Cover image for 🛡️ CodeAegis AI
Production-Grade Multi-Agent Code

🛡️ CodeAegis AI Production-Grade Multi-Agent Code Reviewer & Security Intelligence Platform CodeAegis AI is a production-grade multi-agent code analysis platform that automatically reviews source code and Git diffs across four critical engineering dimensions: Security, Architecture, Performance, and Testability. The platform combines Python AST-based static analysis with LLM-powered multi-agent orchestration to identify potential vulnerabilities, architectural issues, performance bottlenecks, code-quality problems, and testing gaps. Instead of returning a generic AI-generated review, CodeAegis uses specialized analysis agents to evaluate code from different engineering perspectives and consolidate their findings into a unified assessment.* 🔑Key Features ⁕ Multi-Agent Code Review — Specialized agents analyze security, architecture, performance, and testability independently. ⁕ Security Intelligence — Detects suspicious patterns and potential security vulnerabilities using static analysis and AI reasoning. ⁕ AST-Based Analysis — Parses Python source code structurally for reliable static analysis beyond simple text matching. ⁕ Git Diff Analysis — Focuses reviews on newly introduced or modified code. ⁕ Health Scoring — Generates an overall code health assessment based on multiple analysis dimensions. ⁕ Inline Recommendations — Provides actionable feedback tied directly to problematic code. ⁕ AI-Powered Refactoring — Generates improved code fixes that developers can review and apply. ⁕ Automated Testing — Includes a pytest-based test suite for validating core functionality and API behavior. ⚙️Technology Stack ⁕ Backend: FastAPI, Uvicorn, Pydantic, Jinja2 ⁕ AI & Analysis: LLM-based agent orchestration, Python AST static analysis, Git diff analysis ⁕ Testing: pytest, HTTPX ⁕ Frontend: HTML5, CSS3, JavaScript (ES6+), Font Awesome, Google Fonts 💭Engineering Focus The project was designed around a practical developer workflow: analyze → identify → explain → score → fix. The goal is to make AI-assisted code review more structured, actionable, and engineering-focused by combining deterministic static analysis with the reasoning capabilities of specialized AI agents.

Aniket Potabatti

Data Scientist & Analyst