DiffMem

AI

agent memory

SHOW HN2025

DiffMem

I replaced vector databases with Git for AI memory (PoC)

github.com

#ai memory#git#bm25#markdown#llm memory

198points
45comments
#332of 3,837 in 2025
Top 9%of 2025 by points

How HN took it

CuriousMostly questions and neutral comments

6 positive, 13 neutral or asking, 1 mixed, 5 critical, out of 25 comments about the project. Thread score +0.08.

Idea: 4 for, 4 againstDocs: 0 for, 1 against

The thread, comment by comment

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

What HN said

Objections

  • Vector databases solve semantic search that BM25 keyword matching cannot

    From 4 comments
    • I'm failing to grasp how it solves/replaces what vector db were created for in the first place (high-dimensional neighborhood searching, where the space to be searched grows by…
      BenoitP on HN
    • Somehow i feel like the op doesn't get what vector DB's are actually for, semantic search isn't the same as a markdown memory file
      cchance on HN
    • I mean these are two completely different approaches for completely different purposes. So saying that git replaces vector database is like saying that that a filing cabinet…
      _pdp_ on HN
    • The submission and the readme fail to explain the important thing, which is how BM25 is run. If it creates bags of words for every document for every query, that would be…
      OutOfHere on HN
  • Git is unnecessary complexity; a simpler store would do

    From 1 comment
    • Maybe you can use Mercurial for this? You don’t need the complexity of Git for memory.
      cc_ashby on HN

Requests

  • Evaluations comparing it against other memory approaches

    From 1 comment
    • Seems really interesting! It would be great to see some evals to understand how well this works when stacked up against different approaches and use cases.
      jmtulloss on HN
  • MCP server version

    From 1 comment
    • 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)
      thecopy on HN

Questions

  • How BM25 index is built, updated and stored

    From 1 comment
    • The submission and the readme fail to explain the important thing, which is how BM25 is run. If it creates bags of words for every document for every query, that would be…
      OutOfHere on HN
  • How it compares to Graphiti/Zep temporal knowledge graphs

    From 2 comments
    • Great idea! How does it compare to Graphiti / Zep? They use real time knowledge-graphs to tackle the problem you are trying to solve https://github.com/getzep/graphiti
      albertdessaint on HN
    • I could be wrong, but this seems to be exactly what Zep’s “Graphiti” library is made for, build as a semantic temporal knowledge graph.
      fudged71 on HN

Alternatives named

  • Zep Graphiti temporal knowledge graph

    From 3 comments
    • Great idea! How does it compare to Graphiti / Zep? They use real time knowledge-graphs to tackle the problem you are trying to solve https://github.com/getzep/graphiti
      albertdessaint on HN
    • I could be wrong, but this seems to be exactly what Zep’s “Graphiti” library is made for, build as a semantic temporal knowledge graph.
      fudged71 on HN
    • Very nice. We tackled the temporal issue "versioned" in your case with a temporal knowledge graph: https://www.falkordb.com/blog/building-temporal-knowledge-graphs-graphiti/
      danshalev7 on HN
  • Standard RAG with vector embeddings

    From 2 comments
    • > I have struggled to get any of the RAG approaches to handle this effectively. You need to annotate your text chunks. For example you can use a LLM to look over the chunks and…
      visarga on HN
    • Super simplistic example, but say i mention my Daughter, who is 9. Then mention she is 10, a few years later she is 12 but now i call her by her name. I have struggled to get any…
      alexmrv on HN

Category, keywords, summaries and labels on this page were made by AI (Thread Reader v2) and not checked by hand. Each point links to the comments it came from.

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