I replaced vector databases with Git for AI memory (PoC)
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
In plain words
This proof-of-concept system stores AI conversation memory as markdown files in a Git repository instead of using traditional vector databases. It treats each conversation as a commit, uses Git's diff and history features to track how understanding evolves, and employs BM25 search without embeddings. Designed for developers and AI users seeking a simpler alternative to vector stores, it offers version control built-in and retrieves results sub-second while keeping a year's worth of conversations under 100MB of RAM. The creator notes it remains rough and experimental rather than production-ready.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual changes in understanding, not just similarity scores. This is very much a PoC - rough edges everywhere, not production ready. But it's been working surprisingly well for personal use. The entire index for a year of conversations fits in ~100MB RAM with sub-second retrieval. The cool part: You can git checkout to any point in time and see exactly what the AI knew then. Perfect reproducibility, human-readable storage, and you can manually edit memories if needed. GitHub: https://github.com/Growth-Kinetics/DiffMem Stack: Python, GitPython, rank-bm25, OpenRouter for LLM orchestration. MIT licensed. Would love feedback on the approach. Is this crazy or clever? What am I missing that will bite me later?
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