Raindrop AI's Workshop: Your Local Debugger for AI Agents – Everything You Need to Know

Raindrop AI has launched an open-source tool called Workshop, designed to give developers a local debugging and evaluation environment for AI agents. With the rise of agentic AI, debugging these autonomous systems has been a challenge. Workshop solves this by streaming real-time traces—every token, tool call, and decision—into a single SQLite database file, all running locally on your machine. This means no data leaves your environment, and you can inspect exactly what your agent was doing, including errors. Below, we answer key questions about this MIT-licensed tool, from installation to its standout self-healing eval loop.

What exactly is Workshop and why do developers need it?

Workshop is an open-source, MIT-licensed tool from Raindrop AI that functions as a local daemon and dashboard for debugging AI agents. Think of it as a debugger specifically for autonomous code agents—like Claude Code, Cursor, or Devin—that logs every action in real time. Until now, developers often had to rely on polling or external services to see what their agent was doing, which introduced latency and privacy concerns. Workshop eliminates both by storing all traces in a lightweight SQLite file on your machine. If your agent makes a mistake—say, it fails to ask a necessary follow-up question—you can open the dashboard at localhost:5899 and see the exact sequence of events, including tool calls and decisions. This immediate feedback loop is crucial for iterating on agents quickly and confidently.

How does Workshop provide real-time debugging without privacy risks?

Workshop runs entirely on your local machine. When you launch it, it starts a daemon that streams every token, tool call, and decision from your agent to a local dashboard—typically hosted at localhost:5899—the moment it occurs. This is real-time telemetry, meaning there is zero latency from polling external servers. All data is stored in a single .db file (SQLite) that takes up minimal memory. According to Raindrop’s CTO Ben Hylak, this addresses a growing developer concern about sending local traces to external servers for debugging. Your data never leaves your machine, so you maintain full privacy and data sovereignty. This is especially important for enterprises handling sensitive information or working under strict compliance requirements.

How can I install Workshop on my system?

Workshop is available for macOS, Linux, and Windows. The easiest way to install it is via a one-line shell command that automatically downloads the binary and configures your PATH for bash, zsh, and fish shells. For developers who prefer to build from source, the repository is hosted on GitHub and uses the Bun runtime. Detailed instructions are provided in the README. The installation process is designed to be frictionless, so you can start debugging your agents within minutes. Once installed, simply run the Workshop daemon, and it will start listening for traces from any compatible agent you have running locally.

What is the “self-healing eval loop” and how does it work?

This is Workshop’s standout feature. The self-healing eval loop allows coding agents like Claude Code to read traces from Workshop, write evaluations against the codebase, and autonomously fix broken code. Here’s a practical example: Suppose you have a veterinary assistant agent that fails to ask follow-up questions. Workshop captures the full trajectory of that interaction. Claude Code then reads the trace, writes a specific evaluation (eval) that detects the missing questions, identifies the logic error in the prompt or code, and re-runs the agent until all assertions pass. This creates a closed-loop debugging cycle where the agent improves itself without manual intervention. It’s a powerful way to iterate on agent behavior rapidly.

Which programming languages and frameworks does Workshop support?

Workshop is built to be language-agnostic. It is compatible with TypeScript, Python, Rust, and Go. On the framework side, it integrates seamlessly with popular SDKs such as the Vercel AI SDK, OpenAI, Anthropic, LangChain, LlamaIndex, and CrewAI. This broad compatibility means you can use Workshop with virtually any agent or orchestration framework you already have in your stack. Additionally, it works with various coding agents including Claude Code, Cursor, Devin, and OpenCode – likely any agent that can output traces in a supported format. This makes Workshop a universal debugger for the growing ecosystem of autonomous AI agents.

What are the licensing terms and how does the open-source nature benefit the community?

Workshop is released under the MIT License, one of the most permissive open-source licenses. This means it is completely free to use, modify, and distribute – both for personal projects and commercial applications. The MIT License encourages community contributions: developers can submit pull requests, report bugs, and suggest features. For enterprise users, this licensing ensures data sovereignty because you can run Workshop entirely on your own infrastructure without any vendor lock-in. Raindrop AI’s co-founder Ben Hylak stated that the tool was built to provide a “sane” way to debug agents locally, and the open-source release aims to change how developers build autonomous systems collaboratively.

How does Workshop ensure data privacy compared to cloud-based debugging tools?

Privacy was a core design principle of Workshop. Unlike cloud-based debugging tools that require you to send your agent’s traces to external servers, Workshop stores everything locally in a SQLite database file. No data ever leaves your machine unless you choose to share it. This is critical for developers working with sensitive data, proprietary logic, or in regulated industries. The real-time streaming also means there is no need to poll an external API for logs, reducing both latency and exposure. For teams that need to collaborate, the trace file can be safely shared via version control or encrypted channels, but the default operation is fully offline and private.