Agitrack is a traceability and analytics tool for AI agents that captures each agent interaction as a commit with full execution details.
The tool addresses the challenge of understanding and optimizing AI agent behavior by recording every agent turn as a traceable commit that includes the complete interaction trace, the model used, and token costs incurred. This approach creates a persistent, queryable record of agent activity that developers can examine to understand what their agents are doing and why. The system includes a live dashboard for real-time visibility into agent operations and a coach component that learns from your own session history to provide personalized guidance on improving both agent prompts and codebase quality.
Agitrack suits developers working with AI coding agents who need visibility into agent decision-making and want to optimize their agent interactions over time. It is particularly valuable for teams that want to reduce token waste, understand failure modes, and iteratively improve their agent configurations based on historical data rather than guesswork. The project is designed for those who view agent interactions as learnable patterns worth studying systematically.
The project shows active development with regular commits addressing core functionality and user-facing features. Work spans multiple areas including the dashboard interface, the underlying traceability infrastructure, and the coaching system that synthesizes insights from session data. The codebase receives ongoing refinement and expansion of its capabilities for capturing and analyzing agent behavior.
