CodeRabbit

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Point an agent at llms.txt or agent-oriented docs G

Agent access

Use an official CLI G

Agent access

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Run the product headlessly / in CI for automation G

Agent access

Build against official SDKs G

Agent access

Explore an interactive API reference with runnable examples G

Api quality

Rely on versioned APIs with a documented deprecation policy G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Prevent my data from being used to train AI models G

Review comments include committable suggested diffs I can apply with one click C

Suggestions

I configure the reviewer with a versioned config file in my repo — path filters, per-path instructions, review profiles C

Config

I turn a review finding into an applied fix — a committed patch or an agent-generated follow-up — without leaving the PR C

Fixes

The reviewer installs as a GitHub/GitLab app and posts reviews as native inline comments on my pull requests within minutes C

Platforms

Define rules that trigger actions automatically on events G

Review comments reflect the whole repository — call sites, related modules, existing conventions — not just the changed hunks C

Context

Self-host the core product G

The reviewer catches real bugs in my PR — logic errors, race conditions, broken edge cases — not just style nits C

Detection

The reviewer keeps noise low — few false positives, deduplicated comments, severity labels — so my team doesn't tune it out C

Noise

Export all of my data in open formats and leave G

Every PR gets an auto-generated summary and change walkthrough so human reviewers orient fast C

Summaries

I encode my team's own review guidelines — natural-language rules, AST patterns, or linked style guides — and the reviewer enforces them C

Rules

I get the same review inside my IDE before I push, catching issues while the code is still in my editor C

Ide

I reply to the reviewer in the PR thread to ask questions, get explanations, or issue commands — and it answers in context C

Chat

Pushing new commits triggers an incremental re-review that tracks what was fixed instead of repeating old comments C

Updates

The reviewer builds a persistent memory of my team's conventions and past review decisions and applies it to future PRs C

Memory

Push back on a bad review comment and the reviewer learns — it stops repeating the same rejected feedback C

Learning

Review findings hand off cleanly to my coding agent — copyable fix prompts or direct integration with Claude Code, Cursor, or Codex C

Handoff

The reviewer can gate merges — a required status check or blocking review that enforces resolution of critical findings C

Gates

The reviewer understands changes that span multiple repositories or a large monorepo and reviews them coherently C

Context

Control data retention and deletion G

I define custom agentic pre-merge checks in plain language — 'docs updated', 'tests cover new paths' — that run on every PR C

Checks

Reviews flag security problems in the diff — injection risks, leaked secrets, insecure patterns — alongside functional bugs C

Security

The reviewer holds the line on AI-generated PRs — it verifies agent-authored code at a volume no human team could review C

Ai authored

Choose where my data is stored (region/residency) G

I run reviews from a CLI against local diffs or in CI scripts, with machine-readable output my tooling can consume C

Cli

Opt out of telemetry and usage tracking G

Perform bulk operations across many items at once G

Do everything through the API that I can do in the UI G

Schedule recurring jobs or workflows G

Read the product's source under an open license G

I control when reviews run — skip drafts, trigger on demand, filter by branch or label — so the bot shows up only when wanted C

Control

I see dashboards of findings, acceptance rates, and review coverage across my org C

Analytics

I roll out org-level review defaults across hundreds of repos and manage exceptions centrally C

Governance

Version, review, and roll back my automations G