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README.md

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-Edit this `README.md` markdown file to author your organization card.
+# 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.
+
+## Contributing
+
+Contributions are welcome!  Please see the [GitHub repository](https://github.com/langchain-ai/agent-chat-ui) for issues and pull requests.
+
+## License
+
+This organisation is an opensource community project, always check the licences in the repositories + report any ambiguities.