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skegdb/skeg

A multi-tenant vector database focused on extreme RAM efficiency. Lightweight, scalable, and optimized for high-density deployments.

#Vector database
Visit GitHub
Rust
35
3
1 contributors
Apache License 2.0
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Momentum

How fast skegdb/skeg is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Sep 25 – Nov 13, 2026

Stars +1 in 50 days

01
1

Forks +0 in 50 days

01

Merged PRs 0 in 50 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Sep 25Oct 2Oct 9Oct 16Oct 23Oct 30Nov 6Nov 13
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Fri, Sep 25, 2026+1+00
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Total+1+00
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Aug 61 post

    • For those who do local AI or want a rag system that doesn't eat up all their RAM, I released Skeg a few months ago. I'm currently finishing up the optimizations for x86 and Linux.

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

  2. 7 days earlier
  3. Jul 301 post

    • I saw @Qdrant Edge + Nvidia, and I thought... #Skeg can already do that! So thanks, @Nvidia, for the recognition models, and enjoy the demo. running on my old MacBook Pro M1 ~9.5mb in RAM…

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

  4. 9 days earlier
  5. Jul 212 posts

    • Introducing Skeg : A Rust vector DB that prioritizes low memory and production reliability

      Read full on Reddit

      r/vectordatabase

    • Introducing Skeg : A Rust vector DB that prioritizes low memory and production reliability

      Read full on Reddit

      r/Rag

  6. 9 days earlier
  7. Jul 121 post

    • 28 ephemeral browser microVMs, in parallel, in 37 seconds. Each forks in ms, browses a different site, and self-destructs on disconnect. 555 docs → 1,267 vectors → semantic recall across all of them.…

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

  8. 1 month earlier
  9. May 291 post

    • What am I doing? It's simple. One morning I woke up and decided to create a vector database that's different from the rest. Why? Because I think it’s good to try different approaches every now and…

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

  10. May 281 post

    • I'm starting to publish the Skeg benchmarks. This includes the case with Llama 3.2, which uses various databases to perform RAG. ⭐️ 📊

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

  11. May 263 posts

    • Tired of vector DBs killing your RAM? Skeg is made for big LLMs on the same machine. - 1M vectors → ~400 MB RAM - 1-4 bit quantization + reranking - Fast Rust Long-term memory for your agent without…

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

    • I'm terrible at “selling” my work. I'm trying to get better at it, but I'm not sure I'll succeed.

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

    • apparently @antirez and I have been independently obsessing over RAM efficiency for AI workloads. he published first. i took the scenic route.

      Read full on X, posted by @lupodevelop

      @lupodevelop · x.com

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