Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ai-in-pm/AI-Agent-Ecosystem

A powerful, scalable ecosystem for managing and monitoring AI agents. This system provides a framework for deploying, managing, and monitoring multiple AI agents working together to accomplish complex tasks.
https://github.com/ai-in-pm/AI-Agent-Ecosystem

Last synced: 3 days ago
JSON representation

Host: GitHub
URL: https://github.com/ai-in-pm/AI-Agent-Ecosystem
Owner: ai-in-pm
License: mit
Created: 2024-12-26T05:48:52.000Z (13 days ago)
Default Branch: main
Last Pushed: 2024-12-26T05:51:26.000Z (13 days ago)
Last Synced: 2024-12-26T06:28:46.853Z (13 days ago)
Language: Python
Size: 0 Bytes
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

awesome_ai_agents - Ai-Agent-Ecosystem - A powerful, scalable ecosystem for managing and monitoring AI agents. This system provides a framework for deploying, managing, and monit… (Building / Deployment)
awesome_ai_agents - Ai-Agent-Ecosystem - A powerful, scalable ecosystem for managing and monitoring AI agents. This system provides a framework for deploying, managing, and monit… (Building / Deployment)

README

# AI Agent Ecosystem

## Features

- **Multiple Agent Types**
- ROI Optimization Agent
- Marketplace Manager Agent
- Analytics Agent

- **Real-time Monitoring**
- Health checks
- Performance metrics
- Resource utilization
- Error tracking

- **Modern Web Interface**
- Real-time dashboard
- Agent management
- Metrics visualization
- System configuration

## Architecture

The system consists of several components:

1. **Backend (FastAPI)**
- RESTful API endpoints
- Agent management
- Metrics collection
- Health monitoring

2. **Frontend (React)**
- Modern Material-UI interface
- Real-time updates
- Interactive dashboards
- Configuration management

3. **Monitoring Stack**
- Prometheus for metrics collection
- Grafana for visualization
- Custom dashboards

## Prerequisites

- Python 3.8+
- Node.js 14+
- Docker and Docker Compose
- Git

## Installation

1. Clone the repository:
```bash
git clone https://github.com/yourusername/ai-agent-ecosystem.git
cd ai-agent-ecosystem
```

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

3. Install frontend dependencies:
```bash
cd frontend
npm install
cd ..
```

4. Start the monitoring stack:
```bash
docker compose up -d
```

5. Start the backend server:
```bash
python -m src.main
```

6. Start the frontend development server:
```bash
cd frontend
npm start
```

## Configuration

1. Backend configuration is managed through environment variables. Copy `.env.sample` to `.env` and adjust as needed.

2. Frontend configuration can be modified in `frontend/.env`.

3. Monitoring stack configuration:
- Prometheus: `prometheus.yml`
- Grafana: `grafana/provisioning/`

## Usage

1. Access the web interface at http://localhost:3002

2. Monitor your agents:
- Dashboard: http://localhost:3002/
- Agents: http://localhost:3002/agents
- Metrics: http://localhost:3002/metrics
- Settings: http://localhost:3002/settings

3. Access monitoring tools:
- Prometheus: http://localhost:9090
- Grafana: http://localhost:3003 (admin/admin)

## API Documentation

- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc

## Development

1. **Adding New Agents**
- Extend the `BaseAgent` class
- Implement required methods
- Register in `AgentFactory`

2. **Adding Metrics**
- Use the `MetricsCollector` class
- Define new metrics in `metrics.py`
- Update Grafana dashboards

3. **Custom Dashboards**
- Add JSON definitions in `grafana/dashboards/`
- Update provisioning configuration

## Testing

```bash
pytest tests/
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments

- FastAPI for the backend framework
- React and Material-UI for the frontend
- Prometheus and Grafana for monitoring
- All contributors and users of this project