Every pull request is reviewed by AI that indexes your full repository with hybrid RAG retrieval — not just the diff. Catches cross-file bugs, security issues, and architectural problems that surface-level tools miss.
CodeRaven AI Code Review is an automated pull request review system that indexes your entire codebase using Tree-sitter parsing and vector embeddings, then retrieves relevant context via hybrid RAG (keyword + semantic search) to review diffs with full cross-file awareness. It works with GitHub, GitLab, and Bitbucket.
Traditional code review tools analyze only the diff — the lines that changed in a pull request. This means they miss bugs that span multiple files, architectural violations that only become apparent in context, and security vulnerabilities that depend on how different modules interact.
CodeRaven takes a fundamentally different approach. When you connect a repository, it clones the entire codebase and builds a dual-layer index: Tree-sitter parsing captures the structural relationships between functions, classes, and modules, while OpenAI embeddings create a semantic map of what each piece of code does. When a pull request is opened, the AI uses hybrid RAG (combining keyword search with vector similarity) to retrieve the most relevant context from across the entire repository — not just the changed files.
The result is a review that understands cross-file dependencies, catches regressions in code that wasn't directly modified, identifies security patterns that span multiple modules, and enforces architectural conventions that only make sense when viewed at the project level. Each finding includes a risk classification and confidence score, so you can focus your attention where it matters most.
Real outcomes that improve how your team builds software.
The AI understands how changes in one file affect other parts of the codebase, catching regressions that diff-only tools miss entirely.
Every PR gets the same thorough review regardless of reviewer availability, time pressure, or team workload.
Automated first-pass reviews reduce the time senior engineers spend on routine code quality checks significantly.
Define team-specific review prompts to enforce coding conventions, architecture patterns, and security requirements consistently.
No complex setup required — connect your tools and start seeing results.
Link your GitHub, GitLab, or Bitbucket account. CodeRaven clones the repo and builds a searchable index using Tree-sitter for code structure and OpenAI embeddings for semantic understanding.
When a PR is opened, the AI retrieves relevant context from across the entire codebase using hybrid RAG — not just the changed files. It understands cross-file dependencies and architecture patterns.
The AI posts review comments directly on the PR with risk levels, confidence scores, and specific suggestions. Low-risk issues can be auto-fixed with a commit.
Reduce review bottlenecks and catch bugs before they reach production. Every PR gets reviewed consistently, whether senior engineers are available or not.
Catch security vulnerabilities, SQL injection patterns, exposed credentials, and unsafe API usage automatically across every code change.
Handle high PR volume with AI-powered triage. Review contributions for code quality, style consistency, and potential regressions automatically.
See it in action — no credit card required.
Start Free TrialWhen the AI reviewer finds low-risk issues — formatting, naming conventions, simple bugs — it commits the fix automatically. Higher-risk findings still go through human review.
Learn moreAssign a ticket from Jira, Linear, or ClickUp to the AI. It reads the requirements, clones your repo, writes the implementation, creates a branch, and opens a pull request — fully automatically.
Learn moreAutomate ticket status updates, assignments, and notifications based on pull request events. Template variables for dynamic comments. No code required.
Learn moreAI code review, ticket execution, project management, and documentation for your whole organization starting at $24/mo.