CodeCrow is an enterprise-grade, AI-powered code review platform designed to automate the security and quality analysis of your pull requests and branches. By combining large language models with a Retrieval-Augmented Generation (RAG) pipeline, CodeCrow understands your entire codebase, providing deep, context-aware feedback directly in your VCS platform.
CodeCrow supports multiple version control systems with varying levels of integration. Below is the current feature matrix:
| Feature | Bitbucket | GitHub | GitLab |
|---|---|---|---|
| PR Analysis | + | + | - |
| Branch Analysis | + | + | - |
| Task Context Retrieval | - | - | - |
| /ask | + | + | - |
| /analyze | + | + | - |
| /summarize | + | + | - |
| Continuous Analysis | + | + | - |
| RAG Pipeline | + | + | - |
- Context-Aware Reviews: Powered by a custom RAG (Retrieval-Augmented Generation) pipeline using Qdrant vector storage.
- Incremental Analysis: Only scans changed code to keep feedback fast and cost-efficient.
- Multi-Tenant Architecture: Securely manage multiple teams and projects from a single dashboard.
- Interactive Commands: Command CodeCrow directly from PR comments using
/ask,/analyze, and/summarize.
For full setup guides, architectural deep-dives, and API reference, please visit our documentation portal:
High level components:
- Web frontend (
frontend/) – React-based UI for workspaces, projects, dashboards, and issue views. - Web server / API (
java-ecosystem/services/web-server/) – main backend API, auth, workspaces/projects, and orchestration. - Pipeline agent (
java-ecosystem/services/pipeline-agent/) – receives VCS webhooks, fetches repo/PR data, and coordinates analysis. - MCP client (
python-ecosystem/mcp-client/) – executes analyzers and calls LLMs using the Model Context Protocol. - RAG pipeline (
rag-pipeline/) – indexes code and review artifacts into Qdrant for semantic search.
Contributions are welcome. Please see our Development Guide for more information.