---
title: TraceMind AI
emoji: 🔍
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
short_description: AI agent evaluation with MCP-powered intelligence
pinned: false
tags:
  - mcp-in-action-track-enterprise
  - agent-evaluation
  - mcp-client
  - leaderboard
  - gradio
---

# 🔍 TraceMind-AI

Agent Evaluation Platform with MCP-Powered Intelligence

## Overview

TraceMind-AI is a comprehensive platform for evaluating AI agent performance across different models, providers, and configurations. It provides real-time insights, cost analysis, and detailed trace visualization powered by the Model Context Protocol (MCP).

## Features

- **📊 Real-time Leaderboard**: Live evaluation data from HuggingFace datasets
- **🤖 MCP Integration**: AI-powered analysis using remote MCP servers
- **💰 Cost Estimation**: Calculate evaluation costs for different models and configurations
- **🔍 Trace Visualization**: Detailed OpenTelemetry trace analysis
- **📈 Performance Metrics**: GPU utilization, CO2 emissions, token usage tracking

## MCP Integration

TraceMind-AI demonstrates enterprise MCP client usage by connecting to [TraceMind-mcp-server](https://huggingface.co/spaces/kshitijthakkar/TraceMind-mcp-server) via the Model Context Protocol.

**MCP Tools Used:**
- `analyze_leaderboard` - AI-generated insights about evaluation trends
- `estimate_cost` - Cost estimation with hardware recommendations
- `debug_trace` - Interactive trace analysis and debugging
- `compare_runs` - Side-by-side run comparison
- `analyze_results` - Test case analysis with optimization recommendations

## Quick Start

### Prerequisites
- Python 3.10+
- HuggingFace account (for authentication)
- HuggingFace token (optional, for private datasets)

### Installation

1. Clone the repository:
```bash
git clone https://github.com/Mandark-droid/TraceMind-AI.git
cd TraceMind-AI
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

3. Configure environment:
```bash
cp .env.example .env
# Edit .env with your configuration
```

4. Run the application:
```bash
python app.py
```

Visit http://localhost:7860

## Configuration

Create a `.env` file with the following variables:

```env
# HuggingFace Configuration
HF_TOKEN=your_token_here

# MCP Server URL
MCP_SERVER_URL=https://kshitijthakkar-tracemind-mcp-server.hf.space/gradio_api/mcp/

# Dataset Configuration
LEADERBOARD_REPO=kshitijthakkar/smoltrace-leaderboard

# Development Mode (optional - disables OAuth for local testing)
DISABLE_OAUTH=true
```

## Data Sources

TraceMind-AI loads evaluation data from HuggingFace datasets:

- **Leaderboard**: Aggregate statistics for all evaluation runs
- **Results**: Individual test case results
- **Traces**: OpenTelemetry trace data
- **Metrics**: GPU metrics and performance data

## Architecture

### Project Structure

```
TraceMind-AI/
├── app.py                 # Main Gradio application
├── data_loader.py         # HuggingFace dataset integration
├── mcp_client/            # MCP client implementation
│   ├── client.py          # Async MCP client
│   └── sync_wrapper.py    # Synchronous wrapper
├── utils/                 # Utilities
│   ├── auth.py            # HuggingFace OAuth
│   └── navigation.py      # Screen navigation
├── screens/               # UI screens
├── components/            # Reusable components
└── styles/                # Custom CSS
```

### MCP Client Integration

TraceMind-AI uses the MCP Python SDK to connect to remote MCP servers:

```python
from mcp_client.sync_wrapper import get_sync_mcp_client

# Initialize MCP client
mcp_client = get_sync_mcp_client()
mcp_client.initialize()

# Call MCP tools
insights = mcp_client.analyze_leaderboard(
    metric_focus="overall",
    time_range="last_week",
    top_n=5
)
```

## Usage

### Viewing the Leaderboard

1. Log in with your HuggingFace account
2. Navigate to the "Leaderboard" tab
3. Click "Load Leaderboard" to fetch the latest data
4. View AI-powered insights generated by the MCP server

### Estimating Costs

1. Navigate to the "Cost Estimator" tab
2. Enter the model name (e.g., `openai/gpt-4`)
3. Select agent type and number of tests
4. Click "Estimate Cost" for AI-powered analysis

### Viewing Trace Details

1. Select an evaluation run from the leaderboard
2. Click on a specific test case
3. View detailed OpenTelemetry trace visualization
4. Ask questions about the trace using MCP-powered analysis

## Technology Stack

- **UI Framework**: Gradio 5.49.1
- **MCP Protocol**: MCP integration via Gradio
- **Data**: HuggingFace Datasets API
- **Authentication**: HuggingFace OAuth
- **AI**: Google Gemini 2.5 Flash (via MCP server)

## Development

### Running Locally

```bash
# Install dependencies
pip install -r requirements.txt

# Set development mode (optional - disables OAuth)
export DISABLE_OAUTH=true

# Run the app
python app.py
```

### Running on HuggingFace Spaces

This application is configured for deployment on HuggingFace Spaces using the Gradio SDK. The `app.py` file serves as the entry point.

## Documentation

For detailed implementation documentation, see:
- [Data Loader API](data_loader.py) - Dataset loading and caching
- [MCP Client API](mcp_client/client.py) - MCP protocol integration
- [Authentication](utils/auth.py) - HuggingFace OAuth integration

## Demo Video

[Link to demo video showing the application in action]

## Social Media

[Link to social media post about this project]

## License

MIT License - See LICENSE file for details

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## Acknowledgments

- **MCP Team** - For the Model Context Protocol specification
- **Gradio Team** - For Gradio 6 with MCP integration
- **HuggingFace** - For Spaces hosting and dataset infrastructure
- **Google** - For Gemini API access

## Links

- **Live Demo**: https://huggingface.co/spaces/kshitijthakkar/TraceMind-AI
- **MCP Server**: https://huggingface.co/spaces/kshitijthakkar/TraceMind-mcp-server
- **GitHub**: https://github.com/Mandark-droid/TraceMind-AI
- **MCP Specification**: https://modelcontextprotocol.io

---

**MCP's 1st Birthday Hackathon Submission**
*Track: MCP in Action - Enterprise*