Local Code Index for Coding Agents: Resolves Imports Without Language Server

Basemind is an MIT-licensed Rust tool that builds a local code index for AI coding agents, resolving imports and building a call graph without a language server. It runs fully offline with no build step and no per-language daemon, which matters when the repo doesn't compile because an agent is halfway through editing it.

The tool tiers language support by understanding depth: JS/TS goes through oxc for a real scope tree, symbol table, import/export resolution, and tsconfig path aliases in monorepos. Python and Java use GitHub's stack-graphs (archived, but forked and maintained with four upstream rule bugs fixed that used to abort resolution on typed splat parameters or chained assignments). Everything else gets intra-file lexical scope binding only.

371 grammars parse, about 100 give up symbols and calls, and three get real resolution. The key design choice: when you ask for a function's callers, it returns every call site a name scan finds, annotated with which ones resolution could prove, rather than only the proven subset. This avoids the failure mode of an earlier version that filtered to proven hits and reported 2 callers on a file with 172, with no truncation flag.

Performance: scanning the TypeScript compiler (81k files) takes ~18 seconds on an M4; git history queries land in tens of microseconds regardless of history depth. The index lives in a local cache.

The author concedes this is not LSP-precision; for generics, macros, dynamic dispatch, DI containers, and monkey-patching, a language server will be right where Basemind is wrong. But for agents mid-edit on a non-compiling repo, it's a practical alternative.

Who it's for: Developers building or using AI coding agents that need fast, offline code navigation without the overhead of language servers.