Greptile

Greptile is an AI-powered pull request review agent built by Greptile, Inc., a YC W24 company founded by Daksh Gupta and co-founders who met at Georgia Tech. The company raised a $25 million Series A led by Benchmark Capital in September 2025. The core problem Greptile solves is a structural limitation shared by most AI code reviewers: they analyze only the lines that changed in a PR, not what those lines interact with across the rest of the codebase. Greptile builds a dependency graph indexing files, functions, and module relationships, then deploys parallel agents to evaluate each PR change against that broader context.

Greptile v4, released March 5, 2026, is the current production version. It posts review comments automatically when a PR opens on GitHub or GitLab, flagging bugs, security issues, and convention violations with links to the affected code. The platform learns team-specific coding standards by reading historical PR comments, enforces those standards on new PRs, and integrates with Jira and Notion to pull ticket context directly into review. An MCP server allows coding agents like Cline and Cursor to consume and resolve Greptile's review comments programmatically. Early-access TREX (autonomous test generation and sandbox execution) extends the platform from review into validation.

What Greptile actually does in 2026

When a repository is connected to Greptile, the platform crawls it and builds a semantic graph: every function, class, variable, and file mapped against what it calls and what calls it. This graph is updated incrementally as new code lands. When a PR opens, Greptile queries the graph to retrieve not just the changed files but their dependencies, callers, and related patterns. A team of parallel agents then reviews the diff in that extended context.

In practice this catches a class of bug that diff-only tools routinely miss. A change to a shared utility function might look correct in isolation but break three downstream callers. A new API endpoint might duplicate logic that already exists elsewhere in the service. A renamed variable might violate a naming convention the team enforced consistently for two years. Greptile's graph gives it the information to flag all of these. Without the graph, no amount of LLM reasoning compensates for simply not knowing the affected code exists.

Custom rules let teams write plain-English enforcement criteria: "all database queries must use parameterized inputs," "every new endpoint must have a corresponding OpenAPI annotation." Greptile checks these on every PR. The learnings system reads the comments engineers leave on PRs over time, identifying patterns in what the team accepts and rejects, and folding those into future reviews automatically.

The MCP integration routes Greptile's PR comments directly into the coding agent IDE sessions used to address them. If a developer is fixing issues flagged by Greptile inside Cursor, the relevant Greptile comments and codebase rules are automatically available as context. This closes the loop between review and remediation without copy-pasting across tools.

"Greptile outperforms them all by a mile. Honestly the only AI reviewer that doesn't annoy the s*** out of me." -- James Reggio, CTO at Brex, greptile.com, 2025

The v4 release metrics show meaningful improvement over v3: comments addressed per PR increased 74% (from 0.92 to 1.60), the share of comments that PR authors actually acted on rose from 30% to 43%, and positive developer reactions (upvotes) on review comments increased 60%. These are engagement metrics, not accuracy benchmarks, but they track whether the comments are considered useful rather than noise.

In independent benchmarks published by Greptile in 2025, Greptile reported an 82% bug catch rate against a test set, compared to CodeRabbit at 44% and GitHub Copilot in the mid-50s. The catch rate on critical-severity bugs was 58% for Greptile versus 33% for CodeRabbit. The same benchmarks show Greptile flagged 11 false positives compared to CodeRabbit's 2, which is the persistent tradeoff: higher recall, higher noise.

Where Greptile sits versus CodeRabbit and Sourcegraph Cody

CodeRabbit is the current market leader in mid-market AI code review, with 2 million repositories connected and 13 million PRs reviewed by late 2025 according to YipitData. CodeRabbit's architecture applies LLM reasoning to the PR diff plus basic file context, but does not build a persistent dependency graph of the full repository. This makes CodeRabbit reviews lower-noise (2 false positives per benchmark run vs Greptile's 11) but shallower on cross-file impact. CodeRabbit also supports Bitbucket and Azure DevOps in addition to GitHub and GitLab, which Greptile does not. Mid-market ACV for CodeRabbit is approximately $6,700 vs Greptile's $2,000, reflecting CodeRabbit's wider platform coverage and more mature enterprise motion.

The practical decision point between them: if your codebase is highly interconnected and bugs routinely cross file boundaries, Greptile's graph-based approach finds things CodeRabbit will not. If signal-to-noise matters more than maximum recall, and especially if you need Bitbucket or Azure DevOps support, CodeRabbit is the safer default.

Sourcegraph Cody is a different product category that overlaps with Greptile only at the surface. Cody is a RAG-based code comprehension assistant built on Sourcegraph's search infrastructure: pre-indexed vector embeddings and a code search API that retrieves relevant snippets from across hundreds of repositories in response to developer queries. Cody is interactive and synchronous -- a developer asks it a question about the codebase in their IDE and gets an answer. It is not an automated PR review agent. Sourcegraph's ACV is approximately $300,000, targeting Fortune 500 engineering organizations with massive multi-repo monorepos. Cody's core strength is navigating sprawling legacy codebases; Greptile's core strength is automated bug detection at PR merge time. A team could use both: Cody for codebase Q&A in VS Code or JetBrains, Greptile for automated review on every PR. They serve different moments in the development workflow.

For teams evaluating the full spectrum, Augment Code is another codebase-aware coding assistant worth comparing, oriented toward real-time suggestions in the IDE rather than async PR review.

What the daily review reality looks like

Setup involves connecting a GitHub or GitLab organization, authorizing repository access, and waiting for the initial index to build. For large repositories this indexing phase can take several hours, and early reviews on a newly connected repo may lack full context depth until the index completes. Teams that skip configuring custom rules often find the initial experience noisier than expected, because Greptile's generic defaults lack knowledge of project-specific conventions.

Once configured, the workflow is passive: open a PR, and Greptile comments within 2 to 4 minutes. Developers address, resolve, or dismiss comments inline. Comments that get consistently dismissed feed the learning system, gradually tightening the signal.

"Greptile really surprised me with how much context it could gleam, including the PR description: the better the description, the better the result, like with a human reviewer!" -- Emily Burak, Medium, May 2025 -- noting that during a database integration PR test, Greptile "identified several critical issues that could have caused production trouble: a database connection being prematurely closed, table name mismatches, and more."

The false positive experience is real and documented. In a widely-cited Hacker News thread from January 2026, a developer described abandoning Greptile after 3 PRs: it suggested silencing exceptions without understanding prior context, incorrectly claimed "python 3.14 does not exist yet," and gave vague architectural critiques rated 4/5 confidence despite being irrelevant. The same thread had another developer report the opposite experience with properly configured, use-case-specific prompts, catching production bugs human reviewers missed. The divergence suggests Greptile rewards investment in configuration and punishes default-settings deployments.

Teams using GitHub Copilot or other AI coding agents that open high volumes of small PRs hit the per-review pricing ceiling faster than traditional human-authored PR workflows. At 300 PRs per developer per month, Greptile costs approximately $339/seat versus CodeRabbit Pro's flat $24. This is the sharpest friction point in the current product.

Who Greptile is built for

Greptile is the right choice for engineering teams where bugs crossing file or module boundaries are a recurring problem: services-heavy backends, shared library codebases, monorepos with complex dependency trees. The full graph indexing delivers its clearest value when the thing going wrong in production is not visible in the diff alone. Teams at the intersection of fast-moving AI-assisted development (lots of Cursor, Copilot, or Aider-generated code) and complex existing codebases get disproportionate value from a reviewer that understands the whole picture.

The startup discount (50% off for companies under $2M revenue) and YC alumni pricing (one month free) make the economics accessible for early-stage teams, which aligns with Greptile's own YC origin and early customer base.

SOC 2 compliance and air-gapped self-hosting through the Enterprise plan open the door for regulated industries and security-conscious organizations, though enterprise sales cycles are early-stage compared to CodeRabbit or Sourcegraph.

What Greptile is not

Greptile is not a drop-in replacement for teams on Bitbucket or Azure DevOps. Platform support is a hard constraint with no workaround as of April 2026.

It is not optimized for high-velocity agentic workflows where AI agents are opening 50 to 200 PRs per developer per month. The $1/review overage pricing becomes punishing at that volume, and the March 2026 pricing controversy demonstrated how quickly bills can spike for teams that did not model their PR volume before committing to the new pricing structure.

It is not a low-effort deployment. Teams that want useful reviews from day one need to invest in custom rules and allow the learnings system to accumulate team-specific signal. The out-of-the-box experience has a documented noise problem that configuration resolves but does not eliminate entirely.

It is not a codebase search or IDE assistant tool in the way Sourcegraph Cody or Cursor are. Greptile operates at PR time, asynchronously, as a review agent. It does not provide in-IDE completions, real-time suggestions, or interactive Q&A about the codebase outside the PR review interface.