BugBot vs Greptile: Precision vs Codebase-Graph AI Review

BugBot and Greptile both promise quieter pull-request review, though they reach that goal through different context systems. BugBot starts with the changed code, then adds Cursor rules, learned feedback, and connected tools. Greptile builds a graph of the repository before a pull request arrives and uses that graph during review.

The hard choice appears when a team can only justify one extra reviewer. A focused Cursor handoff can shorten the trip from comment to repair.

A persistent repository graph can inspect relationships outside the edited files. Each benefit sounds useful, so the decision needs a concrete failure case rather than a list of vendor features.

The BugBot tool page covers triggers, nested rules, effort, and spend controls. The Greptile tool page covers graph testing, configuration, feedback, and agent handoffs. The comparison below tests the job each product adds to a review stack.

BugBot and Greptile in One View

checked July 28, 2026: these positions come from Cursor's BugBot documentation, Greptile's product overview, and both vendors' current pricing pages. Public product terms can move after this date.

Dimension	Cursor BugBot	Greptile
Context base	Diff, rules, and tools	Persistent codebase graph
First setup	Connect and enable	Connect, index, configure
Manual trigger	bugbot run	@greptileai
Local guidance	BUGBOT.md	.greptile/ files
Feedback memory	Learned rules	Reaction-based learning
Fix destination	Cursor or web	Choice of coding agent
Billing shape	Usage per run	Seat plus credits
Free entry	Account trial or usage	Starter and OSS paths

Two Models of Context

BugBot analyzes pull-request changes and adds project guidance from .cursor/BUGBOT.md. The root file always applies, while more-specific files join the review when changed files sit under their directories. This makes a rule's scope visible in the repository and lets an API folder carry checks that would be noise in a frontend package.

Cursor also lets Teams and Enterprise accounts connect MCP servers for review context. Learned rules add another layer by turning repeated pull-request feedback into candidate guidance. BugBot therefore has more context than a bare diff, though its public docs frame changed code as the review's starting point.

Greptile makes a broader claim about repository structure. It indexes functions, classes, and dependencies into a repository graph, then uses that graph when a pull request changes one part of the system. The review can refer to related code outside the diff because those relationships existed in its index before the pull request opened.

The repository graph also has its own lifecycle. Greptile's quickstart says very large repositories can take one or two hours to index, while ordinary review comments usually arrive in about three minutes after setup. A large reorganization or a stale index can affect the context a reviewer expects to be current.

Greptile's .greptile/ configuration can add instructions, scoped rules, referenced files, ignored paths, and related repositories. Cross-repository context is useful for a shared types package or SDK whose contract lives outside the application repository. It also expands the access decision, so administrators should grant only the repositories the test requires.

The split is persistent structure against review-time guidance. BugBot can receive precise rules and external tool context for the changed area. Greptile maintains a graph designed to expose relationships before anyone writes a rule for the specific caller.

Context Access Grows Differently

BugBot's context can expand through repository rules and MCP servers. A security rule might point the reviewer toward an authorization invariant, while an MCP connection can supply issue or service data. Each added source should answer a named review question because broad tool access raises both cost and disclosure risk.

Greptile expands through indexed repositories and files referenced by .greptile/ configuration. A service can name a shared schema repository or an SDK that defines its public types. The graph can then connect code across that boundary during review.

These context paths create different administrative audit records. BugBot administrators need an inventory of connected tools, approved rule owners, and which account can change them. Greptile administrators need the indexed repository list, cross-repository links, and the credentials that keep each graph source current.

Start with local repository context for both products. Add one external system only after a missed finding shows why it is needed. A wide context set can make a comment sound informed while leaving the reviewer unable to tell which source carried the claim.

The access choice also affects later removal work. Disconnecting an MCP server ends BugBot's route to that tool, while removing a Greptile context repository requires checking what indexed material and derived relationships remain under the current vendor terms. Ask each vendor for the applicable retention behavior before a sensitive rollout.

Test a Contract Break

A cross-file contract break gives both products a fair, useful test. Choose a shared function whose result is consumed outside its own file. A permission helper, API response type, event payload, or database mapper works well because the caller has a clear expectation that can fail.

Create a small pull request that changes the producer while leaving one consumer untouched. Keep the failing relationship out of the pull-request description. The reviewer should find the link from code and repository context rather than a hint written for the experiment.

1. Name the producer and hidden consumer in a private scorecard.
2. Run BugBot and Greptile on the same commit.
3. Record whether each comment names the affected consumer.
4. Require a reachable failure or a testable contract.
5. Repeat with a compatible change that should stay quiet.

The safe companion change matters as much as the seeded break. A reviewer that flags every shared-function edit can appear thorough while moving the investigation burden onto humans. The useful result identifies the harmful relationship and leaves the compatible case alone.

Run a second pass after adding one narrow repository rule to each product. Put the BugBot rule in the closest applicable BUGBOT.md.

Put the Greptile rule in a scoped .greptile/rules.md or structured configuration entry. The wording should describe the contract without naming the planted bug.

This test separates graph discovery from rule compliance. A product may miss the relationship unaided and catch it after guidance, which still has worth. The scorecard should say which kind of context produced the finding instead of reducing the result to a win percentage.

Rules, Feedback, and Noise

BugBot rules follow the repository's directory tree. A project-wide security invariant can sit at the root, while an API package carries a stricter rule for authentication or transaction handling. Reviewers can inspect the same file that guided the bot, which makes a questionable comment easier to trace.

Cursor's learned-rules feature watches reactions, replies, and human review comments. Repeated signals can become candidate rules, then lose status if later evidence stops backing them. Teams should review promoted rules because an old convention can become a reliable source of stale comments after the architecture moves.

Greptile offers dashboard controls for strictness and comment types, plus repository configuration for rules and ignored paths. Its quickstart lists Syntax, Logic, and Style controls. Teams can also filter reviews by labels, authors, branches, and pull-request text, which helps keep generated or low-risk changes out of the automatic queue.

Greptile says reactions and replies teach the reviewer what the team cares about, with unwanted feedback declining over time. That claim belongs in the pilot scorecard. Save two or three dismissed patterns, then submit equivalent safe changes later and check whether the comments return.

Review noise takes two distinct forms in this matchup. Comment noise wastes review attention, while trigger noise spends money on a pull request that never needed another automated pass. Rule tuning handles the first; review policy handles the second.

Review Timing Changes the Queue

BugBot can review every pull-request update, one selected point, or a manual comment. Greptile can filter automatic review by branch, label, author, and pull-request text. Those controls decide whether the reviewer meets a stable change or comments while the author is still rewriting it.

Cursor's automatic update mode fits teams that want a fresh pass after each meaningful push. The same mode can spend several review runs on one pull request. Mention-only or once-per-pull-request settings keep the queue smaller during calibration.

Greptile skips drafts by default and says a normal review arrives in about three minutes after indexing. A team can reserve automatic review for a ready label, then use @greptileai for an older or unusually risky change. That policy gives the persistent graph a stable revision to inspect.

Measure waiting time from the developer's ready signal to a validated finding. Review speed alone can reward a fast stream of low-use comments. The better queue policy gets one useful pass to the human reviewer before approval work begins.

From Comment to Repair

BugBot's repair path is tightly bound to Cursor. A finding can open through "Fix in Cursor" or move to Cursor's web agent. Cursor also documents Fix All and Autofix behavior, though the BugBot repair guide covers the safety checks in more detail.

That handoff removes the search for the affected line and brings the finding into the editor where a Cursor user already works. The proposed code still needs inspection and tests. A fast handoff can make a weak finding more tempting, so the team should require evidence before accepting the repair.

Greptile's "Fix with your Agent" action is built around choice. Its current overview lists Claude Code, Codex, Conductor, Cursor, and Devin. The handoff includes the issue, file paths, line numbers, and suggested code, giving the chosen agent a compact repair brief.

Agent choice helps a team that already splits work across coding tools. It also adds variation to the repair environment, permissions, and output. Keep the same verification rule no matter which agent receives the comment: inspect the diff, run the relevant tests, and preserve human approval.

The comparison ultimately turns on workflow ownership. BugBot gives Cursor the review and repair loop.

Greptile lets its review feed a separate coding agent. Teams with one standard editor may prefer the first arrangement; teams with several approved agents can use the second without forcing a new repair tool.

Provider and Security Fit

Greptile's quickstart documents GitHub App installation and a GitLab token-and-webhook flow. Repositories must be enabled for indexing, and the product needs enough access to build its graph. Large organizations should decide whether all repositories, selected repositories, or cross-repository context matches the smallest useful access boundary.

Greptile's Enterprise plan lists self-hosting, SSO/SAML, GitHub Enterprise coverage, custom terms, and a data-processing agreement. Public plan text cannot answer a team's retention or residency questions. Request the current architecture and contract details before connecting regulated or customer-sensitive code.

Cursor's main BugBot setup page documents GitHub administrator access and repository enablement. Cursor's product changelog also lists GitHub Enterprise Server and GitLab. Teams using self-hosted providers should confirm the exact integration path and plan with Cursor because the public setup material is much fuller for GitHub.

BugBot can use MCP servers on Teams and Enterprise plans. That can pull issue or service context into a review, and it creates another outbound access path to approve. Greptile cross-repository context creates a similar decision inside the source-control estate.

For either product, start with one repository and the smallest set of external systems. Record who can enable a repository, who can change review rules, and which agent can write fixes. Review quality cannot compensate for an unclear code-access boundary.

What the Price Difference Buys

Cursor says an average BugBot run costs $1.00 to $1.50 under its usage model, with pull-request size and effort affecting the result. Teams draw from on-demand spend, while Individuals draw from included usage before any extra spend.

Greptile Pro costs $30 per seat each month and includes 50 credits per seat. A standard review uses one credit, a Trex review uses three, and extra credits cost $1 each. The free Starter plan includes 50 monthly credits for one active developer.

Published plan terms, checked August 8, 2026.

Dimension	BugBot	Greptile
Billing unit	Usage per run	Seat plus credits
Paid floor	$1.00 to $1.50 average run	$30 per seat monthly
Included pool	Plan usage, then on-demand	50 credits per seat
Overage	On-demand spend	$1 per extra credit

The useful difference is what the fixed payment reserves. Greptile's seat charge buys an included review pool and the persistent graph service. BugBot starts with variable review usage and keeps the economics closer to the number and effort of runs.

For twenty standard reviews in a quiet month, BugBot's published average may sit near or below a Greptile Pro seat. For sixty Greptile standard reviews, the base and ten extra credits create a simple bill. Those examples exclude plan discounts, legacy terms, Trex reviews, and unusually large BugBot runs.

Use the full BugBot pricing section and Greptile pricing section for each product's current terms. In this matchup, measure the premium paid for persistent graph context against the time saved by Cursor's direct repair path.

Where the Context Bet Lands

Greptile is the stronger single purchase for a midsize or large team adding a dedicated reviewer to find cross-file contract breaks. The persistent graph gives the product a distinct job beside ordinary pull-request review, while its agent handoff keeps repair-tool choice open.

BugBot wins for a small Cursor-standardized team that prizes the shortest path from comment to edited code and wants spend to follow review volume. The trade-off is persistent repository context against a tighter Cursor loop: Greptile buys the graph, while BugBot buys the handoff and a variable meter.

FAQ

Do BugBot and Greptile work with GitLab?

Yes, both vendors currently describe GitLab coverage for their reviewers. Greptile's quickstart documents its GitLab token and webhook setup, while Cursor's product changelog lists BugBot on GitLab and GitHub Enterprise Server. Confirm self-hosted requirements, plan eligibility, and repository permissions with each vendor before connecting production code.

How do you trigger each reviewer manually?

On GitHub, Cursor documents the comments cursor review, bugbot run, and cursor run. Greptile documents tagging @greptileai on a GitHub or GitLab pull request, including prompts that ask for a focused check. Manual triggers are useful during a pilot because each review event can be measured.

Do both reviewers learn from feedback?

Both vendors describe feedback systems in which Cursor says BugBot turns repeated pull-request signals into candidate learned rules that can be promoted or disabled. Greptile says reactions and replies teach the reviewer what the team cares about over time. Treat both as product claims and test repeated cases.

Is Greptile free for open-source projects?

Greptile's pricing page says qualified non-commercial projects using MIT or Apache licences can apply for free service. The same page lists a free Starter plan with one active developer and 50 monthly credits. Eligibility and credit terms may change later.