Mira
Self-hosted open-source AI code reviewer that indexes your repo and works with any LLM.
Mira is the most complete self-hosted AI reviewer we've tested—no gating, no license key, and its learning loop's quarantine prevents rule drift. Deploying it requires DevOps chops, but for teams that prize data sovereignty and want to avoid per-seat SaaS fees, it's exceptional value. If you need a managed zero-setup service, CodeRabbit or Copilot Code Review are easier, but you'll pay per seat and send your code to a third party.
Verified 1d ago · liveness 74/100 · cite: rightaichoice.com/tools/mira
- Engineering teams wanting cost-effective AI code review with full data control
- Privacy-conscious organizations that cannot send code to third-party APIs
- Teams using GitHub, GitLab, or Forgejo who want to reduce manual review burden
- Developers who want to customize review rules and learn from team patterns
- Teams wanting a fully managed SaaS with zero deployment effort
- Organizations relying on Bitbucket (not yet supported, though planned)
- Beginners unfamiliar with Docker, GitHub App setup, and basic DevOps
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Skip Mira if you need a fully managed, zero-setup AI reviewer, rely on Bitbucket, or lack the Docker and DevOps skills to deploy and maintain a self-hosted service.
You pay for LLM usage through OpenRouter at your chosen model's rate, which can add up fast on large repos or active PR flows—there's no flat SaaS fee but token costs vary by model.
Mira is $0/mo for the software itself—you only pay your LLM provider's usage fees. For a team of 10, that's a fraction of CodeRabbit's per-seat pricing, but you trade that saving for DevOps effort and infrastructure costs.
In short
Mira — Self-hosted open-source AI code reviewer that indexes your repo and works with any LLM. Best for Engineering teams wanting cost-effective AI code review with full data control, Privacy-conscious organizations that cannot send code to third-party APIs, Teams using GitHub, GitLab, or Forgejo who want to reduce manual review burden. Free to use.
What's new in Mira
Checked yesterdayAcross the latest 2 updates: 2 changelog entries.
Mira v0.7.0: Forgejo support, cross-PR overlap detection, PR author allow/deny lists, review dashboard, contribution analytics, threaded activity timeline
Adds Forgejo (incl. Codeberg) support, cross-PR overlap detection, author allow/deny lists, an admin review dashboard, contribution analytics, and a threaded activity timeline, plus fixes to comment anchoring and Postgres resilience.
Mira v0.6.0: GitLab support with full parity
Adds GitLab support with full parity to GitHub, a dynamic platform layer for easier git-host additions, and fixes for malformed LLM JSON recovery and stale Postgres connections.
What people actually say about Mira — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
86 mentions across 6 sources (Hacker News, YouTube, Product Hunt, App Store, GitHub, Lemmy) · researched Aug 27, 2026.
- +Full-repo indexing gives project-wide context, not just a diff.
- +Bring-your-own-LLM: pay providers directly, no markup.
- +Fully open-source and self-hosted; complete data control.
- +Severity and confidence scoring helps triage review comments.
- +Deduplication and comment cap reduce noise on large PRs.
- −Setup requires Docker and DevOps knowledge; not turnkey.
- −Documentation is sparse for advanced setups like Postgres.
- −GitHub Actions integration requires manual webhook setup.
- −Learning loop's quarantine can hide valid findings.
- −Community support is thin; sparse on-topic discussions.
- • Infrastructure costs: you must run a server or container (Docker/Postgres) yourself
- • LLM API costs: you pay the model provider directly for every review call
- • Time cost: DevOps setup and maintenance are on you
Viability Score
How well maintained and how widely used is Mira? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Full-repository indexing for context-aware review
- Inline comments with severity and confidence scoring
- PR walkthrough with Mermaid sequence diagrams
- Cross-PR overlap detection (merge-conflict risk and duplicate effort)
- Deduplication across chunks and files
- GitHub suggestion blocks for one-click fixes
- Custom per-repo and global rules in .mira.yaml
- Learning loop that synthesizes rules from rejected comments and quarantines new ones
- PR author allow/deny lists (filter bots like dependabot)
- Admin review dashboard: stale/waiting PRs, reviewer-responsiveness leaderboard, throughput trends, rubber-stamp detection
- Contribution analytics: commits, PRs, lines, year-long heatmap, review-quality signal
- Threaded PR activity timeline (replies nested under comments, newest first)
- OSV.dev vulnerability scanning tied to codebase index
- Org-wide package search across repositories
- Webhooks for Slack, Teams, and generic endpoints
About Mira
Mira is a self-hostable, fully open-source AI code reviewer. It connects to GitHub, GitLab, or Forgejo (including Codeberg), watches your pull and merge requests, and posts concise inline comments and walkthroughs using the LLM of your choice: Anthropic, OpenAI, Google, DeepSeek, or anything else available through OpenRouter. Everything is included: the review engine, codebase indexing, vulnerability scanning, custom rules, org-wide package search, the dashboard, and the learning loop. There's no paid tier and no license key to buy. You bring your own LLM, so you pay your provider directly with nothing added on top. Mira indexes your whole repository, so the model reviews each diff with full project context instead of just the changed lines. Confidence thresholds, deduplication, severity sorting, and per-PR comment caps keep the noise low. It learns your team's preferences by synthesizing rules from the comments you reject and from how your reviewers comment on merged PRs, then quarantines new rules until they prove themselves. You can write custom per-repo and global rules in .mira.yaml, scan dependencies with OSV.dev, and search packages across your organization. Everything ships in a single Docker image backed by SQLite or Postgres, with no telemetry and no license key. Deployment options include Docker, Railway, Fly.io, and Render; the dashboard offers a threaded PR timeline, contributor analytics, and a reviewer-responsiveness leaderboard. Recent releases added GitLab support with full parity, Forgejo support, cross-PR overlap detection, and admin review analytics. If you prioritize data control and want to avoid per-seat SaaS costs, Mira is a strong alternative to hosted reviewers like CodeRabbit. It requires Docker and basic DevOps, so it's not for teams that want zero setup. The Apache 2.0 license and lack of a paid tier make it attractive for privacy-conscious engineering teams.
Behind the Verdict
Mira delivers a lot for a self-hosted tool. The standout is its bring-your-own-LLM approach: you pick the model, point Mira at OpenRouter, and pay your provider directly—no per-seat fees, no markup. For teams already holding API keys for Anthropic, OpenAI, Google, or DeepSeek, that's a cost lever. And because Mira indexes your entire repo, reviews carry project context, not just diff lines—so comments reflect how a change fits the codebase, not just the lines touched. The learning loop is genuinely differentiated. It synthesizes rules from rejected comments and from how your reviewers comment on merged PRs, then quarantines new rules until they prove themselves. That prevents the rule drift you see in other tools that just dump static rules. Combined with custom rules in .mira.yaml, it gives you fine-grained control that hosted tools rarely match. The recent 0.7.0 release adds Forgejo (including Codeberg) support, cross-PR overlap detection, PR author allow/deny lists, an admin review dashboard, contribution analytics, and a threaded activity timeline. That's a fast-moving project—0.6.0 brought full GitLab parity. The provider-agnostic platform layer (a platforms.json registry) means adding hosts is data, not code, so Bitbucket is on the roadmap and the architecture keeps the engine, indexer, and dashboard provider-agnostic. Weaknesses: you need Docker and DevOps skills. Deployment, maintenance, scaling, and upgrades are on you. There's no managed SaaS option—if you want click-and-forget, this isn't it. Bitbucket isn't supported yet, though planned. Native adapters for direct LLM backends (Anthropic, OpenAI, Google Vertex, Ollama, vLLM) are coming but not yet available—you route through OpenRouter for now, which may be a data-residency issue for some orgs. Where it fits: engineering teams that want cost-effective, context-aware AI code review without sending code to third-party SaaS. Privacy-conscious orgs that can't expose code to hosted reviewers. Teams on GitHub, GitLab, or Forgejo who want to reduce manual review load. Orgs that want vulnerability scanning tied to their codebase index. Where it doesn't: teams wanting zero setup, those on Bitbucket, or beginners without Docker experience. If you're on a fully managed workflow, CodeRabbit is easier but costs per seat and handles code off-site.
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Real-world workflow fit
Concrete scenarios for the personas Mira actually fits — and what changes day-one when you adopt it.
Deploy Mira on Railway with Postgres, connect it to their GitHub org, and set an OpenAI key via OpenRouter.
Outcome: Within the first sprint, every PR gets contextual inline comments and a walkthrough; the engineer sees cross-PR overlap warnings and can mute dependabot PRs with an allow/deny list.
Set MIRA_GITLAB_TOKEN + webhook, enable the review dashboard, and review the first batch of MRs.
Outcome: The team sees reviewer-responsiveness metrics and rubber-stamp detection, which surfaces bottlenecks and encourages deeper reviews; Mira's learning loop starts proposing rules from rejected comments.
Connect Forgejo with an access token and webhook, set MIRA_FORGEJO_API_URL for a self-hosted instance.
Outcome: PRs get auto-reviews with inline comments and a walkthrough; the maintainer uses author allow/deny lists to suppress bot noise and can quickly check which files are affected by a new OSV vulnerability.
Use Cases
- Review every pull request with contextual comments based on your entire codebase.
- Enforce team coding standards with custom rules that run automatically on new PRs.
- Quickly answer which repositories are affected by a newly disclosed CVE.
- Reduce code review cycle time by catching common issues before human reviewers.
- Continuously improve review quality as Mira learns from your team's feedback.
- Self-host the entire pipeline to keep source code and review data on-premises.
- Detect duplicate or overlapping open PRs before they cause merge conflicts.
Models Under the Hood
as of 2026-08-31
Limitations
- Mira is a self-hosted AI code reviewer that requires deploying a single Docker image backed by SQLite or Postgres.
- It currently supports GitHub, GitLab, and Forgejo (including Codeberg); Bitbucket is on the roadmap.
- You bring your own LLM via OpenRouter, so you pay your provider directly; native adapters for Anthropic, OpenAI, Google Vertex, Ollama, and vLLM are planned but not yet available.
- There is no paid tier and no license key, but you must handle deployment, maintenance, and scaling yourself.
- The dashboard is admin-only.
as of 2026-09-01
Verification history
We have re-verified Mira 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Mira tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Self-Hosted
$0/mo
Ideal for
Engineering teams with Docker/DevOps skills that want full data control and no per-seat fees, e.g. a 5-20 person startup or privacy-conscious org on GitHub/GitLab/Forgejo.
What this tier adds
This is the only and starting tier: free, Apache 2.0, no license key. Includes all features—indexing, learning loop, dashboard, vulnerability scanning—with no usage caps.
Where the pricing makes sense
The company stage and team size where Mira's pricing actually pencils out — and where peers do it cheaper.
Mira is $0/mo for the software itself—you only pay your LLM provider's usage fees. For a team of 10, that's a fraction of CodeRabbit's per-seat pricing, but you trade that saving for DevOps effort and infrastructure costs.
Setup time & first value
How long it actually takes to get something useful out of Mira — broken out by persona, not the marketing-page minute.
To first review on GitHub: ~10-15 minutes per the quickstart (create GitHub App, set permissions, generate private key, deploy Docker image, set env vars). GitLab: set a token and webhook, similar time. Forgejo: set a token and webhook, similar time. Add ~30-60 minutes for full repo indexing on a large codebase before first contextual review.
Switching to or from Mira
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From CodeRabbit: point Mira at your GitHub or GitLab repos, deploy the Docker image, and let it index — you'll keep inline comments but drop per-seat fees and send less code off-site.
- →From a custom review bot: replace your scripts with Mira's .mira.yaml rules and learning loop, and use its webhooks for Slack/Teams notifications.
- →From manual review: deploy Mira and let it draft comments on new PRs; human reviewers just approve or reject, feeding the learning loop.
- ↗To CodeRabbit: if you need a managed service, export your .mira.yaml rules (as documentation) and move to CodeRabbit's UI — expect per-seat pricing and hosted data.
- ↗To GitHub Copilot Code Review: adopt Copilot's native comments for zero self-hosted ops—but you'll lose repo indexing and custom rule learning.
- ↗To a custom OpenAI pipeline: if you want more control, extract Mira's custom rules and prompt design into your own scripts—expect to rebuild indexing and learning.
Integrations
Resources & Guides
- Quickstartdocs.miracode.ai
Quickstart · Mira
Get up and running fast from docs.miracode.ai
- Resourcedocs.miracode.ai
Github · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Gitlab · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Forgejo · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Configuration · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Overview · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Features · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Security · Mira
Helpful link from docs.miracode.ai
- Resourcedocs.miracode.ai
Changelog · Mira
Helpful link from docs.miracode.ai
Tutorials & Learning
Official links
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Featured Head-to-Head Comparisons
Mira vs Voyage Ai
Choose Voyage AI if your priority is building a high-accuracy RAG pipeline with domain-specialized embeddings and rerankers, and you have budget for enterprise pricing. Choose Mira if you need a free, self-hosted AI code reviewer with full privacy, customizable rules, and the flexibility to use any LLM. They solve entirely different problems, so your decision hinges on whether you need retrieval or code review.
Mira vs Temporal Ai
If you need to build fault-tolerant, long-running AI agents or microservices that survive crashes and retries, Temporal is the clear choice. If your pain point is manual code review and you want a self-hosted, cost-effective AI reviewer that learns your team's rules, Mira is a fantastic pick. They solve completely different problems — pick based on whether you're orchestrating durable workflows or reviewing pull requests.
Mira vs Spider Cloud
These tools serve completely different functions: Mira is for AI-powered code review on GitHub (self-hosted, free), while Spider Cloud is for web crawling and scraping (SaaS API, freemium). Choose Mira if you need automated PR review with full control over data and privacy; choose Spider Cloud if you're building AI agents or RAG systems that need fresh web content. They are not direct competitors but complementary tools for different stages of development.
Alternatives to Mira
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Frequently Asked Questions
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