RAraghavchamadiya·Apr 6, 2026Submission
Show HN: Repowise – Codebase intelligence for AI coding agents (open source)
Every AI coding session starts from zero. The agent doesn't know who owns a file, which files change together, which ones are dead, or why something was built that way.
I got tired of Claude Code asking me "what does this service do?" on a codebase I've worked on for two years. The context is all there (git history, dependency graph, past decisions), it's just not in a format the agent can use.
Repowise indexes your codebase into four layers: dependency graph (tree-sitter → NetworkX), git intelligence (hotspots, ownership, co-change patterns), auto-generated docs, and architectural decision records. These get exposed via 8 MCP tools. On each commit, only changed files get re-indexed. No full rebuilds, under 30 seconds.
The decisions layer is the part I'm most excited about. It surfaces architectural intent from commit history: large diffs
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, migration patterns, tradeoff language. It also tracks staleness as code evolves. Works well on repos with decent commit messages, less well on "fix" history.
pip install repowise
repowise init # ~25 min first time, <30s incremental
repowise serve # MCP server + local dashboard
Works with Claude Code, Cursor, and any MCP-compatible agent. AGPL-3.0, self-hosted, your code stays on your machine.
I'm running early benchmarks on SWE-Bench and SWE-QA. Results are promising, especially on tasks where grounded context matters. Publishing the full methodology soon.
What's rough: Windows untested, language coverage is Python/TS/Go/Rust/Java/C/C++, decisions UX needs work. Happy to hear what breaks.
Pre-indexed demos (FastAPI, Starlette) on the site. Working on Slack, Jira, and Linear integrations for the hosted team version.
GitHub: github.com/repowise-dev/repowise
Site: repowise.dev