ARIA
Structural intelligence for unfamiliar codebases
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ARIA - Structural intelligence for unfamiliar codebases

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ARIA helps developers understand unfamiliar codebases before making changes. It combines AST-based code analysis, dependency and call graphs, semantic search, API intelligence, and repository-aware AI to turn complex repositories into structured, searchable engineering knowledge.

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Hey Product Hunt! 👋 I built ARIA to solve a problem I kept running into: understanding an unfamiliar codebase before making changes to it. Most AI code tools primarily rely on semantic search. ARIA adds structural context by analyzing the repository itself — symbols, dependencies, call relationships, API surfaces, and change impact. The goal is simple: help developers answer questions like “If I change this, what else could be affected?” with more context than text similarity alone can provide. ARIA is open source and self-hosted. I’ve also included a reproducible evaluation package with a pinned 10-task benchmark and qualitative case studies. I’d love feedback from developers working with large codebases, static analysis, code search, RAG, or developer tooling. What would you want ARIA to understand about your codebase that current AI coding tools often miss?