agentmap

agentmap

The TS/JS-accurate repo map for your coding agent — a compiler-grade ts-morph import/symbol graph that answers "where is it / what breaks / does this already exist" in ~98% fewer context tokens.

Your AI coding agent re-learns your codebase every session — opening files and grepping to find what connects to what, burning tokens before it writes a line. agentmap gives it a queryable, ranked code-relationship map for TypeScript/JavaScript repos instead — a ts-morph import/symbol graph (the real TypeScript compiler, so aliases, vite/webpack resolve.alias, package.json #imports subpaths, and workspace cross-package imports all resolve) ranked by personalized PageRank. Ask it to "add a field" or "fix the login bug" and it finds the right files, their imports, and what already exists in ~98% fewer context tokens on average (up to ~99.9% per task; figures are chars/4 estimates applied equally to both sides) — kept current by a post-commit auto-refresh and actually used via a PreToolUse(Grep) hook.

agentmap's wedge in one line: it's the TS/JS-accurate repo map — a real TypeScript-compiler graph, not a tree-sitter approximation — with a published, honest accuracy eval to back it. That precision is the point; the auto-refresh/nudge wiring below is convenience, not the moat.

One file, one runtime dependency (ts-morph, which bundles the TypeScript compiler — ~10 MB installed). No vector DB, no embedding API, no server. npx @raymondchins/agentmap --any <query> and you have a ranked answer.

Fully local — no network calls, no telemetry, no data leaves your machine. agentmap reads your code, writes a cache under .claude/agentmap/, and never phones home (there is not a single fetch/http call in the source). Your code is never sent anywhere.

⚠️ Always install the scoped name: @raymondchins/agentmap. npx agentmap (unscoped) runs an unrelated package by a different author — this project is @raymondchins/agentmap, and the scoped name is required in every install and command. npmjs.com/package/@raymondchins/agentmap

Benchmark

Every task you hand a coding agent starts with the same hidden step — find the relevant code. Here's the token cost of that step, reading raw files vs querying agentmap, on a real 154-file Next.js app (vercel/ai-chatbot). Every figure is captured tool output (node benchmark/bench.mjs <repo> at the pinned sha):

_{Context tokens the agent burns to answer each question — token est = chars/4, applied to both sides.}