ThrillhouseBot

“Everything’s coming up Thrillhouse!”

A self-hosted, GraalVM-native PR review bot, built as a GitHub App with Quarkus. It reviews pull requests using any OpenAI-compatible chat API, so the review is language-agnostic and you can pick the provider that suits you — including a local Ollama model, so no code has to leave your network.

Features

• Reviews diffs for correctness, security, regressions, stale comments, and code quality
• Token-budgeted whole-PR review for large diffs — split into parallel map-reduce batches with omitted files named, not silently dropped
• Configurable auto-review triggers — skip drafts, gate on labels, or filter by base branch — plus an optional per-PR auto-review interval (AUTO_REVIEW_MIN_INTERVAL) when you want to cap spend on noisy PRs (off by default; use /pause to silence a PR)
• Inline code suggestions on review comments that you can apply with one click
• Every finding is tagged critical, high, medium, or low
• Follow-up reviews track whether earlier findings were addressed or justified
• Every finding can be closed by a maintainer: reply on its review thread, or — for one raised below the inline-posting bar, which has no thread — comment @thrillhousebot resolved <path>:<line> — <title> on the PR
• Maintainer 👍/👎 (and "not useful" replies) on finding comments are recorded for a future learnings pipeline — see Finding feedback
• Conversational replies: @thrillhousebot it in a PR thread or finding reply and the bot answers in context
• A summary comment on the first run, with a risk breakdown and a changed-files walkthrough
• Operable from the PR with comment commands — /help, /review, /summary, /describe, /changelog, /add-docs, /improve, /generate-tests, /resolve, /pause, /resume
• Live dashboard (Next.js) with a WebSocket activity feed, cost charts, and token tracking
• OpenTelemetry traces, token histograms, cost counters, and latency metrics
• Optional reasoning-effort dial and per-model generation/budget caps for OpenAI-compatible endpoints
• Reads per-repo instructions from .github/thrillhousebot.md, falling back to Copilot/Claude/Agents files
• Lets each repository add its own ignore globs in .github/thrillhousebot.yml, unioned with the deployment default, and scope extra review rules to a path glob
• Compiles ahead-of-time with GraalVM/Mandrel, so it starts fast and stays small

Where to go next

• Getting started — create the GitHub App with the hosted installer and run the bot with Docker Compose.
• Commands — drive the bot from a PR: /review, /describe, /changelog, /add-docs, /improve, /generate-tests, and more.
• Configuration — every environment variable, with defaults.
• AI providers — point the bot at the OpenAI-compatible endpoint of your choice.
• Architecture — how a review flows through the system.
• Finding feedback — maintainer 👍/👎 capture for the learnings pipeline.
• How it compares — an honest look at where ThrillhouseBot sits next to other AI code-review tools.
• Contributing — development setup and the CI bar.

Dashboard

The built-in dashboard (Next.js, served by the bot itself) shows summary cards, a live activity feed that streams the model’s output as a review runs, cost charts by model, token breakdowns, and a paginated session history:

Community and license

Questions and setup help belong in GitHub Discussions; bugs and feature requests in Issues.

Licensed under the Apache License 2.0 (SPDX: Apache-2.0).