RepoPilot : Agentic AI Onboarding

Every time a new developer joins a team or contributes to an open-source project, they face the same expensive barrier: context acquisition. Understanding a complex codebase—its architecture, business logic, entry points, and hidden invariants—typically takes weeks of manual exploration, outdated documentation reading, and interrupting senior engineers. This "week-one tax" costs companies thousands of dollars in lost productivity and delays critical feature delivery. Existing tools (static linters, basic README generators, or generic chatbots) fail because they lack full-repository context and cannot provide evidence-backed guidance tied to specific lines of code. The Solution: RepoPilot RepoPilot is an agentic AI platform that ingests any GitHub repository and autonomously produces a personalized, interactive, and evidence-backed onboarding experience in minutes. By leveraging IBM Bob 2.0 as its core reasoning engine, RepoPilot doesn't just summarize code—it understands it. It acts as an expert senior engineer pair-programming with the new hire, guiding them through the codebase via an interactive 8-Step Code Tour, grounded Q&A, and dynamic Architecture Visualization. How It Works (The Agentic Workflow) Ingest & Parse: The user pastes a GitHub URL. RepoPilot clones the repo, uses Tree-sitter to parse ASTs across Python/TypeScript/JavaScript, and chunks the code semantically. Parallel Agent Analysis (IBM Bob 2.0): Two specialized Bob subagents run in parallel: Architecture Agent: Maps modules, entry points, and data flow. Business Logic Agent: Identifies domain concepts, invariants, and state transitions. Synthesis & Narration: Findings are merged, ranked by conceptual dependency (not file hierarchy), and narrated by Bob into human-readable explanations. Interactive Delivery: The frontend renders a live Code Tour Viewer (with syntax highlighting and keyboard navigation) and a Grounded Q&A Panel where every answer cites exact file:start-end line ranges. Key Features & Innovation Evidence-Backed Citations: Every claim made by the AI is backed by a clickable citation (path:start-end). No hallucinations—just verifiable code references. Personalized Role-Based Tours: Whether you're a Backend, Frontend, or DevOps engineer, RepoPilot tailors the tour steps to your specific role. Live Progress Streaming: Real-time WebSocket updates show the agentic pipeline in action (Ingest → Parse → Analyze → Synthesize), building trust in the AI's process. Grounded Q&A: Ask questions like "How does retry logic work?" and get answers derived only from the retrieved code chunks, with direct links to the source. Deep Integration with IBM Bob 2.0 RepoPilot was built AI-first around IBM Bob 2.0's unique capabilities: Bob Shell CLI Integration: We engineered a robust Python wrapper (bob_client.py) that invokes Bob non-interactively via bob -p, handling JSON extraction, timeout management, and automatic repair retries for malformed outputs. Agentic Subagents: We leveraged Bob's ability to spawn specialized subagents (Architecture vs. Business Logic) to parallelize analysis, reducing total processing time by 40%. Full-Repo Context: Unlike RAG-based chatbots that see snippets, Bob analyzes the entire repository structure, enabling holistic architectural insights. Verified Usage: Over 15+ Bob session screenshots and a detailed BOB_PLAYBOOK.md document our prompt engineering journey, including how we solved schema-echo failures and optimized tool-call efficiency (e.g., 17 tool calls for deep ky analysis). Tech Stack AI Engine: IBM Bob 2.0 (Bob Shell CLI, Agent Mode) Orchestration: LangGraph (Python) Backend: FastAPI, PostgreSQL + pgvector (Supabase), Tree-sitter Frontend: Next.js 14, Tailwind CSS, shadcn/ui, WebSockets DevOps: Docker, Vercel/Railway-ready, Sentry monitoring