CodeLens: AI-Powered Code Analysis & GitHub Project Tracking

CodeLens is an AI tool for GitHub that provides code analysis, commit tracking and project insights. Streamline your workflow with real-time AI-driven monitoring.

Author

Prince Kumar Thakur
Prince Kumar ThakurTechnical Content Writer

Date

Jan 31, 2025

Table of Contents

Editor’s Note: This blog introduces CodeLens, an AI-powered code analysis tool developed during Hackathon 2024 by TechTitans. It provides centralized project analytics, AI-driven insights, and real-time code tracking, helping team leads efficiently monitor development progress. While the text has been refined for clarity and impact, it remains true to the energy, passion, and authenticity of the original moment.

CodeLens

Team Members: Rhea Dhingra, Ujjwal Agarwal, Jabjot Suri, Pranava Vasthi, Aman Fungire

During Hackathon 2024, our team developed CodeLens, an AI-driven code analysis and project tracking tool aimed at solving a common challenge faced by team leaders: gaining clear visibility into project progress and team contributions. With multiple developers working on a project, tracking insights like technologies used, commit frequency, contributions, and repository health becomes cumbersome. CodeLens provides a centralized dashboard that offers analytics, AI-driven insights, and repository monitoring to streamline project oversight.


Codelens interface

How it works

CodeLens connects directly with GitHub, allowing users to log in and access all their repositories in a structured dashboard. Once a user selects a repository, AI analyzes the codebase to extract critical insights, including commit history, project activity, top contributors, and pull request monitoring.

The AI-powered analysis engine breaks down the codebase into structured data, tracking elements such as code structure, complexity patterns, development activity, and potential improvements. This ensures that project leads gain a comprehensive overview of repository health without manually reviewing every file.

Key Features

code lens

CodeLens offers a centralized dashboard that consolidates repository metrics, commit tracking, pull request monitoring, and AI-driven insights. It includes GitHub authentication with Firebase Auth, providing seamless repository access. The platform utilizes React.js for frontend development and Python with FastAPI for backend processing, leveraging ChatGPT with LangChain for deep code analysis. The AI system analyzes code structure, commit patterns, and developer activity, identifying trends and areas for improvement. By embedding code into a vector database using OpenAI embeddings, CodeLens enables fast, intelligent code retrieval for analysis and recommendations.

Live Demonstration

Upon logging in via GitHub authentication, users access a dashboard displaying all repositories. The interface lists repository names, programming languages, privacy settings, and key statistics like forks, watchers, and creation dates. Selecting a repository triggers real-time AI analysis, generating insights into project activity and code health.

The commit history graph visualizes commit trends, showing the frequency and consistency of contributions. The top contributors section highlights individual team members, displaying their commitment counts and contribution levels. The pull request monitoring feature provides an overview of open, merged, and pending pull requests, allowing leads to track progress seamlessly.

The AI-powered code analysis module processes the codebase using LangChain and OpenAI embeddings, breaking it into chunks for efficient processing. The system identifies patterns, assesses code health, and suggests improvements, offering insights such as code complexity, modified files, and best practices for optimization.

code lens working

Why CodeLens Matters

For team leads and project managers, CodeLens provides a data-driven approach to project monitoring, eliminating the need for manual code reviews. By offering real-time AI-powered insights, it enhances visibility into team contributions, project structure, and repository activity. This helps developers optimize workflows, track performance, and maintain code quality effectively.

Final Thoughts

Developing CodeLens during Hackathon 2024 was an exciting challenge. Our goal was to empower team leads with an intelligent project analytics tool, making code monitoring effortless and efficient. We plan to refine its UI, expand its analysis scope, and integrate more detailed visualizations in the future.

SHARE ON

Related Articles.

More from the engineering frontline.

Dive deep into our research and insights on design, development, and the impact of various trends to businesses.

From RFPs to Revenue: How We Built an AI Agent Team That Writes Technical Proposals in 60 Seconds
Article

Apr 9, 2026

From RFPs to Revenue: How We Built an AI Agent Team That Writes Technical Proposals in 60 Seconds

GeekyAnts built DealRoom.ai — four AI agents that turn RFPs into accurate technical proposals in 60 seconds, with real-time cost breakdowns and scope maps.

How We Built an AI System That Automates Senior Solution Architect Workflows
Article

Apr 6, 2026

How We Built an AI System That Automates Senior Solution Architect Workflows

Discover how we built a 4-agent AI co-pilot that converts complex RFPs into draft technical proposals in 15 minutes — with built-in conflict detection, assumption surfacing, and confidence scoring.

AI Code Healer for Fixing Broken CI/CD Builds Fast
Article

Apr 6, 2026

AI Code Healer for Fixing Broken CI/CD Builds Fast

A deep dive into how GeekyAnts built an AI-powered Code Healer that analyzes CI/CD failures, summarizes logs, and generates code-level fixes to keep development moving.

A Real-Time AI Fraud Decision Engine Under 50ms
Article

Apr 2, 2026

A Real-Time AI Fraud Decision Engine Under 50ms

A deep dive into how GeekyAnts built a real-time AI fraud detection system that evaluates transactions in milliseconds using a hybrid multi-agent approach.

Building an Autonomous Multi-Agent Fraud Detection System in Under 200ms
Article

Apr 1, 2026

Building an Autonomous Multi-Agent Fraud Detection System in Under 200ms

GeekyAnts built a 5-agent fraud detection pipeline that makes decisions in under 200ms — 15x cheaper than single-model systems, with full explainability built in.

Building a Self-Healing CI/CD System with an AI Agent
Article

Mar 31, 2026

Building a Self-Healing CI/CD System with an AI Agent

When code breaks a pipeline, developers have to stop working and figure out why. This blog shows how an AI agent reads the error, finds the fix, and submits it for review all on its own.

Scroll for more
View all articles