Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developers running AI coding agents (Claude Code, Codex, Devin, Cursor, OpenCode) who need to see exactly what the agent is doing in real time and turn observed failures into permanent regression tests: a `raindrop` binary that opens a local browser debugger the moment the agent runs, a `/instrument-agent` skill that wires the agent into the debugger, and a self-healing eval loop where
Who should use it
Who should skip it
Skip raindrop-ai/workshop if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
raindrop-ai/workshop is tracked by RepoRadar as a local ai agent debugger + self-h in the MIT `raindrop` binary that streams every token/t section. It was first seen on 2026-06-25 and last updated on 2026-06-25. The current verdict is 'try now' with a Gold tier and easy setup difficulty. Across RepoRadar's eight signals, raindrop-ai/workshop is strongest on workflow potential (9.5) and maturity (9.1) and weakest on momentum (7.0) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned raindrop-ai/workshop a composite score of 8.4 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 896.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Risk explanation
**Optional hosted product (Raindrop Cloud at `app.raindrop.ai`) is gated by OAuth and writes `RAINDROP_WRITE_KEY` to `./.env`.** The README is explicit that the cloud path is opt-in and the local install and the cloud coexist with distinct MCP server names (`workshop` vs `raindrop`) and separate install registries; users who only want the local debugger should pass no `--cloud` flag at install time, decline the `raindrop setup` interactive cloud-prompt, and the project stays self-hosted; users who adopt the cloud path should know that `RAINDROP_WRITE_KEY` is written to `./.env` (gitignore it) and the OAuth token cache lives in `~/.raindrop` — `raindrop logout` clears it; **Local debugger binds to port 5899 by default.** The HTTP + WS port is configurable via `RAINDROP_WORKSHOP_PORT` but the default is `5899`; users running multiple Raindrop installs on the same host should override the port to avoid collisions, and users behind a strict firewall should know the debugger is local-only (not exposed to the network) by design; **Trace storage is local SQLite (`~/.raindrop/raindrop_workshop.db`) and unbounded by default.** Long-running instrumented agents will grow the SQLite database; the README does not ship a built-in retention policy, so users running the local debugger on production agent workloads should plan a periodic prune or rotate `RAINDROP_WORKSHOP_DB_PATH` to a project-local path they control.