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Raindrop Workshop (GitHub Repo)
Raindrop Workshop, a new local debugger for AI agents, allows Claude Code to watch traces, write evaluations, and self-heal code.
What: Raindrop Workshop is an open-source tool compatible with TypeScript, Python, Go, and Rust that provides livestreamed traces of AI agent activity, coding-agent integration (specifically with Claude Code, Codex, Devin, Cursor, OpenCode), a self-healing evaluation loop, and local replay capabilities for debugging. It works with popular SDKs like Vercel AI SDK, OpenAI Agents SDK, and LangChain.
Why it matters: This tool addresses a critical need in AI agent development by providing advanced debugging and self-correction mechanisms, moving beyond traditional observability to enable agents to identify and fix their own errors locally.
Takeaway: Developers working with AI agents, particularly those using Claude Code or similar models, should explore Raindrop Workshop for local debugging and self-healing evaluation loops.
Deep dive
- Raindrop Workshop is an open-source local debugger for AI agents.
- It offers livestreamed traces, showing every token, tool call, and decision an agent makes.
- It integrates with coding agents like Claude Code, Codex, Devin, Cursor, and OpenCode.
- A key feature is its "self-healing eval loop," where an agent can write an eval, run itself, detect failures, fix the code, and re-run until assertions pass.
- It supports local replay to debug production traces.
- Compatible with TypeScript, Python, Go, and Rust, and various AI SDKs (Vercel AI, OpenAI Agents, Anthropic, LangChain, etc.) and providers (AWS Bedrock, Azure OpenAI, Vertex AI).
- Installation is via a
curlcommand or building from source withbun. - The UI runs locally via
http://localhost:5899. - Licensed under MIT.
Decoder
- AI Agent: An artificial intelligence program designed to autonomously perform tasks by observing its environment, processing information, making decisions, and taking actions.
- Trace: A record of the sequence of operations, events, and data flows within a program or system, used for debugging and understanding execution.
- Eval loop: A process where an AI agent generates code, runs an evaluation or test against it, identifies errors, and then iteratively attempts to fix the code based on the evaluation feedback.
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