Greptile

Greptile is an AI-powered code review and software engineering platform designed to help development teams identify bugs, understand code changes, enforce engineering standards, and validate pull requests. Rather than operating primarily as an AI code-completion tool, Greptile focuses on codebase-aware validation: it analyzes changes in the context of the wider repository so that potential problems can be identified beyond the files directly modified in a pull request.

Greptile’s current platform describes itself as an AI code reviewer that uses a graph of the codebase together with multiple AI agents to review pull requests. The system indexes files, functions, classes, and dependencies to understand how different parts of a repository are connected. It then applies that context when analyzing changes.

AI code review

Greptile’s main use case is automated pull-request review. Once connected to GitHub or GitLab, Greptile can automatically review pull requests and post findings as comments.

Its codebase graph is intended to provide more context than traditional tools that analyze individual files or isolated diffs. Greptile can examine how a change affects other parts of the application and identify potential issues involving dependencies, architecture, or related code.

The platform says reviews generally appear within approximately three minutes. Developers can also use a Fix with your Agent function to send a finding, including relevant file paths, line numbers, and suggested code, to compatible coding agents such as Claude Code, Codex, Cursor, Conductor, or Devin.

Codebase intelligence

Greptile builds a detailed representation of the codebase so its AI can understand relationships between files, functions, classes, and dependencies.

This approach is central to the product. Instead of asking an AI model to review a pull request with only the changed lines, Greptile provides broader repository context so that the reviewer can reason about how a change fits into the existing system.

The platform can also index adjacent repositories, which can be useful for organizations where applications depend on shared libraries or services stored in separate repositories.

Learning from engineering teams

Greptile can learn from the way a development team responds to its reviews. The platform uses reactions, replies, and merged changes to improve the relevance of future reviews.

Teams can also define custom rules describing their coding standards, architectural requirements, or project-specific conventions. Rules can be scoped to repositories, directories, or file types.

Greptile supports configuration through greptile.json and can automatically index existing rule files such as CLAUDE.md, AGENTS.md, and Cursor rules. This allows organizations to incorporate existing development standards into AI-assisted code review.

Knowledge base

Greptile maintains a self-updating knowledge base describing how a codebase works. The knowledge base can document components of the repository, relationships between different parts of the system, and potential risks.

As the codebase changes, the knowledge base can be updated so that reviews continue to use current repository information.

The knowledge base can also provide context to AI agents, allowing development agents to work with a deeper understanding of the application without requiring teams to manually explain the architecture each time.

TREX runtime testing

Greptile has expanded beyond static pull-request analysis with TREX, its runtime validation agent.

TREX runs a pull-request branch in a sandbox environment and is designed to detect problems that may only become visible when software actually runs. It can start services, mock inputs, interact with user interfaces, and collect logs, screenshots, and traces.

This makes TREX complementary to Greptile’s standard code review because static analysis and runtime validation can identify different categories of problems.

Third-party API context

Greptile also provides partner-specific context for software teams using third-party APIs and SDKs.

Its partner program currently includes companies such as OpenAI, Vercel, Stripe, PostHog, Datadog, WorkOS, Braintrust, and Mintlify. When Greptile detects that a pull request interacts with one of these supported APIs or SDKs, it can review the implementation against partner-provided documentation, rules, and common failure patterns.

This can be particularly useful for API-heavy applications where a code change may be syntactically correct but still violate provider-specific implementation requirements.

GitHub and GitLab

Greptile supports GitHub and GitLab, including enterprise and self-managed deployments.

The platform can work with GitHub Cloud, GitHub Enterprise, GitHub Enterprise Server, GitLab Cloud, and GitLab Self-Managed. Enterprise customers can also deploy Greptile within their own infrastructure and support air-gapped environments.

Engineering workflow integrations

Greptile can connect with other engineering tools to provide additional context for code reviews.

Its integrations include development platforms, issue-management systems, CI/CD tools, observability services, security platforms, and collaboration tools. The company has also supported MCP-based context connections to services such as Jira, Notion, and Google Docs.

These integrations allow Greptile to consider information outside the source code itself when evaluating changes. For example, Jira or Linear context can help determine whether a pull request actually implements the requirements associated with an engineering task.

API

Greptile provides an API that organizations can use to build custom software-engineering workflows.

The company describes examples including connecting Greptile to Sentry or Datadog to diagnose alerts, integrating it with testing pipelines to suggest fixes for failed tests, and connecting it with documentation systems to generate knowledge-base content from codebase context.

Greptile’s current website also confirms that API use is available with discounted bulk pricing for customers building their own products or integrations.

Security and enterprise deployment

Security is a major focus of Greptile’s enterprise offering. The company states that it is SOC 2 Type II compliant and offers enterprise capabilities including SSO/SAML, audit logs, dedicated support, and self-hosted deployment.

Self-hosted Greptile can run using Docker Compose or Kubernetes and can be deployed in AWS, Google Cloud, Azure, and air-gapped environments. Organizations can also configure their own LLM providers, including OpenAI-compatible APIs and AWS Bedrock.

Greptile’s enterprise page additionally states that the platform supports HIPAA and GDPR compliance.

Programming languages

Greptile supports mainstream programming languages including Python, JavaScript, TypeScript, Go, Elixir, Java, C, C++, C#, Swift, PHP, and Rust.

The company states that other programming languages are also supported, although response quality may be somewhat lower for languages outside its primary supported set.

AI development ecosystem

Greptile positions itself as a validation layer that can work alongside different AI coding agents rather than requiring developers to adopt a single AI coding environment.

Its integrations include coding agents such as Claude Code, Codex, Cursor, Conductor, and Devin. This allows teams to use AI-generated code while applying Greptile as an independent review and validation layer.

This positioning is particularly relevant as development teams increasingly use multiple AI coding tools. Instead of replacing those tools, Greptile can evaluate the changes they produce.

Pricing

Greptile currently uses a subscription plus usage-based pricing model.

The official pricing page lists three plans:

Starter — Free

The Starter plan is designed for individual developers. It includes unlimited repositories, 50 credits per month, and one active developer. One standard review uses one credit, while one TREX review uses three credits.

Pro — $30/seat/month

The Pro plan is designed for teams. Each seat includes 50 credits per month, with additional credits priced at $1 each. Repositories remain unlimited.

Enterprise — Custom pricing

The Enterprise plan adds self-hosting, security and compliance capabilities, SSO/SAML, GitHub Enterprise support, dedicated Slack support, and custom invoicing and contractual terms.

Greptile also offers a 14-day free trial for its paid offering. Qualified open-source projects can receive free access, and eligible pre-Series A startups with under $2 million in revenue over the previous 12 months can receive a 50% discount.

Review

Greptile is one of the more specialized AI tools in the software-development market because its primary goal is not to generate code but to validate code with deep repository context.

Its strongest feature is the codebase graph. By understanding relationships among files, functions, classes, and dependencies, Greptile can look beyond the immediate pull-request diff. This makes it potentially valuable for large repositories where a seemingly small change can have consequences elsewhere in the application.

The learning system is another advantage. Teams can define their own rules and allow Greptile to learn from review feedback, which can make its comments increasingly aligned with the organization’s development practices.

TREX expands the platform into runtime validation, giving Greptile a way to test pull requests in a sandbox rather than relying exclusively on static analysis. This is a meaningful extension for teams concerned about bugs that only become visible when an application executes.

Greptile also has strong enterprise capabilities. GitHub and GitLab support, self-hosting, air-gapped deployment, SSO/SAML, audit logs, and custom LLM configurations make it suitable for organizations with strict security or compliance requirements.

The primary limitation is specialization. Greptile is fundamentally a software-engineering and code-validation product, so its value is concentrated among development teams rather than general business users. The credit-based system also means teams with unusually high review volumes can incur additional usage charges.

AI review should also remain complementary to human engineering judgment. A tool that identifies potential bugs can reduce review workload, but architecture, security, product requirements, and production risk still require experienced developers to make the final decisions.

Greptile is best suited to software engineering teams, DevOps organizations, platform teams, startups, enterprises, and teams using AI coding agents that need an independent layer of automated code validation.