Raindrop's Workshop: An Open-Source Debugger and Evaluator for AI Agents — Now Available

Introduction

Developers working with AI agents have long faced a challenge: understanding exactly what their autonomous systems are doing in real time, without sacrificing privacy or adding latency. Raindrop AI, an observability startup, has addressed this with the launch of Workshop, an open-source tool (MIT licensed) that provides a local debugger and evaluation environment specifically designed for AI agents. Whether you are building with Claude Code, Cursor, or any other agent framework, Workshop offers a lightweight, single-file SQLite database to capture every trace of agent activity, streamed instantly to a local dashboard.

This article explores Workshop's core features, installation process, standout functionality like the self-healing eval loop, compatibility with popular stacks, and the implications of its open-source licensing.

What Is Workshop?

Workshop runs as a local daemon and user interface that streams every token, tool call, and decision made by an AI agent to a dashboard typically hosted at localhost:5899. As soon as an event occurs, developers can see it in real time — including errors, mistakes, and logic gaps. All data is stored in a single .db file using Structured Query Language (SQLite), occupying minimal disk space, as noted by Raindrop co-founder and CTO Ben Hylak (a former Apple and SpaceX engineer).

Installation and Platform Support

Workshop is available for macOS, Linux, and Windows. Developers can install it with a one-line shell command that automatically places the binary and configures PATH for bash, zsh, and fish shells. Alternatively, those who prefer building from source can clone the GitHub repository, which uses the Bun runtime. This flexibility ensures Workshop fits into diverse development workflows.

Key Features and Functionality

Real-Time Telemetry and Privacy

Traditional debugging often relies on logging and polling, which introduces latency and raises privacy concerns when traces are sent to external servers. Workshop eliminates both issues by streaming telemetry directly to a local dashboard. Every action — from token generation to tool calls — is visible the moment it happens, allowing developers to pinpoint issues immediately without exposing sensitive data to third parties.

The Self-Healing Eval Loop

The standout feature of Workshop is its self-healing eval loop. This integration allows coding agents like Claude Code to read Workshop traces, write evaluations against the codebase, and autonomously fix broken code. For example, consider a veterinary assistant agent that fails to ask necessary follow-up questions during a consultation. Workshop captures the complete trajectory of the agent's decisions. Claude Code then reads this trace, writes a specific evaluation, identifies the logic error in the prompt or code, and re-runs the agent until all assertions pass. This creates a closed-loop debugging process that reduces manual intervention and accelerates development.

Compatibility and Ecosystem Integration

Language Support

Workshop supports a broad range of programming languages, including TypeScript, Python, Rust, and Go. This makes it suitable for projects built with diverse tech stacks.

Framework and SDK Integration

The tool integrates seamlessly with popular AI development frameworks and SDKs:

• Vercel AI SDK
• OpenAI
• Anthropic
• LangChain
• LlamaIndex
• CrewAI

This broad compatibility ensures Workshop can be added to existing agent pipelines with minimal friction.

Agent Compatibility

Workshop works out of the box with coding agents such as Claude Code, Cursor, Devin, and OpenCode. Developers using these tools can immediately benefit from Workshop's real-time visibility and self-healing capabilities.

Licensing and Community Impact

Raindrop has released Workshop under the MIT License, making it fully open source and free for all users. This permissive licensing encourages community contributions and allows enterprises to maintain full data sovereignty by keeping traces on local machines. According to Hylak, Workshop was built to provide a “sane” way to debug agents locally, transforming how his team and early customers build autonomous systems.

To celebrate the launch, Raindrop is offering limited-edition physical merchandise for early adopters. Developers are encouraged to try Workshop and contribute to its development on GitHub.

Conclusion

Raindrop's Workshop addresses a critical gap in AI agent development: local, real-time observability without compromising privacy. Its self-healing eval loop, broad compatibility, and open-source model make it a valuable tool for any developer working with autonomous agents. By providing a lightweight, single-file trace database and an intuitive local dashboard, Workshop empowers developers to build more reliable and transparent agent systems.