Greptile

Sweet spot: an engineering team of 10–100 with steady PR throughput and at least one shared codebase with real cross-file dependencies. Greptile pays for itself when senior-engineer review time is the bottleneck — it absorbs the "is this safe? where else is this used?" pass that humans skip on tired afternoons. Failure modes. The graph-indexing approach is great for monoliths and medium monorepos, weaker for chaotic 100k-file mega-repos where the graph itself is noisy. Custom-rule tuning is mandatory — out of the box, Greptile will comment on style nits your team genuinely does not care about, and the noise erodes trust within two weeks. The team-learning loop only works if engineers actually engage with comments (resolve, dismiss with reason); silently-ignored comments do not teach the system. What to pilot. Install Greptile on one active repo for two weeks. After week one, write 5–10 custom rules to suppress the comment categories your team rejects. By week two, measure: (a) percent of Greptile comments engineers act on, (b) bugs Greptile flagged that would have shipped, (c) review wall-time saved. If percentage acted-on is above 30% and at least one real bug was caught, scale to all repos. Below 30% and you have a custom-rules problem, not a Greptile problem.

Greptile is an AI code-review agent that comments on GitHub and GitLab pull requests with full-codebase awareness. Where GitHub Copilot Chat reviews the diff in isolation, Greptile first builds a graph of the entire repository — files, functions, imports, call sites — and then reasons about each PR change in the context of where else those symbols are used. That is what lets it catch a multi-file logic bug from a single-file change, the kind of issue most AI reviewers miss entirely. Architecturally, the platform runs a "swarm of agents" in parallel: a style-checker, a security-scanner, a logic-impact analyzer, a dependency-aware critic. Each posts its own review comments. A learning loop reads how engineers respond to past comments — accept, ignore, push back — and tunes the model's thresholds for that team. Custom rules are written in plain English in a config file; no DSL to learn. Greptile competes most directly with CodeRabbit (similar AI-PR-review category, broader marketing surface) and Sourcegraph Cody (codebase Q&A tool that added review). Vs CodeRabbit, Greptile leans more on graph-based context and less on summarisation; reviews tend to be fewer but higher-signal. Vs GitHub Copilot Chat reviews, Greptile is in a different league — Copilot Chat reviews the diff, Greptile reviews the diff against the codebase. Used by 9,000+ teams including Brex, Zapier, and PostHog. Pricing is straightforward: $30/seat/month with 50 reviews included, $1/review after. Free tier for qualified open-source projects; discounts for pre-Series A startups.