---
title: Multi Agent Chat
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: true
license: apache-2.0
tags:
- Agents-MCP-Hackathon
- mcp-server-track
- agent-demo-track
short_description: A multi-agent chat application and Gradio MCP Server
---
# Multi-Agent Chat

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Agents-MCP-Hackathon/multi-agent-chat)

This project is a multi-channel chat application where human users can interact with each other and with an intelligent, autonomous AI agent powered by Google's Gemini. The application is not just a chatbot; it's a fully-fledged multi-agent system designed to be both a compelling agentic demo and a functional MCP Server.

## 🎥 Video Demo
https://www.loom.com/share/f5673ab2b9e644b782b539afd6f06a64?sid=27578356-aa75-42e5-b786-86337c9b937e#Activity

## ✨ Core Features & Agentic Capabilities (Track 3)

This application showcases a powerful and creative use of AI agents in a collaborative environment.

### 1. **Autonomous & Proactive AI Agent (Gemini)**
The core of the application is an AI agent named Gemini with a distinct personality and behavior set. Unlike passive chatbots, this agent:
- **Listens Actively:** It continuously processes the conversation context.
- **Decides Autonomously:** It uses a "Two-Pass" reasoning architecture. A fast, logical **Triage Agent** first decides *if* participation is valuable, understanding nuances like typos ("Gmni") or implicit references ("what about you?").
- **Acts Contextually:** If the decision is to act, a creative **Actor Agent** formulates a human-like, contextual response, respecting its persona (no meta-comments, no inventing personal experiences).

### 2. **Multi-Agent System (MAS)**
The application is a true multi-agent environment where different agents coexist and interact:
- **Human Agents:** Users like "Lucy" and "Eliseu" who drive the conversation.
- **Gemini Participant Agent:** The main AI that enriches the discussion.
- **Specialized Tool Agents:**
    - A **Moderation Agent** that acts as a gatekeeper, filtering messages for safety before they are processed.
    - A **Summarization Agent** that can be invoked to provide a factual, "who-said-what" report of the conversation.
    - An **Opinion Agent** that analyzes the social dynamics and sentiment of the chat, providing a high-level, emotional takeaway.

### 3. **Dynamic & Persistent Environment**
- **Multi-Channel Chat:** Users can join different, persistent chat channels (e.g., `#general`, `#dev`).
- **Session Management:** The system handles user logins, ensures unique usernames within a channel (by appending numbers, e.g., `Lucy_2`), and announces when users join or leave, creating a realistic chat experience.

## 🛠️ MCP Server / Tool Capabilities (Track 1)

This Gradio application is fully compliant with the Model Control Protocol (MCP), acting as a powerful server that exposes its core functionalities as tools for other agents or applications.

### Exposed Tools

A client connecting to this Space's MCP endpoint will discover the following tools:

1.  **`login_user(channel: str, username: str) -> Tuple[str, str]`**
    - **Description:** Logs a user into a specific chat channel. Handles username uniqueness and returns the final username and channel.
    - **Use Case:** An external orchestrator agent could use this to programmatically add a bot or user to a conversation.

2.  **`exit_chat(channel: str, username: str)`**
    - **Description:** Logs a user out of a channel, removing them from the active user list.
    - **Use Case:** Allows for clean session management by external clients.

3.  **`send_message(channel: str, username: str, message: str) -> List[Dict]`**
    - **Description:** The primary interaction tool. It sends a message from a user to a channel, triggers the full AI agent logic (moderation, triage, response), and returns the complete, unformatted conversation history.
    - **Use Case:** This allows an external agent to fully participate in the chat, just like a human user.

4.  **`get_summary(channel: str, chat_history: List[Dict]) -> List[Dict]`**
    - **Description:** Invokes the Summarization Agent to analyze the provided history and generate a factual summary.
    - **Use Case:** An external agent could use this to quickly get up to speed on a long-running conversation without processing the entire transcript.

5.  **`get_opinion(channel: str, chat_history: List[Dict]) -> List[Dict]`**
    - **Description:** Invokes the Opinion Agent to analyze the conversation's social dynamics.
    - **Use Case:** A monitoring agent could use this tool to gauge the health or sentiment of a community conversation.

## 🚀 Future Work & Potential Improvements

This project serves as a robust foundation, but there are many exciting avenues for future development:

- **Enhanced Session Control:** Implement a more robust session management system.
- **Streaming Responses:** Implement true streaming for the Gemini responses (`stream=True` in the API call) and handle the streamed chunks in the Gradio UI. This would make the AI's responses appear token-by-token, feeling more immediate and interactive.
- **WebSockets for Real-Time UI:** Replace the `gr.Timer` polling mechanism with a full WebSocket implementation. This would provide instantaneous updates to all clients without any delay, creating a truly real-time experience and eliminating the need for a refresh loop.
- **Dynamic Tool Creation:** Allow users to define new "tool agents" on the fly by providing a prompt and a name, further expanding the MCP server's capabilities.
- **Persistent Storage:** Integrate a database (like SQLite or a vector database) to store chat histories permanently, so conversations are not lost when the Gradio app restarts.

## 🛠️ How to Run Locally

1.  **Clone the repository:**
    ```bash
    git clone https://huggingface.co/spaces/Agents-MCP-Hackathon/multi-agent-chat
    cd multi-agent-chat
    ```
2.  **Create a virtual environment:**
    ```bash
    python -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
    ```
3.  **Install dependencies:**
    ```bash
    pip install -r requirements.txt
    ```
4.  **Set up your environment variables:**
    - Create a file named `.env`.
    - Add your Google API key to it: `GOOGLE_API_KEY="your_api_key_here"`
5.  **Run the application:**
    ```bash
    python app.py
    ```