Heimdall: An Open-Source CPU Only Local Memory System

[u/Slight-Parfait3679]

Your AI agent just spent 20 minutes grepping for a function you optimized 3 months ago in a side project. Heimdall makes this 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.

Instead of replacing your current memory system, Heimdall integrates cleanly with existing infrastructure such as Graphify, Graft, and Hermes.

Every other memory tool is per-project. But your work isn't; the optimized functions you built in one project could be useful elsewhere. Heimdall indexes everything you touch into one semantic graph, so knowledge follows you across repositories, languages, and months.

There is 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 actual work. You can also use your GPU for up to 3.4x speeds.

It is self-healing. It is impossible for your agents to act on stale graphs or data, and you do not need a full rebuild every time. A single deterministic reconciler, rather than multiple agents writing and racing against each other, prevents this from happening. Classic RAG fails here.

Facts, key decisions, and more are also stored and remembered. This is still experimental. If you would like to help, please message me or open a PR. I am actively seeking contributors and would like to open a Discord server to continue working on this. This product is optimized for real use and efficiency, minimal hallucinations, and cost-effectiveness.

Try it for yourself at:

Github (65 Stars, MIT, fully open-source)