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198 points alexmrv | 1 comments | | HN request time: 0.207s | source

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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thecopy ◴[] No.44970015[source]
This is cool! Do you have any plans on releasing it as an MCP server? Would love to add this to my mcp gateway service https://mcp-boss.com/ (shameless plug)
replies(2): >>44970039 #>>44970073 #
1. alexmrv ◴[] No.44970073[source]
Would have to be productionized, the current repo is a PoC, but if there is enough interest we could def commit some resources to hardening this and adding things like MCP.