl=https://hamilton-praise-heart-costumes.trycloudflare.com
+```
+indicates the endpoint where your agent server is exposed.
+
+Open that URL in Safari and Studio should load immediately.
+
+#### Alternative — use a Chromium‑based browser
+
+Chrome, Edge, and Brave allow HTTP on localhost, so a plain `langgraph dev` should work without extra steps.
+
+#### If it’s still not loading
+
+1. Make sure the `baseUrl` query parameter in the studio URL points to the **tunnel URL** NOT to localhost.
+2. Confirm your CLI version with `langgraph --version`.
+
+No other configuration, certificates, or CORS tweaks are required.
@@ -139,7 +139,7 @@ langgraph dev --no-browser
    }
    ```

-Now let's try to chat with our bot. If we've implemented authentication correctly, we should only be able to access the bot if we provide a valid token in the request header. Users will still, however, be able to access each other's resources until we add [resource authorization handlers](../../concepts/auth.md#resource-authorization) in the next section of our tutorial.
+Now let's try to chat with our bot. If we've implemented authentication correctly, we should only be able to access the bot if we provide a valid token in the request header. Users will still, however, be able to access each other's resources until we add [resource authorization handlers](../../concepts/auth.md#resource-specific-handlers) in the next section of our tutorial.

 ![Authentication, no authorization handlers](./img/authentication.png)

@@ -6,7 +6,7 @@
    2. Resource Authorization (you are here) - Let users have private conversations
    3. [Production Auth](add_auth_server.md) - Add real user accounts and validate using OAuth2

-In this tutorial, we will extend our chatbot to give each user their own private conversations. We'll add [resource-level access control](../../concepts/auth.md#resource-level-access-control) so users can only see their own threads.
+In this tutorial, we will extend our chatbot to give each user their own private conversations. We'll add [resource-level access control](../../concepts/auth.md#single-owner-resources) so users can only see their own threads.

 ![Authorization handlers](./img/authorization.png)

@@ -35,7 +35,7 @@ langgraph dev --no-browser

 Recall that in the last tutorial, the [`Auth`](../../cloud/reference/sdk/python_sdk_ref.md#langgraph_sdk.auth.Auth) object let us register an [authentication function](../../concepts/auth.md#authentication), which the LangGraph platform uses to validate the bearer tokens in incoming requests. Now we'll use it to register an **authorization** handler.

-Authorization handlers are functions that run **after** authentication succeeds. These handlers can add [metadata](../../concepts/auth.md#resource-metadata) to resources (like who owns them) and filter what each user can see.
+Authorization handlers are functions that run **after** authentication succeeds. These handlers can add [metadata](../../concepts/auth.md#filter-operations) to resources (like who owns them) and filter what each user can see.

 Let's update our `src/security/auth.py` and add one authorization handler that is run on every request:

@@ -211,7 +211,7 @@ This means:

 ## Adding scoped authorization handlers {#scoped-authorization}

-The broad `@auth.on` handler matches on all [authorization events](../../concepts/auth.md#authorization-events). This is concise, but it means the contents of the `value` dict are not well-scoped, and we apply the same user-level access control to every resource. If we want to be more fine-grained, we can also control specific actions on resources.
+The broad `@auth.on` handler matches on all [authorization events](../../concepts/auth.md#supported-resources). This is concise, but it means the contents of the `value` dict are not well-scoped, and we apply the same user-level access control to every resource. If we want to be more fine-grained, we can also control specific actions on resources.

 Update `src/security/auth.py` to add handlers for specific resource types:

@@ -290,7 +290,7 @@ Notice that instead of one global handler, we now have specific handlers for:
 2. Reading threads
 3. Accessing assistants

-The first three of these match specific **actions** on each resource (see [resource actions](../../concepts/auth.md#resource-actions)), while the last one (`@auth.on.assistants`) matches _any_ action on the `assistants` resource. For each request, LangGraph will run the most specific handler that matches the resource and action being accessed. This means that the four handlers above will run rather than the broadly scoped "`@auth.on`" handler.
+The first three of these match specific **actions** on each resource (see [resource actions](../../concepts/auth.md#resource-specific-handlers)), while the last one (`@auth.on.assistants`) matches _any_ action on the `assistants` resource. For each request, LangGraph will run the most specific handler that matches the resource and action being accessed. This means that the four handlers above will run rather than the broadly scoped "`@auth.on`" handler.

 Try adding the following test code to your test file:

@@ -1627,7 +1627,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
@@ -1,5 +1,5 @@
-site_name: ""
-site_description: Build language agents as graphs
+site_name: "LangGraph"
+site_description: Build reliable, stateful AI systems, without giving up control
 site_url: https://langchain-ai.github.io/langgraph/
 repo_url: https://github.com/langchain-ai/langgraph
 edit_uri: edit/main/docs/docs/
@@ -104,23 +104,19 @@ nav:
      - How-to Guides:
        - how-tos/index.md
        - LangGraph:
-          - LangGraph: how-tos#langgraph
          - Graph API Basics:
-              - Graph API Basics: how-tos#graph-api-basics
              - how-tos/state-reducers.ipynb
              - how-tos/sequence.ipynb
              - how-tos/branching.ipynb
              - how-tos/recursion-limit.ipynb
              - how-tos/visualization.ipynb
          - Controllability:
-              - Controllability: how-tos#controllability
              - how-tos/map-reduce.ipynb
              - how-tos/command.ipynb
              - how-tos/configuration.ipynb
              - how-tos/node-retries.ipynb
              - how-tos/return-when-recursion-limit-hits.ipynb
          - Persistence:
-              - Persistence: how-tos#persistence
              - how-tos/persistence.ipynb
              - how-tos/subgraph-persistence.ipynb
              - how-tos/cross-thread-persistence.ipynb
@@ -130,13 +126,11 @@ nav:
              - how-tos/persistence-functional.ipynb
              - how-tos/cross-thread-persistence-functional.ipynb
          - Memory:
-              - Memory: how-tos#memory
              - how-tos/memory/manage-conversation-history.ipynb
              - how-tos/memory/delete-messages.ipynb
              - how-tos/memory/add-summary-conversation-history.ipynb
              - how-tos/memory/semantic-search.ipynb
          - Human-in-the-loop:
-              - Human-in-the-loop: how-tos#human-in-the-loop
              - how-tos/human_in_the_loop/breakpoints.ipynb
              - how-tos/human_in_the_loop/dynamic_breakpoints.ipynb
              - how-tos/human_in_the_loop/edit-graph-state.ipynb
@@ -146,7 +140,6 @@ nav:
              - how-tos/wait-user-input-functional.ipynb
              - how-tos/review-tool-calls-functional.ipynb
          - Streaming:
-              - Streaming: how-tos#streaming
              - how-tos/streaming.ipynb
              - how-tos/streaming-tokens.ipynb
              - how-tos/streaming-specific-nodes.ipynb
@@ -154,7 +147,6 @@ nav:
              - how-tos/streaming-subgraphs.ipynb
              - how-tos/disable-streaming.ipynb
          - Tool calling:
-              - Tool calling: how-tos#tool-calling
              - how-tos/tool-calling.ipynb
              - how-tos/tool-calling-errors.ipynb
              - how-tos/pass-run-time-values-to-tools.ipynb
@@ -162,31 +154,26 @@ nav:
              - how-tos/pass-config-to-tools.ipynb
              - how-tos/many-tools.ipynb
          - Subgraphs:
-              - Subgraphs: how-tos#subgraphs
              - how-tos/subgraph.ipynb
              - how-tos/subgraphs-manage-state.ipynb
              - how-tos/subgraph-transform-state.ipynb
          - Multi-agent:
-              - Multi-agent: how-tos#multi-agent
              - how-tos/agent-handoffs.ipynb
              - how-tos/multi-agent-network.ipynb
              - how-tos/multi-agent-multi-turn-convo.ipynb
              - how-tos/multi-agent-network-functional.ipynb
              - how-tos/multi-agent-multi-turn-convo-functional.ipynb
          - State Management:
-              - State Management: how-tos#state-management
              - how-tos/state-model.ipynb
              - how-tos/input_output_schema.ipynb
              - how-tos/pass_private_state.ipynb
          - Other:
-              - Other: how-tos#other
              - how-tos/async.ipynb
              - how-tos/react-agent-structured-output.ipynb
              - how-tos/run-id-langsmith.ipynb
              - how-tos/autogen-integration.ipynb
              - how-tos/autogen-integration-functional.ipynb
          - Prebuilt ReAct Agent:
-              - Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
              - how-tos/create-react-agent.ipynb
              - how-tos/create-react-agent-memory.ipynb
              - how-tos/create-react-agent-system-prompt.ipynb
@@ -196,9 +183,7 @@ nav:
              - how-tos/react-agent-from-scratch.ipynb
              - how-tos/react-agent-from-scratch-functional.ipynb
        - LangGraph Platform:
-            - LangGraph Platform: how-tos#langgraph-platform
            - Application Structure:
-                - Application Structure: how-tos#application-structure
                - cloud/deployment/setup.md
                - cloud/deployment/setup_pyproject.md
                - cloud/deployment/setup_javascript.md
@@ -208,7 +193,6 @@ nav:
                - cloud/deployment/graph_rebuild.md
                - how-tos/autogen-langgraph-platform.ipynb
            - Deployment:
-                - Deployment: how-tos#deployment
                - cloud/deployment/cloud.md
                - cloud/deployment/self_hosted_data_plane.md
                - cloud/deployment/self_hosted_control_plane.md
@@ -219,25 +203,20 @@ nav:
            - Data Management:
                - how-tos/ttl/configure_ttl.md
            - Authentication & Access Control:
-                - Authentication & Access Control: how-tos#authentication-access-control
                - how-tos/auth/custom_auth.md
                - how-tos/auth/openapi_security.md
            - Assistants:
-                - Assistants: how-tos#assistants
                - cloud/how-tos/configuration_cloud.md
                - cloud/how-tos/assistant_versioning.md
            - Threads:
-                - Threads: how-tos#threads
                - cloud/how-tos/copy_threads.md
                - cloud/how-tos/check_thread_status.md
            - Runs:
-                - Runs: how-tos#runs
                - cloud/how-tos/background_run.md
                - cloud/how-tos/same-thread.md
                - cloud/how-tos/cron_jobs.md
                - cloud/how-tos/stateless_runs.md
            - Streaming:
-                - Streaming: how-tos#streaming_1
                - cloud/how-tos/stream_values.md
                - cloud/how-tos/stream_updates.md
                - cloud/how-tos/stream_messages.md
@@ -247,14 +226,12 @@ nav:
                - cloud/how-tos/use_stream_react.md
                - cloud/how-tos/generative_ui_react.md
            - Human-in-the-loop:
-                - Human-in-the-loop: how-tos#human-in-the-loop_1
                - cloud/how-tos/human_in_the_loop_breakpoint.md
                - cloud/how-tos/human_in_the_loop_user_input.md
                - cloud/how-tos/human_in_the_loop_edit_state.md
                - cloud/how-tos/human_in_the_loop_time_travel.md
                - cloud/how-tos/human_in_the_loop_review_tool_calls.md
            - Double-texting:
-                - Double-texting: how-tos#double-texting
                - cloud/how-tos/interrupt_concurrent.md
                - cloud/how-tos/rollback_concurrent.md
                - cloud/how-tos/reject_concurrent.md
@@ -264,12 +241,10 @@ nav:
            - Cron Jobs:
                - cloud/how-tos/cron_jobs.md
            - Modifying the API:
-                - Modifying the API: how-tos#modifying-the-api
                - how-tos/http/custom_lifespan.md
                - how-tos/http/custom_middleware.md
                - how-tos/http/custom_routes.md
            - LangGraph Studio:
-                - LangGraph Studio: how-tos#langgraph-studio
                - cloud/how-tos/test_deployment.md
                - cloud/how-tos/test_local_deployment.md
                - cloud/how-tos/invoke_studio.md
@@ -281,7 +256,6 @@ nav:
      - Concepts:
        - concepts/index.md
        - LangGraph:
-          - LangGraph: concepts#langgraph
          - concepts/high_level.md
          - concepts/low_level.md
          - concepts/agentic_concepts.md
@@ -297,9 +271,7 @@ nav:
          - concepts/durable_execution.md
          - concepts/pregel.md
        - LangGraph Platform:
-          - LangGraph Platform: concepts#langgraph-platform
          - High Level:
-            - High Level: concepts#high-level
            - concepts/langgraph_platform.md
            - concepts/platform_architecture.md
            - concepts/scalability_and_resilience.md
@@ -308,7 +280,6 @@ nav:
            - concepts/plans.md
            - concepts/template_applications.md
          - Components:
-            - Components: concepts#components
            - concepts/langgraph_control_plane.md
            - concepts/langgraph_data_plane.md
            - concepts/langgraph_server.md
@@ -317,13 +288,11 @@ nav:
            - concepts/sdk.md
            - how-tos/use-remote-graph.md
          - LangGraph Server:
-            - LangGraph Server: concepts#langgraph-server
            - concepts/application_structure.md
            - concepts/assistants.md
            - concepts/double_texting.md
            - concepts/auth.md
          - Deployment Options:
-            - Deployment Options: concepts#deployment-options
            - concepts/langgraph_cloud.md
            - concepts/langgraph_self_hosted_data_plane.md
            - concepts/langgraph_self_hosted_control_plane.md
@@ -332,18 +301,15 @@ nav:
      - Tutorials:
        - tutorials/index.md
        - Quick Start:
-          - Quick Start: tutorials#quick-start
          - tutorials/introduction.ipynb
          - tutorials/workflows/index.md
          - tutorials/langgraph-platform/local-server.md
          - cloud/quick_start.md
        - Chatbots:
-            - Chatbots: tutorials#chatbots
            - tutorials/customer-support/customer-support.ipynb
            - tutorials/chatbots/information-gather-prompting.ipynb
            - tutorials/code_assistant/langgraph_code_assistant.ipynb
        - RAG:
-            - RAG: tutorials#rag
            - tutorials/rag/langgraph_adaptive_rag.ipynb
            - tutorials/rag/langgraph_adaptive_rag_local.ipynb
            - tutorials/rag/langgraph_agentic_rag.ipynb
@@ -353,37 +319,30 @@ nav:
            - tutorials/rag/langgraph_self_rag_local.ipynb
            - tutorials/sql-agent.ipynb
        - Agent Architectures:
-            - Agent Architectures: tutorials#agent-architectures
            - Multi-Agent Systems:
-                - Multi-Agent Systems: tutorials#multi-agent-systems
                - tutorials/multi_agent/multi-agent-collaboration.ipynb
                - tutorials/multi_agent/agent_supervisor.ipynb
                - tutorials/multi_agent/hierarchical_agent_teams.ipynb
            - Planning Agents:
-                - Planning Agents: tutorials#planning-agents
                - tutorials/plan-and-execute/plan-and-execute.ipynb
                - tutorials/rewoo/rewoo.ipynb
                - tutorials/llm-compiler/LLMCompiler.ipynb
            - Reflection & Critique:
-                - Reflection & Critique: tutorials#reflection-critique
                - tutorials/reflection/reflection.ipynb
                - tutorials/reflexion/reflexion.ipynb
                - tutorials/tot/tot.ipynb
                - tutorials/lats/lats.ipynb
                - tutorials/self-discover/self-discover.ipynb
        - Evaluation & Analysis:
-            - Evaluation & Analysis: tutorials#evaluation
            - tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
            - tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb
        - Experimental:
-            - Experimental: tutorials#experimental
            - tutorials/storm/storm.ipynb
            - tutorials/tnt-llm/tnt-llm.ipynb
            - tutorials/web-navigation/web_voyager.ipynb
            - tutorials/usaco/usaco.ipynb
            - tutorials/extraction/retries.ipynb
        - LangGraph Platform:
-            - LangGraph Platform: concepts#langgraph-platform
            - tutorials/auth/getting_started.md
            - tutorials/auth/resource_auth.md
            - tutorials/auth/add_auth_server.md
@@ -400,6 +359,7 @@ nav:
          - troubleshooting/errors/MULTIPLE_SUBGRAPHS.md
          - troubleshooting/errors/INVALID_CHAT_HISTORY.md
          - troubleshooting/errors/INVALID_LICENSE.md
+          - troubleshooting/studio.md
      - LangGraph Academy Course: https://academy.langchain.com/courses/intro-to-langgraph

  - Agents:
@@ -532,6 +492,9 @@ extra:
          data: 0
          note: >-
            Thanks for your feedback! Please help us improve this page by adding to the discussion below.
+  shared_analytics:
+    provider: google
+    property: G-47WX3HKKY2
 validation:
  # https://www.mkdocs.org/user-guide/configuration/
  # We are still raising for omitted files because they determine the breadcrumbs for pages.
@@ -549,3 +512,4 @@ copyright: >
  Copyright &copy; 2025 LangChain, Inc | <a href="#__consent">Consent Preferences</a>
 extra_css:
  - stylesheets/version_admonitions.css
+  - stylesheets/logos.css
@@ -21,17 +21,17 @@ langgraph-checkpoint-sqlite = { path = "../libs/checkpoint-sqlite", develop = tr
 langgraph-checkpoint-postgres = { path = "../libs/checkpoint-postgres", develop = true }
 langgraph-sdk = {path = "../libs/sdk-py", develop = true}
 langchain-ollama = "^0.2.3"
-mkdocs = "^1.6.0"
-mkdocs-autorefs = ">=1.0.1,<1.1.0"
-mkdocstrings = "^0.25.1"
-mkdocstrings-python = "^1.10.4"
-mkdocs-minify-plugin = "^0.8.0"
-mkdocs-rss-plugin = "^1.13.1"
-mkdocs-git-committers-plugin-2 = "^2.3.0"
-mkdocs-material = {extras = ["imaging"], version = "^9.5.27"}
-markdown-callouts = "^0.4.0"
-markdown-include = "^0.8.1"
-mkdocs-exclude = "^1.0.2"
+mkdocs = "*"
+mkdocs-autorefs = "*"
+mkdocstrings = "*"
+mkdocstrings-python = "*"
+mkdocs-minify-plugin = "*"
+mkdocs-rss-plugin = "*"
+mkdocs-git-committers-plugin-2 = "*"
+mkdocs-material = {extras = ["imaging"], version = "*"}
+markdown-callouts = "*"
+markdown-include = "*"
+mkdocs-exclude = "*"
 psycopg = {extras = ["binary"], version = "^3.2.0"}
 psycopg-pool = "^3.2.0"
 pygments-ansi-color = ">=0.3"
@@ -49,6 +49,7 @@ langchain-anthropic = "^0.3.8"
 langchain-nomic = "^0.1.3"
 langchain-fireworks = "^0.2.0"
 langchain-community = "^0.3.0"
+langchain-tavily = "^0.1.5"
 langchain-experimental = "^0.3.2"
 langchain-mistralai = "^0.2.6"
 langgraph-checkpoint-mongodb = "^0.1.0"
@@ -279,31 +279,31 @@ test = ["pytest (>=6)"]

 [[package]]
 name = "h11"
-version = "0.14.0"
+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]

 [[package]]
 name = "httpcore"
-version = "1.0.7"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
-    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
 ]

 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"

 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
@@ -215,54 +215,51 @@ test = ["pytest (>=6)"]

 [[package]]
 name = "h11"
-version = "0.14.0"
+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.7"
-groups = ["main", "dev"]
-files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
-]
-
-[[package]]
-name = "httpcore"
-version = "1.0.5"
-description = "A minimal low-level HTTP client."
-optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
-    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
+]
+
+[[package]]
+name = "httpcore"
+version = "0.13.2"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.6"
+groups = ["main", "dev"]
+files = [
+    {file = "httpcore-0.13.2-py3-none-any.whl", hash = "sha256:52b7d9413f6f5592a667de9209d70d4d41aba3fb0540dd7c93475c78b85941e9"},
+    {file = "httpcore-0.13.2.tar.gz", hash = "sha256:c16efbdf643e1b57bde0adc12c53b08645d7d92d6d345a3f71adfc2a083e7fd2"},
 ]

 [package.dependencies]
-certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = "==0.*"
+sniffio = "==1.*"

 [package.extras]
-asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
-socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]

 [[package]]
 name = "httpx"
-version = "0.27.2"
+version = "0.25.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
 files = [
-    {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
-    {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
+    {file = "httpx-0.25.1-py3-none-any.whl", hash = "sha256:fec7d6cc5c27c578a391f7e87b9aa7d3d8fbcd034f6399f9f79b45bcc12a866a"},
+    {file = "httpx-0.25.1.tar.gz", hash = "sha256:ffd96d5cf901e63863d9f1b4b6807861dbea4d301613415d9e6e57ead15fc5d0"},
 ]

 [package.dependencies]
 anyio = "*"
 certifi = "*"
-httpcore = "==1.*"
+httpcore = "*"
 idna = "*"
 sniffio = "*"

@@ -271,7 +268,6 @@ brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-zstd = ["zstandard (>=0.18.0)"]

 [[package]]
 name = "idna"
@@ -93,6 +93,7 @@ class InMemorySaver(
        if factory is not defaultdict:
            self.stack.enter_context(self.storage)  # type: ignore[arg-type]
            self.stack.enter_context(self.writes)  # type: ignore[arg-type]
+            self.stack.enter_context(self.blobs)  # type: ignore[arg-type]

    def __enter__(self) -> "InMemorySaver":
        return self.stack.__enter__()
@@ -212,31 +212,31 @@ test = ["pytest (>=6)"]

 [[package]]
 name = "h11"
-version = "0.14.0"
+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]

 [[package]]
 name = "httpcore"
-version = "1.0.7"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
-    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
 ]

 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"

 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-checkpoint"
-version = "2.0.24"
+version = "2.0.25"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 authors = []
 license = "MIT"
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.

 [[package]]
 name = "anyio"
@@ -6,6 +6,7 @@ version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
@@ -28,6 +29,7 @@ version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
@@ -39,6 +41,7 @@ version = "8.1.7"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
@@ -53,6 +56,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main"]
+markers = "platform_system == \"Windows\""
 files = [
    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -64,6 +69,8 @@ version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version < \"3.11\""
 files = [
    {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
@@ -74,45 +81,48 @@ test = ["pytest (>=6)"]

 [[package]]
 name = "h11"
-version = "0.14.0"
+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+groups = ["main"]
 files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]

 [[package]]
 name = "httpcore"
-version = "1.0.5"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
-    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
-    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
 ]

 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"

 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.26.0)"]
+trio = ["trio (>=0.22.0,<1.0)"]

 [[package]]
 name = "httpx"
-version = "0.27.0"
+version = "0.28.1"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
-    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
-    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
 ]

 [package.dependencies]
@@ -120,13 +130,13 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"

 [package.extras]
 brotli = ["brotli", "brotlicffi"]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]

 [[package]]
 name = "httpx-sse"
@@ -134,6 +144,7 @@ version = "0.4.0"
 description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
    {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
    {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
@@ -145,6 +156,7 @@ version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
+groups = ["main"]
 files = [
    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
@@ -156,6 +168,7 @@ version = "0.1.52"
 description = "CLI for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["main"]
 files = []
 develop = true

@@ -172,6 +185,7 @@ version = "0.1.29"
 description = "SDK for interacting with LangGraph API"
 optional = false
 python-versions = "^3.9.0,<4.0"
+groups = ["main"]
 files = []
 develop = true

@@ -190,6 +204,7 @@ version = "3.10.5"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
    {file = "orjson-3.10.5-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:545d493c1f560d5ccfc134803ceb8955a14c3fcb47bbb4b2fee0232646d0b932"},
    {file = "orjson-3.10.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4324929c2dd917598212bfd554757feca3e5e0fa60da08be11b4aa8b90013c1"},
@@ -245,6 +260,7 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -256,12 +272,14 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version < \"3.11\""
 files = [
    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]

 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.9.0,<4.0"
 content-hash = "ec5109729f30d2033a10a10e8f8d3ed94c7d96d5d31025b4815b0123664bb063"
@@ -619,20 +619,30 @@
    zod "^3.22.4"
    zod-to-json-schema "^3.22.3"

-"@langchain/langgraph-checkpoint@~0.0.6":
-  version "0.0.6"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph-checkpoint/-/langgraph-checkpoint-0.0.6.tgz#69f0c5c9aeefd48dcf0fa1ffa0744d8139a9f27d"
-  integrity sha512-hQsznlUMFKyOCaN9VtqNSSemfKATujNy5ePM6NX7lruk/Mmi2t7R9SsBnf9G2Yts+IaIwv3vJJaAFYEHfqbc5g==
+"@langchain/langgraph-checkpoint@~0.0.17":
+  version "0.0.17"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph-checkpoint/-/langgraph-checkpoint-0.0.17.tgz#d0a8824eb0769567da54262adebe65db4ee6d58f"
+  integrity sha512-6b3CuVVYx+7x0uWLG+7YXz9j2iBa+tn2AXvkLxzEvaAsLE6Sij++8PPbS2BZzC+S/FPJdWsz6I5bsrqL0BYrCA==
  dependencies:
    uuid "^10.0.0"

-"@langchain/langgraph@^0.2.5":
-  version "0.2.5"
-  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-0.2.5.tgz#c42743a59adef03f2e1fea0c198a01694ae34d51"
-  integrity sha512-H4OgZyGRWZHBaiXXIb9avyB8zI6+3OewKn+UOZ+wUzYLKyF3cnq0cNF4/Ps+gxCa5RtOnsHIqQyRkojfXIOqgA==
+"@langchain/langgraph-sdk@~0.0.32":
+  version "0.0.70"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph-sdk/-/langgraph-sdk-0.0.70.tgz#9589f984b47de5e4a669b6008cbf01427a3d41d0"
+  integrity sha512-O8I12bfeMVz5fOrXnIcK4IdRf50IqyJTO458V56wAIHLNoi4H8/JHM+2M+Y4H2PtslXIGnvomWqlBd0eY5z/Og==
  dependencies:
-    "@langchain/langgraph-checkpoint" "~0.0.6"
-    double-ended-queue "^2.1.0-0"
+    "@types/json-schema" "^7.0.15"
+    p-queue "^6.6.2"
+    p-retry "4"
+    uuid "^9.0.0"
+
+"@langchain/langgraph@^0.2.5":
+  version "0.2.67"
+  resolved "https://registry.yarnpkg.com/@langchain/langgraph/-/langgraph-0.2.67.tgz#f154e4e8534ca78b2b73a2cd3a901d82790b0d1b"
+  integrity sha512-tu/ewNIvhIPzeW5GxGzqjmGHinnU/qbNAoLM9czdpci0PCbMysbEJ2pbJrZs7ZjaReWSnr/THkeLPQwqGOM9xw==
+  dependencies:
+    "@langchain/langgraph-checkpoint" "~0.0.17"
+    "@langchain/langgraph-sdk" "~0.0.32"
    uuid "^10.0.0"
    zod "^3.23.8"

@@ -748,7 +758,7 @@
    expect "^29.0.0"
    pretty-format "^29.0.0"

-"@types/json-schema@^7.0.9":
+"@types/json-schema@^7.0.15", "@types/json-schema@^7.0.9":
  version "7.0.15"
  resolved "https://registry.yarnpkg.com/@types/json-schema/-/json-schema-7.0.15.tgz#596a1747233694d50f6ad8a7869fcb6f56cf5841"
  integrity sha512-5+fP8P8MFNC+AyZCDxrB2pkZFPGzqQWUzpSeuuVLvm8VMcorNYavBqoFcxK8bQz4Qsbn4oUEEem4wDLfcysGHA==
@@ -1422,11 +1432,6 @@ dotenv@^16.4.5:
  resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-16.4.5.tgz#cdd3b3b604cb327e286b4762e13502f717cb099f"
  integrity sha512-ZmdL2rui+eB2YwhsWzjInR8LldtZHGDoQ1ugH85ppHKwpUHL7j7rN0Ti9NCnGiQbhaZ11FpR+7ao1dNsmduNUg==

-double-ended-queue@^2.1.0-0:
-  version "2.1.0-0"
-  resolved "https://registry.yarnpkg.com/double-ended-queue/-/double-ended-queue-2.1.0-0.tgz#103d3527fd31528f40188130c841efdd78264e5c"
-  integrity sha512-+BNfZ+deCo8hMNpDqDnvT+c0XpJ5cUa6mqYq89bho2Ifze4URTqRkcwR399hWoTrTkbZ/XJYDgP6rc7pRgffEQ==
-
 ejs@^3.1.10:
  version "3.1.10"
  resolved "https://registry.yarnpkg.com/ejs/-/ejs-3.1.10.tgz#69ab8358b14e896f80cc39e62087b88500c3ac3b"
@@ -3685,6 +3690,11 @@ uuid@^10.0.0:
  resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
  integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==

+uuid@^9.0.0:
+  version "9.0.1"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-9.0.1.tgz#e188d4c8853cc722220392c424cd637f32293f30"
+  integrity sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA==
+
 v8-to-istanbul@^9.0.1:
  version "9.3.0"
  resolved "https://registry.yarnpkg.com/v8-to-istanbul/-/v8-to-istanbul-9.3.0.tgz#b9572abfa62bd556c16d75fdebc1a411d5ff3175"
@@ -3795,7 +3805,12 @@ zod-to-json-schema@^3.22.3:
  resolved "https://registry.yarnpkg.com/zod-to-json-schema/-/zod-to-json-schema-3.23.2.tgz#bc7e379c8050462538383e382964c03d8fe008f9"
  integrity sha512-uSt90Gzc/tUfyNqxnjlfBs8W6WSGpNBv0rVsNxP/BVSMHMKGdthPYff4xtCHYloJGM0CFxFsb3NbC0eqPhfImw==

-zod@^3.22.4, zod@^3.23.8:
+zod@^3.22.4:
  version "3.23.8"
  resolved "https://registry.yarnpkg.com/zod/-/zod-3.23.8.tgz#e37b957b5d52079769fb8097099b592f0ef4067d"
  integrity sha512-XBx9AXhXktjUqnepgTiE5flcKIYWi/rme0Eaj+5Y0lftuGBq+jyRu/md4WnuxqgP1ubdpNCsYEYPxrzVHD8d6g==
+
+zod@^3.23.8:
+  version "3.24.3"
+  resolved "https://registry.yarnpkg.com/zod/-/zod-3.24.3.tgz#1f40f750a05e477396da64438e0e1c0995dafd87"
+  integrity sha512-HhY1oqzWCQWuUqvBFnsyrtZRhyPeR7SUGv+C4+MsisMuVfSPx8HpwWqH8tRahSlt6M3PiFAcoeFhZAqIXTxoSg==
@@ -168,6 +168,13 @@ def cli():
@OPT_DEBUGGER_BASE_URL
@OPT_WATCH
@OPT_POSTGRES_URI
+@click.option(
+    "--image",
+    type=str,
+    default=None,
+    help="Docker image to use for the langgraph-api service. If specified, skips building and uses this image directly."
+    " Useful if you want to test against an image already built using `langgraph build`.",
+)
@click.option(
    "--wait",
    is_flag=True,
@@ -187,6 +194,7 @@ def up(
    debugger_port: Optional[int],
    debugger_base_url: Optional[str],
    postgres_uri: Optional[str],
+    image: Optional[str],
 ):
    click.secho("Starting LangGraph API server...", fg="green")
    click.secho(
@@ -207,6 +215,7 @@ For production use, requires a license key in env var LANGGRAPH_CLOUD_LICENSE_KE
            debugger_port=debugger_port,
            debugger_base_url=debugger_base_url,
            postgres_uri=postgres_uri,
+            image=image,
        )
        # add up + options
        args.extend(["up", "--remove-orphans"])
@@ -572,6 +581,14 @@ def dockerfile(save_path: str, config: pathlib.Path, add_docker_compose: bool) -
    help="Don't raise errors for synchronous I/O blocking operations in your code.",
    default=False,
 )
+@click.option(
+    "--tunnel",
+    is_flag=True,
+    help="Expose the local server via a public tunnel (in this case, Cloudflare) "
+    "for remote frontend access. This avoids issues with browsers "
+    "or networks blocking localhost connections.",
+    default=False,
+)
@cli.command(
    "dev",
    help="🏃‍♀️‍➡️ Run LangGraph API server in development mode with hot reloading and debugging support",
@@ -588,6 +605,7 @@ def dev(
    wait_for_client: bool,
    studio_url: Optional[str],
    allow_blocking: bool,
+    tunnel: bool,
 ):
    """CLI entrypoint for running the LangGraph API server."""
    try:
@@ -655,6 +673,7 @@ def dev(
        ui_config=config_json.get("ui_config"),
        studio_url=studio_url,
        allow_blocking=allow_blocking,
+        tunnel=tunnel,
    )


@@ -682,6 +701,7 @@ def prepare_args_and_stdin(
    debugger_port: Optional[int] = None,
    debugger_base_url: Optional[str] = None,
    postgres_uri: Optional[str] = None,
+    image: Optional[str] = None,
 ) -> Tuple[List[str], str]:
    assert config_path.exists(), f"Config file not found: {config_path}"
    # prepare args
@@ -691,6 +711,7 @@ def prepare_args_and_stdin(
        debugger_port=debugger_port,
        debugger_base_url=debugger_base_url,
        postgres_uri=postgres_uri,
+        image=image,  # Pass image to compose YAML generator
    )
    args = [
        "--project-directory",
@@ -706,6 +727,7 @@ def prepare_args_and_stdin(
        config,
        watch=watch,
        base_image=langgraph_cli.config.default_base_image(config),
+        image=image,
    )
    return args, stdin

@@ -723,6 +745,7 @@ def prepare(
    debugger_port: Optional[int] = None,
    debugger_base_url: Optional[str] = None,
    postgres_uri: Optional[str] = None,
+    image: Optional[str] = None,
 ) -> Tuple[List[str], str]:
    """Prepare the arguments and stdin for running the LangGraph API server."""
    config_json = langgraph_cli.config.validate_config_file(config_path)
@@ -747,5 +770,6 @@ def prepare(
        debugger_port=debugger_port,
        debugger_base_url=debugger_base_url or f"http://127.0.0.1:{port}",
        postgres_uri=postgres_uri,
+        image=image,
    )
    return args, stdin
@@ -1288,6 +1288,7 @@ def config_to_compose(
    config_path: pathlib.Path,
    config: Config,
    base_image: Optional[str] = None,
+    image: Optional[str] = None,
    watch: bool = False,
 ) -> str:
    base_image = base_image or default_base_image(config)
@@ -1314,19 +1315,28 @@ def config_to_compose(
 """
    else:
        watch_str = ""
+    if image:
+        return f"""
+{textwrap.indent(env_vars_str, "            ")}
+        {env_file_str}
+        {watch_str}
+"""

-    dockerfile, additional_contexts = config_to_docker(config_path, config, base_image)
+    else:
+        dockerfile, additional_contexts = config_to_docker(
+            config_path, config, base_image
+        )

-    additional_contexts_str = "\n".join(
-        f"                - {name}: {path}"
-        for name, path in additional_contexts.items()
-    )
-    if additional_contexts_str:
-        additional_contexts_str = f"""
+        additional_contexts_str = "\n".join(
+            f"                - {name}: {path}"
+            for name, path in additional_contexts.items()
+        )
+        if additional_contexts_str:
+            additional_contexts_str = f"""
            additional_contexts:
 {additional_contexts_str}"""

-    return f"""
+        return f"""
 {textwrap.indent(env_vars_str, "            ")}
        {env_file_str}
        pull_policy: build
@@ -143,6 +143,8 @@ def compose_as_dict(
    debugger_base_url: Optional[str] = None,
    # postgres://user:password@host:port/database?option=value
    postgres_uri: Optional[str] = None,
+    # If you are running against an already-built image, you can pass it here
+    image: Optional[str] = None,
 ) -> dict:
    """Create a docker compose file as a dictionary in YML style."""
    if postgres_uri is None:
@@ -211,6 +213,8 @@ def compose_as_dict(
            "POSTGRES_URI": postgres_uri,
        },
    }
+    if image:
+        services["langgraph-api"]["image"] = image

    # If Postgres is included, add it to the dependencies of langgraph-api
    if include_db:
@@ -244,6 +248,7 @@ def compose(
    debugger_base_url: Optional[str] = None,
    # postgres://user:password@host:port/database?option=value
    postgres_uri: Optional[str] = None,
+    image: Optional[str] = None,
 ) -> str:
    """Create a docker compose file as a string."""
    compose_content = compose_as_dict(
@@ -252,6 +257,7 @@ def compose(
        debugger_port=debugger_port,
        debugger_base_url=debugger_base_url,
        postgres_uri=postgres_uri,
+        image=image,
    )
    compose_str = dict_to_yaml(compose_content)
    return compose_str
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.0 and should not be changed by hand.

 [[package]]
 name = "annotated-types"
@@ -33,7 +33,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}

 [package.extras]
 doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]

 [[package]]
@@ -292,7 +292,7 @@ files = [
 [package.extras]
 dev = ["Pygments", "build", "chardet", "pre-commit", "pytest", "pytest-cov", "pytest-dependency", "ruff", "tomli", "twine"]
 hard-encoding-detection = ["chardet"]
-toml = ["tomli ; python_version < \"3.11\""]
+toml = ["tomli"]
 types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency"]

 [[package]]
@@ -358,10 +358,10 @@ files = [
 cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}

 [package.extras]
-docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=3.0.0) ; python_version >= \"3.8\""]
+docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=3.0.0)"]
 docstest = ["pyenchant (>=3)", "readme-renderer (>=30.0)", "sphinxcontrib-spelling (>=7.3.1)"]
-nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2) ; python_version >= \"3.8\""]
-pep8test = ["check-sdist ; python_version >= \"3.8\"", "click (>=8.0.1)", "mypy (>=1.4)", "ruff (>=0.3.6)"]
+nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2)"]
+pep8test = ["check-sdist", "click (>=8.0.1)", "mypy (>=1.4)", "ruff (>=0.3.6)"]
 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi (>=2024)", "cryptography-vectors (==44.0.2)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
@@ -408,33 +408,33 @@ files = [

 [[package]]
 name = "h11"
-version = "0.14.0"
+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version >= \"3.11\""
 files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]

 [[package]]
 name = "httpcore"
-version = "1.0.8"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version >= \"3.11\""
 files = [
-    {file = "httpcore-1.0.8-py3-none-any.whl", hash = "sha256:5254cf149bcb5f75e9d1b2b9f729ea4a4b883d1ad7379fc632b727cec23674be"},
-    {file = "httpcore-1.0.8.tar.gz", hash = "sha256:86e94505ed24ea06514883fd44d2bc02d90e77e7979c8eb71b90f41d364a1bad"},
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
 ]

 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"

 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
@@ -462,7 +462,7 @@ httpcore = "==1.*"
 idna = "*"

 [package.extras]
-brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+brotli = ["brotli", "brotlicffi"]
 cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
@@ -585,15 +585,15 @@ tests = ["flask (>=2.2.5)", "hypothesis (>=6.79.4)", "pytest (>=7.4.4)"]

 [[package]]
 name = "langchain-core"
-version = "0.3.54"
+version = "0.3.55"
 description = "Building applications with LLMs through composability"
 optional = true
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "langchain_core-0.3.54-py3-none-any.whl", hash = "sha256:cd42155d9089e2fd4695ee02a4b2bc6daf55b9d4e1a37639647cf2455ed4fa04"},
-    {file = "langchain_core-0.3.54.tar.gz", hash = "sha256:55ce38939038e19b1271f36f512335462d7f64057b531598b3651d2b403e1b42"},
+    {file = "langchain_core-0.3.55-py3-none-any.whl", hash = "sha256:b3cb36bf37755a616158a79866657c6697b43a2f7c69dd723ce425f1c76c1baa"},
+    {file = "langchain_core-0.3.55.tar.gz", hash = "sha256:0f2b3e311621116a83510c70b0ac9d959030a0a457a69483535cff18501fedc9"},
 ]

 [package.dependencies]
@@ -630,15 +630,15 @@ xxhash = ">=3.5.0,<4.0.0"

 [[package]]
 name = "langgraph-api"
-version = "0.1.9"
+version = "0.1.12"
 description = ""
 optional = true
 python-versions = "<4.0,>=3.11.0"
 groups = ["main"]
 markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "langgraph_api-0.1.9-py3-none-any.whl", hash = "sha256:f84b11b1855e68dbef9f0a78db803e325b8dc11e2e19613178ab84cb0d99627c"},
-    {file = "langgraph_api-0.1.9.tar.gz", hash = "sha256:3530d82e715b9f99eeb8753c365f4d16c99ce60533fa15530d5ad1d493aeec06"},
+    {file = "langgraph_api-0.1.12-py3-none-any.whl", hash = "sha256:0f9417052ac75f6da892902083b7cf6a515bee12dd035cfbc3f0ddb813738830"},
+    {file = "langgraph_api-0.1.12.tar.gz", hash = "sha256:1646a904121a5dc84cece6a81b9c49693ccfbd6f1a2904e0ed7aa7eb711e64fc"},
 ]

 [package.dependencies]
@@ -712,15 +712,15 @@ blockbuster = ">=1.5.24,<2.0.0"

 [[package]]
 name = "langgraph-sdk"
-version = "0.1.61"
+version = "0.1.63"
 description = "SDK for interacting with LangGraph API"
 optional = true
 python-versions = "<4.0.0,>=3.9.0"
 groups = ["main"]
 markers = "python_version >= \"3.11\""
 files = [
-    {file = "langgraph_sdk-0.1.61-py3-none-any.whl", hash = "sha256:f2d774b12497c428862993090622d51e0dbc3f53e0cee3d74a13c7495d835cc6"},
-    {file = "langgraph_sdk-0.1.61.tar.gz", hash = "sha256:87dd1f07ab82da8875ac343268ece8bf5414632017ebc9d1cef4b523962fd601"},
+    {file = "langgraph_sdk-0.1.63-py3-none-any.whl", hash = "sha256:6fb78a7fc6a30eea43bd0d6401dbc9e3263d0d4c03f63c04035980da7e586b05"},
+    {file = "langgraph_sdk-0.1.63.tar.gz", hash = "sha256:62bf2cc31e5aa6c5b9011ee1702bcf1e36e67e142a60bd97af2611162fb58e18"},
 ]

 [package.dependencies]
@@ -729,15 +729,15 @@ orjson = ">=3.10.1"

 [[package]]
 name = "langsmith"
-version = "0.3.32"
+version = "0.3.33"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = true
 python-versions = "<4.0,>=3.9"
 groups = ["main"]
 markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "langsmith-0.3.32-py3-none-any.whl", hash = "sha256:d79af299038cd13db6d53f99fdc6a7171b731702536b7635f2da132555a6bebc"},
-    {file = "langsmith-0.3.32.tar.gz", hash = "sha256:3d7b1149e9fbe0f388303bc94d8deeeb822acc89cdea34f24151ea1316eb487d"},
+    {file = "langsmith-0.3.33-py3-none-any.whl", hash = "sha256:6fa453942014945e1de7e283880ed3e8031b5d84e0dc75b87d101ecedb62371b"},
+    {file = "langsmith-0.3.33.tar.gz", hash = "sha256:0f439e945528c6d14140137b918cc048aea04c6a987525926dbfda2560002924"},
 ]

 [package.dependencies]
@@ -805,10 +805,10 @@ files = [
 ]

 [package.extras]
-dev = ["attrs", "coverage", "eval-type-backport ; python_version < \"3.10\"", "furo", "ipython", "msgpack", "mypy", "pre-commit", "pyright", "pytest", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "tomli ; python_version < \"3.11\"", "tomli_w"]
+dev = ["attrs", "coverage", "eval-type-backport", "furo", "ipython", "msgpack", "mypy", "pre-commit", "pyright", "pytest", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "tomli", "tomli_w"]
 doc = ["furo", "ipython", "sphinx", "sphinx-copybutton", "sphinx-design"]
-test = ["attrs", "eval-type-backport ; python_version < \"3.10\"", "msgpack", "pytest", "pyyaml", "tomli ; python_version < \"3.11\"", "tomli_w"]
-toml = ["tomli ; python_version < \"3.11\"", "tomli_w"]
+test = ["attrs", "eval-type-backport", "msgpack", "pytest", "pyyaml", "tomli", "tomli_w"]
+toml = ["tomli", "tomli_w"]
 yaml = ["pyyaml"]

 [[package]]
@@ -1071,7 +1071,7 @@ typing-inspection = ">=0.4.0"

 [package.extras]
 email = ["email-validator (>=2.0.0)"]
-timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
+timezone = ["tzdata"]

 [[package]]
 name = "pydantic-core"
@@ -1604,22 +1604,22 @@ files = [
 ]

 [package.extras]
-brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]

 [[package]]
 name = "uvicorn"
-version = "0.34.1"
+version = "0.34.2"
 description = "The lightning-fast ASGI server."
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "python_version >= \"3.11\" and extra == \"inmem\""
 files = [
-    {file = "uvicorn-0.34.1-py3-none-any.whl", hash = "sha256:984c3a8c7ca18ebaad15995ee7401179212c59521e67bfc390c07fa2b8d2e065"},
-    {file = "uvicorn-0.34.1.tar.gz", hash = "sha256:af981725fc4b7ffc5cb3b0e9eda6258a90c4b52cb2a83ce567ae0a7ae1757afc"},
+    {file = "uvicorn-0.34.2-py3-none-any.whl", hash = "sha256:deb49af569084536d269fe0a6d67e3754f104cf03aba7c11c40f01aadf33c403"},
+    {file = "uvicorn-0.34.2.tar.gz", hash = "sha256:0e929828f6186353a80b58ea719861d2629d766293b6d19baf086ba31d4f3328"},
 ]

 [package.dependencies]
@@ -1627,7 +1627,7 @@ click = ">=7.0"
 h11 = ">=0.8"

 [package.extras]
-standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]

 [[package]]
 name = "watchdog"
@@ -2011,4 +2011,4 @@ inmem = ["langgraph-api", "langgraph-runtime-inmem", "python-dotenv"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.9.0,<4.0"
-content-hash = "afc2f8776b4b6144bd1197df49ba34089889e2a1110b8470d8f1b212e0b08380"
+content-hash = "6f3f275ae70749922db5bd1105711fbb0f8ac3b215982f7c75861c93d956e95d"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-cli"
-version = "0.2.5"
+version = "0.2.7"
 description = "CLI for interacting with LangGraph API"
 authors = []
 license = "MIT"
@@ -14,7 +14,7 @@ langgraph = "langgraph_cli.cli:cli"
 [tool.poetry.dependencies]
 python = "^3.9.0,<4.0"
 click = "^8.1.7"
-langgraph-api = { version = ">=0.1.0,<0.2.0", optional = true, python = ">=3.11,<4.0" }
+langgraph-api = { version = ">=0.1.12,<0.2.0", optional = true, python = ">=3.11,<4.0" }
 langgraph-runtime-inmem = { version = ">=0.0.1,<0.1.0", optional = true, python = ">=3.11,<4.0" }
 langgraph-sdk = { version = ">=0.1.0,<0.2.0", optional = true, python = ">=3.11,<4.0" }
 python-dotenv = { version = ">=0.8.0", optional = true }
@@ -160,6 +160,110 @@ services:
    assert clean_empty_lines(actual_stdin) == expected_stdin


+def test_prepare_args_and_stdin_with_image() -> None:
+    # this basically serves as an end-to-end test for using config and docker helpers
+    config_path = pathlib.Path(__file__).parent / "langgraph.json"
+    config = validate_config(
+        Config(dependencies=[".", "../../.."], graphs={"agent": "agent.py:graph"})
+    )
+    port = 8000
+    debugger_port = 8001
+    debugger_graph_url = f"http://127.0.0.1:{port}"
+
+    actual_args, actual_stdin = prepare_args_and_stdin(
+        capabilities=DEFAULT_DOCKER_CAPABILITIES,
+        config_path=config_path,
+        config=config,
+        docker_compose=pathlib.Path("custom-docker-compose.yml"),
+        port=port,
+        debugger_port=debugger_port,
+        debugger_base_url=debugger_graph_url,
+        watch=True,
+        image="my-cool-image",
+    )
+
+    expected_args = [
+        "--project-directory",
+        str(pathlib.Path(__file__).parent.absolute()),
+        "-f",
+        "custom-docker-compose.yml",
+        "-f",
+        "-",
+    ]
+    expected_stdin = f"""volumes:
+    langgraph-data:
+        driver: local
+services:
+    langgraph-redis:
+        image: redis:6
+        healthcheck:
+            test: redis-cli ping
+            interval: 5s
+            timeout: 1s
+            retries: 5
+    langgraph-postgres:
+        image: pgvector/pgvector:pg16
+        ports:
+            - "5433:5432"
+        environment:
+            POSTGRES_DB: postgres
+            POSTGRES_USER: postgres
+            POSTGRES_PASSWORD: postgres
+        command:
+            - postgres
+            - -c
+            - shared_preload_libraries=vector
+        volumes:
+            - langgraph-data:/var/lib/postgresql/data
+        healthcheck:
+            test: pg_isready -U postgres
+            start_period: 10s
+            timeout: 1s
+            retries: 5
+            interval: 60s
+            start_interval: 1s
+    langgraph-debugger:
+        image: langchain/langgraph-debugger
+        restart: on-failure
+        depends_on:
+            langgraph-postgres:
+                condition: service_healthy
+        ports:
+            - "{debugger_port}:3968"
+        environment:
+            VITE_STUDIO_LOCAL_GRAPH_URL: {debugger_graph_url}
+    langgraph-api:
+        ports:
+            - "8000:8000"
+        depends_on:
+            langgraph-redis:
+                condition: service_healthy
+            langgraph-postgres:
+                condition: service_healthy
+        environment:
+            REDIS_URI: redis://langgraph-redis:6379
+            POSTGRES_URI: {DEFAULT_POSTGRES_URI}
+        image: my-cool-image
+        healthcheck:
+            test: python /api/healthcheck.py
+            interval: 60s
+            start_interval: 1s
+            start_period: 10s
+        
+        
+        develop:
+            watch:
+                - path: langgraph.json
+                  action: rebuild
+                - path: .
+                  action: rebuild
+                - path: ../../..
+                  action: rebuild\
+"""
+    assert actual_args == expected_args
+    assert clean_empty_lines(actual_stdin) == expected_stdin
+
+
 def test_version_option() -> None:
    """Test the --version option of the CLI."""
    runner = CliRunner()
@@ -78,7 +78,7 @@ test_watch_all:
 PYTHON_FILES=.
 MYPY_CACHE=.mypy_cache
 lint format: PYTHON_FILES=.
-lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E '\.py$$|\.ipynb$$')
+lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --relative --diff-filter=d main . | grep -E r'\.py$$|\.ipynb$$')
 lint_package: PYTHON_FILES=langgraph
 lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
@@ -1,7 +1,8 @@
 import operator
+from collections.abc import Sequence
 from functools import partial
 from random import choice
-from typing import Annotated, Optional, Sequence
+from typing import Annotated, Optional

 from pydantic import BaseModel, Field, field_validator

@@ -1,7 +1,8 @@
 import operator
+from collections.abc import Sequence
 from functools import partial
 from random import choice
-from typing import Annotated, Optional, Sequence
+from typing import Annotated, Optional

 from typing_extensions import TypedDict

@@ -1,8 +1,9 @@
 import operator
+from collections.abc import Sequence
 from dataclasses import dataclass, field
 from functools import partial
 from random import choice
-from typing import Annotated, Optional, Sequence
+from typing import Annotated, Optional

 from langgraph.constants import END, START
 from langgraph.graph.state import StateGraph
@@ -1,6 +1,6 @@
 import functools
 import warnings
-from typing import Any, Callable, Type, TypeVar, Union, cast
+from typing import Any, Callable, TypeVar, Union, cast


 class LangGraphDeprecationWarning(DeprecationWarning):
@@ -8,7 +8,7 @@ class LangGraphDeprecationWarning(DeprecationWarning):


 F = TypeVar("F", bound=Callable[..., Any])
-C = TypeVar("C", bound=Type[Any])
+C = TypeVar("C", bound=type[Any])


 def deprecated(
@@ -1,4 +1,5 @@
-from typing import Any, Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Any, Generic

 from typing_extensions import Self

@@ -21,12 +22,12 @@ class AnyValue(Generic[Value], BaseChannel[Value, Value, Value]):
        return isinstance(value, AnyValue)

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
-from typing import Any, Generic, Sequence, TypeVar
+from collections.abc import Sequence
+from typing import Any, Generic, TypeVar

 from typing_extensions import Self

@@ -1,5 +1,6 @@
 import collections.abc
-from typing import Callable, Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Callable, Generic

 from typing_extensions import NotRequired, Required, Self

@@ -31,7 +32,7 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):

    __slots__ = ("value", "operator")

-    def __init__(self, typ: Type[Value], operator: Callable[[Value, Value], Value]):
+    def __init__(self, typ: type[Value], operator: Callable[[Value, Value], Value]):
        super().__init__(typ)
        self.operator = operator
        # special forms from typing or collections.abc are not instantiable
@@ -57,12 +58,12 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
        )

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,4 +1,5 @@
-from typing import Any, Generic, NamedTuple, Optional, Sequence, Type, Union
+from collections.abc import Sequence, Set
+from typing import Any, Generic, NamedTuple, Optional, Union

 from typing_extensions import Self

@@ -8,11 +9,11 @@ from langgraph.errors import EmptyChannelError, InvalidUpdateError


 class WaitForNames(NamedTuple):
-    names: set[Any]
+    names: Set[Any]


 class DynamicBarrierValue(
-    Generic[Value], BaseChannel[Value, Union[Value, WaitForNames], set[Value]]
+    Generic[Value], BaseChannel[Value, Union[Value, WaitForNames], Set[Value]]
 ):
    """A channel that switches between two states

@@ -25,10 +26,10 @@ class DynamicBarrierValue(

    __slots__ = ("names", "seen")

-    names: Optional[set[Value]]
+    names: Optional[Set[Value]]
    seen: set[Value]

-    def __init__(self, typ: Type[Value]) -> None:
+    def __init__(self, typ: type[Value]) -> None:
        super().__init__(typ)
        self.names = None
        self.seen = set()
@@ -37,12 +38,12 @@ class DynamicBarrierValue(
        return isinstance(value, DynamicBarrierValue) and value.names == self.names

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -54,11 +55,11 @@ class DynamicBarrierValue(
        empty.seen = self.seen.copy()
        return empty

-    def checkpoint(self) -> tuple[Optional[set[Value]], set[Value]]:
+    def checkpoint(self) -> tuple[Optional[Set[Value]], set[Value]]:
        return (self.names, self.seen)

    def from_checkpoint(
-        self, checkpoint: tuple[Optional[set[Value]], set[Value]]
+        self, checkpoint: tuple[Optional[Set[Value]], set[Value]]
    ) -> Self:
        empty = self.__class__(self.typ)
        empty.key = self.key
@@ -1,4 +1,5 @@
-from typing import Any, Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Any, Generic

 from typing_extensions import Self

@@ -21,12 +22,12 @@ class EphemeralValue(Generic[Value], BaseChannel[Value, Value, Value]):
        return isinstance(value, EphemeralValue) and value.guard == self.guard

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,4 +1,5 @@
-from typing import Any, Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Any, Generic

 from typing_extensions import Self

@@ -25,12 +26,12 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):
        return isinstance(value, LastValue)

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,4 +1,5 @@
-from typing import Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Generic

 from typing_extensions import Self

@@ -15,7 +16,7 @@ class NamedBarrierValue(Generic[Value], BaseChannel[Value, Value, set[Value]]):
    names: set[Value]
    seen: set[Value]

-    def __init__(self, typ: Type[Value], names: set[Value]) -> None:
+    def __init__(self, typ: type[Value], names: set[Value]) -> None:
        super().__init__(typ)
        self.names = names
        self.seen: set[str] = set()
@@ -24,12 +25,12 @@ class NamedBarrierValue(Generic[Value], BaseChannel[Value, Value, set[Value]]):
        return isinstance(value, NamedBarrierValue) and value.names == self.names

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,4 +1,5 @@
-from typing import Any, Generic, Iterator, Sequence, Type, Union
+from collections.abc import Iterator, Sequence
+from typing import Any, Generic, Union

 from typing_extensions import Self

@@ -28,7 +29,7 @@ class Topic(

    __slots__ = ("values", "accumulate")

-    def __init__(self, typ: Type[Value], accumulate: bool = False) -> None:
+    def __init__(self, typ: type[Value], accumulate: bool = False) -> None:
        super().__init__(typ)
        # attrs
        self.accumulate = accumulate
@@ -1,4 +1,5 @@
-from typing import Generic, Sequence, Type
+from collections.abc import Sequence
+from typing import Generic

 from typing_extensions import Self

@@ -12,7 +13,7 @@ class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):

    __slots__ = ("value", "guard")

-    def __init__(self, typ: Type[Value], guard: bool = True) -> None:
+    def __init__(self, typ: type[Value], guard: bool = True) -> None:
        super().__init__(typ)
        self.guard = guard
        self.value = MISSING
@@ -21,12 +22,12 @@ class UntrackedValue(Generic[Value], BaseChannel[Value, Value, Value]):
        return isinstance(value, UntrackedValue) and value.guard == self.guard

    @property
-    def ValueType(self) -> Type[Value]:
+    def ValueType(self) -> type[Value]:
        """The type of the value stored in the channel."""
        return self.typ

    @property
-    def UpdateType(self) -> Type[Value]:
+    def UpdateType(self) -> type[Value]:
        """The type of the update received by the channel."""
        return self.typ

@@ -1,6 +1,7 @@
 import sys
+from collections.abc import Mapping
 from types import MappingProxyType
-from typing import Any, Literal, Mapping, cast
+from typing import Any, Literal, cast

 from langgraph.types import Interrupt, Send  # noqa: F401

@@ -1,5 +1,6 @@
+from collections.abc import Sequence
 from enum import Enum
-from typing import Any, Sequence
+from typing import Any

 from langgraph.checkpoint.base import EmptyChannelError  # noqa: F401
 from langgraph.types import Command, Interrupt
@@ -2,14 +2,13 @@ import asyncio
 import concurrent.futures
 import functools
 import inspect
+from collections.abc import Awaitable, Sequence
 from dataclasses import dataclass
 from typing import (
    Any,
-    Awaitable,
    Callable,
    Generic,
    Optional,
-    Sequence,
    TypeVar,
    Union,
    get_args,
@@ -1,19 +1,17 @@
+from collections.abc import Awaitable, Hashable, Sequence
 from inspect import (
    isfunction,
    ismethod,
    signature,
 )
+from itertools import zip_longest
 from types import FunctionType
 from typing import (
    Any,
-    Awaitable,
    Callable,
-    Hashable,
    Literal,
    NamedTuple,
    Optional,
-    Sequence,
-    Type,
    Union,
    cast,
    get_args,
@@ -29,12 +27,17 @@ from langchain_core.runnables import (

 from langgraph.constants import END, START
 from langgraph.errors import InvalidUpdateError
-from langgraph.pregel.write import ChannelWrite
+from langgraph.pregel.write import PASSTHROUGH, ChannelWrite, ChannelWriteEntry
 from langgraph.types import Send
 from langgraph.utils.runnable import (
    RunnableCallable,
 )

+Writer = Callable[
+    [Sequence[Union[str, Send]], bool],
+    Sequence[Union[ChannelWriteEntry, Send]],
+]
+

 def _get_branch_path_input_schema(
    path: Union[
@@ -42,7 +45,7 @@ def _get_branch_path_input_schema(
        Callable[..., Awaitable[Union[Hashable, list[Hashable]]]],
        Runnable[Any, Union[Hashable, list[Hashable]]],
    ],
-) -> Optional[Type[Any]]:
+) -> Optional[type[Any]]:
    input = None
    # detect input schema annotation in the branch callable
    try:
@@ -85,7 +88,7 @@ class Branch(NamedTuple):
    path: Runnable[Any, Union[Hashable, list[Hashable]]]
    ends: Optional[dict[Hashable, str]]
    then: Optional[str] = None
-    input_schema: Optional[Type[Any]] = None
+    input_schema: Optional[type[Any]] = None

    @classmethod
    def from_path(
@@ -124,9 +127,7 @@ class Branch(NamedTuple):

    def run(
        self,
-        writer: Callable[
-            [Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
-        ],
+        writer: Writer,
        reader: Optional[Callable[[RunnableConfig], Any]] = None,
    ) -> RunnableCallable:
        return ChannelWrite.register_writer(
@@ -138,7 +139,15 @@ class Branch(NamedTuple):
                name=None,
                trace=False,
                func_accepts_config=True,
+            ),
+            list(
+                zip_longest(
+                    writer([e for e in self.ends.values()], True),
+                    [str(la) for la, e in self.ends.items()],
+                )
            )
+            if self.ends
+            else None,
        )

    def _route(
@@ -147,9 +156,7 @@ class Branch(NamedTuple):
        config: RunnableConfig,
        *,
        reader: Optional[Callable[[RunnableConfig], Any]],
-        writer: Callable[
-            [Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
-        ],
+        writer: Writer,
    ) -> Runnable:
        if reader:
            value = reader(config)
@@ -172,9 +179,7 @@ class Branch(NamedTuple):
        config: RunnableConfig,
        *,
        reader: Optional[Callable[[RunnableConfig], Any]],
-        writer: Callable[
-            [Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
-        ],
+        writer: Writer,
    ) -> Runnable:
        if reader:
            value = reader(config)
@@ -193,9 +198,7 @@ class Branch(NamedTuple):

    def _finish(
        self,
-        writer: Callable[
-            [Sequence[Union[str, Send]], RunnableConfig], Optional[ChannelWrite]
-        ],
+        writer: Writer,
        input: Any,
        result: Any,
        config: RunnableConfig,
@@ -212,4 +215,18 @@ class Branch(NamedTuple):
            raise ValueError("Branch did not return a valid destination")
        if any(p.node == END for p in destinations if isinstance(p, Send)):
            raise InvalidUpdateError("Cannot send a packet to the END node")
-        return writer(destinations, config) or input
+        entries = writer(destinations, False)
+        if not entries:
+            return input
+        else:
+            need_passthrough = False
+            for e in entries:
+                if isinstance(e, ChannelWriteEntry):
+                    if e.value is PASSTHROUGH:
+                        need_passthrough = True
+                        break
+            if need_passthrough:
+                return ChannelWrite(entries)
+            else:
+                ChannelWrite.do_write(config, entries)
+                return input
@@ -1,23 +1,17 @@
-import asyncio
 import logging
 from collections import defaultdict
+from collections.abc import Awaitable, Hashable, Sequence
 from typing import (
    Any,
-    Awaitable,
    Callable,
-    Hashable,
    NamedTuple,
    Optional,
-    Sequence,
    Union,
    cast,
    overload,
 )

 from langchain_core.runnables import Runnable
-from langchain_core.runnables.config import RunnableConfig
-from langchain_core.runnables.graph import Graph as DrawableGraph
-from langchain_core.runnables.graph import Node as DrawableNode
 from typing_extensions import Self

 from langgraph.channels.ephemeral_value import EphemeralValue
@@ -32,7 +26,6 @@ from langgraph.constants import (
 )
 from langgraph.graph.branch import Branch
 from langgraph.pregel import Channel, Pregel
-from langgraph.pregel.protocol import PregelProtocol
 from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.types import All, Checkpointer
@@ -182,7 +175,7 @@ class Graph:
        # validate the condition
        if name in self.branches[source]:
            raise ValueError(
-                f"Branch with name `{path.name}` already exists for node " f"`{source}`"
+                f"Branch with name `{path.name}` already exists for node `{source}`"
            )
        # save it
        self.branches[source][name] = Branch.from_path(path, path_map, then, False)
@@ -380,10 +373,10 @@ class CompiledGraph(Pregel):
            cast(list[str], self.nodes[end].channels).append(start)

    def attach_branch(self, start: str, name: str, branch: Branch) -> None:
-        def branch_writer(
-            packets: Sequence[Union[str, Send]], config: RunnableConfig
-        ) -> Optional[ChannelWrite]:
-            writes = [
+        def get_writes(
+            packets: Sequence[Union[str, Send]], static: bool = False
+        ) -> Sequence[Union[ChannelWriteEntry, Send]]:
+            return [
                (
                    ChannelWriteEntry(f"branch:{start}:{name}:{p}" if p != END else END)
                    if not isinstance(p, Send)
@@ -391,14 +384,13 @@ class CompiledGraph(Pregel):
                )
                for p in packets
            ]
-            return ChannelWrite(cast(Sequence[Union[ChannelWriteEntry, Send]], writes))

        # add hidden start node
        if start == START and start not in self.nodes:
            self.nodes[start] = Channel.subscribe_to(START, tags=[TAG_HIDDEN])

        # attach branch writer
-        self.nodes[start] |= branch.run(branch_writer)
+        self.nodes[start] |= branch.run(get_writes)

        # attach branch readers
        ends = branch.ends.values() if branch.ends else [node for node in self.nodes]
@@ -408,171 +400,3 @@ class CompiledGraph(Pregel):
                self.channels[channel_name] = EphemeralValue(Any)
                self.nodes[end].triggers.append(channel_name)
                cast(list[str], self.nodes[end].channels).append(channel_name)
-
-    async def aget_graph(
-        self,
-        config: Optional[RunnableConfig] = None,
-        *,
-        xray: Union[int, bool] = False,
-    ) -> DrawableGraph:
-        """Returns a drawable representation of the computation graph."""
-        from langgraph.pregel.remote import RemoteGraph
-
-        # gather subgraphs
-        if xray:
-            subpregels: dict[str, PregelProtocol] = {
-                k: v
-                async for k, v in self.aget_subgraphs()
-                if isinstance(v, (CompiledGraph, RemoteGraph))
-            }
-            subgraphs = {
-                k: v
-                for k, v in zip(
-                    subpregels,
-                    await asyncio.gather(
-                        *(
-                            p.aget_graph(
-                                config,
-                                xray=xray
-                                if isinstance(xray, bool) or xray <= 0
-                                else xray - 1,
-                            )
-                            for p in subpregels.values()
-                        )
-                    ),
-                )
-            }
-        else:
-            subgraphs = {}
-
-        # draw the graph
-        return self._draw_graph(config, subgraphs=subgraphs)
-
-    def get_graph(
-        self,
-        config: Optional[RunnableConfig] = None,
-        *,
-        xray: Union[int, bool] = False,
-    ) -> DrawableGraph:
-        """Returns a drawable representation of the computation graph."""
-        from langgraph.pregel.remote import RemoteGraph
-
-        # gather subgraphs
-        if xray:
-            subgraphs = {
-                k: v.get_graph(
-                    config,
-                    xray=xray if isinstance(xray, bool) or xray <= 0 else xray - 1,
-                )
-                for k, v in self.get_subgraphs()
-                if isinstance(v, (CompiledGraph, RemoteGraph))
-            }
-        else:
-            subgraphs = {}
-
-        # draw the graph
-        return self._draw_graph(config, subgraphs=subgraphs)
-
-    def _draw_graph(
-        self,
-        config: Optional[RunnableConfig] = None,
-        *,
-        subgraphs: dict[str, DrawableGraph] = {},
-    ) -> DrawableGraph:
-        # create the graph
-        graph = DrawableGraph()
-        start_nodes: dict[str, DrawableNode] = {
-            START: graph.add_node(self.get_input_schema(config), START)
-        }
-        end_nodes: dict[str, DrawableNode] = {}
-
-        def add_edge(
-            start: str,
-            end: str,
-            label: Optional[Hashable] = None,
-            conditional: bool = False,
-        ) -> None:
-            if end == END and END not in end_nodes:
-                end_nodes[END] = graph.add_node(self.get_output_schema(config), END)
-            if start not in start_nodes or end not in end_nodes:
-                logger.warning(
-                    f"Could not add edge from '{start}' to '{end}' due to missing nodes"
-                )
-                return
-            return graph.add_edge(
-                start_nodes[start],
-                end_nodes[end],
-                str(label) if label is not None else None,
-                conditional,
-            )
-
-        for key, n in self.builder.nodes.items():
-            node = n.runnable
-            metadata = n.metadata or {}
-            if key in self.interrupt_before_nodes and key in self.interrupt_after_nodes:
-                metadata["__interrupt"] = "before,after"
-            elif key in self.interrupt_before_nodes:
-                metadata["__interrupt"] = "before"
-            elif key in self.interrupt_after_nodes:
-                metadata["__interrupt"] = "after"
-            if key in subgraphs:
-                subgraph = subgraphs[key]
-                subgraph.trim_first_node()
-                subgraph.trim_last_node()
-                if len(subgraph.nodes) >= 1:
-                    e, s = graph.extend(subgraph, prefix=key)
-                    if e is None:
-                        logger.warning(
-                            f"Could not extend subgraph '{key}' due to missing entrypoint"
-                        )
-                        continue
-                    if s is not None:
-                        start_nodes[key] = s
-                    end_nodes[key] = e
-                else:
-                    nn = graph.add_node(node, key, metadata=metadata or None)
-                    start_nodes[key] = nn
-                    end_nodes[key] = nn
-            else:
-                nn = graph.add_node(node, key, metadata=metadata or None)
-                start_nodes[key] = nn
-                end_nodes[key] = nn
-        for start, end in sorted(self.builder._all_edges):
-            add_edge(start, end)
-        for start, branches in self.builder.branches.items():
-            default_ends = {
-                **{k: k for k in self.builder.nodes if k != start},
-                END: END,
-            }
-            for _, branch in branches.items():
-                if branch.ends is not None:
-                    ends = branch.ends
-                elif branch.then is not None:
-                    ends = {k: k for k in default_ends if k not in (END, branch.then)}
-                else:
-                    ends = cast(dict[Hashable, str], default_ends)
-                for label, end in ends.items():
-                    add_edge(
-                        start,
-                        end,
-                        label if label != end else None,
-                        conditional=True,
-                    )
-                    if branch.then is not None:
-                        add_edge(end, branch.then)
-        for key, n in self.builder.nodes.items():
-            if isinstance(n.ends, dict):
-                for end, label in n.ends.items():
-                    add_edge(key, end, label, conditional=True)
-            elif isinstance(n.ends, tuple):
-                for end in n.ends:
-                    add_edge(key, end, conditional=True)
-
-        return graph
-
-    def _repr_mimebundle_(self, **kwargs: Any) -> dict[str, Any]:
-        """Mime bundle used by Jupyter to display the graph"""
-        return {
-            "text/plain": repr(self),
-            "image/png": self.get_graph().draw_mermaid_png(),
-        }
@@ -1,5 +1,6 @@
 import uuid
 import warnings
+from collections.abc import Sequence
 from functools import partial
 from typing import (
    Annotated,
@@ -7,7 +8,6 @@ from typing import (
    Callable,
    Literal,
    Optional,
-    Sequence,
    Union,
    cast,
 )
@@ -3,10 +3,10 @@ import logging
 import weakref
 from inspect import isclass
 from typing import (
+    Annotated,
    Any,
    Callable,
    Optional,
-    Type,
    Union,
    get_args,
    get_origin,
@@ -15,14 +15,13 @@ from typing import (

 from pydantic import BaseModel
 from pydantic.v1 import BaseModel as BaseModelV1
-from typing_extensions import Annotated

 __all__ = ["SchemaCoercionMapper"]

 logger = logging.getLogger(__name__)


-_cache: weakref.WeakKeyDictionary[Type[Any], dict[int, "SchemaCoercionMapper"]] = (
+_cache: weakref.WeakKeyDictionary[type[Any], dict[int, "SchemaCoercionMapper"]] = (
    weakref.WeakKeyDictionary()
 )

@@ -32,7 +31,7 @@ class SchemaCoercionMapper:

    def __new__(
        cls,
-        schema: Type[Any],
+        schema: type[Any],
        type_hints: Optional[dict[str, Any]] = None,
        *,
        max_depth: int = 12,
@@ -46,7 +45,7 @@ class SchemaCoercionMapper:

    def __init__(
        self,
-        schema: Type[Any],
+        schema: type[Any],
        type_hints: Optional[dict[str, Any]] = None,
        *,
        max_depth: int = 12,
@@ -187,7 +186,7 @@ class SchemaCoercionMapper:
                def dict_coercer(v: Any, d: Any) -> Any:
                    if not isinstance(v, dict):
                        if throw:
-                            raise TypeError("Expected dict, got %s" % type(v))
+                            raise TypeError(f"Expected dict, got {type(v)}")
                    return v

                return dict_coercer
@@ -197,7 +196,7 @@ class SchemaCoercionMapper:
            def dict_coercer(v: Any, d: Any) -> Any:
                if not isinstance(v, dict):
                    if throw:
-                        raise TypeError("Expected dict, got %s" % type(v))
+                        raise TypeError(f"Expected dict, got {type(v)}")
                    return v
                return {k_sub(k, d - 1): v_sub(val, d - 1) for k, val in v.items()}

@@ -3,19 +3,16 @@ import logging
 import typing
 import warnings
 from collections import defaultdict
+from collections.abc import Awaitable, Hashable, Sequence
 from functools import partial
 from inspect import isclass, isfunction, ismethod, signature
 from types import FunctionType
 from typing import (
    Any,
-    Awaitable,
    Callable,
-    Hashable,
    Literal,
    NamedTuple,
    Optional,
-    Sequence,
-    Type,
    Union,
    cast,
    get_args,
@@ -84,7 +81,7 @@ from langgraph.utils.runnable import RunnableLike, coerce_to_runnable
 logger = logging.getLogger(__name__)


-def _warn_invalid_state_schema(schema: Union[Type[Any], Any]) -> None:
+def _warn_invalid_state_schema(schema: Union[type[Any], Any]) -> None:
    if isinstance(schema, type):
        return
    if typing.get_args(schema):
@@ -108,7 +105,7 @@ def _get_node_name(node: RunnableLike) -> str:
 class StateNodeSpec(NamedTuple):
    runnable: Runnable
    metadata: Optional[dict[str, Any]]
-    input: Type[Any]
+    input: type[Any]
    retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]]
    ends: Optional[Union[tuple[str, ...], dict[str, str]]] = EMPTY_SEQ

@@ -166,15 +163,15 @@ class StateGraph(Graph):
    nodes: dict[str, StateNodeSpec]  # type: ignore[assignment]
    channels: dict[str, BaseChannel]
    managed: dict[str, ManagedValueSpec]
-    schemas: dict[Type[Any], dict[str, Union[BaseChannel, ManagedValueSpec]]]
+    schemas: dict[type[Any], dict[str, Union[BaseChannel, ManagedValueSpec]]]

    def __init__(
        self,
-        state_schema: Optional[Type[Any]] = None,
-        config_schema: Optional[Type[Any]] = None,
+        state_schema: Optional[type[Any]] = None,
+        config_schema: Optional[type[Any]] = None,
        *,
-        input: Optional[Type[Any]] = None,
-        output: Optional[Type[Any]] = None,
+        input: Optional[type[Any]] = None,
+        output: Optional[type[Any]] = None,
    ) -> None:
        super().__init__()
        if state_schema is None:
@@ -195,7 +192,7 @@ class StateGraph(Graph):
        self.schemas = {}
        self.channels = {}
        self.managed = {}
-        self.type_hints: dict[Type[Any], dict[str, Any]] = {}
+        self.type_hints: dict[type[Any], dict[str, Any]] = {}
        self.schema = state_schema
        self.input = input
        self.output = output
@@ -211,7 +208,7 @@ class StateGraph(Graph):
            (start, end) for starts, end in self.waiting_edges for start in starts
        }

-    def _add_schema(self, schema: Type[Any], /, allow_managed: bool = True) -> None:
+    def _add_schema(self, schema: type[Any], /, allow_managed: bool = True) -> None:
        if schema not in self.schemas:
            _warn_invalid_state_schema(schema)
            channels, managed, type_hints = _get_channels(schema)
@@ -250,7 +247,7 @@ class StateGraph(Graph):
        node: RunnableLike,
        *,
        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[Type[Any]] = None,
+        input: Optional[type[Any]] = None,
        retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
@@ -275,7 +272,7 @@ class StateGraph(Graph):
        action: RunnableLike,
        *,
        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[Type[Any]] = None,
+        input: Optional[type[Any]] = None,
        retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
@@ -299,7 +296,7 @@ class StateGraph(Graph):
        action: Optional[RunnableLike] = None,
        *,
        metadata: Optional[dict[str, Any]] = None,
-        input: Optional[Type[Any]] = None,
+        input: Optional[type[Any]] = None,
        retry: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
        destinations: Optional[Union[dict[str, str], tuple[str, ...]]] = None,
    ) -> Self:
@@ -527,7 +524,7 @@ class StateGraph(Graph):
        # validate the condition
        if name in self.branches[source]:
            raise ValueError(
-                f"Branch with name `{path.name}` already exists for node " f"`{source}`"
+                f"Branch with name `{path.name}` already exists for node `{source}`"
            )
        # save it
        self.branches[source][name] = Branch.from_path(path, path_map, then, True)
@@ -686,12 +683,12 @@ class StateGraph(Graph):

 class CompiledStateGraph(CompiledGraph):
    builder: StateGraph
-    schema_to_mapper: dict[Type[Any], Optional[Callable[[Any], Any]]]
+    schema_to_mapper: dict[type[Any], Optional[Callable[[Any], Any]]]

    def __init__(
        self,
        *,
-        schema_to_mapper: dict[Type[Any], Optional[Callable[[Any], Any]]],
+        schema_to_mapper: dict[type[Any], Optional[Callable[[Any], Any]]],
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
@@ -774,7 +771,12 @@ class CompiledStateGraph(CompiledGraph):
            ChannelWriteTupleEntry(
                mapper=_get_root if output_keys == ["__root__"] else _get_updates
            ),
-            ChannelWriteTupleEntry(mapper=_control_branch),
+            ChannelWriteTupleEntry(
+                mapper=_control_branch,
+                static=_control_static(node.ends)
+                if node is not None and node.ends is not None
+                else None,
+            ),
        )

        # add node and output channel
@@ -840,30 +842,32 @@ class CompiledStateGraph(CompiledGraph):
    def attach_branch(
        self, start: str, name: str, branch: Branch, *, with_reader: bool = True
    ) -> None:
-        def branch_writer(
-            packets: Sequence[Union[str, Send]], config: RunnableConfig
-        ) -> None:
-            if filtered := [p for p in packets if p != END]:
-                writes = [
-                    (
-                        ChannelWriteEntry(CHANNEL_BRANCH_TO.format(p), None)
-                        if not isinstance(p, Send)
-                        else p
-                    )
-                    for p in filtered
-                ]
-                if branch.then and branch.then != END:
-                    writes.append(
-                        ChannelWriteEntry(
-                            f"branch:{start}:{name}::then",
-                            WaitForNames(
-                                {p.node if isinstance(p, Send) else p for p in filtered}
-                            ),
-                        )
-                    )
-                ChannelWrite.do_write(
-                    config, cast(Sequence[Union[Send, ChannelWriteEntry]], writes)
+        def get_writes(
+            packets: Sequence[Union[str, Send]], static: bool = False
+        ) -> Sequence[Union[ChannelWriteEntry, Send]]:
+            writes = [
+                (
+                    ChannelWriteEntry(CHANNEL_BRANCH_TO.format(p), None)
+                    if not isinstance(p, Send)
+                    else p
                )
+                for p in packets
+                if (True if static else p != END)
+            ]
+            if not writes:
+                return []
+            if branch.then and branch.then != END:
+                writes.append(
+                    ChannelWriteEntry(
+                        f"branch:{start}:{name}::then",
+                        WaitForNames(
+                            frozenset(
+                                p.node if isinstance(p, Send) else p for p in packets
+                            )
+                        ),
+                    )
+                )
+            return writes

        if with_reader:
            # get schema
@@ -891,7 +895,7 @@ class CompiledStateGraph(CompiledGraph):
            reader = None

        # attach branch publisher
-        self.nodes[start].writers.append(branch.run(branch_writer, reader))
+        self.nodes[start].writers.append(branch.run(get_writes, reader))

        # attach then subscriber
        if branch.then and branch.then != END:
@@ -1015,7 +1019,7 @@ class CompiledStateGraph(CompiledGraph):


 def _pick_mapper(
-    state_keys: Sequence[str], schema: Type[Any], type_hints: Optional[dict[str, Any]]
+    state_keys: Sequence[str], schema: type[Any], type_hints: Optional[dict[str, Any]]
 ) -> Optional[Callable[[Any], Any]]:
    if state_keys == ["__root__"]:
        return None
@@ -1027,7 +1031,7 @@ def _pick_mapper(
    return partial(_coerce_state, schema)


-def _coerce_state(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
+def _coerce_state(schema: type[Any], input: dict[str, Any]) -> dict[str, Any]:
    return schema(**input)


@@ -1059,6 +1063,15 @@ def _control_branch(value: Any) -> Sequence[tuple[str, Any]]:
    return rtn


+def _control_static(
+    ends: Union[tuple[str, ...], dict[str, str]],
+) -> Sequence[tuple[str, Any, Optional[str]]]:
+    if isinstance(ends, dict):
+        return [(CHANNEL_BRANCH_TO.format(k), None, label) for k, label in ends.items()]
+    else:
+        return [(CHANNEL_BRANCH_TO.format(e), None, None) for e in ends]
+
+
 def _get_root(input: Any) -> Optional[Sequence[tuple[str, Any]]]:
    if isinstance(input, Command):
        if input.graph == Command.PARENT:
@@ -1083,7 +1096,7 @@ def _get_root(input: Any) -> Optional[Sequence[tuple[str, Any]]]:


 def _get_channels(
-    schema: Type[dict],
+    schema: type[dict],
 ) -> tuple[dict[str, BaseChannel], dict[str, ManagedValueSpec], dict[str, Any]]:
    if not hasattr(schema, "__annotations__"):
        return (
@@ -1137,7 +1150,7 @@ def _get_channel(
    return fallback


-def _is_field_channel(typ: Type[Any]) -> Optional[BaseChannel]:
+def _is_field_channel(typ: type[Any]) -> Optional[BaseChannel]:
    if hasattr(typ, "__metadata__"):
        meta = typ.__metadata__
        if len(meta) >= 1 and isinstance(meta[-1], BaseChannel):
@@ -1147,7 +1160,7 @@ def _is_field_channel(typ: Type[Any]) -> Optional[BaseChannel]:
    return None


-def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]:
+def _is_field_binop(typ: type[Any]) -> Optional[BinaryOperatorAggregate]:
    if hasattr(typ, "__metadata__"):
        meta = typ.__metadata__
        if len(meta) >= 1 and callable(meta[-1]):
@@ -1168,7 +1181,7 @@ def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]:
    return None


-def _is_field_managed_value(name: str, typ: Type[Any]) -> Optional[ManagedValueSpec]:
+def _is_field_managed_value(name: str, typ: type[Any]) -> Optional[ManagedValueSpec]:
    if hasattr(typ, "__metadata__"):
        meta = typ.__metadata__
        if len(meta) >= 1:
@@ -1186,7 +1199,7 @@ def _is_field_managed_value(name: str, typ: Type[Any]) -> Optional[ManagedValueS


 def _get_schema(
-    typ: Type,
+    typ: type,
    schemas: dict,
    channels: dict,
    name: str,
@@ -1,14 +1,11 @@
 from abc import ABC, abstractmethod
+from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager, contextmanager
 from inspect import isclass
 from typing import (
    Any,
-    AsyncIterator,
    Generic,
-    Iterator,
    NamedTuple,
-    Sequence,
-    Type,
    TypeVar,
    Union,
 )
@@ -66,11 +63,11 @@ class WritableManagedValue(Generic[V, U], ManagedValue[V], ABC):


 class ConfiguredManagedValue(NamedTuple):
-    cls: Type[ManagedValue]
+    cls: type[ManagedValue]
    kwargs: dict[str, Any]


-ManagedValueSpec = Union[Type[ManagedValue], ConfiguredManagedValue]
+ManagedValueSpec = Union[type[ManagedValue], ConfiguredManagedValue]


 def is_managed_value(value: Any) -> TypeGuard[ManagedValueSpec]:
@@ -79,7 +76,7 @@ def is_managed_value(value: Any) -> TypeGuard[ManagedValueSpec]:
    )


-def is_readonly_managed_value(value: Any) -> TypeGuard[Type[ManagedValue]]:
+def is_readonly_managed_value(value: Any) -> TypeGuard[type[ManagedValue]]:
    return (
        isclass(value)
        and issubclass(value, ManagedValue)
@@ -90,7 +87,7 @@ def is_readonly_managed_value(value: Any) -> TypeGuard[Type[ManagedValue]]:
    )


-def is_writable_managed_value(value: Any) -> TypeGuard[Type[WritableManagedValue]]:
+def is_writable_managed_value(value: Any) -> TypeGuard[type[WritableManagedValue]]:
    return (isclass(value) and issubclass(value, WritableManagedValue)) or (
        isinstance(value, ConfiguredManagedValue)
        and issubclass(value.cls, WritableManagedValue)
@@ -1,15 +1,16 @@
-from contextlib import asynccontextmanager, contextmanager
+from collections.abc import AsyncIterator, Iterator
+from contextlib import (
+    AbstractAsyncContextManager,
+    AbstractContextManager,
+    asynccontextmanager,
+    contextmanager,
+)
 from inspect import signature
 from typing import (
    Any,
-    AsyncContextManager,
-    AsyncIterator,
    Callable,
-    ContextManager,
    Generic,
-    Iterator,
    Optional,
-    Type,
    Union,
 )

@@ -28,15 +29,15 @@ class Context(ManagedValue[V], Generic[V]):
    def of(
        ctx: Union[
            None,
-            Callable[..., ContextManager[V]],
-            Type[ContextManager[V]],
-            Callable[..., AsyncContextManager[V]],
-            Type[AsyncContextManager[V]],
+            Callable[..., AbstractContextManager[V]],
+            type[AbstractContextManager[V]],
+            Callable[..., AbstractAsyncContextManager[V]],
+            type[AbstractAsyncContextManager[V]],
        ] = None,
        actx: Optional[
            Union[
-                Callable[..., AsyncContextManager[V]],
-                Type[AsyncContextManager[V]],
+                Callable[..., AbstractAsyncContextManager[V]],
+                type[AbstractAsyncContextManager[V]],
            ]
        ] = None,
    ) -> ConfiguredManagedValue:
@@ -98,8 +99,10 @@ class Context(ManagedValue[V], Generic[V]):
        self,
        loop: LoopProtocol,
        *,
-        ctx: Union[None, Type[ContextManager[V]], Type[AsyncContextManager[V]]] = None,
-        actx: Optional[Type[AsyncContextManager[V]]] = None,
+        ctx: Union[
+            None, type[AbstractContextManager[V]], type[AbstractAsyncContextManager[V]]
+        ] = None,
+        actx: Optional[type[AbstractAsyncContextManager[V]]] = None,
    ) -> None:
        self.ctx = ctx
        self.actx = actx
@@ -1,12 +1,9 @@
 import collections.abc
+from collections.abc import AsyncIterator, Iterator, Sequence
 from contextlib import asynccontextmanager, contextmanager
 from typing import (
    Any,
-    AsyncIterator,
-    Iterator,
    Optional,
-    Sequence,
-    Type,
 )

 from typing_extensions import NotRequired, Required, Self
@@ -71,7 +68,7 @@ class SharedValue(WritableManagedValue[Value, Update]):
            yield value

    def __init__(
-        self, loop: LoopProtocol, *, typ: Type[Any], scope: str, key: str
+        self, loop: LoopProtocol, *, typ: type[Any], scope: str, key: str
    ) -> None:
        super().__init__(loop)
        if typ := _strip_extras(typ):
@@ -6,17 +6,11 @@ import concurrent.futures
 import queue
 import weakref
 from collections import defaultdict, deque
+from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
 from functools import partial
 from typing import (
    Any,
-    AsyncIterator,
    Callable,
-    Dict,
-    Iterator,
-    Mapping,
-    Optional,
-    Sequence,
-    Type,
    Union,
    cast,
    get_type_hints,
@@ -93,6 +87,7 @@ from langgraph.pregel.algo import (
 )
 from langgraph.pregel.checkpoint import create_checkpoint, empty_checkpoint
 from langgraph.pregel.debug import tasks_w_writes
+from langgraph.pregel.draw import draw_graph
 from langgraph.pregel.io import map_input, read_channels
 from langgraph.pregel.loop import AsyncPregelLoop, StreamProtocol, SyncPregelLoop
 from langgraph.pregel.manager import AsyncChannelsManager, ChannelsManager
@@ -108,6 +103,7 @@ from langgraph.store.base import BaseStore
 from langgraph.types import (
    All,
    Checkpointer,
+    Interrupt,
    LoopProtocol,
    StateSnapshot,
    StateUpdate,
@@ -141,8 +137,8 @@ class Channel:
        cls,
        channels: str,
        *,
-        key: Optional[str] = None,
-        tags: Optional[list[str]] = None,
+        key: str | None = None,
+        tags: list[str] | None = None,
    ) -> PregelNode: ...

    @overload
@@ -152,16 +148,16 @@ class Channel:
        channels: Sequence[str],
        *,
        key: None = None,
-        tags: Optional[list[str]] = None,
+        tags: list[str] | None = None,
    ) -> PregelNode: ...

    @classmethod
    def subscribe_to(
        cls,
-        channels: Union[str, Sequence[str]],
+        channels: str | Sequence[str],
        *,
-        key: Optional[str] = None,
-        tags: Optional[list[str]] = None,
+        key: str | None = None,
+        tags: list[str] | None = None,
    ) -> PregelNode:
        """Runs process.invoke() each time channels are updated,
        with a dict of the channel values as input."""
@@ -467,7 +463,7 @@ class Pregel(PregelProtocol):

    nodes: dict[str, PregelNode]

-    channels: dict[str, Union[BaseChannel, ManagedValueSpec]]
+    channels: dict[str, BaseChannel | ManagedValueSpec]

    stream_mode: StreamMode = "values"
    """Mode to stream output, defaults to 'values'."""
@@ -476,18 +472,18 @@ class Pregel(PregelProtocol):
    """Whether to force emitting stream events eagerly, automatically turned on
    for stream_mode "messages" and "custom"."""

-    output_channels: Union[str, Sequence[str]]
+    output_channels: str | Sequence[str]

-    stream_channels: Optional[Union[str, Sequence[str]]] = None
+    stream_channels: str | Sequence[str] | None = None
    """Channels to stream, defaults to all channels not in reserved channels"""

-    interrupt_after_nodes: Union[All, Sequence[str]]
+    interrupt_after_nodes: All | Sequence[str]

-    interrupt_before_nodes: Union[All, Sequence[str]]
+    interrupt_before_nodes: All | Sequence[str]

-    input_channels: Union[str, Sequence[str]]
+    input_channels: str | Sequence[str]

-    step_timeout: Optional[float] = None
+    step_timeout: float | None = None
    """Maximum time to wait for a step to complete, in seconds. Defaults to None."""

    debug: bool
@@ -496,44 +492,44 @@ class Pregel(PregelProtocol):
    checkpointer: Checkpointer = None
    """Checkpointer used to save and load graph state. Defaults to None."""

-    store: Optional[BaseStore] = None
+    store: BaseStore | None = None
    """Memory store to use for SharedValues. Defaults to None."""

-    retry_policy: Optional[Sequence[RetryPolicy]] = None
+    retry_policy: Sequence[RetryPolicy] | None = None
    """Retry policies to use when running tasks. Set to None to disable."""

-    config_type: Optional[Type[Any]] = None
+    config_type: type[Any] | None = None

-    input_model: Optional[Type[BaseModel]] = None
+    input_model: type[BaseModel] | None = None

-    config: Optional[RunnableConfig] = None
+    config: RunnableConfig | None = None

    name: str = "LangGraph"

-    trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None
+    trigger_to_nodes: Mapping[str, Sequence[str]] | None = None

    def __init__(
        self,
        *,
        nodes: dict[str, PregelNode],
-        channels: Optional[dict[str, Union[BaseChannel, ManagedValueSpec]]],
+        channels: dict[str, BaseChannel | ManagedValueSpec] | None,
        auto_validate: bool = True,
        stream_mode: StreamMode = "values",
        stream_eager: bool = False,
-        output_channels: Union[str, Sequence[str]],
-        stream_channels: Optional[Union[str, Sequence[str]]] = None,
-        interrupt_after_nodes: Union[All, Sequence[str]] = (),
-        interrupt_before_nodes: Union[All, Sequence[str]] = (),
-        input_channels: Union[str, Sequence[str]],
-        step_timeout: Optional[float] = None,
-        debug: Optional[bool] = None,
-        checkpointer: Optional[BaseCheckpointSaver] = None,
-        store: Optional[BaseStore] = None,
-        retry_policy: Optional[Union[RetryPolicy, Sequence[RetryPolicy]]] = None,
-        config_type: Optional[Type[Any]] = None,
-        input_model: Optional[Type[BaseModel]] = None,
-        config: Optional[RunnableConfig] = None,
-        trigger_to_nodes: Optional[Mapping[str, Sequence[str]]] = None,
+        output_channels: str | Sequence[str],
+        stream_channels: str | Sequence[str] | None = None,
+        interrupt_after_nodes: All | Sequence[str] = (),
+        interrupt_before_nodes: All | Sequence[str] = (),
+        input_channels: str | Sequence[str],
+        step_timeout: float | None = None,
+        debug: bool | None = None,
+        checkpointer: BaseCheckpointSaver | None = None,
+        store: BaseStore | None = None,
+        retry_policy: RetryPolicy | Sequence[RetryPolicy] | None = None,
+        config_type: type[Any] | None = None,
+        input_model: type[BaseModel] | None = None,
+        config: RunnableConfig | None = None,
+        trigger_to_nodes: Mapping[str, Sequence[str]] | None = None,
        name: str = "LangGraph",
    ) -> None:
        self.nodes = nodes
@@ -562,22 +558,87 @@ class Pregel(PregelProtocol):
            self.validate()

    def get_graph(
-        self, config: Optional[RunnableConfig] = None, *, xray: Union[int, bool] = False
+        self, config: RunnableConfig | None = None, *, xray: int | bool = False
    ) -> Graph:
-        raise NotImplementedError
+        """Returns a drawable representation of the computation graph."""
+        # gather subgraphs
+        if xray:
+            subgraphs = {
+                k: v.get_graph(
+                    config,
+                    xray=xray if isinstance(xray, bool) or xray <= 0 else xray - 1,
+                )
+                for k, v in self.get_subgraphs()
+            }
+        else:
+            subgraphs = {}
+
+        return draw_graph(
+            merge_configs(self.config, config),
+            nodes=self.nodes,
+            specs=self.channels,
+            input_channels=self.input_channels,
+            interrupt_after_nodes=self.interrupt_after_nodes,
+            interrupt_before_nodes=self.interrupt_before_nodes,
+            trigger_to_nodes=self.trigger_to_nodes,
+            checkpointer=self.checkpointer,
+            subgraphs=subgraphs,
+        )

    async def aget_graph(
-        self, config: Optional[RunnableConfig] = None, *, xray: Union[int, bool] = False
+        self, config: RunnableConfig | None = None, *, xray: int | bool = False
    ) -> Graph:
-        raise NotImplementedError
+        """Returns a drawable representation of the computation graph."""

-    def copy(self, update: Optional[dict[str, Any]] = None) -> Self:
+        # gather subgraphs
+        if xray:
+            subpregels: dict[str, PregelProtocol] = {
+                k: v async for k, v in self.aget_subgraphs()
+            }
+            subgraphs = {
+                k: v
+                for k, v in zip(
+                    subpregels,
+                    await asyncio.gather(
+                        *(
+                            p.aget_graph(
+                                config,
+                                xray=xray
+                                if isinstance(xray, bool) or xray <= 0
+                                else xray - 1,
+                            )
+                            for p in subpregels.values()
+                        )
+                    ),
+                )
+            }
+        else:
+            subgraphs = {}
+
+        return draw_graph(
+            merge_configs(self.config, config),
+            nodes=self.nodes,
+            specs=self.channels,
+            input_channels=self.input_channels,
+            interrupt_after_nodes=self.interrupt_after_nodes,
+            interrupt_before_nodes=self.interrupt_before_nodes,
+            trigger_to_nodes=self.trigger_to_nodes,
+            checkpointer=self.checkpointer,
+            subgraphs=subgraphs,
+        )
+
+    def _repr_mimebundle_(self, **kwargs: Any) -> dict[str, Any]:
+        """Mime bundle used by Jupyter to display the graph"""
+        return {
+            "text/plain": repr(self),
+            "image/png": self.get_graph().draw_mermaid_png(),
+        }
+
+    def copy(self, update: dict[str, Any] | None = None) -> Self:
        attrs = {**self.__dict__, **(update or {})}
        return self.__class__(**attrs)

-    def with_config(
-        self, config: Optional[RunnableConfig] = None, **kwargs: Any
-    ) -> Self:
+    def with_config(self, config: RunnableConfig | None = None, **kwargs: Any) -> Self:
        return self.copy(
            {"config": merge_configs(self.config, config, cast(RunnableConfig, kwargs))}
        )
@@ -632,9 +693,7 @@ class Pregel(PregelProtocol):
            ]
        ]

-    def config_schema(
-        self, *, include: Optional[Sequence[str]] = None
-    ) -> Type[BaseModel]:
+    def config_schema(self, *, include: Sequence[str] | None = None) -> type[BaseModel]:
        # If the config type is not set explicitly, we will try to infer it.
        # If the config type is provided, but isn't directly supported by pydantic
        # (e.g., vanilla python class), we will also delegate to the parent class,
@@ -654,8 +713,8 @@ class Pregel(PregelProtocol):
        return create_model(self.get_name("Config"), field_definitions=fields)

    def get_config_jsonschema(
-        self, *, include: Optional[Sequence[str]] = None
-    ) -> Dict[str, Any]:
+        self, *, include: Sequence[str] | None = None
+    ) -> dict[str, Any]:
        schema = self.config_schema(include=include)
        if hasattr(schema, "model_json_schema"):
            return schema.model_json_schema()
@@ -669,9 +728,7 @@ class Pregel(PregelProtocol):
            if isinstance(channel, BaseChannel):
                return channel.UpdateType

-    def get_input_schema(
-        self, config: Optional[RunnableConfig] = None
-    ) -> Type[BaseModel]:
+    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        if self.input_model is not None:
            return self.input_model
        config = merge_configs(self.config, config)
@@ -688,8 +745,8 @@ class Pregel(PregelProtocol):
            )

    def get_input_jsonschema(
-        self, config: Optional[RunnableConfig] = None
-    ) -> Dict[str, Any]:
+        self, config: RunnableConfig | None = None
+    ) -> dict[str, Any]:
        schema = self.get_input_schema(config)
        if hasattr(schema, "model_json_schema"):
            return schema.model_json_schema()
@@ -704,8 +761,8 @@ class Pregel(PregelProtocol):
                return channel.ValueType

    def get_output_schema(
-        self, config: Optional[RunnableConfig] = None
-    ) -> Type[BaseModel]:
+        self, config: RunnableConfig | None = None
+    ) -> type[BaseModel]:
        config = merge_configs(self.config, config)
        if isinstance(self.output_channels, str):
            return super().get_output_schema(config)
@@ -720,8 +777,8 @@ class Pregel(PregelProtocol):
            )

    def get_output_jsonschema(
-        self, config: Optional[RunnableConfig] = None
-    ) -> Dict[str, Any]:
+        self, config: RunnableConfig | None = None
+    ) -> dict[str, Any]:
        schema = self.get_output_schema(config)
        if hasattr(schema, "model_json_schema"):
            return schema.model_json_schema()
@@ -736,13 +793,13 @@ class Pregel(PregelProtocol):
        )

    @property
-    def stream_channels_asis(self) -> Union[str, Sequence[str]]:
+    def stream_channels_asis(self) -> str | Sequence[str]:
        return self.stream_channels or [
            k for k in self.channels if isinstance(self.channels[k], BaseChannel)
        ]

    def get_subgraphs(
-        self, *, namespace: Optional[str] = None, recurse: bool = False
+        self, *, namespace: str | None = None, recurse: bool = False
    ) -> Iterator[tuple[str, PregelProtocol]]:
        for name, node in self.nodes.items():
            # filter by prefix
@@ -771,7 +828,7 @@ class Pregel(PregelProtocol):
                    )

    async def aget_subgraphs(
-        self, *, namespace: Optional[str] = None, recurse: bool = False
+        self, *, namespace: str | None = None, recurse: bool = False
    ) -> AsyncIterator[tuple[str, PregelProtocol]]:
        for name, node in self.get_subgraphs(namespace=namespace, recurse=recurse):
            yield name, node
@@ -783,8 +840,8 @@ class Pregel(PregelProtocol):
    def _prepare_state_snapshot(
        self,
        config: RunnableConfig,
-        saved: Optional[CheckpointTuple],
-        recurse: Optional[BaseCheckpointSaver] = None,
+        saved: CheckpointTuple | None,
+        recurse: BaseCheckpointSaver | None = None,
        apply_pending_writes: bool = False,
    ) -> StateSnapshot:
        if not saved:
@@ -832,7 +889,7 @@ class Pregel(PregelProtocol):
            # get the subgraphs
            subgraphs = dict(self.get_subgraphs())
            parent_ns = saved.config[CONF].get(CONFIG_KEY_CHECKPOINT_NS, "")
-            task_states: dict[str, Union[RunnableConfig, StateSnapshot]] = {}
+            task_states: dict[str, RunnableConfig | StateSnapshot] = {}
            for task in next_tasks.values():
                if task.name not in subgraphs:
                    continue
@@ -899,8 +956,8 @@ class Pregel(PregelProtocol):
    async def _aprepare_state_snapshot(
        self,
        config: RunnableConfig,
-        saved: Optional[CheckpointTuple],
-        recurse: Optional[BaseCheckpointSaver] = None,
+        saved: CheckpointTuple | None,
+        recurse: BaseCheckpointSaver | None = None,
        apply_pending_writes: bool = False,
    ) -> StateSnapshot:
        if not saved:
@@ -951,7 +1008,7 @@ class Pregel(PregelProtocol):
            # get the subgraphs
            subgraphs = {n: g async for n, g in self.aget_subgraphs()}
            parent_ns = saved.config[CONF].get(CONFIG_KEY_CHECKPOINT_NS, "")
-            task_states: dict[str, Union[RunnableConfig, StateSnapshot]] = {}
+            task_states: dict[str, RunnableConfig | StateSnapshot] = {}
            for task in next_tasks.values():
                if task.name not in subgraphs:
                    continue
@@ -1019,7 +1076,7 @@ class Pregel(PregelProtocol):
        self, config: RunnableConfig, *, subgraphs: bool = False
    ) -> StateSnapshot:
        """Get the current state of the graph."""
-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1061,7 +1118,7 @@ class Pregel(PregelProtocol):
        self, config: RunnableConfig, *, subgraphs: bool = False
    ) -> StateSnapshot:
        """Get the current state of the graph."""
-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1103,13 +1160,13 @@ class Pregel(PregelProtocol):
        self,
        config: RunnableConfig,
        *,
-        filter: Optional[Dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
    ) -> Iterator[StateSnapshot]:
        config = ensure_config(config)
        """Get the history of the state of the graph."""
-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1154,13 +1211,13 @@ class Pregel(PregelProtocol):
        self,
        config: RunnableConfig,
        *,
-        filter: Optional[Dict[str, Any]] = None,
-        before: Optional[RunnableConfig] = None,
-        limit: Optional[int] = None,
+        filter: dict[str, Any] | None = None,
+        before: RunnableConfig | None = None,
+        limit: int | None = None,
    ) -> AsyncIterator[StateSnapshot]:
        config = ensure_config(config)
        """Get the history of the state of the graph."""
-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1225,7 +1282,7 @@ class Pregel(PregelProtocol):
            RunnableConfig: The updated config.
        """

-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1500,7 +1557,7 @@ class Pregel(PregelProtocol):
                        next_tasks[tid].writes.append((k, v))
                    if tasks := [t for t in next_tasks.values() if t.writes]:
                        apply_writes(checkpoint, channels, tasks, None)
-            valid_updates: list[tuple[str, Optional[dict[str, Any]]]] = []
+            valid_updates: list[tuple[str, dict[str, Any] | None]] = []
            if len(updates) == 1:
                values, as_node = updates[0]
                # find last node that updated the state, if not provided
@@ -1639,7 +1696,7 @@ class Pregel(PregelProtocol):
            RunnableConfig: The updated config.
        """

-        checkpointer: Optional[BaseCheckpointSaver] = ensure_config(config)[CONF].get(
+        checkpointer: BaseCheckpointSaver | None = ensure_config(config)[CONF].get(
            CONFIG_KEY_CHECKPOINTER, self.checkpointer
        )
        if not checkpointer:
@@ -1914,7 +1971,7 @@ class Pregel(PregelProtocol):
                        next_tasks[tid].writes.append((k, v))
                    if tasks := [t for t in next_tasks.values() if t.writes]:
                        apply_writes(checkpoint, channels, tasks, None)
-            valid_updates: list[tuple[str, Optional[dict[str, Any]]]] = []
+            valid_updates: list[tuple[str, dict[str, Any] | None]] = []
            if len(updates) == 1:
                values, as_node = updates[0]
                # find last node that updated the state, if not provided
@@ -2034,8 +2091,8 @@ class Pregel(PregelProtocol):
    def update_state(
        self,
        config: RunnableConfig,
-        values: Optional[Union[dict[str, Any], Any]],
-        as_node: Optional[str] = None,
+        values: dict[str, Any] | Any | None,
+        as_node: str | None = None,
    ) -> RunnableConfig:
        """Update the state of the graph with the given values, as if they came from
        node `as_node`. If `as_node` is not provided, it will be set to the last node
@@ -2047,7 +2104,7 @@ class Pregel(PregelProtocol):
        self,
        config: RunnableConfig,
        values: dict[str, Any] | Any,
-        as_node: Optional[str] = None,
+        as_node: str | None = None,
    ) -> RunnableConfig:
        """Update the state of the graph asynchronously with the given values, as if they came from
        node `as_node`. If `as_node` is not provided, it will be set to the last node
@@ -2059,19 +2116,19 @@ class Pregel(PregelProtocol):
        self,
        config: RunnableConfig,
        *,
-        stream_mode: Optional[Union[StreamMode, list[StreamMode]]],
-        output_keys: Optional[Union[str, Sequence[str]]],
-        interrupt_before: Optional[Union[All, Sequence[str]]],
-        interrupt_after: Optional[Union[All, Sequence[str]]],
-        debug: Optional[bool],
+        stream_mode: StreamMode | list[StreamMode] | None,
+        output_keys: str | Sequence[str] | None,
+        interrupt_before: All | Sequence[str] | None,
+        interrupt_after: All | Sequence[str] | None,
+        debug: bool | None,
    ) -> tuple[
        bool,
        set[StreamMode],
-        Union[str, Sequence[str]],
-        Union[All, Sequence[str]],
-        Union[All, Sequence[str]],
-        Optional[BaseCheckpointSaver],
-        Optional[BaseStore],
+        str | Sequence[str],
+        All | Sequence[str],
+        All | Sequence[str],
+        BaseCheckpointSaver | None,
+        BaseStore | None,
    ]:
        if config["recursion_limit"] < 1:
            raise ValueError("recursion_limit must be at least 1")
@@ -2089,7 +2146,7 @@ class Pregel(PregelProtocol):
            # if being called as a node in another graph, always use values mode
            stream_mode = ["values"]
        if self.checkpointer is False:
-            checkpointer: Optional[BaseCheckpointSaver] = None
+            checkpointer: BaseCheckpointSaver | None = None
        elif CONFIG_KEY_CHECKPOINTER in config.get(CONF, {}):
            checkpointer = config[CONF][CONFIG_KEY_CHECKPOINTER]
        elif self.checkpointer is True:
@@ -2101,7 +2158,7 @@ class Pregel(PregelProtocol):
                f"Checkpointer requires one or more of the following 'configurable' keys: {[s.id for s in checkpointer.config_specs]}"
            )
        if CONFIG_KEY_STORE in config.get(CONF, {}):
-            store: Optional[BaseStore] = config[CONF][CONFIG_KEY_STORE]
+            store: BaseStore | None = config[CONF][CONFIG_KEY_STORE]
        else:
            store = self.store
        return (
@@ -2116,17 +2173,17 @@ class Pregel(PregelProtocol):

    def stream(
        self,
-        input: Union[dict[str, Any], Any],
-        config: Optional[RunnableConfig] = None,
+        input: dict[str, Any] | Any,
+        config: RunnableConfig | None = None,
        *,
-        stream_mode: Optional[Union[StreamMode, list[StreamMode]]] = None,
-        output_keys: Optional[Union[str, Sequence[str]]] = None,
-        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
-        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
-        checkpoint_during: Optional[bool] = None,
-        debug: Optional[bool] = None,
+        stream_mode: StreamMode | list[StreamMode] | None = None,
+        output_keys: str | Sequence[str] | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        checkpoint_during: bool | None = None,
+        debug: bool | None = None,
        subgraphs: bool = False,
-    ) -> Iterator[Union[dict[str, Any], Any]]:
+    ) -> Iterator[dict[str, Any] | Any]:
        """Stream graph steps for a single input.

        Args:
@@ -2135,7 +2192,7 @@ class Pregel(PregelProtocol):
            stream_mode: The mode to stream output, defaults to self.stream_mode.
                Options are:

-                - `"values"`: Emit all values in the state after each step.
+                - `"values"`: Emit all values in the state after each step, including interrupts.
                    When used with functional API, values are emitted once at the end of the workflow.
                - `"updates"`: Emit only the node or task names and updates returned by the nodes or tasks after each step.
                    If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
@@ -2352,7 +2409,7 @@ class Pregel(PregelProtocol):
                ):
                    # we are careful to have a single waiter live at any one time
                    # because on exit we increment semaphore count by exactly 1
-                    waiter: Optional[concurrent.futures.Future] = None
+                    waiter: concurrent.futures.Future | None = None
                    # because sync futures cannot be cancelled, we instead
                    # release the stream semaphore on exit, which will cause
                    # a pending waiter to return immediately
@@ -2403,17 +2460,17 @@ class Pregel(PregelProtocol):

    async def astream(
        self,
-        input: Union[dict[str, Any], Any],
-        config: Optional[RunnableConfig] = None,
+        input: dict[str, Any] | Any,
+        config: RunnableConfig | None = None,
        *,
-        stream_mode: Optional[Union[StreamMode, list[StreamMode]]] = None,
-        output_keys: Optional[Union[str, Sequence[str]]] = None,
-        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
-        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
-        checkpoint_during: Optional[bool] = None,
-        debug: Optional[bool] = None,
+        stream_mode: StreamMode | list[StreamMode] | None = None,
+        output_keys: str | Sequence[str] | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        checkpoint_during: bool | None = None,
+        debug: bool | None = None,
        subgraphs: bool = False,
-    ) -> AsyncIterator[Union[dict[str, Any], Any]]:
+    ) -> AsyncIterator[dict[str, Any] | Any]:
        """Stream graph steps for a single input.

        Args:
@@ -2422,7 +2479,7 @@ class Pregel(PregelProtocol):
            stream_mode: The mode to stream output, defaults to self.stream_mode.
                Options are:

-                - `"values"`: Emit all values in the state after each step.
+                - `"values"`: Emit all values in the state after each step, including interrupts.
                    When used with functional API, values are emitted once at the end of the workflow.
                - `"updates"`: Emit only the node or task names and updates returned by the nodes or tasks after each step.
                    If multiple updates are made in the same step (e.g. multiple nodes are run) then those updates are emitted separately.
@@ -2704,17 +2761,17 @@ class Pregel(PregelProtocol):

    def invoke(
        self,
-        input: Union[dict[str, Any], Any],
-        config: Optional[RunnableConfig] = None,
+        input: dict[str, Any] | Any,
+        config: RunnableConfig | None = None,
        *,
        stream_mode: StreamMode = "values",
-        output_keys: Optional[Union[str, Sequence[str]]] = None,
-        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
-        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
-        checkpoint_during: Optional[bool] = None,
-        debug: Optional[bool] = None,
+        output_keys: str | Sequence[str] | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        checkpoint_during: bool | None = None,
+        debug: bool | None = None,
        **kwargs: Any,
-    ) -> Union[dict[str, Any], Any]:
+    ) -> dict[str, Any] | Any:
        """Run the graph with a single input and config.

        Args:
@@ -2732,10 +2789,11 @@ class Pregel(PregelProtocol):
            If stream_mode is not "values", it returns a list of output chunks.
        """
        output_keys = output_keys if output_keys is not None else self.output_channels
-        if stream_mode == "values":
-            latest: Union[dict[str, Any], Any] = None
-        else:
-            chunks = []
+
+        latest: Union[dict[str, Any], Any] = None
+        chunks: list[Union[dict[str, Any], Any]] = []
+        interrupts: list[Interrupt] = []
+
        for chunk in self.stream(
            input,
            config,
@@ -2748,27 +2806,40 @@ class Pregel(PregelProtocol):
            **kwargs,
        ):
            if stream_mode == "values":
-                latest = chunk
+                if (
+                    isinstance(chunk, dict)
+                    and (ints := chunk.get(INTERRUPT)) is not None
+                ):
+                    interrupts.extend(ints)
+                else:
+                    latest = chunk
            else:
                chunks.append(chunk)
+
        if stream_mode == "values":
+            if interrupts:
+                return (
+                    {**latest, INTERRUPT: interrupts}
+                    if isinstance(latest, dict)
+                    else {INTERRUPT: interrupts}
+                )
            return latest
        else:
            return chunks

    async def ainvoke(
        self,
-        input: Union[dict[str, Any], Any],
-        config: Optional[RunnableConfig] = None,
+        input: dict[str, Any] | Any,
+        config: RunnableConfig | None = None,
        *,
        stream_mode: StreamMode = "values",
-        output_keys: Optional[Union[str, Sequence[str]]] = None,
-        interrupt_before: Optional[Union[All, Sequence[str]]] = None,
-        interrupt_after: Optional[Union[All, Sequence[str]]] = None,
-        checkpoint_during: Optional[bool] = None,
-        debug: Optional[bool] = None,
+        output_keys: str | Sequence[str] | None = None,
+        interrupt_before: All | Sequence[str] | None = None,
+        interrupt_after: All | Sequence[str] | None = None,
+        checkpoint_during: bool | None = None,
+        debug: bool | None = None,
        **kwargs: Any,
-    ) -> Union[dict[str, Any], Any]:
+    ) -> dict[str, Any] | Any:
        """Asynchronously invoke the graph on a single input.

        Args:
@@ -2787,10 +2858,11 @@ class Pregel(PregelProtocol):
        """

        output_keys = output_keys if output_keys is not None else self.output_channels
-        if stream_mode == "values":
-            latest: Union[dict[str, Any], Any] = None
-        else:
-            chunks = []
+
+        latest: Union[dict[str, Any], Any] = None
+        chunks: list[Union[dict[str, Any], Any]] = []
+        interrupts: list[Interrupt] = []
+
        async for chunk in self.astream(
            input,
            config,
@@ -2803,10 +2875,23 @@ class Pregel(PregelProtocol):
            **kwargs,
        ):
            if stream_mode == "values":
-                latest = chunk
+                if (
+                    isinstance(chunk, dict)
+                    and (ints := chunk.get(INTERRUPT)) is not None
+                ):
+                    interrupts.extend(ints)
+                else:
+                    latest = chunk
            else:
                chunks.append(chunk)
+
        if stream_mode == "values":
+            if interrupts:
+                return (
+                    {**latest, INTERRUPT: interrupts}
+                    if isinstance(latest, dict)
+                    else {INTERRUPT: interrupts}
+                )
            return latest
        else:
            return chunks
@@ -3,19 +3,17 @@ import itertools
 import sys
 import threading
 from collections import defaultdict, deque
+from collections.abc import Iterable, Mapping, Sequence
 from copy import copy
 from functools import partial
 from hashlib import sha1
 from typing import (
    Any,
    Callable,
-    Iterable,
    Literal,
-    Mapping,
    NamedTuple,
    Optional,
    Protocol,
-    Sequence,
    Union,
    cast,
    overload,
@@ -544,11 +542,14 @@ def prepare_single_task(
            str(task_path[2]),
        )
        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+        # we append True to the task path to indicate that a call is being
+        # made, so we should not return interrupts from this task (responsibility lies with the parent)
+        task_path = (*task_path[:3], True)
        metadata = {
            "langgraph_step": step,
            "langgraph_node": name,
            "langgraph_triggers": triggers,
-            "langgraph_path": task_path[:3],
+            "langgraph_path": task_path,
            "langgraph_checkpoint_ns": task_checkpoint_ns,
        }
        if task_id_checksum is not None:
@@ -577,7 +578,7 @@ def prepare_single_task(
                            local_read,
                            channels,
                            managed,
-                            PregelTaskWrites(task_path[:3], name, writes, triggers),
+                            PregelTaskWrites(task_path, name, writes, triggers),
                        ),
                        CONFIG_KEY_STORE: (store or configurable.get(CONFIG_KEY_STORE)),
                        CONFIG_KEY_CHECKPOINTER: (
@@ -600,10 +601,10 @@ def prepare_single_task(
                call.retry,
                None,
                task_id,
-                task_path[:3],
+                task_path,
            )
        else:
-            return PregelTask(task_id, name, task_path[:3])
+            return PregelTask(task_id, name, task_path)
    elif task_path[0] == PUSH:
        if len(task_path) == 2:
            # SEND tasks, executed in superstep n+1
@@ -639,11 +640,14 @@ def prepare_single_task(
            logger.warning(f"Ignoring invalid PUSH task path {task_path}")
            return
        task_checkpoint_ns = f"{checkpoint_ns}:{task_id}"
+        # we append False to the task path to indicate that a call is not being made
+        # so we should return interrupts from this task
+        task_path = (*task_path[:3], False)
        metadata = {
            "langgraph_step": step,
            "langgraph_node": packet.node,
            "langgraph_triggers": triggers,
-            "langgraph_path": task_path[:3],
+            "langgraph_path": task_path,
            "langgraph_checkpoint_ns": task_checkpoint_ns,
        }
        if task_id_checksum is not None:
@@ -680,7 +684,7 @@ def prepare_single_task(
                                channels,
                                managed,
                                PregelTaskWrites(
-                                    task_path[:3], packet.node, writes, triggers
+                                    task_path, packet.node, writes, triggers
                                ),
                            ),
                            CONFIG_KEY_STORE: (
@@ -710,12 +714,12 @@ def prepare_single_task(
                    proc.retry_policy,
                    None,
                    task_id,
-                    task_path[:3],
+                    task_path,
                    writers=proc.flat_writers,
                    subgraphs=proc.subgraphs,
                )
        else:
-            return PregelTask(task_id, packet.node, task_path[:3])
+            return PregelTask(task_id, packet.node, task_path)
    elif task_path[0] == PULL:
        # (PULL, node name)
        name = cast(str, task_path[1])
@@ -5,7 +5,8 @@ import functools
 import inspect
 import sys
 import types
-from typing import Any, Callable, Generator, Generic, Optional, Sequence, TypeVar, cast
+from collections.abc import Generator, Sequence
+from typing import Any, Callable, Generic, Optional, TypeVar, cast

 from langchain_core.runnables import Runnable
 from typing_extensions import ParamSpec
@@ -29,16 +30,12 @@ from langgraph.utils.runnable import (
 def _getattribute(obj: Any, name: str) -> Any:
    for subpath in name.split("."):
        if subpath == "<locals>":
-            raise AttributeError(
-                "Can't get local attribute {!r} on {!r}".format(name, obj)
-            )
+            raise AttributeError(f"Can't get local attribute {name!r} on {obj!r}")
        try:
            parent = obj
            obj = getattr(obj, subpath)
        except AttributeError:
-            raise AttributeError(
-                "Can't get attribute {!r} on {!r}".format(name, obj)
-            ) from None
+            raise AttributeError(f"Can't get attribute {name!r} on {obj!r}") from None
    return obj, parent


@@ -1,5 +1,6 @@
+from collections.abc import Mapping
 from datetime import datetime, timezone
-from typing import Mapping, Optional
+from typing import Optional

 from langgraph.channels.base import BaseChannel
 from langgraph.checkpoint.base import Checkpoint
@@ -1,15 +1,12 @@
 from collections import defaultdict
+from collections.abc import Iterable, Iterator, Mapping, Sequence
 from dataclasses import asdict
 from datetime import datetime, timezone
 from pprint import pformat
 from typing import (
    Any,
-    Iterable,
-    Iterator,
    Literal,
-    Mapping,
    Optional,
-    Sequence,
    Union,
 )
 from uuid import UUID
@@ -0,0 +1,239 @@
+from collections import defaultdict
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional, Union, cast
+
+from langchain_core.runnables.config import RunnableConfig
+from langchain_core.runnables.graph import Graph, Node
+
+from langgraph.channels.base import BaseChannel
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.constants import CONF, CONFIG_KEY_SEND, END, INPUT, START
+from langgraph.managed.base import ManagedValueSpec
+from langgraph.pregel.algo import (
+    PregelTaskWrites,
+    apply_writes,
+    increment,
+    prepare_next_tasks,
+)
+from langgraph.pregel.checkpoint import empty_checkpoint
+from langgraph.pregel.io import map_input
+from langgraph.pregel.manager import ChannelsManager
+from langgraph.pregel.read import PregelNode
+from langgraph.pregel.write import ChannelWrite
+from langgraph.types import All, Checkpointer, LoopProtocol
+
+
+def draw_graph(
+    config: RunnableConfig,
+    *,
+    nodes: dict[str, PregelNode],
+    specs: dict[str, Union[BaseChannel, ManagedValueSpec]],
+    input_channels: Union[str, Sequence[str]],
+    interrupt_after_nodes: Union[All, Sequence[str]],
+    interrupt_before_nodes: Union[All, Sequence[str]],
+    trigger_to_nodes: Optional[Mapping[str, Sequence[str]]],
+    checkpointer: Checkpointer,
+    subgraphs: dict[str, Graph],
+) -> Graph:
+    """Get the graph for this Pregel instance.
+
+    Args:
+        config: The configuration to use for the graph.
+        subgraphs: The subgraphs to include in the graph.
+        checkpointer: The checkpointer to use for the graph.
+
+    Returns:
+        The graph for this Pregel instance.
+    """
+    # (src, dest, is_conditional, label)
+    edges: set[tuple[str, str, bool, Optional[str]]] = set()
+
+    step = -1
+    checkpoint = empty_checkpoint()
+    get_next_version = (
+        checkpointer.get_next_version
+        if isinstance(checkpointer, BaseCheckpointSaver)
+        else increment
+    )
+    with ChannelsManager(
+        specs,
+        checkpoint,
+        LoopProtocol(step=step, stop=-1, config=config),
+        skip_context=True,
+    ) as (channels, managed):
+        static_seen: set[Any] = set()
+        sources: dict[str, set[tuple[str, bool, Optional[str]]]] = {}
+        step_sources: dict[str, set[tuple[str, bool, Optional[str]]]] = {}
+        # remove node mappers
+        nodes = {
+            k: v.copy(update={"mapper": None}) if v.mapper is not None else v
+            for k, v in nodes.items()
+        }
+        # apply input writes
+        input_writes = list(map_input(input_channels, {}))
+        _, updated_channels = apply_writes(
+            checkpoint,
+            channels,
+            [
+                PregelTaskWrites((), INPUT, input_writes, []),
+            ],
+            get_next_version,
+        )
+        # prepare first tasks
+        tasks = prepare_next_tasks(
+            checkpoint,
+            [],
+            nodes,
+            channels,
+            managed,
+            config,
+            step,
+            for_execution=True,
+            store=None,
+            checkpointer=None,
+            manager=None,
+            trigger_to_nodes=trigger_to_nodes,
+            updated_channels=updated_channels,
+        )
+        start_tasks = tasks
+        # run the pregel loop
+        while tasks:
+            conditionals: dict[tuple[str, str, Any], Optional[str]] = {}
+            # run task writers
+            for task in tasks.values():
+                for w in task.writers:
+                    # apply regular writes
+                    if isinstance(w, ChannelWrite):
+                        w.invoke(None, task.config)
+                    # apply conditional writes declared for static analysis, only once
+                    if w not in static_seen:
+                        static_seen.add(w)
+                        # apply static writes
+                        if writes := ChannelWrite.get_static_writes(w):
+                            # END writes are not written, but become edges directly
+                            for t in writes:
+                                if t[0] == END:
+                                    edges.add((task.name, t[0], True, t[2]))
+                            writes = [t for t in writes if t[0] != END]
+                            conditionals.update(
+                                {(task.name, *t[:2]): t[2] for t in writes}
+                            )
+                            task.config[CONF][CONFIG_KEY_SEND]([t[:2] for t in writes])
+            # collect sources
+            step_sources = {
+                task.name: {
+                    (
+                        w[0],
+                        (task.name, *w) in conditionals,
+                        conditionals.get((task.name, *w)),
+                    )
+                    for w in task.writes
+                }
+                for task in tasks.values()
+            }
+            sources.update(step_sources)
+            # invert triggers
+            trigger_to_sources: dict[str, set[tuple[str, bool, Optional[str]]]] = (
+                defaultdict(set)
+            )
+            for src, triggers in sources.items():
+                for trigger, cond, label in triggers:
+                    trigger_to_sources[trigger].add((src, cond, label))
+            # apply writes
+            _, updated_channels = apply_writes(
+                checkpoint, channels, tasks.values(), get_next_version
+            )
+            # prepare next tasks
+            tasks = prepare_next_tasks(
+                checkpoint,
+                [],
+                nodes,
+                channels,
+                managed,
+                config,
+                step,
+                for_execution=True,
+                store=None,
+                checkpointer=None,
+                manager=None,
+                trigger_to_nodes=trigger_to_nodes,
+                updated_channels=updated_channels,
+            )
+            # collect edges
+            for task in tasks.values():
+                for trigger in task.triggers:
+                    for src, cond, label in sorted(trigger_to_sources[trigger]):
+                        edges.add((src, task.name, cond, label))
+        # assemble the graph
+        graph = Graph()
+        # add nodes
+        for name, node in nodes.items():
+            metadata = dict(node.metadata or {})
+            if name in interrupt_before_nodes and name in interrupt_after_nodes:
+                metadata["__interrupt"] = "before,after"
+            elif name in interrupt_before_nodes:
+                metadata["__interrupt"] = "before"
+            elif name in interrupt_after_nodes:
+                metadata["__interrupt"] = "after"
+            graph.add_node(node.bound, name, metadata=metadata or None)
+        # add start node
+        if START not in nodes:
+            graph.add_node(None, START)
+            for task in start_tasks.values():
+                add_edge(graph, START, task.name)
+        # add discovered edges
+        for src, dest, is_conditional, label in sorted(edges):
+            add_edge(
+                graph,
+                src,
+                dest,
+                data=label if label != dest else None,
+                conditional=is_conditional,
+            )
+        # add end edges
+        termini = {d for _, d, _, _ in edges if d != END}.difference(
+            s for s, _, _, _ in edges
+        )
+        if termini:
+            for src in sorted(termini):
+                add_edge(graph, src, END)
+        elif len(step_sources) == 1:
+            for src in sorted(step_sources):
+                add_edge(graph, src, END, conditional=True)
+        # replace subgraphs
+        for name, subgraph in subgraphs.items():
+            if (
+                len(subgraph.nodes) > 1
+                and name in graph.nodes
+                and subgraph.first_node()
+                and subgraph.last_node()
+            ):
+                subgraph.trim_first_node()
+                subgraph.trim_last_node()
+                # replace the node with the subgraph
+                graph.nodes.pop(name)
+                first, last = graph.extend(subgraph, prefix=name)
+                for idx, edge in enumerate(graph.edges):
+                    if edge.source == name:
+                        graph.edges[idx] = edge.copy(source=cast(Node, last).id)
+                    elif edge.target == name:
+                        graph.edges[idx] = edge.copy(target=cast(Node, first).id)
+
+        return graph
+
+
+def add_edge(
+    graph: Graph,
+    source: str,
+    target: str,
+    *,
+    data: Optional[Any] = None,
+    conditional: bool = False,
+) -> None:
+    """Add an edge to the graph."""
+    for edge in graph.edges:
+        if edge.source == source and edge.target == target:
+            return
+    if target not in graph.nodes and target == END:
+        graph.add_node(None, END)
+    graph.add_edge(graph.nodes[source], graph.nodes[target], data, conditional)
@@ -1,15 +1,12 @@
 import asyncio
 import concurrent.futures
 import time
-from contextlib import ExitStack
+from collections.abc import Awaitable, Coroutine
+from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
 from contextvars import copy_context
 from types import TracebackType
 from typing import (
-    AsyncContextManager,
-    Awaitable,
    Callable,
-    ContextManager,
-    Coroutine,
    Optional,
    Protocol,
    TypeVar,
@@ -40,7 +37,7 @@ class Submit(Protocol[P, T]):
    ) -> concurrent.futures.Future[T]: ...


-class BackgroundExecutor(ContextManager):
+class BackgroundExecutor(AbstractContextManager):
    """A context manager that runs sync tasks in the background.
    Uses a thread pool executor to delegate tasks to separate threads.
    On exit,
@@ -122,7 +119,7 @@ class BackgroundExecutor(ContextManager):
                    pass


-class AsyncBackgroundExecutor(AsyncContextManager):
+class AsyncBackgroundExecutor(AbstractAsyncContextManager):
    """A context manager that runs async tasks in the background.
    Uses the current event loop to delegate tasks to asyncio tasks.
    On exit,
@@ -1,5 +1,6 @@
 from collections import Counter
-from typing import Any, Iterator, Literal, Mapping, Optional, Sequence, TypeVar, Union
+from collections.abc import Iterator, Mapping, Sequence
+from typing import Any, Literal, Optional, TypeVar, Union
 from uuid import UUID

 from langchain_core.runnables.utils import AddableDict
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
William FHandGitHub	d7a0da8f06	checkpointer: Enter stack for blobs dict in InMemorySaver (#4419 )	2025-04-26 13:57:36 -07:00
Vadym BardaandGitHub	2d9347ec33	docs(cloud): update human-in-the-loop guides to use interrupt() (#4418 )	2025-04-26 13:37:06 -04:00
Nuno CamposandGitHub	8951d162f0	Improve logic for drawing virtual end node (#4409 ) - Don't draw unexpected edges to END node - If a conditional edge has a custom label for END node then draw it	2025-04-25 15:45:12 -07:00
Nuno Campos	d90126b34e	Fix	2025-04-25 15:38:41 -07:00
Nuno CamposandGitHub	adc4569daf	build(deps): bump h11 from 0.14.0 to 0.16.0 in /libs/checkpoint-sqlite (#4403 ) Bumps [h11](https://github.com/python-hyper/h11) from 0.14.0 to 0.16.0. <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/python-hyper/h11/commit/1c5b07581f058886c8bdd87adababd7d959dc7ca"><code>1c5b075</code></a> this time for surer</li> <li><a href="https://github.com/python-hyper/h11/commit/d9c369935e853a7ee1aeb7e481f6dddf9b9c9b8a"><code>d9c3699</code></a> this time for sure...</li> <li><a href="https://github.com/python-hyper/h11/commit/d91b9dd2290a25c8c3f5ec15feb57de5873e6e39"><code>d91b9dd</code></a> blacken</li> <li><a href="https://github.com/python-hyper/h11/commit/5a4683ca466b59bbab9b19cfea20ee157b31cee0"><code>5a4683c</code></a> Soothe mypy</li> <li><a href="https://github.com/python-hyper/h11/commit/9c9567f0a92d13a83a8d8ebdbc757c8c2d384536"><code>9c9567f</code></a> Bump version to 0.16.0</li> <li><a href="https://github.com/python-hyper/h11/commit/114803a29ce50116dc47951c690ad4892b1a36ed"><code>114803a</code></a> Merge commit from fork</li> <li><a href="https://github.com/python-hyper/h11/commit/9462006f6ce4941661888228cbd4ac1ea80689b0"><code>9462006</code></a> Bump version to 0.15.0</li> <li><a href="https://github.com/python-hyper/h11/commit/70a96bea8e55403e5d92db14c111432c6d7a8685"><code>70a96be</code></a> Merge pull request <a href="https://redirect.github.com/python-hyper/h11/issues/181">#181</a> from Julien00859/Julien00859/get_int_max_str_digits</li> <li><a href="https://github.com/python-hyper/h11/commit/60782ad107e538b9312aac7e1c119c8358bf797c"><code>60782ad</code></a> Reject Content-Length longer 1 billion TB</li> <li><a href="https://github.com/python-hyper/h11/commit/dff7cc397a26ed4acdedd92d1bda6c8f18a6ed9f"><code>dff7cc3</code></a> Validate Chunked-Encoding chunk footer</li> <li>Additional commits viewable in <a href="https://github.com/python-hyper/h11/compare/v0.14.0...v0.16.0">compare view</a></li> </ul> </details> <br /> [![Dependabot compatibility score](https://dependabot-badges.githubapp.com/badges/compatibility_score?dependency-name=h11&package-manager=pip&previous-version=0.14.0&new-version=0.16.0)](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/langchain-ai/langgraph/network/alerts). </details>	2025-04-25 15:23:46 -07:00
Nuno Campos	6d70559618	Upgrade	2025-04-25 15:17:06 -07:00
Nuno Campos	70afd2dc2c	Update all	2025-04-25 15:11:32 -07:00
ccurmeandGitHub	14e7215090	docs: use langchain-tavily in quickstart (#4412 ) We want to deprecate `TavilySearchResults` in langchain-community in favor of `TavilySearch` in langchain-tavily. Also update quickstart to use `init_chat_model`.	2025-04-25 17:54:37 -04:00
Nuno Campos	460edde3a7	Lint	2025-04-25 14:37:53 -07:00
Nuno Campos	34d591dedf	Improve logic for drawing virtual end node - Don't draw unexpected edges to END node - If a conditional edge has a custom label for END node then draw it	2025-04-25 14:37:53 -07:00
Daehwi KimandGitHub	746b5f0730	fix(docs): fix typo (#4411 )	2025-04-25 17:10:47 -04:00
Vadym BardaandGitHub	64448404de	ci: don't try to render mermaid images when testing notebooks (#4414 )	2025-04-25 17:09:18 -04:00
Andrew NguonlyandGitHub	9637f3b1be	docs: Document API endpoint for creating new project (#4413 )	2025-04-25 10:42:20 -07:00
Jacob LeeandGitHub	c7fe85585b	docs: Adds additional Google Analytics tag to docs (#4402 ) CC @danielrlambert3	2025-04-24 13:17:52 -07:00
dependabot[bot]andGitHub	c6971c6a58	build(deps): bump h11 from 0.14.0 to 0.16.0 in /libs/checkpoint-sqlite Bumps [h11](https://github.com/python-hyper/h11) from 0.14.0 to 0.16.0. - [Commits](https://github.com/python-hyper/h11/compare/v0.14.0...v0.16.0) --- updated-dependencies: - dependency-name: h11 dependency-version: 0.16.0 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com>	2025-04-24 17:46:01 +00:00
Sydney RunkleandGitHub	2327f8619f	docs: fix xlinks (api and general) (#4391 ) Mostly focusing on the API docs xrefs now working, for example: <img width="728" alt="Screenshot 2025-04-23 at 6 43 40 PM" src="https://github.com/user-attachments/assets/e0939edb-2088-4b4e-92b0-8009aac6a86e" /> --> <img width="737" alt="Screenshot 2025-04-23 at 7 57 28 PM" src="https://github.com/user-attachments/assets/d05e28b5-cf4f-41e0-96f8-9910d19f4657" /> Also fixed some other broken links / references. There are lots of broken links related to langgraph plans / deployment options, I think our new docs writer was going to work on those.	2025-04-24 08:47:08 -07:00
Sydney RunkleandGitHub	4eb124e83d	[breaking]: Improve interrupt behavior when `stream_mode='values'` (#4374 ) This PR does a few things: 1. Surfaces interrupts when `stream_mode='values'` (particularly relevant for `invoke`, where this is the default behavior) 2. Adds an `interrupt_id` property to the `Interrupt` dataclass so that interrupts can effectively be mapped to resumes 3. Minor docs updates to reflect the new pattern (no need for a special section on interrupts with `invoke` and `ainvoke`) * In a different PR (the one with the multiple resume values), as it's more relevant there: add an `interrupts` property to `StateSnapshot` so that `interrupts` can easily be iterated over if users are attempting to map interrupts to resumes. I don't recommend we release this until we have multi-resumes working. ## Example We have the following setup where we're sending multiple prompts to the child graph, which uses `interrupt`: ```py def child_graph(state): human_input = interrupt(state["prompt"]) return { "human_inputs": [human_input], } ``` <img width="142" alt="Screenshot 2025-04-23 at 10 01 12 AM" src="https://github.com/user-attachments/assets/c6238bf1-54ad-4e48-ab0b-60a0bfc18485" /> Old behavior: ```py initial_input = {"prompts": ["a", "b"]} print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values")) #> {'prompts': ['a', 'b'], 'human_inputs': []} print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values")) #> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1']} print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values")) #> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']} ``` New behavior: ```py initial_input = {"prompts": ["a", "b"]} print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values")) """ { "prompts": ["a", "b"], "human_inputs": [], "__interrupt__": [ Interrupt( value="a", resumable=True, ns=["child_graph:38d43a18-a5e7-8ab2-ca83-9d80f6e9ca83"] ), Interrupt( value="b", resumable=True, ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"] ) ] } """ print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values")) """ { "prompts": ["a", "b"], "human_inputs": ["hello 1"], "__interrupt__": [ Interrupt( value="b", resumable=True, ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"] ) ] } """ print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values")) #> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']} ```	2025-04-24 08:21:28 -07:00
Sydney Runkle	84e9ebda91	add back auto gen content	2025-04-24 08:16:03 -07:00
Sydney Runkle	48e8576fcf	remove auto gen file	2025-04-24 08:15:06 -07:00
William FHandGitHub	6263c2f710	Copy with cached property (#4399 )	2025-04-24 07:16:37 -07:00
David DuongandGitHub	17bc55028e	docs(js): update semver constraints (#4401 )	2025-04-24 15:48:17 +02:00
Tat Dat Duong	cd8443f5ce	docs(js): update semver constraints	2025-04-24 15:47:38 +02:00
David DuongandGitHub	7e3f31f97c	docs(js): update required semver ranges (#4396 )	2025-04-24 12:41:12 +02:00
Tat Dat Duong	3db9bf325c	docs(js): update required semver ranges	2025-04-24 12:37:26 +02:00
Sydney Runkle	97337355a2	formatting	2025-04-23 21:21:02 -07:00
Sydney Runkle	d6c12133ad	fixing links	2025-04-23 21:15:55 -07:00
Sydney Runkle	d67abdd112	fix docs build	2025-04-23 20:07:16 -07:00
Sydney Runkle	8dec77e129	remove bounds on mkdocs deps	2025-04-23 18:42:42 -07:00
Sydney Runkle	b81c21f311	docs updates	2025-04-23 17:48:47 -07:00
Sydney Runkle	cd6b086374	await	2025-04-23 16:36:37 -07:00
Sydney Runkle	5929b0b08d	more test fixes	2025-04-23 16:19:46 -07:00
Sydney Runkle	9f33b4e529	fix test to reflect bug fix	2025-04-23 16:03:08 -07:00
Sydney RunkleandGitHub	f0fb8d9187	Merge branch 'main' into sr/better-interrupts	2025-04-23 15:58:57 -07:00
Sydney RunkleandGitHub	0ff913c50b	Fix double interrupt return caused by dynamic tasks (#4389 ) Fix issue found in https://github.com/langchain-ai/langgraph/pull/4374#discussion_r2056701206	2025-04-23 15:56:47 -07:00
Sydney RunkleandGitHub	67075bfb7f	Update libs/langgraph/tests/test_pregel_async.py	2025-04-23 15:50:22 -07:00
Sydney Runkle	37b2956758	helpful comments + test	2025-04-23 14:24:57 -07:00
Sydney Runkle	f42fc971e3	fix double interrupt raise with task path bool flag	2025-04-23 14:16:35 -07:00
Vadym BardaandGitHub	6bcab08f55	langgraph: release 0.3.33 (#4388 )	2025-04-23 16:28:12 -04:00
Sydney Runkle	8fe7c8101f	more explicit tests	2025-04-23 11:49:26 -07:00
Vadym BardaandGitHub	6bca615e6a	langgraph: allow nested lists/dicts of primitives in RemoteGraph config (#4387 )	2025-04-23 14:04:54 -04:00
Vadym BardaandGitHub	0147790937	langgraph: release 0.3.32 (#4386 )	2025-04-23 11:51:29 -04:00
Vadym BardaandGitHub	364c572f0f	langgraph: fix messages streaming for list of Commands (#4379 ) Fixes #4372	2025-04-23 11:46:57 -04:00
langchain-infraandGitHub	8af4b63c69	docs: Add docs for REDIS_KEY_PREFIX, REDIS_CLUSTER env vars (#4334 ) # Description Add documentation for using the REDIS_KEY_PREFIX, REDIS_CLUSTER env vars.	2025-04-23 11:17:03 -04:00
Asamu DavidandGitHub	941b4b1287	Merge branch 'main' into da/4-17/add-redis-prefix-support	2025-04-23 15:52:30 +01:00
Asamu DavidandGitHub	24f04fba37	Update REDIS_CLUSTER env doc.md	2025-04-23 15:52:07 +01:00
William FHandGitHub	05f21a384b	Add image arg to up command (#4385 ) Using this argument, you can get more customization since you can do `langgraph build` or directly `docker build` your image and then re-use the `langgraph up --image my-image` and have it also spin up redis & postgres for you. Easier then writing your own compose file	2025-04-23 07:48:01 -07:00
Vadym BardaandGitHub	29e9ee2d7b	docs(agents): use list of messages format (#4378 )	2025-04-23 01:25:00 +00:00
Sydney Runkle	d6b4ee348f	goodness, last test fix	2025-04-22 17:32:13 -07:00
Sydney Runkle	9e89b68596	fixing prebuilt tests	2025-04-22 17:24:54 -07:00
Sydney Runkle	1251eeaa48	final test fixes, hopefully	2025-04-22 17:21:56 -07:00
Sydney Runkle	85e73653f9	linting and test fixes	2025-04-22 17:10:54 -07:00
Sydney RunkleandGitHub	a8b70cb23c	Merge branch 'main' into sr/better-interrupts	2025-04-22 13:48:03 -07:00
Sydney RunkleandGitHub	173627a94c	langgraph[lint]: Upgrade to Python 3.9+ syntax (#4368 ) Also added "UP" (pyupgrade) rule to `pyproject.toml`	2025-04-22 13:47:18 -07:00
Sydney Runkle	edd7d608cd	final linting	2025-04-22 13:40:53 -07:00
Sydney Runkle	8977a35060	linting post merge	2025-04-22 13:01:40 -07:00
Sydney RunkleandGitHub	0a6e5a18bb	Merge branch 'main' into pyupgrade-39	2025-04-22 12:30:53 -07:00
Sydney Runkle	abc6323c6c	Merge branch 'sr/better-interrupts' of https://github.com/langchain-ai/langgraph into sr/better-interrupts	2025-04-22 11:39:24 -07:00
Sydney Runkle	d5a1bb05f5	revert changes to lockfile	2025-04-22 11:35:56 -07:00
Sydney Runkle	8506c6655b	more linting	2025-04-22 11:34:38 -07:00
Sydney RunkleandGitHub	44af4d8e3e	Merge branch 'main' into sr/better-interrupts	2025-04-22 11:31:46 -07:00
Sydney Runkle	8e6c0be48a	try linting + maybe test fix	2025-04-22 11:28:09 -07:00
Sydney Runkle	35523f4081	linting	2025-04-22 10:34:24 -07:00
Sydney Runkle	9a7b1fa12a	initial pass - surfacing interrupts for stream_mode='values'	2025-04-22 10:15:19 -07:00
Nuno CamposandGitHub	71bf2f9e85	Rewrite graph drawing logic (#4354 ) - It now executes the same pregel algo as when the graph is executed (without running any user code in nodes or conditional edges) to discover all the edges - This means we now support drawing the graph for all Pregel instances, not just StateGraph - This is done in preparation for new edge/node type coming in separate PR - Known changes - custom labels on conditional edges to END are no longer displayed	2025-04-22 09:31:13 -07:00
Nuno Campos	b03c647677	Lint	2025-04-22 08:45:50 -07:00
Nuno Campos	5d49188d3e	Lint	2025-04-22 08:41:51 -07:00
Nuno Campos	deeb2d6e92	Fix	2025-04-22 08:36:42 -07:00
Nuno Campos	0db67d4196	Fix	2025-04-22 08:36:42 -07:00
Nuno Campos	56c9c210c3	Fix	2025-04-22 08:36:42 -07:00
Nuno Campos	3398715258	Rewrite graph drawing logic - It now executes the same pregel algo as when the graph is executed (without running any user code in nodes or conditional edges) to discover all the edges - This means we now support drawing the graph for all Pregel instances, not just StateGraph	2025-04-22 08:36:41 -07:00
William Fu-Hinthorn	38d806733d	Update site_description Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>	2025-04-22 08:18:10 -07:00
William FHandGitHub	86ddd8da10	Add docs on tunneling (#4371 ) Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>	2025-04-22 15:04:04 +00:00
William FHandGitHub	a5f5d0c4df	Expose --tunnel flag to dev command (#4370 ) Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>	2025-04-22 14:23:09 +00:00
Sydney Runkle	cba7d21732	fix tests?	2025-04-21 21:14:14 -07:00
Sydney Runkle	b6ea73ff24	linting for 3.12	2025-04-21 21:01:27 -07:00
Sydney Runkle	1a477e57ff	upgrade to py39 standards	2025-04-21 20:43:40 -07:00
David Asamu	f94eeabebd	add doc for REDIS_CLUSTER env var	2025-04-17 23:20:53 +01:00
Asamu DavidandGitHub	9e22a75423	Merge branch 'main' into da/4-17/add-redis-prefix-support	2025-04-17 22:30:10 +01:00
David Asamu	e0da491fe6	docs: Add docs for REDIS_KEY_PREFIX env var	2025-04-17 21:38:41 +01:00
				`@@ -1 +0,0 @@`
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				`@@ -0,0 +1 @@`
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