I built Satori: a read-only codebase map for AI coding agents

Hamza built Satori, an open-source, read-only codebase map for MCP-compatible AI coding agents. It provides a structured investigation path to help agents efficiently navigate symbols, ownership boundaries, and dependencies without editing source code, reducing token costs and improving context accuracy.

Hi, I’m Hamza, the maker of Satori . Most AI coding agents can search files. That is not enough. Real codebases are not flat text dumps. They have symbols, ownership boundaries, wrappers, callers, callees, stale files, generated output, and logic spread across folders. Without structured repo context, agents build context the hard way. They burn tokens pulling broad file dumps. They still may not have the complete picture. They guess which file owns the behavior. They miss related callers, wrappers, and stale paths. That is expensive for frontier models — and even harder for smaller, local, or non-frontier models where every token matters more. I built Satori to give MCP-compatible coding agents a more efficient investigation path before they touch code. Satori is an open-source, read-only codebase map for AI coding agents. It helps agents move from: plain-English intent → owner symbols → file outline → exact symbol / line reads → advisory caller/callee context → freshness-aware recovery The goal is simple: Satori does not edit your source code. It gives agents better repo context so developers can steer and approve changes with more confidence. Search gives agents access to text. But investigation needs more than nearby text. A real repo has structure: When agents do not have a structured path, they often compensate by pulling more files into context. That increases token cost, but it does not guarantee completeness. More context is not the same thing as better evidence. Satori exposes a small MCP tool surface for repo investigation: The point is not to replace developer judgment. The point is to make the agent show better evidence before it proposes a diff. Instead of asking an agent to grep around a repo and hope it finds the right place, Satori gives it a repeatable path: That makes the investigation cheaper, more inspectable, and less dependent on dumping entire files into the model. npx -y @zokizuan/satori-cli@latest install --client all npx -y @zokizuan/satori-cli@latest doctor Satori works with Codex, Claude Code, OpenCode, and MCP-compatible clients. It is currently pre-alpha , and offline support is planned. Website: https://satori.hamza.my.id/ https://satori.hamza.my.id/ GitHub: https://github.com/ham-zax/satori https://github.com/ham-zax/satori npm: https://www.npmjs.com/package/@zokizuan/satori-cli https://www.npmjs.com/package/@zokizuan/satori-cli Product Hunt: https://www.producthunt.com/products/satori-4?utm source=other&utm medium=social https://www.producthunt.com/products/satori-4?utm source=other&utm medium=social If you use coding agents on real repos, I’d love to hear where they usually lose context. Is it wrong files? Broad file dumps? Missed callers? Stale repo state? Overconfident edits? And if Satori looks useful, a GitHub star, Product Hunt upvote, or comment would really help the launch.