Metadata-Version: 2.4
Name: mesh-health-dashboard
Version: 0.1.0
Summary: Real-time observability dashboard for multi-agent AI systems
Project-URL: Homepage, https://github.com/Zachr89/mesh-health-dashboard
Project-URL: Repository, https://github.com/Zachr89/mesh-health-dashboard
Author-email: Zach <zach@zachos.dev>
License: MIT
License-File: LICENSE
Keywords: agents,ai,dashboard,monitoring,observability
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.11
Requires-Dist: aiosqlite>=0.19.0
Requires-Dist: fastapi>=0.109.0
Requires-Dist: httpx>=0.26.0
Requires-Dist: jinja2>=3.1.3
Requires-Dist: pydantic-settings>=2.1.0
Requires-Dist: pydantic>=2.5.0
Requires-Dist: python-multipart>=0.0.6
Requires-Dist: sqlalchemy>=2.0.25
Requires-Dist: uvicorn[standard]>=0.27.0
Requires-Dist: websockets>=12.0
Provides-Extra: dev
Requires-Dist: black>=23.12.0; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.21.0; extra == 'dev'
Requires-Dist: pytest>=7.4.0; extra == 'dev'
Requires-Dist: ruff>=0.1.9; extra == 'dev'
Description-Content-Type: text/markdown

# mesh-health-dashboard

Real-time observability for multi-agent AI workflows. Monitor agent communication, task completion, token usage, and budget health in one dashboard.

## What is this?

mesh-health-dashboard is a lightweight observability layer for ZachOS's AI agent mesh infrastructure. It provides real-time visibility into agent interactions, resource consumption, and cost metrics—helping you debug workflow bottlenecks, prevent budget overruns, and optimize task routing without friction. Deploy it alongside your existing agents and start monitoring immediately.

## Features

- **Real-time Agent Monitoring** – Track communication patterns, task completion rates, and agent health status
- **Token & Cost Tracking** – Monitor token consumption and budget spend across your mesh with configurable alerts
- **Task Routing Insights** – Visualize task distribution and identify routing inefficiencies
- **Multi-agent Debugging** – Inspect agent interactions and workflow execution traces
- **Zero-friction Deployment** – Docker Compose setup ready to run alongside your infrastructure
- **REST API** – Programmatic access to metrics and health data
- **Lightweight Database** – Embedded metrics storage with minimal operational overhead

## Quick Start

### Prerequisites
- Docker & Docker Compose
- Python 3.10+ (for local development)

### Installation

**Via Docker Compose (Recommended)**

```bash
docker-compose up -d
```

The dashboard will be available at `http://localhost:8000`.

**Local Development**

```bash
# Install dependencies
pip install -e .

# Configure your mesh connection
export MESH_API_URL="http://your-mesh-endpoint:5000"

# Start the server
python -m mesh_health_dashboard.app
```

## Usage

### View the Dashboard

Navigate to `http://localhost:8000` after startup. The dashboard displays:
- Active agents and their current tasks
- Real-time token consumption
- Budget utilization and alerts
- Communication topology between agents

### Query Metrics via API

```bash
# Get agent status
curl http://localhost:8000/api/agents

# Get token usage summary
curl http://localhost:8000/api/metrics/tokens

# Get task completion rate
curl http://localhost:8000/api/metrics/tasks
```

### Configuration

Create a `.env` file or edit `src/mesh_health_dashboard/config.py`:

```env
MESH_API_URL=http://localhost:5000
BUDGET_LIMIT=1000
ALERT_TOKEN_THRESHOLD=80
DATABASE_PATH=./data/metrics.db
```

## Tech Stack

- **Backend**: FastAPI (Python)
- **Database**: SQLite
- **Frontend**: HTML5 + JavaScript (responsive, no build step)
- **Containerization**: Docker & Docker Compose
- **Testing**: pytest

## License

MIT