cylestio-local-server 0.1.19

A lightweight, self-hosted server for collecting, processing, and analyzing telemetry data from AI agents

Cylestio Local Server

A lightweight, self-hosted server for collecting, processing, and analyzing telemetry data from AI agents.

Overview

Cylestio Local Server provides a comprehensive solution for monitoring and analyzing AI agent behavior through telemetry data collection. This server offers powerful tools for tracking agent performance, usage patterns, and identifying potential issues.

Key Features

• ✅ Simple REST API for submitting telemetry events
• ✅ Metrics aggregation and retrieval for performance analysis
• ✅ Trace and span management for distributed operations
• ✅ Filtering and querying capabilities for data exploration
• ✅ Dashboard-ready metrics for visualization
• ✅ LLM interaction tracking for understanding AI behavior

Table of Contents

• Installation
• Quick Start
• Configuration
• API Reference
• Database Design
• Development
• Contributing
• License

Installation

Option 1: Install from PyPI (Recommended)

pip install cylestio-local-server

Option 2: Install from Source

# Clone the repository
git clone https://github.com/cylestio/cylestio-local-server.git
cd cylestio-local-server

# Install in development mode
pip install -e .

Quick Start

Starting the server (from PyPI installation)

# Start with default settings (port 8000, database in current directory)
cylestio-server

# Specify host, port and database path
cylestio-server --host 127.0.0.1 --port 9000 --db-path /path/to/database.db

# Enable development mode with auto-reload
cylestio-server --reload --debug

Starting the server (from source)

# From the repository root
python -m src.main

# With custom port
python -m src.main --port 9000

Verify Installation

1. Open your browser and navigate to:

   http://localhost:8000/docs
   

   This will open the Swagger UI documentation where you can explore and test the API.

2. Test the health endpoint:

   curl http://localhost:8000/v1/health
   

   You should see a response like:

   {"status":"ok"}
   

Configuration

The server can be configured using environment variables, command-line arguments, or a .env file in the root directory.

Configuration Options

Option	Description	Default
HOST	Server host address	0.0.0.0
PORT	Server port	8000
DEBUG	Enable debug mode	false
DATABASE_URL	Database connection string	sqlite:///cylestio.db
API_PREFIX	Prefix for API routes	/api
RATE_LIMIT_PER_MINUTE	API rate limit per client	100
LOG_LEVEL	Logging level	INFO

Example .env File

# Server settings
HOST=0.0.0.0
PORT=8000
DEBUG=false

# Database settings
DATABASE_URL=sqlite:///cylestio.db

# API settings
API_PREFIX=/api
RATE_LIMIT_PER_MINUTE=100

# Logging settings
LOG_LEVEL=INFO

API Reference

The Cylestio Local Server provides a comprehensive API for submitting telemetry events and retrieving metrics.

Main Endpoint Categories

• Telemetry Submission: Submit telemetry events from AI agents
• Agent Metrics: Get metrics for specific agents
• LLM Interactions: Track and analyze LLM usage
• Tool Usage: Monitor tool invocations and performance
• Session Management: Track user sessions and interactions
• Trace Analysis: Analyze execution traces for debugging

For detailed API documentation, refer to the Swagger UI at /docs when the server is running.

Database Design

The server uses a relational database (SQLite by default) with a carefully designed schema optimized for telemetry data:

• Events: Base table for all telemetry events
• LLM Interactions: Tracks LLM requests and responses
• Tool Executions: Records tool invocations and results
• Sessions: Groups related events by session
• Spans: Manages distributed tracing data

The database schema supports:

• JSON attribute storage for flexible metadata
• Relationship tracking between related events
• Efficient querying for analytics

System Architecture

The system is built with a modular architecture consisting of four main layers:

1. REST API Layer: Handles HTTP requests and responses
2. Processing Layer: Validates and normalizes telemetry data
3. Analysis Layer: Computes metrics and insights
4. Database Layer: Persists and retrieves data

This layered approach provides a clean separation of concerns and makes the system maintainable and extensible.

Development

Running Tests

# Run all tests
python -m pytest

# Run with coverage report
python -m pytest --cov=src

Code Structure

• src/api/: API endpoints and controllers
• src/database/: Database models and repositories
• src/processing/: Data processing and validation
• src/analysis/: Metrics computation and analysis
• src/models/: Data models and schemas
• src/utils/: Utility functions and helpers
• src/config/: Configuration management
• src/services/: Business logic services

Contributing

We welcome contributions from the community! Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests to ensure they pass
5. Commit your changes (git commit -m 'Add some amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Please read our Contributing Guidelines for more details.

License

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

Acknowledgements

• FastAPI for the powerful API framework
• SQLAlchemy for the ORM capabilities
• The AI agent monitoring community for feedback and support