Observability startup Raindrop AI’s new open source, MIT Licensed “Workshop” tool, launched today, gives developers something that they’ve likely wanted, perhaps subconsciously, since the agentic AI era kicked off in earnest last year: a local debugger and evaluation tool specifically designed for AI agents, allowing devs to see all the traces of what their agent has been doing in a single, lightweight Structured Query Language (SQL) database file (.db)It functions as a local daemon and UI that streams every token, tool call, and decision to a local dashboard—typically hosted at localhost:5899—the moment it occurs. By visiting their localhost, developers can then see everything their agent was up to — including mistakes or errors — and identify what went wrong, when, and ideally, discern why. It’s all stored in a single .db file, which takes up relatively little memory, according to a X direct message VentureBeat received from Ben Hylak, Raindrop’s co-founder and CTO (and a former Apple and SpaceX engineer). This real-time telemetry eliminates the latency of traditional polling and addresses a growing developer concern regarding the privacy of sending local traces to external servers.The tool is available for macOS, Linux, and Windows. It can be installed through a one-line shell command that automates binary placement and PATH configuration for bash, zsh, and fish shells. For developers who prefer to build from source, the repository is hosted on GitHub and utilizes the Bun runtime. The product: establishing a self-healing eval loopThe platform’s standout feature is the “self-healing eval loop,” which allows coding agents like Claude Code to read traces, write evals against the codebase, and fix broken code autonomously. In a practical application, if a …
