Building Retrace - execution replay for AI agents. Record, replay, and diff agent runs.

Yashwanth Bogam • Peerlist

<h3>AI Agent Execution Replay and Debugging</h3>
Retrace provides a comprehensive solution for AI agent development, enabling developers to record, replay, fork, and share agent executions. It offers deep visibility into every LLM call, tool invocation, and error, facilitating rapid debugging and iteration.
<ul>
<li>Trace Recording: Automatically capture all LLM calls, tool usage, costs, and errors with a simple decorator.</li>
<li>Step-by-step Replay: Re-run recorded agent executions to understand behavior and pinpoint issues.</li>
<li>Execution Forking: Branch from any specific step in a trace, modify inputs, and cascade-replay the subsequent execution to test fixes.</li>
<li>Prove the Fix: Verify the effectiveness of changes by re-running a fix against a failed execution and receiving a verdict.</li>
<li>CI/CD Integration: Implement evaluation gates in CI/CD pipelines to automatically fail builds when agent behavior regresses.</li>
<li>Runtime Guardrails: Enforce limits on cost, loops, or steps to prevent runaway agents and unexpected expenses.</li>
<li>Multi-Agent System Support: Understand complex interactions within multi-agent systems through session grouping and agent topology visualization.</li>
<li>Framework Agnostic: Works seamlessly with Python and TypeScript agents, supporting popular frameworks like LangChain, CrewAI, and LlamaIndex.</li>
</ul>
Retrace offers a free tier with 1,000 traces per month, making it accessible for experimentation and early-stage development. Paid tiers provide increased trace limits, longer retention periods, and advanced features for production environments.