At a glance
| Capability | ReviewGate | CodeRabbit | PR-Agent (Codium) | Generic LLM reviewer |
|---|---|---|---|---|
| Deployment / data residency | Self-hosted CLI; code stays in your infra | Cloud SaaS; code sent to vendor | Cloud / app; code leaves GitHub | Depends on setup |
| LLM provider choice | Any OpenAI-compatible or Anthropic endpoint | Vendor-chosen / managed | Vendor-chosen / limited | Fully up to you |
| Default write permissions | Read-only; no worktree writes or shell execution | Posts comments / suggestions; may auto-apply | Posts comments / suggestions | Varies |
| Findings validation | Line anchoring, deduplication, counter-evidence judge | Static analysis + LLM; limited counter-evidence | LLM + some static tools | Usually none |
| Confidence-based gating | BLOCK / WARN / folded by confidence | Severity labels; configurable rules | Labels / scores | Rarely built-in |
| Incomplete review handling | Degrades to WARN + non-zero exit; never fake PASS | May still report review completion | May report partial review as done | No standardized behavior |
| Pricing model | MIT open source; pay only your LLM usage | Per-seat / usage SaaS subscription | Freemium / usage tiers | Your own API spend |
| Public evaluation logs | Committed to repo under docs/evals/ | Not public | Limited benchmarks | Not common |
When ReviewGate is the better fit
- Your code cannot leave your GitHub org or VPC.
- You already have preferred LLM contracts (DeepSeek, Kimi, GLM, Qwen, Claude, etc.) and want to use them directly.
- You want a CI gate that distinguishes "clean" (exit 0), "blocked by findings" (exit 1), and "tool failure" (exit 2).
- You'd rather fold low-confidence noise than flood PRs with "maybe" comments.
- You need an audit trail of how the tool was evaluated against real PRs and real CVEs.
When CodeRabbit or PR-Agent may be better
- You want a fully managed SaaS with no local binary or CI wiring.
- You value rich PR chat interfaces and auto-apply suggestions above all else.
- You don't mind vendor-chosen models and are okay with code leaving your org.
- You need features ReviewGate doesn't yet provide (e.g., Jira integration, granular role-based access).
What makes ReviewGate different
1. It is a gate, not just a reviewer
Most AI reviewers produce comments. ReviewGate produces a verdict: BLOCK, WARN, or pass, with explicit exit codes for CI. If the review is incomplete because of timeout or oversized context, it reports WARN and exits non-zero — it never silently marks the PR as clean.
2. Built-in skepticism
Every finding is anchored to source lines, deduplicated across dimensions, and sent through a counter-evidence judge. A finding only survives if the model can argue for it and the judge cannot falsify it with evidence from the codebase.
3. Model-agnostic and self-hosted
ReviewGate talks to any OpenAI-compatible or Anthropic endpoint you configure. There is no central service, no per-seat pricing, and no vendor lock-in. You control the model, the key, and where the traffic goes.
4. Public, reproducible evals
All benchmarks are run against real code and committed to the repository. The revert-gold-standard method (reverting a real bug fix and checking if ReviewGate catches it) is documented alongside the results.
Try ReviewGate on your next PR
Install the CLI in one command, configure your LLM endpoint, and run reviewgate review --fail-on block in CI.
Get started →