Mira

Self-hosted AI code review. Your code, your dashboard, your LLM key.

Docs · Community · Self-Host Guide » · Benchmark

Self-host every feature: full review engine, codebase indexing, vulnerability scanning, custom rules, org-wide package search, dashboard, learning loop. No paid tier, no license key, no SaaS upsell.

Mira reviews your pull requests using your choice of LLM (via OpenRouter, which fronts Anthropic, OpenAI, Google, DeepSeek, and more) and posts concise, actionable feedback. The noise filter, confidence clamping, and learning loop ensure you only see comments that matter. See FEATURES.md for the full surface.

Why Teams Choose Mira

• Model agnostic — Run Claude, GPT, Gemini, DeepSeek, Llama, or any OpenAI-compatible endpoint: OpenRouter, vLLM, Ollama, Together, Groq, Fireworks, or AWS Bedrock direct. Per-provider quirks are config, not code, so adding a provider is a one-line entry.
• Zero markup on LLM costs — Bring your own key. You pay the model provider directly; Mira never proxies your spend or adds a multiplier. The dashboard shows real per-repo, per-model cost — not estimates.
• Learns from your context — Mira synthesizes rules from your merged PRs: rejected comments and human review patterns become team rules that shape future reviews.
• You set the rules — Define custom and org-wide review rules in plain language, per-repo via .mira.yaml or from the dashboard.
• Privacy first — Self-hosted by default. Diffs, indexes, review history, and CVE data live in your SQLite or Postgres, on infra you own. No phone-home, no required telemetry, no "is this used for training?"
• Low-noise reviews — Confidence thresholds, dedup, a self-critique pass, and per-PR caps mean every comment is one worth reading — and Mira is the fastest tool on the public Code Review Bench.
• Indexed, cross-file context — A full-repo code index gives the model real project context, not just the diff — plus org-wide package search and hourly OSV.dev CVE scanning across every repo.
• GitHub and GitLab — Auto-reviews every PR and merge request and answers @miracodeai questions inline, with full feature parity across both. Bitbucket and Gitea adapters are next; the engine, indexer, and dashboard are provider-agnostic, so a new host is a data entry plus one provider class.
• Self-host on day one — Docker image with Railway / Fly.io / Render configs, SQLite or Postgres. Every feature included.

Dashboard

Your data, your dashboard

Most AI reviewers are SaaS: your diffs (and often the full surrounding code) leave for a third-party server, and the only "view" you get is the comments that come back on a PR. Mira flips both halves of that:

• Your code never leaves your infra. Diffs, embeddings, indexes, review history, vulnerability data, all stored in your SQLite or Postgres, on infrastructure you own. No phone-home, no required telemetry, no "is this used for training?" question.
• The dashboard you see above is yours. It's not a marketing screenshot of someone else's view of your code. CodeRabbit, Greptile, and similar SaaS reviewers don't expose anything like it. Mira's dashboard surfaces signals you don't get anywhere else:
  • Org-wide package inventory: answer "which repos use lodash@4.17.20?" in one query. Stack it next to your CVE feed for instant blast-radius checks.
  • CVE alerts on every dependency: hourly OSV.dev poll, severity + advisory link + fix version surfaced inline next to the package.
  • Dependency + blast-radius graphs: see exactly which files and repos depend on a symbol before you change it.
  • Per-repo review event stream: every webhook, every chunk, every cost figure, in one place for live troubleshooting.
  • Cost & token telemetry: actual spend per repo and per model, not estimates, because you control the LLM key.
  • Coming soon, change-frequency heatmaps: surface the files that bug fixes keep landing on so you can target review attention.

If your engineering team needs answers like "which of our repos are exposed to this CVE?" or "what's the blast radius of changing this function?", those questions stop being multi-day investigations and start being one-click dashboard pages.

Benchmark

Mira is the fastest tool measured on the public Code Review Bench, and the only one on the speed/quality Pareto frontier: every tool that scores higher on F1 takes 5–14× longer per PR.

Plotted against every published competitor on the same subset, Mira sits in the upper-left corner: everything to the right is slower; everything above it pays 5–14× the wall time for the extra F1.

Measured on the same 50-PR offline benchmark, judged by Claude Sonnet 4.6.

	Mira	Cubic-v2	Greptile	CodeRabbit	GitHub Copilot
F1	44	56	35	32	31
Precision	43%	50%	32%	24%	24%
Recall	46%	65%	40%	50%	43%
Median time / PR	~77s	~9m	~5m	~5m	~10m

Methodology: scores measured against the Martian Code Review Bench offline dataset with Claude Sonnet 4.6 as the judge.

Quick Start

Run Mira self-hosted to auto-review every PR and merge request and answer @miracodeai questions inline. GitHub (as a GitHub App) and GitLab (via a group/project access token) are both fully supported; Bitbucket and Gitea are next.

1. Deploy — one-click on Railway, or with Docker:

# mira.yaml — deployment-wide defaults. Every key is optional.
llm:
  model: "anthropic/claude-sonnet-4-6"
  indexing_model: "anthropic/claude-haiku-4-5"

# .env — secrets only.
MIRA_GITHUB_APP_ID=123456
MIRA_GITHUB_PRIVATE_KEY="$(cat private-key.pem)"
MIRA_WEBHOOK_SECRET=your-secret
OPENROUTER_API_KEY=sk-or-...

docker run -p 8000:8000 --env-file .env \
  -v "$(pwd)/mira.yaml:/app/mira.yaml" \
  ghcr.io/miracodeai/mira:latest --config /app/mira.yaml

2. Install the app on your repos — every PR gets reviewed.

→ Full walkthrough: creating the GitHub App & quickstart · GitLab setup · deploy options · choosing models, custom endpoints & AWS Bedrock

Configuration

mira.yaml (loaded via --config) holds deployment-wide defaults. Drop a .mira.yaml in any repo — or use the dashboard — to override per-repo; both deep-merge over mira.yaml for that repo only:

# .mira.yaml — optional per-repo override
filter:
  confidence_threshold: 0.5  # noisier repo → lower bar
  max_comments: 10

→ Full schema and every key: Configuration docs.

Development

git clone https://github.com/mira-reviewer/mira.git
cd mira
pip install -e ".[dev,serve]"

# Run tests
pytest tests/ -v

# Run the regression suite (hits real GitHub + LLM, ~$1, ~3 min).
# Pinned PRs whose findings have flickered across iterations. Run before
# merging changes that touch prompts, the noise filter, or the engine.
OPENROUTER_API_KEY=... GITHUB_TOKEN=... pytest -m eval -v

# Lint
ruff check src/ tests/

# Type check
mypy src/mira/ --ignore-missing-imports

License

Apache 2.0. See LICENSE.