## 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.