Repo-Tutor

Problem Statement Onboarding software engineers onto complex codebases is time-consuming, expensive, and error-prone. Traditional onboarding relies on outdated documentation or senior engineer hand-holding. New developers often struggle to understand codebase architecture, locate critical components, and verify their comprehension without risking production code. Solution: Repo-Tutor Repo-Tutor (built within workspace Provisio) is a multi-agent AI onboarding assistant that turns static repositories into interactive learning environments. Designed for software engineering teams and technical leads, Repo-Tutor analyzes local codebases, answers natural language questions, and evaluates developer understanding through interactive quizzes. How Users Interact with Repo-Tutor Repository Mapping: The system ingests the codebase, building a structured AST and file-tree map without consuming LLM tokens. Interactive Q&A: Engineers ask codebase-specific questions (e.g., "How does error triage work?"). The Mentor Agent uses TF-IDF retrieval and dynamic context scaling to provide grounded answers with precise line references. Comprehension Assessment: The Coach Agent generates dynamic multiple-choice quizzes based on repo context. As developers submit answers, the system scores them in real time using strict keyword evaluation, providing immediate feedback and recommending targeted starter tasks. Why It Is Creative and Unique Repo-Tutor combines a zero-LLM local analyzer with intelligent LLM agent orchestrations. Instead of relying solely on generic LLM prompts, it uses hybrid retrieval (TF-IDF + Groq Llama-3.1 API) to prevent hallucinations. Furthermore, its interactive quiz and task recommendation engine actively tests comprehension rather than passively generating text, ensuring true onboarding readiness.