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 )
2026-08-29 11:19:54 +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
52 changed files with 856 additions and 2119 deletions
@@ -31,6 +31,12 @@ 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",
    "reference/prebuilt.md": "reference/agents.md"
@@ -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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
@@ -112,7 +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"]

@@ -83,7 +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.

@@ -107,7 +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"]

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

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

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

@@ -183,7 +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(
@@ -231,66 +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.
@@ -92,15 +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
@@ -147,8 +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.
@@ -158,9 +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
@@ -183,7 +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"
@@ -212,7 +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
@@ -231,7 +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."
@@ -261,6 +409,10 @@ store.get(("users",), "user_123").value
 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.
@@ -74,6 +74,71 @@ 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/)
@@ -212,6 +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)
@@ -271,6 +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.
@@ -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.
@@ -1,376 +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",
@@ -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
-}
@@ -1,231 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
-   "metadata": {},
-   "source": [
-    "# How to add a custom system prompt to the prebuilt ReAct agent\n",
-    "\n",
-    "\n",
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Prerequisites</p>\n",
-    "    <p>\n",
-    "        This guide assumes familiarity with the following:\n",
-    "        <ul>\n",
-    "            <li>            \n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/messages/#systemmessage\">\n",
-    "                    SystemMessage\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 tutorial will show how to add a custom system prompt to the [prebuilt ReAct agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_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 custom system prompt by passing a string to the `prompt` param.\n"
-   ]
-  },
-  {
-   "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": "715867c6",
-   "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": 1,
-   "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 typing import Literal\n",
-    "\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",
-    "# We can add our system prompt here\n",
-    "\n",
-    "prompt = \"Respond in Italian\"\n",
-    "\n",
-    "# Define the graph\n",
-    "\n",
-    "from langgraph.prebuilt import create_react_agent\n",
-    "\n",
-    "graph = create_react_agent(model, tools=tools, prompt=prompt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00407425-506d-4ffd-9c86-987921d8c844",
-   "metadata": {},
-   "source": [
-    "## Usage\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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": 4,
-   "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_b02uzBRrIm2uciJa8zDXCDxT)\n",
-      " Call ID: call_b02uzBRrIm2uciJa8zDXCDxT\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",
-      "A New York potrebbe essere nuvoloso.\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
-    "\n",
-    "print_stream(graph.stream(inputs, stream_mode=\"values\"))"
-   ]
-  }
- ],
- "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
-}
@@ -151,21 +151,13 @@ See the below guide for how to integrate with other frameworks using the [Functi

 - [How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks](autogen-integration-functional.ipynb)

-### Prebuilt ReAct Agent
+### Prebuilt Agent

-The LangGraph [prebuilt ReAct agent](../reference/agents.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
+LangGraph comes with a [prebuilt][langgraph.prebuilt.chat_agent_executor.create_react_agent] implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent). See [Agents](../agents/overview.md) guides for more information.

-One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.
+!!! tip

-These guides show how to use the prebuilt ReAct agent:
-
- [How to use the pre-built ReAct agent](create-react-agent.ipynb)
- [How to add thread-level memory to a ReAct Agent](create-react-agent-memory.ipynb)
- [How to add a custom system prompt to a ReAct agent](create-react-agent-system-prompt.ipynb)
- [How to add human-in-the-loop processes to a ReAct agent](create-react-agent-hitl.ipynb)
- [How to return structured output from a ReAct agent](create-react-agent-structured-output.ipynb)
- [How to add semantic search for long-term memory to a ReAct agent](memory/semantic-search.ipynb#using-in-create-react-agent)
- [How to manage message history in a ReAct agent](create-react-agent-manage-message-history.ipynb)
+    One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.

 Interested in further customizing the ReAct agent? This guide provides an
 overview of its underlying implementation to help you customize for your own needs:
@@ -3727,7 +3727,7 @@ url = "../libs/sdk-py"

 [[package]]
 name = "langgraph-supervisor"
-version = "0.0.18"
+version = "0.0.19"
 description = "An implementation of a supervisor multi-agent architecture using LangGraph"
 optional = false
 python-versions = ">=3.10"
@@ -3737,18 +3737,18 @@ develop = false

 [package.dependencies]
 langchain-core = ">=0.3.40,<0.4.0"
-langgraph = ">=0.3.5,<0.4.0"
+langgraph = ">=0.3.5"
 langgraph-prebuilt = ">=0.1.7,<0.2.0"

 [package.source]
 type = "git"
 url = "https://github.com/langchain-ai/langgraph-supervisor-py"
 reference = "HEAD"
-resolved_reference = "5cbfa9748ec809c0ee92c6a821fb18851b9ddfde"
+resolved_reference = "d146c97af19271f435245ab0d4532b7f51ee360b"

 [[package]]
 name = "langgraph-swarm"
-version = "0.0.10"
+version = "0.0.11"
 description = "An implementation of a multi-agent swarm using LangGraph"
 optional = false
 python-versions = ">=3.10"
@@ -3758,13 +3758,13 @@ develop = false

 [package.dependencies]
 langchain-core = ">=0.3.40,<0.4.0"
-langgraph = ">=0.3.5,<0.4.0"
+langgraph = ">=0.3.5"

 [package.source]
 type = "git"
 url = "https://github.com/langchain-ai/langgraph-swarm-py"
 reference = "HEAD"
-resolved_reference = "472a871aed829dc7fd655819cd5fbc8b0291f507"
+resolved_reference = "8879cd91cf799da9e217e382a2c56b5033575d37"

 [[package]]
 name = "langmem"
@@ -1,10 +1,11 @@
 [tool.poetry]
-name = "langgraph-monorepo"
+name = "langgraph-docs"
 version = "0.0.1"
-description = "LangGraph monorepo"
+description = "LangGraph docs"
 authors = []
 license = "MIT"
 readme = "README.md"
+package-mode = false

 [tool.poetry.dependencies]
 python = "^3.10"
@@ -14,8 +14,10 @@ EMPTY_SEQ: tuple[str, ...] = tuple()
 MISSING = object()

 # --- Public constants ---
-TAG_NOSTREAM = sys.intern("langsmith:nostream")
+TAG_NOSTREAM = sys.intern("nostream")
 """Tag to disable streaming for a chat model."""
+TAG_NOSTREAM_ALT = sys.intern("langsmith:nostream")
+"""Tag to disable streaming for a chat model. (Deprecated in favour of "nostream")"""
 TAG_HIDDEN = sys.intern("langsmith:hidden")
 """Tag to hide a node/edge from certain tracing/streaming environments."""
 START = sys.intern("__start__")
@@ -1,6 +1,7 @@
 import functools
 import logging
 import weakref
+from dataclasses import is_dataclass
 from inspect import isclass
 from typing import (
    Annotated,
@@ -13,8 +14,8 @@ from typing import (
    get_type_hints,
 )

-from pydantic import BaseModel
-from pydantic.v1 import BaseModel as BaseModelV1
+from pydantic import BaseModel, ConfigDict, TypeAdapter
+from typing_extensions import is_typeddict

 __all__ = ["SchemaCoercionMapper"]

@@ -45,7 +46,7 @@ class SchemaCoercionMapper:

    def __init__(
        self,
-        schema: type[Any],
+        schema: type[BaseModel],
        type_hints: Optional[dict[str, Any]] = None,
        *,
        max_depth: int = 12,
@@ -63,30 +64,12 @@ class SchemaCoercionMapper:
            else get_type_hints(schema, localns={schema.__name__: schema})
        )

-        if issubclass(schema, BaseModelV1):
-            self._fields = {
-                n: self.type_hints.get(n, f.annotation)
-                for n, f in schema.__fields__.items()
-            }
-            self._construct = schema.construct
-            unhandled_attrs = (
-                "__pre_root_validators__",
-                "__post_root_validators__",
-                "__validators__",
-            )
-            if any(getattr(schema, c, None) for c in unhandled_attrs):
-                self.coerce: Callable[[Any, Any], Union[BaseModelV1, BaseModel]] = (
-                    lambda v, _: schema(**v)
-                )
-            else:
-                self.coerce = self._coerce
-
-        elif issubclass(schema, BaseModel):
+        if issubclass(schema, BaseModel):
            self._fields = {
                n: self.type_hints.get(n, f.annotation)
                for n, f in schema.model_fields.items()
            }
-            self._construct: Callable[..., Any] = schema.model_construct  # type: ignore
+            self._construct: Callable[..., Any] = schema.model_construct
            unhandled_attrs = ("validators", "field_validators", "root_validators")
            if (decorators := getattr(schema, "__pydantic_decorators__", None)) and any(
                getattr(decorators, attr, None) for attr in unhandled_attrs
@@ -94,9 +77,8 @@ class SchemaCoercionMapper:
                self.coerce = lambda v, _: schema.model_validate(v)
            else:
                self.coerce = self._coerce
-
        else:
-            raise TypeError("Schema is neither a Pydantic v1 nor v2 model.")
+            raise TypeError("Schema must be a Pydantic V2 model.")

        self._field_coercers: Optional[dict[str, Callable[[Any, int], Any]]] = None

@@ -138,14 +120,12 @@ class SchemaCoercionMapper:

        if isclass(field_type):
            # This is needed bcs. of issubclass issues on older versions of python
-            is_class_ = True
            try:
-                is_bm_v2 = issubclass(field_type, BaseModel)
+                is_bm_subclass = issubclass(field_type, BaseModel)
            except TypeError:
                # python < 3.11 issue.
-                is_class_ = False
-                is_bm_v2 = False
-            if is_bm_v2 or (is_class_ and issubclass(field_type, BaseModelV1)):
+                is_bm_subclass = False
+            if is_bm_subclass:
                mapper = SchemaCoercionMapper(field_type, max_depth=depth - 1)
                return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v

@@ -265,67 +245,18 @@ _IDENTITY_TYPES: tuple[type[Any], ...] = (
    type(None),
 )

-try:
-    # Pydantic v2.
-    from pydantic import TypeAdapter

+@functools.lru_cache(maxsize=2048)
+def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
    try:
-        import pydantic.v1.types as v1_types_
-        from pydantic.v1 import parse_obj_as
-
-        v1_types = tuple(
-            v for k, v in vars(v1_types_).items() if k in v1_types_.__all__
+        config = (
+            None
+            if (issubclass(tp, BaseModel) or is_dataclass(tp) or is_typeddict(tp))
+            else ConfigDict(arbitrary_types_allowed=True)
        )
-    except ImportError:
-        v1_types = ()
-
-        def parse_obj_as(tp: Any, v: Any) -> Any:  # type: ignore
-            return v
-
-    try:
-        from pydantic.v1 import parse_obj_as
-        from pydantic.v1.main import create_model
-    except ImportError:
-        create_model = None  # type: ignore
-
-    def _get_v1_parser(tp: Any) -> Any:
-        if create_model is not None:
-            try:
-                parser = create_model(
-                    f"ParsingModel[{tp}]",
-                    __root__=(tp, ...),
-                )
-                return lambda v: parser(__root__=v).__root__  # type: ignore
-            except RuntimeError:
-                return lambda v: v
-        return lambda v: parse_obj_as(tp, v)
-
-    @functools.lru_cache(maxsize=2048)
-    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
-        if tp in v1_types:
-            return _get_v1_parser(tp)
-        try:
-            return TypeAdapter(
-                tp, config={"arbitrary_types_allowed": True}
-            ).validate_python
-        except TypeError:
-            # Delayed classes like ConstrainedList
-            return _get_v1_parser(tp)
-
-except ImportError:
-    # Pydantic V1
-    from pydantic.v1.main import create_model
-
-    @functools.lru_cache(maxsize=2048)
-    def _adapter_for(tp: Any) -> Callable[[Any], Any]:  # noqa: D401
-        try:
-            parser = create_model(
-                f"ParsingModel[{tp}]",
-                __root__=(tp, ...),
-            )
-            return lambda v: parser(__root__=v).__root__  # type: ignore
-        except RuntimeError:
-            return lambda v: v
+    except TypeError:
+        config = None
+    return TypeAdapter(tp, config=config).validate_python


 def _get_adapter(tp: Any) -> Callable[[Any], Any]:
@@ -23,7 +23,6 @@ from typing import (

 from langchain_core.runnables import Runnable, RunnableConfig
 from pydantic import BaseModel
-from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import Self

 from langgraph._api.deprecation import LangGraphDeprecationWarning
@@ -625,7 +624,7 @@ class StateGraph(Graph):
                self.input
                if len(self.channels) > 1
                and isclass(self.input)
-                and issubclass(self.input, (BaseModel, BaseModelV1))
+                and issubclass(self.input, BaseModel)
                else None
            ),
            nodes={},
@@ -1011,7 +1010,7 @@ def _pick_mapper(
    if isclass(schema):
        if issubclass(schema, dict):
            return None
-        if issubclass(schema, (BaseModel, BaseModelV1)):
+        if issubclass(schema, BaseModel):
            return SchemaCoercionMapper(schema, type_hints=type_hints)
    return partial(_coerce_state, schema)

@@ -1194,7 +1193,7 @@ def _get_schema(
    channels: dict,
    name: str,
 ) -> type[BaseModel]:
-    if isclass(typ) and issubclass(typ, (BaseModel, BaseModelV1)):
+    if isclass(typ) and issubclass(typ, BaseModel):
        return typ
    else:
        keys = list(schemas[typ].keys())
@@ -1,4 +1,4 @@
-from typing import Any, Literal, Optional, Union
+from typing import Any, Literal, Optional, Union, cast
 from uuid import uuid4

 from langchain_core.messages import AnyMessage
@@ -55,6 +55,7 @@ def push_ui_message(
    metadata: Optional[dict[str, Any]] = None,
    message: Optional[AnyMessage] = None,
    state_key: str = "ui",
+    merge: bool = False,
 ) -> UIMessage:
    """Push a new UI message to update the UI state.

@@ -100,6 +101,7 @@ def push_ui_message(
        "name": name,
        "props": props,
        "metadata": {
+            "merge": merge,
            "run_id": config.get("run_id", None),
            "tags": config.get("tags", None),
            "name": config.get("run_name", None),
@@ -191,6 +193,12 @@ def ui_message_reducer(
                ids_to_remove.add(msg_id)
            else:
                ids_to_remove.discard(msg_id)
+
+                if cast(UIMessage, msg).get("metadata", {}).get("merge", False):
+                    prev_msg = merged[existing_idx]
+                    msg = msg.copy()
+                    msg["props"] = {**prev_msg["props"], **msg["props"]}
+
                merged[existing_idx] = msg
        else:
            if msg.get("type") == "remove-ui":
@@ -13,7 +13,7 @@ from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.messages import BaseMessage
 from langchain_core.outputs import ChatGenerationChunk, LLMResult

-from langgraph.constants import NS_SEP, TAG_HIDDEN, TAG_NOSTREAM
+from langgraph.constants import NS_SEP, TAG_HIDDEN, TAG_NOSTREAM, TAG_NOSTREAM_ALT
 from langgraph.types import Command, StreamChunk

 try:
@@ -93,7 +93,9 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
        metadata: Optional[dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
-        if metadata and (not tags or TAG_NOSTREAM not in tags):
+        if metadata and (
+            not tags or (TAG_NOSTREAM not in tags and TAG_NOSTREAM_ALT not in tags)
+        ):
            self.metadata[run_id] = (
                tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP)),
                metadata,
@@ -3,7 +3,6 @@ from collections.abc import Generator, Sequence
 from typing import Annotated, Any, Optional, Union, get_type_hints

 from pydantic import BaseModel
-from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import NotRequired, ReadOnly, Required, get_origin

 # NOTE: this is redefined here separately from langgraph.constants
@@ -158,12 +157,7 @@ def get_enhanced_type_hints(

 def get_update_as_tuples(input: Any, keys: Sequence[str]) -> list[tuple[str, Any]]:
    """Get Pydantic state update as a list of (key, value) tuples."""
-    # Pydantic v1
-    if isinstance(input, BaseModelV1):
-        keep: Optional[set[str]] = input.__fields_set__
-        defaults = {k: v.default for k, v in input.__fields__.items()}
-    # Pydantic v2
-    elif isinstance(input, BaseModel):
+    if isinstance(input, BaseModel):
        keep = input.model_fields_set
        defaults = {k: v.default for k, v in input.model_fields.items()}
    else:
@@ -1,11 +1,181 @@
 import sys
 import typing
+import warnings
+from contextlib import nullcontext
 from dataclasses import is_dataclass
-from typing import Any, Optional, Union
+from functools import lru_cache
+from typing import (
+    Any,
+    Optional,
+    Union,
+    cast,
+    overload,
+)

-import typing_extensions
-from pydantic import BaseModel
-from pydantic.v1 import BaseModel as BaseModelV1
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    RootModel,
+)
+from pydantic import (
+    create_model as _create_model_base,
+)
+from pydantic.fields import FieldInfo
+from pydantic.json_schema import (
+    DEFAULT_REF_TEMPLATE,
+    GenerateJsonSchema,
+    JsonSchemaMode,
+)
+from typing_extensions import TypedDict
+
+
+@overload
+def get_fields(model: type[BaseModel]) -> dict[str, FieldInfo]: ...
+
+
+@overload
+def get_fields(model: BaseModel) -> dict[str, FieldInfo]: ...
+
+
+def get_fields(
+    model: Union[type[BaseModel], BaseModel],
+) -> dict[str, FieldInfo]:
+    """Get the field names of a Pydantic model."""
+    if hasattr(model, "model_fields"):
+        return model.model_fields
+
+    if hasattr(model, "__fields__"):
+        return model.__fields__  # type: ignore[return-value]
+    msg = f"Expected a Pydantic model. Got {type(model)}"
+    raise TypeError(msg)
+
+
+_SchemaConfig = ConfigDict(
+    arbitrary_types_allowed=True, frozen=True, protected_namespaces=()
+)
+
+NO_DEFAULT = object()
+
+
+def _create_root_model(
+    name: str,
+    type_: Any,
+    module_name: Optional[str] = None,
+    default_: object = NO_DEFAULT,
+) -> type[BaseModel]:
+    """Create a base class."""
+
+    def schema(
+        cls: type[BaseModel],
+        by_alias: bool = True,  # noqa: FBT001,FBT002
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+    ) -> dict[str, Any]:
+        # Complains about schema not being defined in superclass
+        schema_ = super(cls, cls).schema(  # type: ignore[misc]
+            by_alias=by_alias, ref_template=ref_template
+        )
+        schema_["title"] = name
+        return schema_
+
+    def model_json_schema(
+        cls: type[BaseModel],
+        by_alias: bool = True,  # noqa: FBT001,FBT002
+        ref_template: str = DEFAULT_REF_TEMPLATE,
+        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+        mode: JsonSchemaMode = "validation",
+    ) -> dict[str, Any]:
+        # Complains about model_json_schema not being defined in superclass
+        schema_ = super(cls, cls).model_json_schema(  # type: ignore[misc]
+            by_alias=by_alias,
+            ref_template=ref_template,
+            schema_generator=schema_generator,
+            mode=mode,
+        )
+        schema_["title"] = name
+        return schema_
+
+    base_class_attributes = {
+        "__annotations__": {"root": type_},
+        "model_config": ConfigDict(arbitrary_types_allowed=True),
+        "schema": classmethod(schema),
+        "model_json_schema": classmethod(model_json_schema),
+        "__module__": module_name or "langchain_core.runnables.utils",
+    }
+
+    if default_ is not NO_DEFAULT:
+        base_class_attributes["root"] = default_
+    with warnings.catch_warnings():
+        custom_root_type = type(name, (RootModel,), base_class_attributes)
+    return cast("type[BaseModel]", custom_root_type)
+
+
+@lru_cache(maxsize=256)
+def _create_root_model_cached(
+    model_name: str,
+    type_: Any,
+    *,
+    module_name: Optional[str] = None,
+    default_: object = NO_DEFAULT,
+) -> type[BaseModel]:
+    return _create_root_model(
+        model_name, type_, default_=default_, module_name=module_name
+    )
+
+
+@lru_cache(maxsize=256)
+def _create_model_cached(
+    model_name: str,
+    /,
+    **field_definitions: Any,
+) -> type[BaseModel]:
+    return _create_model_base(
+        model_name,
+        __config__=_SchemaConfig,
+        **_remap_field_definitions(field_definitions),
+    )
+
+
+# Reserved names should capture all the `public` names / methods that are
+# used by BaseModel internally. This will keep the reserved names up-to-date.
+# For reference, the reserved names are:
+# "construct", "copy", "dict", "from_orm", "json", "parse_file", "parse_obj",
+# "parse_raw", "schema", "schema_json", "update_forward_refs", "validate",
+# "model_computed_fields", "model_config", "model_construct", "model_copy",
+# "model_dump", "model_dump_json", "model_extra", "model_fields",
+# "model_fields_set", "model_json_schema", "model_parametrized_name",
+# "model_post_init", "model_rebuild", "model_validate", "model_validate_json",
+# "model_validate_strings"
+_RESERVED_NAMES = {key for key in dir(BaseModel) if not key.startswith("_")}
+
+
+def _remap_field_definitions(field_definitions: dict[str, Any]) -> dict[str, Any]:
+    """This remaps fields to avoid colliding with internal pydantic fields."""
+
+    remapped = {}
+    for key, value in field_definitions.items():
+        if key.startswith("_") or key in _RESERVED_NAMES:
+            # Let's add a prefix to avoid colliding with internal pydantic fields
+            if isinstance(value, FieldInfo):
+                msg = (
+                    f"Remapping for fields starting with '_' or fields with a name "
+                    f"matching a reserved name {_RESERVED_NAMES} is not supported if "
+                    f" the field is a pydantic Field instance. Got {key}."
+                )
+                raise NotImplementedError(msg)
+            type_, default_ = value
+            remapped[f"private_{key}"] = (
+                type_,
+                Field(
+                    default=default_,
+                    alias=key,
+                    serialization_alias=key,
+                    title=key.lstrip("_").replace("_", " ").title(),
+                ),
+            )
+        else:
+            remapped[key] = value
+    return remapped


 def create_model(
@@ -13,32 +183,70 @@ def create_model(
    *,
    field_definitions: Optional[dict[str, Any]] = None,
    root: Optional[Any] = None,
-) -> Union[BaseModel, BaseModelV1]:
+) -> type[BaseModel]:
    """Create a pydantic model with the given field definitions.

+    Attention:
+        Please do not use outside of langchain packages. This API
+        is subject to change at any time.
+
    Args:
        model_name: The name of the model.
+        module_name: The name of the module where the model is defined.
+            This is used by Pydantic to resolve any forward references.
        field_definitions: The field definitions for the model.
        root: Type for a root model (RootModel)
+
+    Returns:
+        Type[BaseModel]: The created model.
    """
-    try:
-        # for langchain-core >= 0.3.0
-        from langchain_core.utils.pydantic import create_model_v2
+    field_definitions = field_definitions or {}

-        return create_model_v2(
-            model_name,
-            field_definitions=field_definitions,
-            root=root,
-        )
-    except ImportError:
-        # for langchain-core < 0.3.0
-        from langchain_core.runnables.utils import create_model
+    if root:
+        if field_definitions:
+            msg = (
+                "When specifying __root__ no other "
+                f"fields should be provided. Got {field_definitions}"
+            )
+            raise NotImplementedError(msg)

-        v1_kwargs = {}
-        if root is not None:
-            v1_kwargs["__root__"] = root
+        if isinstance(root, tuple):
+            kwargs = {"type_": root[0], "default_": root[1]}
+        else:
+            kwargs = {"type_": root}

-        return create_model(model_name, **v1_kwargs, **(field_definitions or {}))
+        try:
+            named_root_model = _create_root_model_cached(model_name, **kwargs)
+        except TypeError:
+            # something in the arguments into _create_root_model_cached is not hashable
+            named_root_model = _create_root_model(
+                model_name,
+                **kwargs,
+            )
+        return named_root_model
+
+    # No root, just field definitions
+    names = set(field_definitions.keys())
+
+    capture_warnings = False
+
+    for name in names:
+        # Also if any non-reserved name is used (e.g., model_id or model_name)
+        if name.startswith("model"):
+            capture_warnings = True
+
+    with warnings.catch_warnings() if capture_warnings else nullcontext():
+        if capture_warnings:
+            warnings.filterwarnings(action="ignore")
+        try:
+            return _create_model_cached(model_name, **field_definitions)
+        except TypeError:
+            # something in field definitions is not hashable
+            return _create_model_base(
+                model_name,
+                __config__=_SchemaConfig,
+                **_remap_field_definitions(field_definitions),
+            )


 def is_supported_by_pydantic(type_: Any) -> bool:
@@ -51,14 +259,12 @@ def is_supported_by_pydantic(type_: Any) -> bool:
    if is_dataclass(type_):
        return True

-    # Pydantic does not support mixing .v1 and root namespaces, so
-    # we only check for BaseModel (not pydantic.v1.BaseModel).
    if isinstance(type_, type) and issubclass(type_, BaseModel):
        return True

    if hasattr(type_, "__orig_bases__"):
        for base in type_.__orig_bases__:
-            if base is typing_extensions.TypedDict:
+            if base is TypedDict:
                return True
            elif base is typing.TypedDict:  # noqa: TID251
                # ignoring TID251 since it's OK to use typing.TypedDict in this case.
@@ -26,7 +26,6 @@ description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
-markers = "python_version < \"4.0\""
 files = [
    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -2270,7 +2269,6 @@ description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
-markers = "python_version < \"4.0\""
 files = [
    {file = "pydantic-2.9.2-py3-none-any.whl", hash = "sha256:f048cec7b26778210e28a0459867920654d48e5e62db0958433636cde4254f12"},
    {file = "pydantic-2.9.2.tar.gz", hash = "sha256:d155cef71265d1e9807ed1c32b4c8deec042a44a50a4188b25ac67ecd81a9c0f"},
@@ -2295,7 +2293,6 @@ description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "dev"]
-markers = "python_version < \"4.0\""
 files = [
    {file = "pydantic_core-2.23.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b10bd51f823d891193d4717448fab065733958bdb6a6b351967bd349d48d5c9b"},
    {file = "pydantic_core-2.23.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4fc714bdbfb534f94034efaa6eadd74e5b93c8fa6315565a222f7b6f42ca1166"},
@@ -3306,7 +3303,6 @@ files = [
    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
-markers = {main = "python_version < \"4.0\""}

 [[package]]
 name = "tzdata"
@@ -3677,4 +3673,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9"
-content-hash = "0e0c3fc2d5a5348c8df102497c7221a1f052f37a788b315eb82dfc7d63423e17"
+content-hash = "770dcaa5816fffb667b5e3639c0ee5add24df0a99bb9a89a6d8fb2c4a79fb185"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.4.0"
+version = "0.4.1"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"
@@ -14,6 +14,7 @@ langgraph-checkpoint = "^2.0.10"
 langgraph-sdk = { version = ">=0.1.42", python = "<4.0" }
 langgraph-prebuilt = { version = ">=0.1.8", python = "<4.0" }
 xxhash = "^3.5.0"
+pydantic = { version = ">=2.7.4"}

 [tool.poetry.group.dev.dependencies]
 pytest = "^8.3.2"
@@ -6,22 +6,18 @@ from pydantic import BaseModel
 # define these objects to avoid importing langchain_core.agents
 # and therefore avoid relying on core Pydantic version
 class AgentAction(BaseModel):
+    """
+    Represents a request to execute an action by an agent.
+
+    The action consists of the name of the tool to execute and the input to pass
+    to the tool. The log is used to pass along extra information about the action.
+    """
+
    tool: str
    tool_input: Union[str, dict]
    log: str
    type: Literal["AgentAction"] = "AgentAction"

-    model_config = {
-        "json_schema_extra": {
-            "description": (
-                """Represents a request to execute an action by an agent.
-
-The action consists of the name of the tool to execute and the input to pass
-to the tool. The log is used to pass along extra information about the action."""
-            )
-        }
-    }
-

 class AgentFinish(BaseModel):
    """Final return value of an ActionAgent.
@@ -32,12 +28,3 @@ class AgentFinish(BaseModel):
    return_values: dict
    log: str
    type: Literal["AgentFinish"] = "AgentFinish"
-    model_config = {
-        "json_schema_extra": {
-            "description": (
-                """Final return value of an ActionAgent.
-
-Agents return an AgentFinish when they have reached a stopping condition."""
-            )
-        }
-    }
@@ -12,13 +12,11 @@ from langchain_core.messages import (
    ToolMessage,
 )
 from pydantic import BaseModel
-from pydantic.v1 import BaseModel as BaseModelV1
 from typing_extensions import TypedDict

 from langgraph.graph import add_messages
 from langgraph.graph.message import REMOVE_ALL_MESSAGES, MessagesState
 from langgraph.graph.state import END, START, StateGraph
-from tests.conftest import IS_LANGCHAIN_CORE_030_OR_GREATER
 from tests.messages import _AnyIdHumanMessage

 _, CORE_MINOR, CORE_PATCH = (int(v) for v in langchain_core.__version__.split("."))
@@ -175,19 +173,11 @@ def test_delete_all():
    assert result == expected_result


-MESSAGES_STATE_SCHEMAS = [MessagesState]
-if IS_LANGCHAIN_CORE_030_OR_GREATER:
+class MessagesStatePydantic(BaseModel):
+    messages: Annotated[list[AnyMessage], add_messages]

-    class MessagesStatePydantic(BaseModel):
-        messages: Annotated[list[AnyMessage], add_messages]

-    MESSAGES_STATE_SCHEMAS.append(MessagesStatePydantic)
-else:
-
-    class MessagesStatePydanticV1(BaseModelV1):
-        messages: Annotated[list[AnyMessage], add_messages]
-
-    MESSAGES_STATE_SCHEMAS.append(MessagesStatePydanticV1)
+MESSAGES_STATE_SCHEMAS = [MessagesState, MessagesStatePydantic]


@pytest.mark.parametrize("state_schema", MESSAGES_STATE_SCHEMAS)
@@ -33,6 +33,7 @@ from langchain_core.runnables import (
 )
 from langchain_core.runnables.graph import Edge
 from langsmith import traceable
+from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
 from typing_extensions import TypedDict
@@ -2596,14 +2597,12 @@ def test_in_one_fan_out_state_graph_waiting_edge_via_branch(


@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1(
+def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2(
    snapshot: SnapshotAssertion,
    mocker: MockerFixture,
    request: pytest.FixtureRequest,
    checkpointer_name: str,
 ) -> None:
-    from pydantic.v1 import BaseModel, ValidationError
-
    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
    setup = mocker.Mock()
    teardown = mocker.Mock()
@@ -2642,8 +2641,7 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1(
        yo: int

    class State(BaseModel):
-        class Config:
-            arbitrary_types_allowed = True
+        model_config = ConfigDict(arbitrary_types_allowed=True)

        query: str
        inner: Annotated[InnerObject, lambda x, y: y]
@@ -2651,197 +2649,6 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic1(
        docs: Annotated[list[str], sorted_add]
        client: Annotated[httpx.Client, Context(make_httpx_client)]

-    class Input(BaseModel):
-        query: str
-        inner: InnerObject
-
-    class Output(BaseModel):
-        answer: str
-        docs: list[str]
-
-    class StateUpdate(BaseModel):
-        query: Optional[str] = None
-        answer: Optional[str] = None
-        docs: Optional[list[str]] = None
-
-    class UpdateDocs34(BaseModel):
-        docs: list[str] = ["doc3", "doc4"]
-
-    def rewrite_query(data: State) -> State:
-        assert isinstance(data.inner, InnerObject)
-        return {"query": f"query: {data.query}"}
-
-    def analyzer_one(data: State) -> State:
-        assert isinstance(data.inner, InnerObject)
-        return StateUpdate(query=f"analyzed: {data.query}")
-
-    def retriever_one(data: State) -> State:
-        return {"docs": ["doc1", "doc2"]}
-
-    def retriever_two(data: State) -> State:
-        time.sleep(0.1)
-        return UpdateDocs34()
-
-    def qa(data: State) -> State:
-        return {"answer": ",".join(data.docs)}
-
-    def decider(data: State) -> str:
-        assert isinstance(data, State)
-        return "retriever_two"
-
-    workflow = StateGraph(State, input=Input, output=Output)
-
-    workflow.add_node("rewrite_query", rewrite_query)
-    workflow.add_node("analyzer_one", analyzer_one)
-    workflow.add_node("retriever_one", retriever_one)
-    workflow.add_node("retriever_two", retriever_two)
-    workflow.add_node("qa", qa)
-
-    workflow.set_entry_point("rewrite_query")
-    workflow.add_edge("rewrite_query", "analyzer_one")
-    workflow.add_edge("analyzer_one", "retriever_one")
-    workflow.add_conditional_edges(
-        "rewrite_query", decider, {"retriever_two": "retriever_two"}
-    )
-    workflow.add_edge(["retriever_one", "retriever_two"], "qa")
-    workflow.set_finish_point("qa")
-
-    app = workflow.compile()
-
-    if checkpointer_name == "memory":
-        assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
-        assert app.get_input_jsonschema() == snapshot
-        assert app.get_output_jsonschema() == snapshot
-
-    with pytest.raises(ValidationError), assert_ctx_once():
-        app.invoke({"query": {}})
-
-    with assert_ctx_once():
-        assert app.invoke({"query": "what is weather in sf", "inner": {"yo": 1}}) == {
-            "docs": ["doc1", "doc2", "doc3", "doc4"],
-            "answer": "doc1,doc2,doc3,doc4",
-        }
-
-    with assert_ctx_once():
-        assert [
-            *app.stream({"query": "what is weather in sf", "inner": {"yo": 1}})
-        ] == [
-            {"rewrite_query": {"query": "query: what is weather in sf"}},
-            {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
-            {"retriever_two": {"docs": ["doc3", "doc4"]}},
-            {"retriever_one": {"docs": ["doc1", "doc2"]}},
-            {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
-        ]
-
-    app_w_interrupt = workflow.compile(
-        checkpointer=checkpointer,
-        interrupt_after=["retriever_one"],
-    )
-    config = {"configurable": {"thread_id": "1"}}
-
-    with assert_ctx_once():
-        assert [
-            c
-            for c in app_w_interrupt.stream(
-                {"query": "what is weather in sf", "inner": {"yo": 1}}, config
-            )
-        ] == [
-            {"rewrite_query": {"query": "query: what is weather in sf"}},
-            {"analyzer_one": {"query": "analyzed: query: what is weather in sf"}},
-            {"retriever_two": {"docs": ["doc3", "doc4"]}},
-            {"retriever_one": {"docs": ["doc1", "doc2"]}},
-            {"__interrupt__": ()},
-        ]
-
-    with assert_ctx_once():
-        assert [c for c in app_w_interrupt.stream(None, config)] == [
-            {"qa": {"answer": "doc1,doc2,doc3,doc4"}},
-        ]
-
-    with assert_ctx_once():
-        assert app_w_interrupt.update_state(
-            config, {"docs": ["doc5"]}, as_node="rewrite_query"
-        ) == {
-            "configurable": {
-                "thread_id": "1",
-                "checkpoint_id": AnyStr(),
-                "checkpoint_ns": "",
-            }
-        }
-
-
-@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2(
-    snapshot: SnapshotAssertion,
-    mocker: MockerFixture,
-    request: pytest.FixtureRequest,
-    checkpointer_name: str,
-) -> None:
-    from pydantic import BaseModel, ConfigDict, Field, ValidationError
-    from pydantic.v1 import BaseModel as BaseModelV1
-
-    IS_V1 = BaseModel is BaseModelV1
-
-    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")
-    setup = mocker.Mock()
-    teardown = mocker.Mock()
-
-    @contextmanager
-    def assert_ctx_once() -> Iterator[None]:
-        assert setup.call_count == 0
-        assert teardown.call_count == 0
-        try:
-            yield
-        finally:
-            assert setup.call_count == 1
-            assert teardown.call_count == 1
-            setup.reset_mock()
-            teardown.reset_mock()
-
-    @contextmanager
-    def make_httpx_client() -> Iterator[httpx.Client]:
-        setup()
-        with httpx.Client() as client:
-            try:
-                yield client
-            finally:
-                teardown()
-
-    def sorted_add(
-        x: list[str], y: Union[list[str], list[tuple[str, str]]]
-    ) -> list[str]:
-        if isinstance(y[0], tuple):
-            for rem, _ in y:
-                x.remove(rem)
-            y = [t[1] for t in y]
-        return sorted(operator.add(x, y))
-
-    class InnerObject(BaseModel):
-        yo: int
-
-    if IS_V1:
-
-        class State(BaseModel):
-            class Config:
-                arbitrary_types_allowed = True
-
-            query: str
-            inner: Annotated[InnerObject, lambda x, y: y]
-            answer: Optional[str] = None
-            docs: Annotated[list[str], sorted_add]
-            client: Annotated[httpx.Client, Context(make_httpx_client)]
-
-    else:
-
-        class State(BaseModel):
-            model_config = ConfigDict(arbitrary_types_allowed=True)
-
-            query: str
-            inner: Annotated[InnerObject, lambda x, y: y]
-            answer: Optional[str] = None
-            docs: Annotated[list[str], sorted_add]
-            client: Annotated[httpx.Client, Context(make_httpx_client)]
-
    class StateUpdate(BaseModel):
        query: Optional[str] = None
        answer: Optional[str] = None
@@ -2966,8 +2773,6 @@ def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic_inp
    request: pytest.FixtureRequest,
    checkpointer_name: str,
 ) -> None:
-    from pydantic import BaseModel
-
    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")

    def sorted_add(
@@ -4415,7 +4220,6 @@ def test_remove_message_from_node():

 def test_xray_lance(snapshot: SnapshotAssertion):
    from langchain_core.messages import AnyMessage, HumanMessage
-    from pydantic import BaseModel, Field

    class Analyst(BaseModel):
        affiliation: str = Field(
@@ -5533,8 +5337,6 @@ def test_dict_mixed_return() -> None:


 def test_command_pydantic_dataclass() -> None:
-    from pydantic import BaseModel
-
    class PydanticState(BaseModel):
        foo: str

@@ -6351,9 +6153,7 @@ def test_double_interrupt_subgraph(


@pytest.mark.parametrize("checkpointer_name", ALL_CHECKPOINTERS_SYNC)
-def test_multi_resume(
-    request: pytest.FixtureRequest, checkpointer_name: str
-) -> None:
+def test_multi_resume(request: pytest.FixtureRequest, checkpointer_name: str) -> None:
    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")

    class ChildState(TypedDict):
@@ -6362,11 +6162,11 @@ def test_multi_resume(
        human_inputs: list[str]

    def get_human_input(state: ChildState):
-        human_input = interrupt(state['prompt'])
+        human_input = interrupt(state["prompt"])

        return {
-            'human_input': human_input,
-            'human_inputs': [human_input],
+            "human_input": human_input,
+            "human_inputs": [human_input],
        }

    child_graph = (
@@ -6385,13 +6185,13 @@ def test_multi_resume(
        return [
            Send(
                "child_graph",
-                {'prompt': prompt},
+                {"prompt": prompt},
            )
-            for prompt in state['prompts']
+            for prompt in state["prompts"]
        ]

    def cleanup(state: ParentState):
-        assert len(state['human_inputs']) == len(state["prompts"])
+        assert len(state["human_inputs"]) == len(state["prompts"])

    parent_graph = (
        StateGraph(ParentState)
@@ -6404,21 +6204,19 @@ def test_multi_resume(
    )

    thread_config: RunnableConfig = {
-        'configurable': {
-            'thread_id': uuid.uuid4(),
+        "configurable": {
+            "thread_id": uuid.uuid4(),
        },
    }

-    prompts = ['a', 'b', 'c', 'd', 'e']
+    prompts = ["a", "b", "c", "d", "e"]

    events = parent_graph.invoke(
-        {'prompts': prompts},
-        thread_config,
-        stream_mode='values'
+        {"prompts": prompts}, thread_config, stream_mode="values"
    )

-    assert len(events['__interrupt__']) == len(prompts)
-    interrupt_values = {i.value for i in events['__interrupt__']}
+    assert len(events["__interrupt__"]) == len(prompts)
+    interrupt_values = {i.value for i in events["__interrupt__"]}
    assert interrupt_values == set(prompts)

    resume_map: dict[str, str] = {
@@ -6428,11 +6226,8 @@ def test_multi_resume(

    result = parent_graph.invoke(Command(resume=resume_map), thread_config)
    assert result == {
-        'prompts': prompts,
-        'human_inputs': [
-            f"human input for prompt {prompt}"
-            for prompt in prompts
-        ],
+        "prompts": prompts,
+        "human_inputs": [f"human input for prompt {prompt}" for prompt in prompts],
    }


@@ -7169,8 +6964,6 @@ def test_node_destinations() -> None:


 def test_pydantic_none_state_update() -> None:
-    from pydantic import BaseModel
-
    class State(BaseModel):
        foo: Optional[str]

@@ -7182,8 +6975,6 @@ def test_pydantic_none_state_update() -> None:


 def test_pydantic_state_update_command() -> None:
-    from pydantic import BaseModel
-
    class State(BaseModel):
        foo: Optional[str]

@@ -7215,8 +7006,6 @@ def test_pydantic_state_update_command() -> None:


 def test_pydantic_state_mutation() -> None:
-    from pydantic import BaseModel, Field
-
    class Inner(BaseModel):
        a: int = 0

@@ -7249,8 +7038,6 @@ def test_pydantic_state_mutation() -> None:


 def test_pydantic_state_mutation_command() -> None:
-    from pydantic import BaseModel, Field
-
    class Inner(BaseModel):
        a: int = 0

@@ -7529,8 +7316,6 @@ def test_interrupt_subgraph_reenter_checkpointer_true(


 def test_empty_invoke() -> None:
-    from pydantic import BaseModel
-
    def reducer_merge_dicts(
        dict1: dict[Any, Any], dict2: dict[Any, Any]
    ) -> dict[Any, Any]:
@@ -7582,8 +7367,6 @@ def test_empty_invoke() -> None:
 def test_parallel_interrupts(
    request: pytest.FixtureRequest, checkpointer_name: str
 ) -> None:
-    from pydantic import BaseModel, Field
-
    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")

    # --- CHILD GRAPH ---
@@ -7759,8 +7542,6 @@ def test_parallel_interrupts(
 def test_parallel_interrupts_double(
    request: pytest.FixtureRequest, checkpointer_name: str
 ) -> None:
-    from pydantic import BaseModel, Field
-
    checkpointer = request.getfixturevalue(f"checkpointer_{checkpointer_name}")

    # --- CHILD GRAPH ---
@@ -8214,8 +7995,6 @@ def test_batch_update_as_input(


 def test_migration_graph(snapshot: SnapshotAssertion) -> None:
-    from pydantic import BaseModel
-
    class DummyState(BaseModel):
        pass_count: int = 0

@@ -26,6 +26,7 @@ import pytest
 from langchain_core.language_models import GenericFakeChatModel
 from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
 from langchain_core.utils.aiter import aclosing
+from pydantic import BaseModel, ConfigDict, Field, ValidationError
 from pytest_mock import MockerFixture
 from syrupy import SnapshotAssertion
 from typing_extensions import TypedDict
@@ -4663,16 +4664,9 @@ async def test_in_one_fan_out_state_graph_waiting_edge_via_branch(
        ]


-@pytest.mark.parametrize("version", ["v1", "v2"])
-async def test_nested_pydantic_models(version: str) -> None:
+async def test_nested_pydantic_models() -> None:
    """Test that nested Pydantic models are properly constructed from leaf nodes up."""

-    # Define nested Pydantic models
-    if version == "v1":
-        from pydantic.v1 import BaseModel, Field
-    else:
-        from pydantic import BaseModel, Field
-
    class NestedModel(BaseModel):
        value: int
        name: str
@@ -4799,8 +4793,6 @@ async def test_nested_pydantic_models(version: str) -> None:
 async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
    snapshot: SnapshotAssertion, mocker: MockerFixture, checkpointer_name: str
 ) -> None:
-    from pydantic.v1 import BaseModel, ValidationError
-
    setup = mocker.Mock()
    teardown = mocker.Mock()

@@ -4835,8 +4827,7 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
        return sorted(operator.add(x, y))

    class State(BaseModel):
-        class Config:
-            arbitrary_types_allowed = True
+        model_config = ConfigDict(arbitrary_types_allowed=True)

        query: str
        answer: Optional[str] = None
@@ -4992,8 +4983,6 @@ async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class(
 async def test_in_one_fan_out_state_graph_waiting_edge_custom_state_class_pydantic2(
    snapshot: SnapshotAssertion, checkpointer_name: str
 ) -> None:
-    from pydantic import BaseModel, ValidationError
-
    def sorted_add(
        x: list[str], y: Union[list[str], list[tuple[str, str]]]
    ) -> list[str]:
@@ -8,7 +8,18 @@ import uuid
 from enum import Enum
 from typing import Annotated, Literal, Optional, Union

-import pytest
+from pydantic import (
+    BaseModel,
+    ByteSize,
+    Field,
+    SecretStr,
+    confloat,
+    conint,
+    conlist,
+    constr,
+    field_validator,
+    model_validator,
+)

 from langgraph.constants import END, START
 from langgraph.graph.state import StateGraph
@@ -45,50 +56,10 @@ def test_is_supported_by_pydantic() -> None:

    assert is_supported_by_pydantic(PydanticModel) is True

-    if hasattr(pydantic, "v1"):

-        class PydanticModelV1(pydantic.v1.BaseModel):
-            x: int
-
-        assert is_supported_by_pydantic(PydanticModelV1) is False
-
-    assert is_supported_by_pydantic(int) is False
-
-
-@pytest.mark.parametrize("version", ["v1", "v2"])
-def test_nested_pydantic_models(version: str) -> None:
+def test_nested_pydantic_models() -> None:
    """Test that nested Pydantic models are properly constructed from leaf nodes up."""

-    # Define nested Pydantic models
-    # Import necessary modules
-
-    if version == "v1":
-        from pydantic.v1 import (  # type: ignore
-            BaseModel,
-            ByteSize,
-            Field,
-            SecretStr,
-            confloat,
-            conint,
-            conlist,
-            constr,
-        )
-    else:
-        from pydantic import (  # type: ignore
-            BaseModel,
-            ByteSize,
-            Field,
-            SecretStr,
-            confloat,
-            conint,
-            conlist,
-            constr,
-        )
-        from pydantic.v1 import BaseModel as BaseModelV1
-
-        if BaseModel is BaseModelV1:
-            pytest.skip("Cannot test pydantic v2 using installed version < 2")
-
    class NestedModel(BaseModel):
        value: int
        name: str
@@ -123,10 +94,7 @@ def test_nested_pydantic_models(version: str) -> None:
        name: str
        friends: list[str] = Field(default_factory=list)  # IDs of friends

-    if version == "v2":
-        conlist_type = conlist(item_type=int, min_length=2, max_length=5)
-    else:
-        conlist_type = conlist(item_type=int, min_items=2, max_items=5)
+    conlist_type = conlist(item_type=int, min_length=2, max_length=5)

    class State(BaseModel):
        # Basic nested model tests
@@ -314,8 +282,6 @@ def test_nested_pydantic_models(version: str) -> None:


 def test_pydantic_state_field_validator():
-    from pydantic import BaseModel, field_validator, model_validator
-
    class State(BaseModel):
        name: str
        text: str = ""
@@ -346,32 +312,3 @@ def test_pydantic_state_field_validator():
    g = builder.compile()
    res = g.invoke(input_state)
    assert res["text"] == "Hello, Validated John!"
-
-
-def test_pydantic_v1_state_root_validator():
-    from pydantic.v1 import BaseModel, root_validator
-
-    class State(BaseModel):
-        name: str
-        text: str = ""
-        only_root: int = 13
-
-        @root_validator(pre=True)
-        @classmethod
-        def validate(cls, values: dict):
-            values["name"] = "Validated " + values["name"]
-            return values | {"only_root": 396}
-
-    input_state = {"name": "John"}
-
-    def process_node(state: State):
-        assert State(**input_state) == state
-        return {"text": "Hello, " + state.name + "!"}
-
-    builder = StateGraph(state_schema=State)
-    builder.add_node("process", process_node)
-    builder.add_edge(START, "process")
-    builder.add_edge("process", END)
-    g = builder.compile()
-    res = g.invoke(input_state)
-    assert res["text"] == "Hello, Validated John!"
@@ -7,7 +7,7 @@ from typing import Annotated as Annotated2

 import pytest
 from langchain_core.runnables import RunnableConfig, RunnableLambda
-from pydantic.v1 import BaseModel
+from pydantic import BaseModel
 from typing_extensions import NotRequired, Required, TypedDict

 from langgraph.graph.state import StateGraph, _get_node_name, _warn_invalid_state_schema
@@ -1,6 +1,6 @@
 {
  "name": "@langchain/langgraph-sdk",
-  "version": "0.0.71",
+  "version": "0.0.73",
  "description": "Client library for interacting with the LangGraph API",
  "type": "module",
  "packageManager": "yarn@1.22.19",
@@ -1,6 +1,7 @@
 import {
  Assistant,
  AssistantGraph,
+  AssistantSortBy,
  AssistantVersion,
  CancelAction,
  Checkpoint,
@@ -16,8 +17,10 @@ import {
  Run,
  RunStatus,
  SearchItemsResponse,
+  SortOrder,
  Subgraphs,
  Thread,
+  ThreadSortBy,
  ThreadState,
  ThreadStatus,
 } from "./schema.js";
@@ -423,6 +426,8 @@ export class AssistantsClient extends BaseClient {
    metadata?: Metadata;
    limit?: number;
    offset?: number;
+    sortBy?: AssistantSortBy;
+    sortOrder?: SortOrder;
  }): Promise<Assistant[]> {
    return this.fetch<Assistant[]>("/assistants/search", {
      method: "POST",
@@ -431,6 +436,8 @@ export class AssistantsClient extends BaseClient {
        metadata: query?.metadata ?? undefined,
        limit: query?.limit ?? 10,
        offset: query?.offset ?? 0,
+        sort_by: query?.sortBy ?? undefined,
+        sort_order: query?.sortOrder ?? undefined,
      },
    });
  }
@@ -621,12 +628,12 @@ export class ThreadsClient<
    /**
     * Sort by.
     */
-    sortBy?: "thread_id" | "status" | "created_at" | "updated_at";
+    sortBy?: ThreadSortBy;
    /**
     * Sort order.
     * Must be one of 'asc' or 'desc'.
     */
-    sortOrder?: "asc" | "desc";
+    sortOrder?: SortOrder;
  }): Promise<Thread<ValuesType>[]> {
    return this.fetch<Thread<ValuesType>[]>("/threads/search", {
      method: "POST",
@@ -8,6 +8,8 @@ export {
 } from "./client.js";
 export {
  uiMessageReducer,
+  isUIMessage,
+  isRemoveUIMessage,
  type UIMessage,
  type RemoveUIMessage,
 } from "./types.js";
@@ -38,21 +38,42 @@ export const typedUi = <Decl extends Record<string, ElementType>>(
  const runId = (config.metadata?.run_id as string | undefined) ?? config.runId;
  if (!runId) throw new Error("run_id is required");

-  const handlePush = <K extends keyof PropMap & string>(
+  function handlePush<K extends keyof PropMap & string>(
    message: {
      id?: string;
      name: K;
      props: PropMap[K];
      metadata?: Record<string, unknown>;
    },
-    options?: { message?: MessageLike },
-  ): UIMessage => {
-    const evt: UIMessage = {
+    options?: { message?: MessageLike; merge?: boolean },
+  ): UIMessage<K, PropMap[K]>;
+
+  function handlePush<K extends keyof PropMap & string>(
+    message: {
+      id?: string;
+      name: K;
+      props: Partial<PropMap[K]>;
+      metadata?: Record<string, unknown>;
+    },
+    options: { message?: MessageLike; merge: true },
+  ): UIMessage<K, Partial<PropMap[K]>>;
+
+  function handlePush<K extends keyof PropMap & string>(
+    message: {
+      id?: string;
+      name: K;
+      props: PropMap[K] | Partial<PropMap[K]>;
+      metadata?: Record<string, unknown>;
+    },
+    options?: { message?: MessageLike; merge?: boolean },
+  ): UIMessage<K, PropMap[K] | Partial<PropMap[K]>> {
+    const evt: UIMessage<K, PropMap[K] | Partial<PropMap[K]>> = {
      type: "ui" as const,
      id: message?.id ?? uuidv4(),
      name: message?.name,
      props: message?.props,
      metadata: {
+        merge: options?.merge || undefined,
        run_id: runId,
        tags: config.tags,
        name: config.runName,
@@ -64,7 +85,7 @@ export const typedUi = <Decl extends Record<string, ElementType>>(
    config.writer?.(evt);
    config.configurable?.__pregel_send?.([[stateKey, evt]]);
    return evt;
-  };
+  }

  const handleDelete = (id: string): RemoveUIMessage => {
    const evt: RemoveUIMessage = { type: "remove-ui", id };
@@ -1,10 +1,14 @@
-export interface UIMessage {
+export interface UIMessage<
+  TName extends string = string,
+  TProps extends Record<string, unknown> = Record<string, unknown>,
+> {
  type: "ui";

  id: string;
-  name: string;
-  props: Record<string, unknown>;
+  name: TName;
+  props: TProps;
  metadata: {
+    merge?: boolean;
    run_id?: string;
    name?: string;
    tags?: string[];
@@ -18,6 +22,20 @@ export interface RemoveUIMessage {
  id: string;
 }

+export function isUIMessage(message: unknown): message is UIMessage {
+  if (typeof message !== "object" || message == null) return false;
+  if (!("type" in message)) return false;
+  return message.type === "ui";
+}
+
+export function isRemoveUIMessage(
+  message: unknown,
+): message is RemoveUIMessage {
+  if (typeof message !== "object" || message == null) return false;
+  if (!("type" in message)) return false;
+  return message.type === "remove-ui";
+}
+
 export function uiMessageReducer(
  state: UIMessage[],
  update: UIMessage | RemoveUIMessage | (UIMessage | RemoveUIMessage)[],
@@ -33,7 +51,9 @@ export function uiMessageReducer(

    const index = state.findIndex((ui) => ui.id === event.id);
    if (index !== -1) {
-      newState[index] = event;
+      newState[index] = event.metadata.merge
+        ? { ...event, props: { ...state[index].props, ...event.props } }
+        : event;
    } else {
      newState.push(event);
    }
@@ -301,3 +301,14 @@ export interface CronCreateForThreadResponse
  extends Omit<CronCreateResponse, "thread_id"> {
  thread_id: string;
 }
+
+export type AssistantSortBy =
+  | "assistant_id"
+  | "graph_id"
+  | "name"
+  | "created_at"
+  | "updated_at";
+
+export type ThreadSortBy = "thread_id" | "status" | "created_at" | "updated_at";
+
+export type SortOrder = "asc" | "desc";
@@ -33,6 +33,7 @@ import langgraph_sdk
 from langgraph_sdk.schema import (
    All,
    Assistant,
+    AssistantSortBy,
    AssistantVersion,
    CancelAction,
    Checkpoint,
@@ -52,10 +53,12 @@ from langgraph_sdk.schema import (
    RunCreate,
    RunStatus,
    SearchItemsResponse,
+    SortOrder,
    StreamMode,
    StreamPart,
    Subgraphs,
    Thread,
+    ThreadSortBy,
    ThreadState,
    ThreadStatus,
    ThreadUpdateStateResponse,
@@ -767,6 +770,8 @@ class AssistantsClient:
        graph_id: Optional[str] = None,
        limit: int = 10,
        offset: int = 0,
+        sort_by: Optional[AssistantSortBy] = None,
+        sort_order: Optional[SortOrder] = None,
        headers: Optional[dict[str, str]] = None,
    ) -> list[Assistant]:
        """Search for assistants.
@@ -777,6 +782,8 @@ class AssistantsClient:
                The graph ID is normally set in your langgraph.json configuration.
            limit: The maximum number of results to return.
            offset: The number of results to skip.
+            sort_by: The field to sort by.
+            sort_order: The order to sort by.
            headers: Optional custom headers to include with the request.

        Returns:
@@ -799,6 +806,10 @@ class AssistantsClient:
            payload["metadata"] = metadata
        if graph_id:
            payload["graph_id"] = graph_id
+        if sort_by:
+            payload["sort_by"] = sort_by
+        if sort_order:
+            payload["sort_order"] = sort_order
        return await self.http.post(
            "/assistants/search",
            json=payload,
@@ -1043,10 +1054,8 @@ class ThreadsClient:
        status: Optional[ThreadStatus] = None,
        limit: int = 10,
        offset: int = 0,
-        sort_by: Optional[
-            Literal["thread_id", "status", "created_at", "updated_at"]
-        ] = None,
-        sort_order: Optional[Literal["asc", "desc"]] = None,
+        sort_by: Optional[ThreadSortBy] = None,
+        sort_order: Optional[SortOrder] = None,
        headers: Optional[dict[str, str]] = None,
    ) -> list[Thread]:
        """Search for threads.
@@ -95,6 +95,23 @@ Action to take when cancelling the run.
 - "rollback": Cancel the run. Then delete the run and associated checkpoints.
 """

+AssistantSortBy = Literal[
+    "assistant_id", "graph_id", "name", "created_at", "updated_at"
+]
+"""
+The field to sort by.
+"""
+
+ThreadSortBy = Literal["thread_id", "status", "created_at", "updated_at"]
+"""
+The field to sort by.
+"""
+
+SortOrder = Literal["asc", "desc"]
+"""
+The order to sort by.
+"""
+

 class Config(TypedDict, total=False):
    """Configuration options for a call."""
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph-sdk"
-version = "0.1.64"
+version = "0.1.65"
 description = "SDK for interacting with LangGraph API"
 authors = []
 license = "MIT"
Author	SHA1	Message	Date
lc-arjunandGitHub	03c34bf2cf	feat: assistants sorting sdk spec (#4484 )	2025-04-30 17:05:37 -04:00
Vadym BardaandGitHub	536c1c2bba	docs: remove prebuilt how-tos and add redirects to agents tab (#4485 )	2025-04-30 15:55:55 -04:00
Vadym Barda GitHub Lauren Hirata Singh	c5de8f4e50	docs(agents): update manage message history section (#4482 ) Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>	2025-04-30 19:11:45 +00:00
Vadym BardaandGitHub	d08ed5f42e	docs(agents): add store semantic search link (#4483 )	2025-04-30 15:08:18 -04:00
David DuongandGitHub	0269dd8818	release(langgraph): 0.4.1 (#4480 )	2025-04-30 20:24:48 +02:00
David DuongandGitHub	aa722ac084	release(sdk-js): 0.0.72 (#4481 )	2025-04-30 20:22:01 +02:00
Tat Dat Duong	91e9f12b54	release(sdk-js): 0.0.72	2025-04-30 20:19:30 +02:00
Tat Dat Duong	5779d9079f	release(langgraph): 0.4.1	2025-04-30 20:18:25 +02:00
David DuongandGitHub	e02c4b06db	feat(ui): add `merge` option to UI messages (#4473 ) - Add docs about (partial) streaming UI components from LLMs - Add missing support for "nostream" in LangGraph	2025-04-30 20:15:57 +02:00
Vadym BardaandGitHub	5b44886da0	docs(agents): add disable_streaming (#4475 )	2025-04-30 14:07:19 -04:00
Tat Dat Duong	be1af772f5	Fix lint	2025-04-30 20:04:48 +02:00
Vadym BardaandGitHub	33e409e12b	docs(agents): add model fallbacks (#4478 )	2025-04-30 17:56:58 +00:00
Vadym BardaandGitHub	ce2ba47aff	docs: cross link working w/ memory in tools (#4464 )	2025-04-30 13:52:10 -04:00
Tat Dat Duong	b3371a1d63	Add missing "nostream" support	2025-04-30 19:50:14 +02:00
Tat Dat Duong	5804e788d8	Add docs	2025-04-30 19:46:14 +02:00
Vadym BardaandGitHub	aa9651910c	docs: set package-mode to false for pyproject (#4477 )	2025-04-30 12:09:18 -04:00
Sydney RunkleandGitHub	49c10a9188	packaging: removing pydantic v1 support (#4448 ) Also moving over any logic from `langchain-core` to here as we slowly drop `langchain-core` dependency. Pydantic v1 is no longer undergoing active maintenance and v2 has been out for almost 2 years, so it seems like an appropriate time to drop v1 scar tissue.	2025-04-30 09:10:47 -04:00
Tat Dat Duong	1b6e12ef14	feat(ui): add `merge` option to UI messages	2025-04-30 11:52:47 +02:00
William Fu-Hinthorn	91de85a8e6	fix test	2025-04-29 15:55:54 -07:00
Sydney Runkle	ef1e1659c6	conditional for config	2025-04-29 18:22:18 -04:00
Sydney Runkle	c712e09fbc	conditional for config	2025-04-29 18:17:33 -04:00
Sydney Runkle	80dca9b9a5	removing remaining v1 logic	2025-04-29 15:11:37 -04:00
Sydney Runkle	da0994b741	removing langchain-core pydantic utilities	2025-04-29 14:31:28 -04:00
Sydney Runkle	64cfbb0d02	remove v1 test	2025-04-29 13:51:45 -04:00
Sydney Runkle	e99028cfc6	what would it look like to remove pydantic v1 support?	2025-04-28 21:44:17 -04:00
				`@@ -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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