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🔍 RepoLens — Understand Any Codebase in Seconds

🧠 Paste a GitHub URL. Get a full visual architecture walkthrough, module breakdown, key code flows, and an AI chat to explore deeper.

RepoLens Railtracks Augment


💡 The Problem

Every developer knows the pain: you open a new repo and face a wall of unfamiliar code. READMEs are outdated. Architecture docs don't exist. You spend hours jumping between files trying to piece together how things work.

What if you could understand any codebase in under 60 seconds?

✨ What RepoLens Does

  1. 📎 Paste a GitHub repo URL
  2. 🤖 AI agents analyze the entire codebase — architecture, modules, data flows, patterns
  3. 📊 Get an interactive visual walkthrough:
    • 🗺️ Architecture Diagram — auto-generated Mermaid diagram showing how components connect
    • 📦 Module Breakdown — every major module explained with key files highlighted
    • 🔄 Key Code Flows — step-by-step walkthroughs of critical paths (auth, API lifecycle, data pipeline, etc.) with annotated code snippets
  4. 💬 Ask follow-up questions — chat with the codebase using semantic code search

🏗️ How It Works

  GitHub Repo URL
        │
        ▼
  ┌─────────────────┐    ┌──────────────────────────────────────┐
  │  🌐 Next.js UI   │◄──►│  ⚡ FastAPI Backend                   │
  └─────────────────┘    └──────────────┬───────────────────────┘
                                        │
                                        ▼
                          ┌──────────────────────────────┐
                          │  🤖 Railtracks Agent Pipeline  │
                          │                              │
                          │  IndexerAgent ──────────────►│
                          │  ArchitectureAgent ────────►│
                          │  ModuleAgent ──────────────►│
                          │  FlowAgent ────────────────►│
                          │  DiagramAgent (+ validator) ►│
                          │  ComposerAgent ────────────►│
                          └──────┬──────────┬───────────┘
                                 │          │
                      ┌──────────▼┐  ┌──────▼──────────┐
                      │ 🧬 Augment │  │ 🌊 DigitalOcean  │
                      │  Context   │  │  Gradient AI    │
                      │  Engine    │  │  Inference      │
                      └───────────┘  └─────────────────┘

🔗 The Agent Pipeline (Built with Railtracks)

RepoLens uses 7 specialized AI agents orchestrated as a sequential flow with a validation loop:

Agent Job
🗂️ IndexerAgent Indexes the repo using Augment Context Engine via MCP
🏛️ ArchitectureAgent Identifies architecture patterns, tech stack, and design decisions
📦 ModuleAgent Breaks the codebase into logical modules with descriptions
🔄 FlowAgent Discovers 3-5 critical code flows with step-by-step walkthroughs
📊 DiagramAgent Generates a Mermaid.js architecture diagram
DiagramReviewer Validates diagram syntax and completeness (validation loop)
🎼 ComposerAgent Assembles everything into a structured response for the UI

Plus a 💬 ChatAgent for follow-up Q&A powered by semantic code retrieval.

🧬 Augment Code — The Brain of RepoLens

Augment's Context Engine is the centerpiece — it's what makes the analysis actually understand code rather than just pattern-match.

  • Context Connectors index any GitHub repo into a semantic search engine
  • Agents query the index via MCP (Model Context Protocol) to find relevant code
  • The ChatAgent uses semantic retrieval for grounded, accurate answers
  • Every code snippet shown in the UI comes from Augment's semantic search — not naive grep
# Railtracks agents connect to Augment via MCP
augment_server = connect_mcp(
    MCPHttpParams(url="http://localhost:8081/mcp")
)

ArchitectureAgent = rt.agent_node(
    tool_nodes=augment_server.tools,  # 🧬 Augment search tools
    llm=do_gradient_llm,              # 🌊 DigitalOcean inference
    system_message="Analyze this codebase's architecture..."
)

🌊 DigitalOcean — Infrastructure & Intelligence

  • Gradient AI Inference Hub — powers all LLM calls across every agent (OpenAI-compatible API)
  • App Platform — one-click deployment for both frontend and backend
  • All inference runs through inference.do-ai.run — zero infrastructure to manage

🤖 Railtracks — The Orchestration Layer

  • Flows are just Python — no config files, no DSLs, just async/await
  • Sequential pipeline with a validation loop for diagram quality
  • Built-in MCP support connects directly to Augment's Context Engine
  • Full observability and error handling out of the box

🛠️ Tech Stack

Layer Technology
🌐 Frontend Next.js + Tailwind CSS + shadcn/ui
⚡ Backend Python + FastAPI + uvicorn
🤖 Agents Railtracks — flows are just Python
🧬 Code Intelligence Augment Code Context Connectors + Context Engine SDK
🧠 LLM Inference DigitalOcean Gradient AI Inference Hub
📊 Diagrams Mermaid.js
🚀 Deployment DigitalOcean App Platform

🚀 Quick Start

# Clone
git clone https://github.com/your-org/repolens.git && cd repolens

# Backend
cd backend
cp .env.example .env   # Add your API keys
uv sync
uv run uvicorn main:app --reload --port 8000

# Frontend (new terminal)
cd frontend
pnpm install && pnpm dev

Open http://localhost:3000 and paste a GitHub URL 🎉

🔑 Environment Variables

DO_MODEL_ACCESS_KEY=     # DigitalOcean Gradient AI key
AUGMENT_SESSION_AUTH=    # From `auggie token print`
GITHUB_TOKEN=            # GitHub PAT for repo access

🎬 Demo Script (3 Minutes)

Time Action
0:00 🎤 "Every developer wastes hours understanding new codebases. We fixed that."
0:15 📎 Paste a GitHub repo URL into RepoLens
0:30 ⏳ Show the agent pipeline working — "7 AI agents powered by Railtracks, using Augment's Context Engine to deeply understand the code"
1:00 🗺️ Walk through the architecture diagram
1:30 📦 Show module breakdown + key code flows with annotated snippets
2:00 💬 Ask a follow-up question in the chat — "How does authentication work?"
2:30 🌊 "Deployed live on DigitalOcean App Platform, LLM inference via Gradient AI"
2:45 🎯 "Try it yourself" — share the live URL

👥 Team

Built with ❤️ and ☕ at the Multimodal Hackathon 2026


🤖 Built with Railtracks · 🧬 Powered by Augment Code · 🌊 Deployed on DigitalOcean

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