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---
title: ChatUI
emoji: 🧠
colorFrom: yellow
colorTo: indigo
sdk: static
pinned: false
short_description: Langchain / LangGraph Chat UI
---

## LangGraph Agent Chat UI: Your Gateway to Agent Interaction

The Agent Chat UI,, is a React/Vite application that provides a clean, chat-based interface for interacting with your LangGraph agents. Here's why it's a valuable tool:

*   **Easy Connection:** Connect to local or deployed LangGraph agents with a simple URL and graph ID.
*   **Intuitive Chat:**  Interact naturally with your agents, sending and receiving messages in a familiar chat format.
*   **Visualize Agent Actions:** See tool calls and their results rendered directly in the UI.
*   **Human-in-the-Loop Made Easy:** Seamlessly integrate human input using LangGraph's `interrupt` feature. The UI handles the presentation and interaction, allowing for approvals, edits, and responses.
*   **Explore Execution Paths:** Use the UI to travel through time, inspect checkpoints, and fork conversations, all powered by LangGraph's state management.
*   **Debug and Understand:** Inspect the full state of your LangGraph thread at any point.

## Get Started with the Agent Chat UI (and LangGraph!)

You have several options to start using the UI:

### 1. Try the Deployed Version (No Setup Required!)

*   **Visit:** [agentchat.vercel.app](https://agentchat.vercel.app/)
*   **Connect:** Enter your LangGraph deployment URL and graph ID (the `path` you set with `langserve.add_routes`). If using a production deployment, also include your LangSmith API key.
*   **Chat!** You're ready to interact with your agent.

### 2. Run Locally (for Development and Customization)

*   **Option A: Clone the Repository:**
    ```bash
    git clone https://github.com/langchain-ai/agent-chat-ui.git
    cd agent-chat-ui
    pnpm install # Or npm install/yarn install
    pnpm dev     # Or npm run dev/yarn dev
    ```
*   **Option B: Quickstart with `npx`:**
    ```bash
    npx create-agent-chat-app
    cd agent-chat-app
    pnpm install # Or npm install/yarn install
    pnpm dev     # Or npm run dev/yarn dev
    ```
    
    Open your browser to `http://localhost:5173` (or the port indicated in your terminal).

# LangGraph Agent Chat UI

This project provides a simple, intuitive user interface (UI) for interacting with LangGraph agents. It's built with React and Vite, offering a responsive chat-like experience for testing and demonstrating your LangGraph deployments.  It's designed to work seamlessly with LangGraph's core concepts, including checkpoints, thread management, and human-in-the-loop capabilities.

## Features

*   **Easy Connection:** Connect to both local and production LangGraph deployments by simply providing the deployment URL and graph ID (the path used when defining the graph).
*   **Chat Interface:** Interact with your agents through a familiar chat interface, sending and receiving messages in real-time.  The UI manages the conversation thread, automatically using checkpoints for persistence.
*   **Tool Call Rendering:** The UI automatically renders tool calls and their results, making it easy to visualize the agent's actions. This is compatible with LangGraph's [tool calling and function calling capabilities](https://python.langchain.com/docs/guides/tools/custom_tools).
*   **Human-in-the-Loop Support:** Seamlessly integrate human intervention using LangGraph's `interrupt` function. The UI presents a dedicated interface for reviewing, editing, and responding to interrupt requests (e.g., for approval or modification of agent actions), following the standardized schema.
*   **Thread History:** View and navigate through past chat threads, enabling you to review previous interactions. This leverages LangGraph's checkpointing for persistent conversation history.
*   **Time Travel and Forking:** Leverage LangGraph's powerful state management features, including [checkpointing](https://python.langchain.com/docs/modules/agents/concepts#checkpointing) and thread manipulation.  Run the graph from specific checkpoints, explore different execution paths, and edit previous messages.
*   **State Inspection:** Examine the current state of your LangGraph thread for debugging and understanding the agent's internal workings. This allows you to inspect the full state object managed by LangGraph.
*   **Multiple Deployment Options:**
    *   **Deployed Site:** Use the hosted version at [agentchat.vercel.app](https://agentchat.vercel.app/)
    *   **Local Development:** Clone the repository and run it locally for development and customization.
    *   **Quick Setup:** Use `npx create-agent-chat-app` for a fast, streamlined setup.
* **Langsmith API key:** When utilizing a product deployment you must provide an Langsmith API key.

## Getting Started

There are three main ways to use the Agent Chat UI:

### 1. Using the Deployed Site (Easiest)

1.  **Navigate:** Go to [agentchat.vercel.app](https://agentchat.vercel.app/).
2.  **Enter Details:**
    *   **Deployment URL:** The URL of your LangGraph deployment (e.g., `http://localhost:2024` for a local deployment using LangServe, or the URL provided by LangSmith for a production deployment).
    *   **Assistant / Graph ID:** The path of the graph you want to interact with (e.g., `chat`, `email_agent`). This is defined when adding routes with `add_routes(..., path="/your_path")`.
     *   **LangSmith API Key** (Production Deployments Only):  If you are connecting to a deployment hosted on LangSmith, you will need to provide your LangSmith API key for authentication.  *This is NOT required for local LangGraph servers.*  The key is stored locally in your browser's storage.
3.  **Click "Continue":** You'll be taken to the chat interface, ready to interact with your agent.

### 2. Local Development (Full Control)

1.  **Clone the Repository:**

    ```bash
    git clone https://github.com/langchain-ai/agent-chat-ui.git
    cd agent-chat-ui
    ```

2.  **Install Dependencies:**

    ```bash
    pnpm install  # Or npm install, or yarn install
    ```

3.  **Start the Development Server:**

    ```bash
    pnpm dev  # Or npm run dev, or yarn dev
    ```

4.  **Open in Browser:** The application will typically be available at `http://localhost:5173` (the port may vary; check your terminal output). Follow the instructions in "Using the Deployed Site" to connect to your LangGraph.

### 3. Quick Setup with `npx create-agent-chat-app`

This method creates a new project directory with the Agent Chat UI already set up.

1.  **Run the Command:**

    ```bash
    npx create-agent-chat-app
    ```

2.  **Follow Prompts:**  You'll be prompted for a project name (default is `agent-chat-app`).

3.  **Navigate to Project Directory:**

    ```bash
    cd agent-chat-app
    ```

4.  **Install and Run:**

    ```bash
    pnpm install # Or npm install, or yarn install
    pnpm dev     # Or npm run dev, or yarn dev
    ```

5.  **Open in Browser:**  The application will be available at `http://localhost:5173`.  Follow the instructions in "Using the Deployed Site" to connect.

## LangGraph Setup (Prerequisites)

Before using the Agent Chat UI, you need a running LangGraph agent served via LangServe. Below are examples using both a simple agent and an agent with human-in-the-loop.

### Basic LangGraph Example (Python)

```python
# agent.py (Example LangGraph agent - Python)
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.prebuilt import create_agent_executor
from langchain_core.tools import tool

# FastAPI and LangServe for serving the graph
from fastapi import FastAPI
from langserve import add_routes


@tool
def get_weather(city: str):
    """
    Gets the weather for a specified city
    """
    if city.lower() == "new york":
        return "The weather in New York is nice today with a high of 75F."
    else:
        return "The weather for that city is not supported"


# Define the tools
tools = [get_weather]

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant"),
        MessagesPlaceholder(variable_name="messages"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)

model = ChatOpenAI(temperature=0).bind_tools(tools)


@chain
def transform_messages(data):
    messages = data["messages"]
    if not isinstance(messages[-1], HumanMessage):
        messages.append(
            AIMessage(
                content="I don't know how to respond to messages other than a final answer"
            )
        )
    return {"messages": messages}


agent = (
    {
        "messages": transform_messages,
        "agent_scratchpad": lambda x: [],  # No tools in this simple example
    }
    | prompt
    | model
)

# Wrap the agent in a RunnableGraph
app = create_agent_executor(agent, tools)

# Serve the graph using FastAPI and langserve
fastapi_app = FastAPI(
    title="LangGraph Agent",
    version="1.0",
    description="A simple LangGraph agent server",
)

# Mount LangServe at the /agent endpoint
add_routes(
    fastapi_app,
    app,
    path="/chat",  # Matches the graph ID we'll use in the UI
)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(fastapi_app, host="localhost", port=2024)

```
To run this example:

1.  Save the code as `agent.py`.
2.  Install necessary packages:  `pip install langchain langchain-core langchain-openai langgraph fastapi uvicorn "langserve[all]"` (add any other packages for your tools).
3.  Set your OpenAI API key: `export OPENAI_API_KEY="your-openai-api-key"`
4.  Run the script: `python agent.py`
5. Your LangGraph agent will be running at `http://localhost:2024/chat`, and the graph ID to enter into the ui is `chat`.

### LangGraph with Human-in-the-Loop Example (Python)

```python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import chain
from langchain_openai import ChatOpenAI
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.prebuilt import create_agent_executor, ToolInvocation, interrupt
from langchain_core.tools import tool
from fastapi import FastAPI
from langserve import add_routes


@tool
def write_email(subject: str, body: str, to: str):
    """
    Drafts an email with a specified subject, body and recipient
    """
    print(f"Writing email with subject '{subject}' to '{to}'")  # Debugging
    return f"Draft email to {to} with subject {subject} sent."


tools = [write_email]

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant that drafts emails."),
        MessagesPlaceholder(variable_name="messages"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)


model = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview").bind_tools(tools)


@chain
def transform_messages(data):
    messages = data["messages"]
    if not isinstance(messages[-1], HumanMessage):
        messages.append(
            AIMessage(
                content="I don't know how to respond to messages other than a final answer"
            )
        )
    return {"messages": messages}



def handle_interrupt(state):
    """Handles human-in-the-loop interruptions."""
    print("---INTERRUPT---")  # Debugging
    messages = state["messages"]
    last_message = messages[-1]

    if isinstance(last_message, AIMessage) and isinstance(
        last_message.content, list
    ):
        # Find the tool call
        for msg in last_message.content:
            if isinstance(msg, ToolInvocation):
                tool_name = msg.name
                tool_args = msg.args
                if tool_name == "write_email":
                    # Construct the human interrupt request
                    interrupt_data = {
                        "type": "interrupt",
                        "args": {
                            "type": "response",
                            "studio": {  # optional
                                "subject": tool_args["subject"],
                                "body": tool_args["body"],
                                "to": tool_args["to"],
                            },
                            "description": "Response Instruction: \n\n- **Response**: Any response submitted will be passed to an LLM to rewrite the email.  It can rewrite the email body, subject, or recipient.\n\n- **Edit or Accept**: Editing/Accepting the email.",
                        },
                    }
                    # Call the interrupt function and return the new state
                    return interrupt(messages, interrupt_data)
    return {"messages": messages}


agent = (
    {
        "messages": transform_messages,
        "agent_scratchpad": lambda x: x.get("agent_scratchpad", []),
    }
    | prompt
    | model
    | handle_interrupt  # Add the interrupt handler
)

# Wrap the agent in a RunnableGraph
app = create_agent_executor(agent, tools)

# Serve the graph using FastAPI and langserve
fastapi_app = FastAPI(
    title="LangGraph Agent",
    version="1.0",
    description="A simple LangGraph agent server",
)

# Mount LangServe at the /agent endpoint
add_routes(
    fastapi_app,
    app,
    path="/email_agent",  # Matches the graph ID we'll use in the UI
)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(fastapi_app, host="localhost", port=2024)

```
To run this example:

1.  Save the code as `agent.py`.
2.  Install necessary packages:  `pip install langchain langchain-core langchain-openai langgraph fastapi uvicorn "langserve[all]"` (add any other packages for your tools).
3.  Set your OpenAI API key: `export OPENAI_API_KEY="your-openai-api-key"`
4.  Run the script: `python agent.py`
5. Your LangGraph agent will be running at `http://localhost:2024/email_agent`, and the graph ID to enter into the ui is `email_agent`.

## Key Concepts (LangGraph Integration)

*   **Messages Key:** The Agent Chat UI expects your LangGraph state to include a `messages` key, which holds a list of `langchain_core.messages.BaseMessage` instances (e.g., `HumanMessage`, `AIMessage`, `SystemMessage`, `ToolMessage`).  This is standard practice in LangChain and LangGraph for conversational agents.
*   **Checkpoints:** The UI automatically utilizes LangGraph's checkpointing mechanism to save and restore the conversation state. This ensures that you can resume conversations and explore different branches without losing progress.
*    **`add_routes` and `path`:**  The `path` argument in `add_routes` (from `langserve`) determines the "Graph ID" that you'll enter in the UI.  This is crucial for the UI to connect to the correct LangGraph endpoint.
*   **Tool Calling:** If you use `bind_tools` with your LLM, tool calls and tool results will be rendered in the UI, with clear labels showing the function call and the response.

## Human-in-the-Loop Details

The Agent Chat UI supports human-in-the-loop interactions using the standard LangGraph interrupt schema. Here's how it works:

1.  **Interrupt Schema:** Your LangGraph agent should call the `interrupt` function (from `langgraph.prebuilt`) with a specific schema to pause execution and request human input. The schema should include:
    *   `type`: `interrupt`.
    *   `args`: A dictionary containing information about the interruption. This is where you provide the data the human needs to review (e.g., a draft email, a proposed action).
        *   `type`: Can be one of `"response"`, `"accept"`, or `"ignore"`. This indicates the type of human interaction expected.
        *   `args`: Further arguments specific to the interrupt type. For instance, if the interrupt type is `response`, the `args` could contain a message to give to the user.
        *   `studio`: *Optional.* If included, this must contain `subject`, `body`, and `to` keys for interrupt requests.
        *   `description`: *Optional.* If used, this provides a static prompt to the user that displays the fields the human needs to complete.
    *   `name` (optional): A name for the interrupt.
    *   `id` (optional): A unique identifier for the interrupt.

2.  **UI Rendering:** When the Agent Chat UI detects an interrupt with this schema, it will automatically render a user-friendly interface for human interaction. This interface allows the user to:
    *   **Inspect:** View the data provided in the `args` of the interrupt (e.g., the content of a draft email).
    *   **Edit:** Modify the data (if the interrupt schema allows for it).
    *   **Respond:** Provide a response (if the interrupt type is `"response"`).
    *   **Accept/Reject:** Approve or reject the proposed action (if the interrupt type is `"accept"`).
    *   **Ignore:** Ignore the interrupt (if the interrupt type is `"ignore"`).

3.  **Resuming Execution:** After the human interacts with the interrupt, the UI sends the response back to the LangGraph via LangServe, and execution resumes.