Bubo 🦉

This wise owl is Bubo, an agentic AI code reviewer that reviews your GitLab MRs and GitHub PRs with the LLM model of your choice, and posts only the findings worth acting on as inline threads — no chatbot noise, no praise, no summaries.

• Signal over noise — evidence-backed inline findings, confidence gates, optional verification, and deduplication.
• Your model, your infrastructure — OpenAI, Anthropic, or another CLI-backed reviewer with your own deployment.
• Feedback-driven reviews — recorded outcomes help tune thresholds and suppress recurring unwanted findings.
• Review evidence, in one place — local dashboards and reports for findings, usage, policy decisions, and outcomes.
• Compliance - cosign-signed releases with SBOMs, auditable on-prem report
• Metrics and RoI — Read about real world metrics and ROI

Run continuous reviews through polling, or trigger reviews through MCP. The experimental GitHub Action.

Install

uv tool install bubo     # or: pipx install bubo
bubo init                # idempotent; seeds config + workspace + DB
bubo doctor              # verify before the first poll
bubo-poller service start # start the review service (polling + outcome sync)

Container → docker pull ghcr.io/mountainowl/bubo - Bring your own LLM CLI, you can use multiple architectures.

Documentation

Everything lives on the docs site — this README is just the front door.

Recipes	Copy-paste GitLab / GitHub / in-house-model setups.
Features	The full capability list.
Configuration	Every setting, per section, plus a quick-start config.
Operate	Deploy, schedule, grade outcomes, governance report.
Troubleshooting	Host / infra fixes (sandbox, AppArmor).
Metrics & telemetry	Emitted llm_review.* metrics and dashboards.
Updates & recovery	Automatic/manual updates, safe deferrals and issue-ready failure reports; platform acceptance status.

Community

Contributing · Security policy · Release verification · Support · Code of conduct · License: MIT