Launching today

CodeTrace-AI

Launching today

Ground your AI coding agent in your actual codebase

5 followers

Ground your AI coding agent in your actual codebase

5 followers

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CodeTrace-AI is an autonomous engineering intelligence platform that understands entire repositories through structural analysis, semantic search, dependency mapping, call graphs, and AI reasoning.

Free

Launch tags:Open Source•Developer Tools•Artificial Intelligence

Launch Team

Otari by Mozilla AIControl Your LLM Infrastructure A/B

Promoted

Maker

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Hey — I'm Viraj, solo dev on this. CodeTrace-AI started from a frustration I kept hitting with AI coding agents on real codebases: they'd confidently refactor something and miss that a function was called via a dynamic import three files away, or invent an API that didn't exist. The agents were smart. They just weren't grounded. So I built the grounding layer. CodeTrace-AI indexes your repo locally using Tree-sitter for parsing, ChromaDB for semantic search, and a SQLite/NetworkX graph for structural relationships. It exposes all of this to your agent through an MCP server, so Claude Code / Cursor / any MCP client can actually ask "who calls this function" or "what's the blast radius of changing this signature" — and get a grounded answer instead of a plausible-sounding guess. Two things I care about that most competitors don't do: 1. **Local-first, air-gap deployable.** Your code never leaves your machine. Works fully offline. Regulated environments can actually use it. 2. **Honest about uncertainty.** Dynamic imports and config-driven wiring are hard. Instead of pretending, CodeTrace-AI labels every edge in the graph as STATIC_RESOLVED, STATIC_INFERRED, DYNAMIC_UNRESOLVED, or CONFIG_DECLARED — so your agent (and you) know how much to trust each connection. There's also an interactive HTML architecture visualizer that I honestly should have marketed harder from day one. The core is open source. A runtime-static correlation layer is coming as a paid tier. Would love feedback — especially on the MCP server integration story.

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3d ago

A dependency map for test coverage would be a great addition, showing which unit and integration tests actually exercise each module or function call chain. Right now I can see what calls what, but not what gets validated, which makes it harder to spot untested critical paths before refactoring. Could your AI flag functions with no direct or indirect test coverage and suggest missing test cases for high-risk code?

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3h ago

Otari by Mozilla AIControl Your LLM Infrastructure A/B

Promoted

Maker

📌

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3d ago

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3h ago