This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
CodeTrace-AI
Ground your AI coding agent in your actual codebase
CodeTrace-AI is an autonomous engineering intelligence platform that understands entire repositories through structural analysis, semantic search, dependency mapping, call graphs, and AI reasoning.
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.
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?
About CodeTrace-AI on Product Hunt
“Ground your AI coding agent in your actual codebase”
CodeTrace-AI was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #100 on the daily leaderboard. CodeTrace-AI is an autonomous engineering intelligence platform that understands entire repositories through structural analysis, semantic search, dependency mapping, call graphs, and AI reasoning.
CodeTrace-AI was featured in Open Source (68.6k followers), Developer Tools (515.9k followers), Artificial Intelligence (473.7k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 221.7k products, making this a competitive space to launch in.
Who hunted CodeTrace-AI?
CodeTrace-AI was hunted by viraj sawant. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how CodeTrace-AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
viraj sawant