feat: assistants sorting sdk spec (#4484 )

docs: remove prebuilt how-tos and add redirects to agents tab (#4485 )
docs(agents): update manage message history section (#4482 )
2026-08-18 05:35:43 +02:00 · 2025-04-30 17:05:37 -04:00 · 2025-04-30 15:55:55 -04:00 · 2025-04-30 19:11:45 +00:00 · 2025-04-30 15:08:18 -04:00 · 2025-04-30 20:24:48 +02:00
202 changed files with 6505 additions and 5851 deletions
@@ -22,6 +22,12 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
        "create_react_agent",
        "prebuilt",
    ),
+    (
+        [],
+        "langgraph.prebuilt.chat_agent_executor",
+        "AgentState",
+        "prebuilt",
+    ),
    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
    (
        ["langgraph.prebuilt"],
@@ -63,6 +69,18 @@ MANUAL_API_REFERENCES_LANGGRAPH = [
    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
+    # other prebuilts
+    (["langgraph_supervisor"], "langgraph_supervisor.supervisor", "create_supervisor", "supervisor"),
+    (["langgraph_supervisor"], "langgraph_supervisor.handoff", "create_handoff_tool", "supervisor"),
+    ([], "langgraph_supervisor.handoff", "create_forward_message_tool", "supervisor"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "create_swarm", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "add_active_agent_router", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.swarm", "SwarmState", "swarm"),
+    (["langgraph_swarm"], "langgraph_swarm.handoff", "create_handoff_tool", "swarm"),
+    ([], "langchain_mcp_adapters.client", "MultiServerMCPClient", "mcp"),
+    ([], "langchain_mcp_adapters.tools", "load_mcp_tools", "mcp"),
+    ([], "langchain_mcp_adapters.prompts", "load_mcp_prompt", "mcp"),
+    ([], "langchain_mcp_adapters.resources", "load_mcp_resources", "mcp"),
 ]

 WELL_KNOWN_LANGGRAPH_OBJECTS = {
@@ -144,7 +162,9 @@ def get_imports(code: str, path: str) -> List[ImportInformation]:
    for found_import in found_imports:
        module = found_import["source"]

-        if module.startswith("langchain"):
+        if module.startswith("langchain_mcp_adapters"):
+            package_ecosystem = "langgraph"
+        elif module.startswith("langchain"):
            # Handles things like `langchain` or `langchain_anthropic`
            package_ecosystem = "langchain"
        elif module.startswith("langgraph"):
@@ -1,7 +1,6 @@
 import ast
 import os
 import re
-from pathlib import Path
 from typing import Literal

 import nbformat
@@ -26,7 +25,7 @@ def _uses_input(source: str) -> bool:


 def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.:w
+    """Process a code block that uses cell magic.

    - Lines starting with "%%capture" are ignored.
    - Lines starting with "%pip" are rewritten by removing the leading "%" character.
@@ -52,10 +51,14 @@ def _rewrite_cell_magic(code: str) -> str:
        if stripped.startswith("%%capture"):
            continue
        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%pip"):
-            # Drop the leading '%' character
-            rewritten_lines.append(stripped[1:])
-        # Anything else is not supported
+        elif stripped.startswith("%") or stripped.startswith("!"):
+            # Drop the leading '%' character and then drop all leading whitespace
+            stripped = stripped.lstrip("%! \t")
+            # Check if the line starts with "pip"
+            if stripped.startswith("pip"):
+                rewritten_lines.append(stripped)
+            else:
+                raise NotImplementedError(f"Unhandled line: {line}")
        else:
            raise NotImplementedError(f"Unhandled line: {line}")

@@ -247,13 +250,10 @@ class EscapePreprocessor(Preprocessor):
            )
            cell.metadata["exec"] = is_exec

-            if self.markdown_exec_migration:
-                # For markdown exec migration we'll re-write cell magic as bash commands
-                if source.startswith("%%"):
-                    cell.source = _rewrite_cell_magic(source)
-                    cell.metadata["language"] = "shell"
-
-                cell.metadata["has_output"] = _has_output(source)
+            # For markdown exec migration we'll re-write cell magic as bash commands
+            if source.startswith("%%"):
+                cell.source = _rewrite_cell_magic(source)
+                cell.metadata["language"] = "shell"

            # Remove noqa comments
            cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
@@ -352,7 +352,7 @@ exporter = MarkdownExporter(


 def convert_notebook(
-    notebook_path: Path,
+    notebook_path: str,
    mode: Literal["markdown", "exec"] = "markdown",
 ) -> str:
    with open(notebook_path) as f:
@@ -1,5 +1,18 @@
 {% extends 'markdown/index.md.j2' %}

+{% block input %}{# cell.metadata.language is an addition of our docs pipeline. #}
+```{%- if 'language' in cell.metadata -%}
+{{ cell.metadata.language }}
+{%- elif 'magics_language' in cell.metadata -%}
+{{ cell.metadata.magics_language }}
+{%- elif 'name' in nb.metadata.get('language_info', {}) -%}
+{{ nb.metadata.language_info.name }}
+{%- endif %}
+{{ cell.source }}
+```
+{% endblock input %}
+
+
 {%- block traceback_line -%}
 ```output
 {{ line.rstrip() | strip_ansi }}
@@ -8,13 +21,13 @@

 {%- block stream -%}
 ```output
-{{ output.text.rstrip() }}
+{{ output.text.rstrip() | strip_ansi }}
 ```
 {%- endblock stream -%}

 {%- block data_text scoped -%}
 ```output
-{{ output.data['text/plain'].rstrip() }}
+{{ output.data['text/plain'].rstrip() | strip_ansi }}
 ```
 {%- endblock data_text -%}

@@ -31,8 +31,15 @@ REDIRECT_MAP = {
    "cloud/concepts/api.md": "concepts/langgraph_server.md",
    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+    # prebuit redirects
+    "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
+    "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
+    "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
+    "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
+    "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
    # misc
-    "prebuilt.md": "agents/prebuilt.md"
+    "prebuilt.md": "agents/prebuilt.md",
+    "reference/prebuilt.md": "reference/agents.md"
 }


@@ -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 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -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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
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Agents

 ## What is an agent?
@@ -103,7 +112,7 @@ from langgraph.prebuilt import create_react_agent

 # highlight-next-line
 def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config.get("configurable", {}).get("user_name")
+    user_name = config["configurable"].get("user_name")
    system_msg = f"You are a helpful assistant. Address the user as {user_name}."
    return [{"role": "system", "content": system_msg}] + state["messages"]

@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Context

 Agents often require more than a list of messages to function effectively. They need **context**.
@@ -74,7 +83,7 @@ agent.invoke({

 For context that spans *across* conversations or sessions, LangGraph allows access to **long-term memory** via a `store`. This can be used to read or update persistent facts (e.g., user profiles, preferences, prior interactions). For more, see the [Memory guide](./memory.md).

-## Customizing Prompts with Context
+## Customizing Prompts with Context { #prompts }

 Prompts define how the agent behaves. To incorporate runtime context, you can dynamically generate prompts based on the agent's state or config.

@@ -98,7 +107,7 @@ Common use cases:
        config: RunnableConfig,
    ) -> list[AnyMessage]:
        # highlight-next-line
-        user_name = config.get("configurable", {}).get("user_name")
+        user_name = config["configurable"].get("user_name")
        system_msg = f"You are a helpful assistant. User's name is {user_name}"
        return [{"role": "system", "content": system_msg}] + state["messages"]

@@ -153,7 +162,7 @@ Common use cases:
    })
    ```

-## Tools
+## Accessing Context in Tools { #tools }

 Tools can access context through special parameter **annotations**.

@@ -174,7 +183,7 @@ Tools can access context through special parameter **annotations**.
    ) -> str:
        """Look up user info."""
        # highlight-next-line
-        user_id = config.get("configurable", {}).get("user_id")
+        user_id = config["configurable"].get("user_id")
        return "User is John Smith" if user_id == "user_123" else "Unknown user"

    agent = create_react_agent(
@@ -222,66 +231,6 @@ Tools can access context through special parameter **annotations**.
    })
    ```

+### Update Context from Tools

-## Update context from tools
-
-Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
-
-```python
-from typing import Annotated
-from langchain_core.tools import InjectedToolCallId
-from langchain_core.messages import ToolMessage
-from langgraph.prebuilt import InjectedState
-from langgraph.types import Command
-
-class CustomState(AgentState):
-    # highlight-next-line
-    user_name: str
-
-def get_user_info(
-    # highlight-next-line
-    tool_call_id: Annotated[str, InjectedToolCallId],
-    # highlight-next-line
-    config: RunnableConfig
-) -> Command:
-    """Look up user info."""
-    # highlight-next-line
-    user_id = config.get("configurable", {}).get("user_id")
-    name = "John Smith" if user_id == "user_123" else "Unknown user"
-    return Command(update={
-        # highlight-next-line
-        "user_name": name,
-        # update the message history
-        # highlight-next-line
-        "messages": [
-            ToolMessage(
-                "Successfully looked up user information",
-                # highlight-next-line
-                tool_call_id=tool_call_id
-            )
-        ]
-    })
-
-def greet(
-    # highlight-next-line
-    state: Annotated[CustomState, InjectedState]
-) -> str:
-    """Use this to greet the user once you found their info."""
-    user_name = state["user_name"]
-    return f"Hello {user_name}!"
-
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info, greet],
-    # highlight-next-line
-    state_schema=CustomState
-)
-
-agent.invoke(
-    {"messages": [{"role": "user", "content": "greet the user"}]},
-    # highlight-next-line
-    config={"configurable": {"user_id": "user_123"}}
-)
-```
-
-For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
+Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Deployment

 To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Evals

 To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:
@@ -1,6 +1,17 @@
+---
+search:
+  boost: 2
+tags:
+  - human-in-the-loop
+  - hil
+  - agent
+hide:
+  - tags
+---
+
 # Human-in-the-loop

-To review, edit and approve tool calls in an agent you can use LangGraph's built-in [human-in-the-loop](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.
+To review, edit and approve tool calls in an agent you can use LangGraph's built-in [Human-In-the-Loop (HIL)](../concepts/human_in_the_loop.md) features, specifically the [`interrupt()`][langgraph.types.interrupt] primitive.

 LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received.

@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # MCP Integration

 [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the `langchain-mcp-adapters` library.
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Memory

 LangGraph supports two types of memory essential for building conversational agents:
@@ -83,15 +92,26 @@ When the agent is invoked the second time with the same `thread_id`, the origina

    If you're using [LangGraph Platform](./deployment.md), during deployment your checkpointer will be automatically configured to use a production-ready database.

-### Message history summarization 
+### Manage message history
+
+Long conversations can exceed the LLM's context window. Common solutions are:
+
+* [Summarization](#summarize-message-history): Maintain a running summary of the conversation
+* [Trimming](#trim-message-history): Remove first or last N messages in the history
+
+This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+
+To manage message history, specify `pre_model_hook` — a function ([node](../concepts/low_level.md#nodes)) that will always run before calling the language model.
+
+#### Summarize message history

 <figure markdown="1">
 ![image](./assets/summary.png){: style="max-height:400px"}
-<figcaption>Message history can grow quickly and exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.
+<figcaption>Long conversations can exceed the LLM's context window. A common solution is to maintain a running summary of the conversation. This allows the agent to keep track of the conversation without exceeding the LLM's context window.
 </figcaption>
 </figure>

-Long conversations can exceed the LLM's context window. To handle this, you can summarize older messages by specifying a [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent], such as the prebuilt [`SummarizationNode`](https://langchain-ai.github.io/langmem/reference/short_term/#langmem.short_term.SummarizationNode):
+To summarize message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with a prebuilt [`SummarizationNode`](https://langchain-ai.github.io/langmem/reference/short_term/#langmem.short_term.SummarizationNode):

 ```python
 from langchain_anthropic import ChatAnthropic
@@ -138,8 +158,144 @@ agent = create_react_agent(
 4. The `pre_model_hook` is set to the `SummarizationNode`. This node will summarize the message history before sending it to the LLM. The summarization node will automatically handle the summarization process and update the agent's state with the new summary. You can replace this with a custom implementation if you prefer. Please see the [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] API reference for more details.
 5. The `state_schema` is set to the `State` class, which is the custom state that contains an extra `context` key.

+#### Trim message history
+
+To trim message history, you can use [`pre_model_hook`][langgraph.prebuilt.chat_agent_executor.create_react_agent] with [`trim_messages`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html) function:
+
+```python
+# highlight-next-line
+from langchain_core.messages.utils import (
+    # highlight-next-line
+    trim_messages,
+    # highlight-next-line
+    count_tokens_approximately
+# highlight-next-line
+)
+from langgraph.prebuilt import create_react_agent
+
+# This function will be called every time before the node that calls LLM
+def pre_model_hook(state):
+    trimmed_messages = trim_messages(
+        state["messages"],
+        strategy="last",
+        token_counter=count_tokens_approximately,
+        max_tokens=384,
+        start_on="human",
+        end_on=("human", "tool"),
+    )
+    # highlight-next-line
+    return {"llm_input_messages": trimmed_messages}
+
+checkpointer = InMemorySaver()
+agent = create_react_agent(
+    model,
+    tools,
+    # highlight-next-line
+    pre_model_hook=pre_model_hook,
+    checkpointer=checkpointer,
+)
+```
+
 To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)

+### Read in tools { #read-short-term }
+
+LangGraph allows agent to access its short-term memory (state) inside the tools.
+
+```python
+from typing import Annotated
+from langgraph.prebuilt import InjectedState, create_react_agent
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_id: str
+
+def get_user_info(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Look up user info."""
+    # highlight-next-line
+    user_id = state["user_id"]
+    return "User is John Smith" if user_id == "user_123" else "Unknown user"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info],
+    # highlight-next-line
+    state_schema=CustomState,
+)
+
+agent.invoke({
+    "messages": "look up user information",
+    # highlight-next-line
+    "user_id": "user_123"
+})
+```
+
+See the [Context](./context.md#__tabbed_2_2) guide for more information.
+
+### Write from tools { #write-short-term }
+
+To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
+
+```python
+from typing import Annotated
+from langchain_core.tools import InjectedToolCallId
+from langchain_core.runnables import RunnableConfig
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt import InjectedState, create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.types import Command
+
+class CustomState(AgentState):
+    # highlight-next-line
+    user_name: str
+
+def update_user_info(
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    config: RunnableConfig
+) -> Command:
+    """Look up and update user info."""
+    user_id = config["configurable"].get("user_id")
+    name = "John Smith" if user_id == "user_123" else "Unknown user"
+    # highlight-next-line
+    return Command(update={
+        # highlight-next-line
+        "user_name": name,
+        # update the message history
+        "messages": [
+            ToolMessage(
+                "Successfully looked up user information",
+                tool_call_id=tool_call_id
+            )
+        ]
+    })
+
+def greet(
+    # highlight-next-line
+    state: Annotated[CustomState, InjectedState]
+) -> str:
+    """Use this to greet the user once you found their info."""
+    user_name = state["user_name"]
+    return f"Hello {user_name}!"
+
+agent = create_react_agent(
+    model="anthropic:claude-3-7-sonnet-latest",
+    tools=[get_user_info, greet],
+    # highlight-next-line
+    state_schema=CustomState
+)
+
+agent.invoke(
+    {"messages": [{"role": "user", "content": "greet the user"}]},
+    # highlight-next-line
+    config={"configurable": {"user_id": "user_123"}}
+)
+```
+
+For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
+
 ## Long-term memory

 Use long-term memory to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
@@ -149,9 +305,10 @@ To use long-term memory, you need to:
 1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
 2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.

-### Reading
+### Read { #read-long-term }

 ```python title="A tool the agent can use to look up user information"
+from langchain_core.runnables import RunnableConfig
 from langgraph.config import get_store
 from langgraph.prebuilt import create_react_agent
 from langgraph.store.memory import InMemoryStore
@@ -174,7 +331,7 @@ def get_user_info(config: RunnableConfig) -> str:
    # Same as that provided to `create_react_agent`
    # highlight-next-line
    store = get_store() # (6)!
-    user_id = config.get("configurable", {}).get("user_id")
+    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    user_info = store.get(("users",), user_id) # (7)!
    return str(user_info.value) if user_info else "Unknown user"
@@ -194,7 +351,7 @@ agent.invoke(
 )
 ```

-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.
@@ -203,7 +360,7 @@ agent.invoke(
 7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
 8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.

-### Writing
+### Write { #write-long-term }

 ```python title="Example of a tool that updates user information"
 from typing_extensions import TypedDict
@@ -222,7 +379,7 @@ def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
    # Same as that provided to `create_react_agent`
    # highlight-next-line
    store = get_store() # (4)!
-    user_id = config.get("configurable", {}).get("user_id")
+    user_id = config["configurable"].get("user_id")
    # highlight-next-line
    store.put(("users",), user_id, user_info) # (5)!
    return "Successfully saved user info."
@@ -245,13 +402,17 @@ 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.
 5. The `put` method is used to store data in the store. The first argument is the namespace, and the second argument is the key. This will store the user information in the store.
 6. The `user_id` is passed in the config. This is used to identify the user whose information is being updated.

+### Semantic search
+
+LangGraph also allows you to [search](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/#using-in-create-react-agent) for items in long-term memory by semantic similarity.
+
 ### Prebuilt memory tools

 **LangMem** is a LangChain-maintained library that offers tools for managing long-term memories in your agent. See the [LangMem documentation](https://langchain-ai.github.io/langmem/) for usage examples.
@@ -1,3 +1,14 @@
+---
+search:
+  boost: 2
+tags:
+  - anthropic
+  - openai
+  - agent
+hide:
+  - tags
+---
+
 # Models

 This page describes how to configure the chat model used by an agent.
@@ -63,7 +74,72 @@ agent = create_react_agent(

    The example above uses `ChatAnthropic`, which is already supported by `init_chat_model`. This pattern is shown to illustrate how to manually instantiate a model not available through init_chat_model.

+## Disable streaming
+
+To disable streaming of the individual LLM tokens, set `disable_streaming=True` when initializing the model:
+
+=== "`init_chat_model`"
+
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    model = init_chat_model(
+        "anthropic:claude-3-7-sonnet-latest",
+        # highlight-next-line
+        disable_streaming=True
+    )
+    ```
+
+=== "`ChatModel`"
+
+    ```python
+    from langchain_anthropic import ChatAnthropic
+
+    model = ChatAnthropic(
+        model="claude-3-7-sonnet-latest",
+        # highlight-next-line
+        disable_streaming=True
+    )
+    ```
+
+Refer to the [API reference](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.disable_streaming) for more information on `disable_streaming`
+
+## Adding model fallbacks
+
+You can add a fallback to a different model or a different LLM provider using `model.with_fallbacks([...])`:
+
+=== "`init_chat_model`"
+
+    ```python
+    from langchain.chat_models import init_chat_model
+
+    model_with_fallbacks = (
+        init_chat_model("anthropic:claude-3-5-haiku-latest")
+        # highlight-next-line
+        .with_fallbacks([
+            init_chat_model("openai:gpt-4.1-mini"),
+        ])
+    )
+    ```
+
+=== "`ChatModel`"
+
+    ```python
+    from langchain_anthropic import ChatAnthropic
+    from langchain_openai import ChatOpenAI
+
+    model_with_fallbacks = (
+        ChatAnthropic(model="claude-3-5-haiku-latest")
+        # highlight-next-line
+        .with_fallbacks([
+            ChatOpenAI(model="gpt-4.1-mini"),
+        ])
+    )
+    ```
+
+See this [guide](https://python.langchain.com/docs/how_to/fallbacks/#fallback-to-better-model) for more information on model fallbacks.
+
 ## Additional resources

 - [Model integration directory](https://python.langchain.com/docs/integrations/chat/)
- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
+- [Universal initialization with `init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/)
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Multi-agent

 A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a [multi-agent system](../concepts/multi_agent.md).
@@ -1,5 +1,11 @@
 ---
 title: Overview
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
 ---

 # Agent development with LangGraph
@@ -1,3 +1,10 @@
+---
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Community Agents

 To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
@@ -1,7 +1,16 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # 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
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Streaming

 Streaming is key to building responsive applications. There are a few types of data you’ll want to stream:
@@ -203,6 +212,12 @@ You can specify multiple streaming modes by passing stream mode as a list: `stre
        print("\n")
    ```

+## Disable streaming
+
+In some applications you might need to disable streaming of individual tokens for a given model. This is useful in [multi-agent](./multi-agent.md) systems to control which agents stream their output.
+
+See the [Models](./models.md#disable-streaming) guide to learn how to disable streaming.
+
 ## Additional resources

 * [Streaming in LangGraph](https://langchain-ai.github.io/langgraph/how-tos/streaming)
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # Tools

 [Tools](https://python.langchain.com/docs/concepts/tools/) are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
@@ -262,6 +271,13 @@ By default, the agent will catch all exceptions raised during tool calls and wil

 See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.

+## Working with memory
+
+LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
+
+* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
+* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
+
 ## Prebuilt tools

 LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.
@@ -1,3 +1,12 @@
+---
+search:
+  boost: 2
+tags:
+  - agent
+hide:
+  - tags
+---
+
 # UI

 You can use a prebuilt chat UI for interacting with any LangGraph agent through the [Agent Chat UI](https://github.com/langchain-ai/agent-chat-ui). Using the [deployed version](https://agentchat.vercel.app) is the quickest way to get started, and allows you to interact with both local and deployed graphs.
@@ -107,13 +107,13 @@ After installing and authorizing LangChain's `hosted-langserve` GitHub app, repo
 All traffic from `LangGraph Platform` deployments created after January 6th 2025 will come through a NAT gateway.
 This NAT gateway will have several static ip addresses depending on the region you are deploying in. Refer to the table below for the list of IP addresses to whitelist:

-| US             | EU             |
-|----------------|----------------|
-| 35.197.29.146  | 34.13.192.67   |
-| 34.145.102.123 | 34.147.105.64  |
-| 34.169.45.153  | 34.90.22.166   |
-| 34.82.222.17   | 34.147.36.213  |
-| 35.227.171.135 | 34.32.137.113  | 
-| 34.169.88.30   | 34.91.238.184  |
-| 34.19.93.202   | 35.204.101.241 |
-| 34.19.34.50    | 35.204.48.32   |
+| US             | EU              |
+|----------------|-----------------|
+| 35.197.29.146  | 34.90.213.236   |
+| 34.145.102.123 | 34.13.244.114   |
+| 34.169.45.153  | 34.32.180.189   |
+| 34.82.222.17   | 34.34.69.108    |
+| 35.227.171.135 | 34.32.145.240   | 
+| 34.169.88.30   | 34.90.157.44    |
+| 34.19.93.202   | 34.141.242.180  |
+| 34.19.34.50    | 34.32.141.108   |
@@ -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.

@@ -0,0 +1,86 @@
+# Configurable Headers
+
+LangGraph allows runtime configuration to modify agent behavior and permissions dynamically. When using the [LangGraph Platform](../quick_start.md), you can pass this configuration in the request body (`config`) or specific request headers. This enables adjustments based on user identity or other request data (see the [configuration how-to](../../how-tos/configuration.ipynb) for more details on how to access within your graph).
+
+For privacy, control which headers are passed to the runtime configuration via the `http.configurable_headers` section in your `langgraph.json` file.
+
+Here's how to customize the included and excluded headers:
+
+```json
+{
+  "http": {
+    "configurable_headers": {
+      "include": ["x-user-id", "x-organization-id", "my-prefix-*"],
+      "exclude": ["authorization", "x-api-key"]
+    }
+  }
+}
+```
+
+
+The `include` and `exclude` lists accept exact header names or patterns using `*` to match any number of characters. For your security, no other regex patterns are supported.
+
+## Using within your graph
+
+You can access the included headers in your graph using the `config` argument of any node.
+
+```python
+def my_node(state, config):
+  organization_id = config["configurable"].get("x-organization-id")
+  ...
+```
+
+Or by fetching from context (useful in tools and or within other nested functions).
+
+```python
+from langgraph.config import get_config
+
+def search_everything(query: str):
+  organization_id = get_config()["configurable"].get("x-organization-id")
+  ...
+```
+
+
+You can even use this to dynamically compile the graph.
+
+```python
+# my_graph.py.
+import contextlib
+
+@contextlib.asynccontextmanager
+async def generate_agent(config):
+  organization_id = config["configurable"].get("x-organization-id")
+  if organization_id == "org1":
+    graph = ...
+    yield graph
+  else:
+    graph = ...
+    yield graph
+
+```
+
+```json
+{
+  "graphs": {"agent": "my_grph.py:generate_agent"}
+}
+```
+
+For more examples on how to use runtime configuration, check out the [configuration how-to](../../how-tos/configuration.ipynb).
+
+### Opt-out of configurable headers
+
+If you'd like to opt-out of configurable headers, you can simply set a wildcard pattern in the `exclude` list:
+
+```json
+{
+  "http": {
+    "configurable_headers": {
+      "exclude": ["*"]
+    }
+  }
+}
+```
+
+This will exclude all headers from being added to your run's configuration.
+
+Note that exclusions take precedence over inclusions.
@@ -207,18 +207,6 @@ Behind the scenes, `LoadExternalComponent` will fetch the JS and CSS for the UI

 ## How-to guides

-### Show loading UI when components are loading
-
-You can provide a fallback UI to be rendered when the components are loading.
-
-```tsx
-<LoadExternalComponent
-  stream={thread}
-  message={ui}
-  fallback={<div>Loading...</div>}
-/>
-```
-
 ### Provide custom components on the client side

 If you already have the components loaded in your client application, you can provide a map of such components to be rendered directly without fetching the UI code from LangGraph Platform.
@@ -235,6 +223,18 @@ const clientComponents = {
 />;
 ```

+### Show loading UI when components are loading
+
+You can provide a fallback UI to be rendered when the components are loading.
+
+```tsx
+<LoadExternalComponent
+  stream={thread}
+  message={ui}
+  fallback={<div>Loading...</div>}
+/>
+```
+
 ### Customise the namespace of UI components.

 By default `LoadExternalComponent` will use the `assistantId` from `useStream()` hook to fetch the code for UI components. You can customise this by providing a `namespace` prop to the `LoadExternalComponent` component.
@@ -316,9 +316,9 @@ const WeatherComponent = (props: { city: string }) => {
 };
 ```

-### Streaming UI updates before the node execution is finished
+### Streaming UI messages from the server

-You can stream UI updates before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook.
+You can stream UI messages before the node execution is finished by using the `onCustomEvent` callback of the `useStream()` hook. This is especially useful when updating the UI component as the LLM is generating the response.

 ```tsx
 import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";
@@ -335,6 +335,162 @@ const { thread, submit } = useStream({
 });
 ```

+Then you can pushing updates to the UI component by calling `ui.push()` / `push_ui_message()` with the same ID as the UI message you wish to update.
+
+=== "Python"
+
+    ```python
+    from typing import Annotated, Sequence, TypedDict
+
+    from langchain_anthropic import ChatAnthropic
+    from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
+    from langgraph.graph import StateGraph
+    from langgraph.graph.message import add_messages
+    from langgraph.graph.ui import AnyUIMessage, push_ui_message, ui_message_reducer
+
+
+    class AgentState(TypedDict):  # noqa: D101
+        messages: Annotated[Sequence[BaseMessage], add_messages]
+        ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]
+
+
+    class CreateTextDocument(TypedDict):
+        """Prepare a document heading for the user."""
+
+        title: str
+
+
+    async def writer_node(state: AgentState):
+        model = ChatAnthropic(model="claude-3-5-sonnet-latest")
+        message: AIMessage = await model.bind_tools(
+            tools=[CreateTextDocument],
+            tool_choice={"type": "tool", "name": "CreateTextDocument"},
+        ).ainvoke(state["messages"])
+
+        tool_call = next(
+            (x["args"] for x in message.tool_calls if x["name"] == "CreateTextDocument"),
+            None,
+        )
+
+        if tool_call:
+            ui_message = push_ui_message("writer", tool_call, message=message)
+            ui_message_id = ui_message["id"]
+
+            # We're already streaming the LLM response to the client through UI messages
+            # so we don't need to stream it again to the `messages` stream mode.
+            content_stream = model.with_config({"tags": ["nostream"]}).astream(
+                f"Create a document with the title: {tool_call['title']}"
+            )
+
+            content: AIMessageChunk | None = None
+            async for chunk in content_stream:
+                content = content + chunk if content else chunk
+
+                push_ui_message(
+                    "writer",
+                    {"content": content.text()},
+                    id=ui_message_id,
+                    message=message,
+                    # Use `merge=rue` to merge props with the existing UI message
+                    merge=True,
+                )
+
+        return {"messages": [message]}
+    ```
+
+=== "JS"
+
+    ```tsx
+    import {
+      Annotation,
+      MessagesAnnotation,
+      type LangGraphRunnableConfig,
+    } from "@langchain/langgraph";
+    import { z } from "zod";
+    import { ChatAnthropic } from "@langchain/anthropic";
+    import {
+      typedUi,
+      uiMessageReducer,
+    } from "@langchain/langgraph-sdk/react-ui/server";
+    import type { AIMessageChunk } from "@langchain/core/messages";
+
+    import type ComponentMap from "./ui";
+
+    const AgentState = Annotation.Root({
+      ...MessagesAnnotation.spec,
+      ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
+    });
+
+    async function writerNode(
+      state: typeof AgentState.State,
+      config: LangGraphRunnableConfig
+    ): Promise<typeof AgentState.Update> {
+      const ui = typedUi<typeof ComponentMap>(config);
+
+      const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+      const message = await model
+        .bindTools(
+          [
+            {
+              name: "create_text_document",
+              description: "Prepare a document heading for the user.",
+              schema: z.object({ title: z.string() }),
+            },
+          ],
+          { tool_choice: { type: "tool", name: "create_text_document" } }
+        )
+        .invoke(state.messages);
+
+      type ToolCall = { name: "create_text_document"; args: { title: string } };
+      const toolCall = message.tool_calls?.find(
+        (tool): tool is ToolCall => tool.name === "create_text_document"
+      );
+
+      if (toolCall) {
+        const { id, name } = ui.push(
+          { name: "writer", props: { title: toolCall.args.title } },
+          { message }
+        );
+
+        const contentStream = await model
+          // We're already streaming the LLM response to the client through UI messages
+          // so we don't need to stream it again to the `messages` stream mode.
+          .withConfig({ tags: ["nostream"] })
+          .stream(`Create a short poem with the topic: ${message.text}`);
+
+        let content: AIMessageChunk | undefined;
+        for await (const chunk of contentStream) {
+          content = content?.concat(chunk) ?? chunk;
+
+          ui.push(
+            { id, name, props: { content: content?.text } },
+            // Use `merge: true` to merge props with the existing UI message
+            { message, merge: true }
+          );
+        }
+      }
+
+      return { messages: [message] };
+    }
+    ```
+
+=== "`ui.tsx`"
+
+    ```tsx
+    function WriterComponent(props: { title: string; content?: string }) {
+      return (
+        <article>
+          <h2>{props.title}</h2>
+          <p style={{ whiteSpace: "pre-wrap" }}>{props.content}</p>
+        </article>
+      );
+    }
+
+    export default {
+      weather: WriterComponent,
+    };
+    ```
+
 ### Remove UI messages from state

 Similar to how messages can be removed from the state by appending a RemoveMessage you can remove an UI message from the state by calling `remove_ui_message` / `ui.delete` with the ID of the UI message.
@@ -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}}}]}}
@@ -1,4 +1,4 @@
-# Interrupt
+# How to use the interrupt option

 This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).

@@ -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.

@@ -1,4 +1,5 @@
-# Rollback
+# How to use the Rollback option
+

 This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).

@@ -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.

@@ -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.",
@@ -49,7 +49,7 @@ The LangGraph CLI requires a JSON configuration file that follows this [schema](
    | <span style="white-space: nowrap;">`pip_config_file`</span>  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
    | <span style="white-space: nowrap;">`dockerfile_lines`</span> | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
    | <span style="white-space: nowrap;">`checkpointer`</span>   | Configuration for the checkpointer. Contains a `ttl` field which is an object with the following keys: <ul><li>`strategy`: How to handle expired checkpoints (e.g., `"delete"`).</li><li>`sweep_interval_minutes`: How often to check for expired checkpoints (integer).</li><li>`default_ttl`: Default time-to-live for checkpoints in **minutes** (integer). Defines how long checkpoints are kept before the specified strategy is applied.</li></ul> |
-    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li></ul> |
+    | <span style="white-space: nowrap;">`http`</span>            | HTTP server configuration with the following fields: <ul><li>`app`: Path to custom Starlette/FastAPI app (e.g., `"./src/agent/webapp.py:app"`). See [custom routes guide](../../how-tos/http/custom_routes.md).</li><li>`disable_assistants`: Disable `/assistants` routes</li><li>`disable_threads`: Disable `/threads` routes</li><li>`disable_runs`: Disable `/runs` routes</li><li>`disable_store`: Disable `/store` routes</li><li>`disable_meta`: Disable `/ok`, `/info`, `/metrics`, and `/docs` routes</li><li>`cors`: CORS configuration with fields for `allow_origins`, `allow_methods`, `allow_headers`, etc.</li><li>`configurable_headers`: Define which request headers to exclude or include as a run's configurable values.</li></ul> |

 === "JS"

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Agent architectures

 Many LLM applications implement a particular control flow of steps before and / or after LLM calls. As an example, [RAG](https://github.com/langchain-ai/rag-from-scratch) performs retrieval of documents relevant to a user question, and passes those documents to an LLM in order to ground the model's response in the provided document context. 
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Application Structure

 !!! info "Prerequisites"
@@ -15,7 +20,7 @@ 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 +82,7 @@ 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 +150,18 @@ 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.

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Assistants

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Authentication & Access Control

 LangGraph Platform provides a flexible authentication and authorization system that can integrate with most authentication schemes.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Breakpoints

 Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Bring Your Own Cloud (BYOC)

 !!! note Prerequisites
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Deployment Options

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Double Texting

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Durable Execution

 **Durable execution** is a technique in which a process or workflow saves its progress at key points, allowing it to pause and later resume exactly where it left off. This is particularly useful in scenarios that require [human-in-the-loop](./human_in_the_loop.md), where users can inspect, validate, or modify the process before continuing, and in long-running tasks that might encounter interruptions or errors (e.g., calls to an LLM timing out). By preserving completed work, durable execution enables a process to resume without reprocessing previous steps -- even after a significant delay (e.g., a week later). 
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # FAQ

 Common questions and their answers!
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Functional API

 ## Overview
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Why LangGraph?

 ## LLM applications
@@ -1,3 +1,13 @@
+---
+search:
+  boost: 2
+tags:
+  - human-in-the-loop
+  - hil
+hide:
+  - tags
+---
+
 # Human-in-the-loop

 !!! tip "This guide uses the new `interrupt` function."
@@ -409,39 +419,6 @@ 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
@@ -473,6 +450,22 @@ Upon **resuming** the graph, the counter will be incremented a second time, resu
 The value of counter is: 2
 ```

+### Resuming multiple interrupts with one invocation
+
+If you have multiple interrupts in the task queue, you can use `Command.resume` with a dictionary mapping
+of interrupt ids to resume values to resume multiple interrupts with a single `invoke` / `stream` call.
+
+For example, once your graph has been interrupted (multiple times, theoretically) and is stalled:
+
+```python
+resume_map = {
+    i.interrupt_id: f"human input for prompt {i.value}"
+    for i in parent.get_state(thread_config).interrupts
+}
+
+parent_graph.invoke(Command(resume=resume_map), config=thread_config)
+```
+
 ## Common Pitfalls

 ### Side-effects
@@ -1,6 +1,8 @@
 ---
 title: Concepts
 description: Conceptual Guide for LangGraph
+search:
+  boost: 0.5
 ---

 # Conceptual Guide
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph CLI

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Cloud SaaS (Beta)

 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy to Cloud SaaS](../cloud/deployment/cloud.md).
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Control Plane

 The term "control plane" is used broadly to refer to the Control Plane UI where users create and update [LangGraph Servers](./langgraph_server.md) (deployments) and the Control Plane APIs that support the UI experience.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Data Plane

 The term "data plane" is used broadly to refer to [LangGraph Servers](./langgraph_server.md) (deployments), the corresponding infrastructure for each server, and the "listener" application that continuously polls for updates from the [LangGraph Control Plane](./langgraph_control_plane.md).
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Self-Hosted Control Plane (Beta)

 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Control Plane](../cloud/deployment/self_hosted_control_plane.md).
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Self-Hosted Data Plane (Beta)

 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy the Self-Hosted Data Plane](../cloud/deployment/self_hosted_data_plane.md).
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Server

 !!! info "Prerequisites"
@@ -20,7 +25,7 @@ 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.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Standalone Container

 To deploy a [LangGraph Server](../concepts/langgraph_server.md), follow the how-to guide for [how to deploy a Standalone Container](../cloud/deployment/standalone_container.md).
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Studio

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Glossary

 ## Graphs
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Memory

 ## What is Memory?
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Multi-agent Systems

 An [agent](./agentic_concepts.md#agent-architectures) is _a system that uses an LLM to decide the control flow of an application_. As you develop these systems, they might grow more complex over time, making them harder to manage and scale. For example, you might run into the following problems:
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Persistence

 LangGraph has a built-in persistence layer, implemented through checkpointers. When you compile graph with a checkpointer, the checkpointer saves a `checkpoint` of the graph state at every super-step. Those checkpoints are saved to a `thread`, which can be accessed after graph execution. Because `threads` allow access to graph's state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible. See [this how-to guide](../how-tos/persistence.ipynb) for an end-to-end example on how to add and use checkpointers with your graph. Below, we'll discuss each of these concepts in more detail. 
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Platform Plans


@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Platform Architecture

 ![](img/langgraph_platform_deployment_architecture.png)
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph's Runtime (Pregel)

 [Pregel][langgraph.pregel.Pregel] implements LangGraph's runtime, managing the execution of LangGraph applications.
@@ -22,7 +27,7 @@ Repeat until no **actors** are selected for execution, or a maximum number of st

 ## Actors 

-An **actor** is a [PregelNode][langgraph.pregel.read.PregelNode]. It subscribes to channels, reads data from them, and writes data to them. It can be thought of as an **actor** in the Pregel algorithm. [PregelNodes][langgraph.pregel.read.PregelNode] implement LangChain's Runnable interface.
+An **actor** is a `PregelNode`. It subscribes to channels, reads data from them, and writes data to them. It can be thought of as an **actor** in the Pregel algorithm. `PregelNodes` implement LangChain's Runnable interface.

 ## Channels

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph Platform: Scalability & Resilience

 LangGraph Platform is designed to scale horizontally with your workload. Each instance of the service is stateless, and keeps no resources in memory. The service is designed to gracefully handle new instances being added or removed, including hard shutdown cases.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # LangGraph SDK

 !!! info "Prerequisites"
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Self-Hosted

 !!! note Prerequisites
@@ -7,19 +12,17 @@

 ## 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

@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Streaming

 Building a responsive app for end-users? Real-time updates are key to keeping users engaged as your app progresses.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Template Applications

 Templates are open source reference applications designed to help you get started quickly when building with LangGraph. They provide working examples of common agentic workflows that can be customized to your needs.
@@ -1,3 +1,8 @@
+---
+search:
+  boost: 2
+---
+
 # Time Travel ⏱️

 !!! note "Prerequisites"
@@ -1,376 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
-    "\n",
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Prerequisites</p>\n",
-    "    <p>\n",
-    "        This guide assumes familiarity with the following:\n",
-    "        <ul>\n",
-    "            <li>            \n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/\">\n",
-    "                    Human-in-the-loop\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/\">\n",
-    "                    Agent Architectures\n",
-    "                </a>                   \n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
-    "                    Chat Models\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
-    "                    Tools\n",
-    "                </a>\n",
-    "            </li>            \n",
-    "        </ul>\n",
-    "    </p>\n",
-    "</div> \n",
-    "\n",
-    "This guide will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](../create-react-agent) for how to get started with the prebuilt ReAct agent\n",
-    "\n",
-    "You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First, let's install the required packages and set our API keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4c5c054",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
-    "    <p style=\"padding-top: 5px;\">\n",
-    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we initialize the model we want to use.\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "\n",
-    "\n",
-    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-    "from typing import Literal\n",
-    "\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str):\n",
-    "    \"\"\"Use this to get weather information from a given location.\"\"\"\n",
-    "    if location.lower() in [\"nyc\", \"new york\"]:\n",
-    "        return \"It might be cloudy in nyc\"\n",
-    "    elif location.lower() in [\"sf\", \"san francisco\"]:\n",
-    "        return \"It's always sunny in sf\"\n",
-    "    else:\n",
-    "        raise AssertionError(\"Unknown Location\")\n",
-    "\n",
-    "\n",
-    "tools = [get_weather]\n",
-    "\n",
-    "# We need a checkpointer to enable human-in-the-loop patterns\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(\n",
-    "    model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_stream(stream):\n",
-    "    \"\"\"A utility to pretty print the stream.\"\"\"\n",
-    "    for s in stream:\n",
-    "        message = s[\"messages\"][-1]\n",
-    "        if isinstance(message, tuple):\n",
-    "            print(message)\n",
-    "        else:\n",
-    "            message.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "what is the weather in SF, CA?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_YjOKDkgMGgUZUpKIasYk1AdK)\n",
-      " Call ID: call_YjOKDkgMGgUZUpKIasYk1AdK\n",
-      "  Args:\n",
-      "    location: SF, CA\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "\n",
-    "config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
-    "inputs = {\"messages\": [(\"user\", \"what is the weather in SF, CA?\")]}\n",
-    "\n",
-    "print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca40a719",
-   "metadata": {},
-   "source": [
-    "We can verify that our graph stopped at the right place:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Next step:  ('tools',)\n"
-     ]
-    }
-   ],
-   "source": [
-    "snapshot = graph.get_state(config)\n",
-    "print(\"Next step: \", snapshot.next)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7de6ca78",
-   "metadata": {},
-   "source": [
-    "Now we can either approve or edit the tool call before proceeding to the next node. If we wanted to approve the tool call, we would simply continue streaming the graph with `None` input. If we wanted to edit the tool call we need to update the state to have the correct tool call, and then after the update has been applied we can continue.\n",
-    "\n",
-    "We can try resuming and we will see an error arise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "740bbaeb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_YjOKDkgMGgUZUpKIasYk1AdK)\n",
-      " Call ID: call_YjOKDkgMGgUZUpKIasYk1AdK\n",
-      "  Args:\n",
-      "    location: SF, CA\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_weather\n",
-      "\n",
-      "Error: AssertionError('Unknown Location')\n",
-      " Please fix your mistakes.\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_CLu9ofeBhtWF2oheBspxXkfE)\n",
-      " Call ID: call_CLu9ofeBhtWF2oheBspxXkfE\n",
-      "  Args:\n",
-      "    location: San Francisco, CA\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1cf5950",
-   "metadata": {},
-   "source": [
-    "This error arose because our tool argument of \"San Francisco, CA\" is not a location our tool recognizes.\n",
-    "\n",
-    "Let's show how we would edit the tool call to search for \"San Francisco\" instead of \"San Francisco, CA\" - since our tool as written treats \"San Francisco, CA\" as an unknown location. We will update the state and then resume streaming the graph and should see no errors arise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1c81ed9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'configurable': {'thread_id': '42',\n",
-       "  'checkpoint_ns': '',\n",
-       "  'checkpoint_id': '1ef801d1-5b93-6bb9-8004-a088af1f9cec'}}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = graph.get_state(config)\n",
-    "\n",
-    "last_message = state.values[\"messages\"][-1]\n",
-    "last_message.tool_calls[0][\"args\"] = {\"location\": \"San Francisco\"}\n",
-    "\n",
-    "graph.update_state(config, {\"messages\": [last_message]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_CLu9ofeBhtWF2oheBspxXkfE)\n",
-      " Call ID: call_CLu9ofeBhtWF2oheBspxXkfE\n",
-      "  Args:\n",
-      "    location: San Francisco\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_weather\n",
-      "\n",
-      "It's always sunny in sf\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The weather in San Francisco is currently sunny.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_stream(graph.stream(None, config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8202a5f9",
-   "metadata": {},
-   "source": [
-    "Fantastic! Our graph updated properly to query the weather in San Francisco and got the correct \"It's always sunny in sf\" response from the tool, and then responded to the user accordingly."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -17,8 +17,8 @@
    "\n",
    "Message history can grow quickly and exceed LLM context window size, whether you're building chatbots with many conversation turns or agentic systems with numerous tool calls. There are several strategies for managing the message history:\n",
    "\n",
-    "* [message trimming](#keep-the-original-message-history-unmodified) - remove first or last N messages in the history\n",
-    "* [summarization](#summarizing-message-history) - summarize earlier messages in the history and replace them with a summary\n",
+    "* [message trimming](#keep-the-original-message-history-unmodified) — remove first or last N messages in the history\n",
+    "* [summarization](#summarizing-message-history) — summarize earlier messages in the history and replace them with a summary\n",
    "* custom strategies (e.g., message filtering, etc.)\n",
    "\n",
    "To manage message history in `create_react_agent`, you need to define a `pre_model_hook` function or [runnable](https://python.langchain.com/docs/concepts/runnables/) that takes graph state an returns a state update:\n",
@@ -691,7 +691,8 @@
    "\n",
    "checkpointer = InMemorySaver()\n",
    "graph = create_react_agent(\n",
-    "    model,\n",
+    "    # limit the output size to ensure consistent behavior\n",
+    "    model.bind(max_tokens=256),\n",
    "    tools,\n",
    "    # highlight-next-line\n",
    "    pre_model_hook=summarization_node,\n",
@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to add thread-level memory to a ReAct Agent\n",
-    "\n",
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Prerequisites</p>\n",
-    "    <p>\n",
-    "        This guide assumes familiarity with the following:\n",
-    "        <ul>\n",
-    "            <li>            \n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/persistence/\">\n",
-    "                    LangGraph Persistence\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>            \n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/persistence/#checkpointer-interface\">\n",
-    "                    Checkpointer interface\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/\">\n",
-    "                    Agent Architectures\n",
-    "                </a>                   \n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models/\">\n",
-    "                    Chat Models\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/tools/\">\n",
-    "                    Tools\n",
-    "                </a>\n",
-    "            </li>\n",
-    "        </ul>\n",
-    "    </p>\n",
-    "</div> \n",
-    "\n",
-    "This guide will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](../create-react-agent) for how to get started with the prebuilt ReAct agent\n",
-    "\n",
-    "We can add memory to the agent, by passing a [checkpointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/) to the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First, let's install the required packages and set our API keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87a00ce9",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
-    "    <p style=\"padding-top: 5px;\">\n",
-    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we initialize the model we want to use.\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "\n",
-    "\n",
-    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-    "\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(location: str) -> str:\n",
-    "    \"\"\"Use this to get weather information.\"\"\"\n",
-    "    if any([city in location.lower() for city in [\"nyc\", \"new york city\"]]):\n",
-    "        return \"It might be cloudy in nyc\"\n",
-    "    elif any([city in location.lower() for city in [\"sf\", \"san francisco\"]]):\n",
-    "        return \"It's always sunny in sf\"\n",
-    "    else:\n",
-    "        return f\"I am not sure what the weather is in {location}\"\n",
-    "\n",
-    "\n",
-    "tools = [get_weather]\n",
-    "\n",
-    "# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
-    "# to retain the chat context between interactions\n",
-    "from langgraph.checkpoint.memory import MemorySaver\n",
-    "\n",
-    "memory = MemorySaver()\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(model, tools=tools, checkpointer=memory)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "Let's interact with it multiple times to show that it can remember"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_stream(stream):\n",
-    "    for s in stream:\n",
-    "        message = s[\"messages\"][-1]\n",
-    "        if isinstance(message, tuple):\n",
-    "            print(message)\n",
-    "        else:\n",
-    "            message.pretty_print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's the weather in NYC?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "Tool Calls:\n",
-      "  get_weather (call_xM1suIq26KXvRFqJIvLVGfqG)\n",
-      " Call ID: call_xM1suIq26KXvRFqJIvLVGfqG\n",
-      "  Args:\n",
-      "    city: nyc\n",
-      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
-      "Name: get_weather\n",
-      "\n",
-      "It might be cloudy in nyc\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "The weather in NYC might be cloudy.\n"
-     ]
-    }
-   ],
-   "source": [
-    "config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-    "\n",
-    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
-   "metadata": {},
-   "source": [
-    "Notice that when we pass the same thread ID, the chat history is preserved."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "187479f9-32fa-4611-9487-cf816ba2e147",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m Human Message \u001b[0m=================================\n",
-      "\n",
-      "What's it known for?\n",
-      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
-      "\n",
-      "New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable aspects include:\n",
-      "\n",
-      "1. **Statue of Liberty**: A symbol of freedom and democracy.\n",
-      "2. **Times Square**: Known for its bright lights, Broadway theaters, and bustling atmosphere.\n",
-      "3. **Central Park**: A large urban park offering a green oasis in the middle of the city.\n",
-      "4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.\n",
-      "5. **Broadway**: Famous for its world-class theater productions.\n",
-      "6. **Wall Street**: The financial hub of the United States.\n",
-      "7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
-      "8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.\n",
-      "9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.\n",
-      "10. **Fashion**: A global fashion capital, home to New York Fashion Week.\n",
-      "\n",
-      "These are just a few highlights of what makes NYC a unique and vibrant city.\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
-    "print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c461eb47-b4f9-406f-8923-c68db7c5687f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
@@ -1,287 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to return structured output from the prebuilt ReAct agent\n",
-    "\n",
-    "!!! info \"Prerequisites\"\n",
-    "    This guide assumes familiarity with the following:\n",
-    "    \n",
-    "    - [Agent Architectures](../../concepts/agentic_concepts/)\n",
-    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
-    "    - [Tools](https://python.langchain.com/docs/concepts/tools/)\n",
-    "    - [Structured Output](https://python.langchain.com/docs/concepts/structured_outputs/)\n",
-    "\n",
-    "To return structured output from the prebuilt ReAct agent you can provide a `response_format` parameter with the desired output schema to [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent]:\n",
-    "\n",
-    "```python\n",
-    "class ResponseFormat(BaseModel):\n",
-    "    \"\"\"Respond to the user in this format.\"\"\"\n",
-    "    my_special_output: str\n",
-    "\n",
-    "\n",
-    "graph = create_react_agent(\n",
-    "    model,\n",
-    "    tools=tools,\n",
-    "    # specify the schema for the structured output using `response_format` parameter\n",
-    "    response_format=ResponseFormat\n",
-    ")\n",
-    "```\n",
-    "\n",
-    "Prebuilt ReAct makes an additional LLM call at the end of the ReAct loop to produce a structured output response. Please see [this guide](../react-agent-structured-output) to learn about other  strategies for returning structured outputs from a tool-calling agent."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First, let's install the required packages and set our API keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a213e11a-5c62-4ddb-a707-490d91add383",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "%pip install -U langgraph langchain-openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "23a1885c-04ab-4750-aefa-105891fddf3e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def _set_env(var: str):\n",
-    "    if not os.environ.get(var):\n",
-    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
-    "\n",
-    "\n",
-    "_set_env(\"OPENAI_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87a00ce9",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
-    "    <p style=\"padding-top: 5px;\">\n",
-    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
-    "    </p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
-   "metadata": {},
-   "source": [
-    "## Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7a154152-973e-4b5d-aa13-48c617744a4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First we initialize the model we want to use.\n",
-    "from langchain_openai import ChatOpenAI\n",
-    "\n",
-    "model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
-    "\n",
-    "# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
-    "\n",
-    "from typing import Literal\n",
-    "from langchain_core.tools import tool\n",
-    "\n",
-    "\n",
-    "@tool\n",
-    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
-    "    \"\"\"Use this to get weather information.\"\"\"\n",
-    "    if city == \"nyc\":\n",
-    "        return \"It might be cloudy in nyc\"\n",
-    "    elif city == \"sf\":\n",
-    "        return \"It's always sunny in sf\"\n",
-    "    else:\n",
-    "        raise AssertionError(\"Unknown city\")\n",
-    "\n",
-    "\n",
-    "tools = [get_weather]\n",
-    "\n",
-    "# Define the structured output schema\n",
-    "\n",
-    "from pydantic import BaseModel, Field\n",
-    "\n",
-    "\n",
-    "class WeatherResponse(BaseModel):\n",
-    "    \"\"\"Respond to the user in this format.\"\"\"\n",
-    "\n",
-    "    conditions: str = Field(description=\"Weather conditions\")\n",
-    "\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(\n",
-    "    model,\n",
-    "    tools=tools,\n",
-    "    # specify the schema for the structured output using `response_format` parameter\n",
-    "    response_format=WeatherResponse,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n",
-    "\n",
-    "Let's now test our agent:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-    "response = graph.invoke(inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50e273a0-fbdb-4eee-89ca-580fbfb52daf",
-   "metadata": {},
-   "source": [
-    "You can see that the agent output contains a `structured_response` key with the structured output conforming to the specified `WeatherResponse` schema, in addition to the message history under `messages` key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "300748d4-0ed2-470d-8dbc-7c14231e73b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "WeatherResponse(conditions='cloudy')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response[\"structured_response\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd9e3487-2cec-44cf-9472-0a51eebeddff",
-   "metadata": {},
-   "source": [
-    "### Customizing prompt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a608548d-77fc-4d7a-845c-32ae9ec0489a",
-   "metadata": {},
-   "source": [
-    "You might need to further customize the second LLM call for the structured output generation and provide a system prompt. To do so, you can pass a tuple (prompt, schema):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d1386f99-ffd1-4b36-86ec-cabb3357d929",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graph = create_react_agent(\n",
-    "    model,\n",
-    "    tools=tools,\n",
-    "    # specify both the system prompt and the schema for the structured output\n",
-    "    response_format=(\"Always return capitalized weather conditions\", WeatherResponse),\n",
-    ")\n",
-    "\n",
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-    "response = graph.invoke(inputs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91f34991-b406-4fd2-a776-4dd03e3dc3dd",
-   "metadata": {},
-   "source": [
-    "You can verify that the structured response now contains a capitalized value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ba43a67f-127c-45e7-982c-a8210d97a3ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "WeatherResponse(conditions='Cloudy')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response[\"structured_response\"]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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				`@@ -1 +0,0 @@`
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