open-code-review vs no-mistakes
Two of the top projects in Code review and testing, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
open-code-review
CLI that reviews Git diffs with an LLM agent and line-level comments
no-mistakes
Local git proxy that runs AI review, tests and lint before opening a PR
Verdict
open-code-review ranks higher, 76 to 60. Of the 3 score parts we measure for both, open-code-review leads on 1, no-mistakes on none and 2 are even.
- Adoptionopen-code-review leads by 58 points, 75 to 17: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score no-mistakes on this; open-code-review has 91.
- Easy to runWe do not score no-mistakes on this; open-code-review has 50.
- Agent-readyEven at 70.
Pick open-code-review if you are
Teams adding LLM code review to local workflows or CI pipelines
- Deterministic file selection and bundling; each bundle runs as an isolated sub-agent
- Reviews workspace changes, branch ranges, single commits, or full files via `ocr scan`
Pick no-mistakes if you are
Developers who want agent-checked branches before raising PRs
- Runs in an isolated worktree, so your working directory is not disturbed
- Works with claude, codex, grok, opencode, copilot, cursor and others, with ordered fallbacks
| What we compare | open-code-review | no-mistakes |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 75, popular | 17, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 47.2k | 8.8k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Go | Go |
| Docker | No | No |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Mentioned | Not stated |
open-code-review
Open Code Review is a Go CLI (`ocr`) that reads a Git diff, groups related files into bundles, and runs each bundle through a tool-using LLM agent that produces structured review comments. File selection, rule matching, comment positioning and reflection are done by deterministic code rather than prompts. `ocr scan` reviews whole files when there is no diff, and delegation mode lets an existing coding agent do the review with its own model.
Who it is for: Teams adding LLM code review to local workflows or CI pipelines
Strengths
- Deterministic file selection and bundling; each bundle runs as an isolated sub-agent
- Reviews workspace changes, branch ranges, single commits, or full files via `ocr scan`
- Sessions can be resumed and browsed in a viewer; JSON output for automation
- Plugins for Claude Code, Codex, Cursor, Kimi Code, OpenCode; CI docs for GitHub, GitLab, Gerrit
Weaknesses
- Requires Git >= 2.41 and a configured LLM endpoint, unless delegation mode is used
- Recall is lower than general-purpose agents by the README's own benchmark
- Benchmark figures are published by the project itself, not independently verified
- No Docker or Compose files detected
- no GPU
- Needs Git >= 2.41, LLM provider API
- Models: configurable LLM providers, custom providers
no-mistakes
no-mistakes places a local git remote in front of your real one. Pushing to it creates a disposable worktree and runs a pipeline (review, test, docs, lint, push, PR, CI) driven by a coding agent CLI such as claude or codex. The branch is forwarded to the real remote only after every gate passes, and a PR is opened for you. Safe fixes are applied automatically; other findings wait for approval in a TUI.
Who it is for: Developers who want agent-checked branches before raising PRs
Strengths
- Runs in an isolated worktree, so your working directory is not disturbed
- Works with claude, codex, grok, opencode, copilot, cursor and others, with ordered fallbacks
- Three entry points: git push, a TUI, or a /no-mistakes agent skill
- Watches CI after the PR opens and auto-repairs failures
Weaknesses
- Requires a runnable coding agent CLI configured for every gate
- Agent calls spend API quota; RAM and cost figures are unknown
- CI repairs may need another full review pass if revalidate_repairs is set
- Auto PR creation and CI watching are documented only for configured push targets such as GitHub
- Needs git, a coding agent CLI (claude, codex, opencode, etc.)
- Models: claude, codex, grok, rovodev, opencode
More in Code review and testing
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.