Heimdall

Heimdall

**Your agent keeps rebuilding work you already did. Heimdall makes it stop.**

Heimdall gives AI coding agents **persistent memory across every repository and project you work on** so the question *"did I already solve this in another project?"* gets answered by one verified search instead of twenty minutes of grep, `find`, and `ls` loops.

The problem it solves

**1. Memory that doesn't live in one repository.** Every other memory tool is per-project. But your work isn't: the optimized functions you built in one project could be useful somewhere else. Heimdall indexes *everything you touch* into one semantic graph, so knowledge follows you across repositories, languages, and months.

**2. Orientation time, cut to seconds.** A fresh agent session burns dozens of bash commands just figuring out the lay of the land — `ls`, `grep`, re-reading files it read last week. Heimdall injects the relevant prior work into the session's first prompt and backs a single `kb_search` call: ranked, scoped, verified. Fewer commands, fewer tokens, faster first useful action.

**3. Zero token spend.** Memory maintenance is a local daemon: file watching, tree-sitter AST parsing, sqlite. Indexing a file costs **CPU only — never an LLM call**. Retrieval is hybrid ranked search (lexical + semantic + graph walk) over locally-computed embeddings. Your context window stays for your a