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, 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