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sqliteai
sqlite-vector
C2025–2026
sqliteai / sqlite-vector

sqlite-vector

SQLite-Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database.

5Distinct people
14Mentions
0Explicit recommenders
1.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

SQLite-Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month14 dated mentions

Observed mentions: 2025-05-22 17:06:48 UTC to 2026-10-02 12:58:09 UTC.

0122025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 20252025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsJan 20262026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2026-10-01 00:00:00 UTC to 2026-11-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsOct 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC101
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC110
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC220
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC202
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC211
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC211
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC110
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC000
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC000
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC220
Oct 20262026-10-01 00:00:00 UTC2026-11-01 00:00:00 UTC110

What Hacker News says

2 sources
This is a neat project - good API design, performance looks impressive. Note that this one isn't open source: https://github.com/sqliteai/sqlite-vector/blob/main/LICENSE.... The announcement says: > We believe the community could benefit from sqlite-vector, which is why we’ve made it entirely free for open-source projects. But that's not really how this works. If my open source projects incorporate aspects of this project they're no longer open source!
In Ultra efficient vector extension for SQLite
I think the whole field of vector databases is mostly just one huge misunderstanding. Most of you are not Google or any other big tech company so so won't have billions of embeddings. It's crazy how people add bloat and complexity to their stuff just because they want to do medium scale RAG with ca. 2 million embeddings. Here comes the punchline, you do not need a fancy vector database in this case. I stumbled over https://github.com/sqliteai/sqlite-vector which is a SQLite extension and I wonder why no one else did this before, but it simply implements a highly optimized brute force search over the vectors, so you get sub 100ms queries over millions of vectors with perfect recall. It uses dynamic runtime dispatch that makes use of the available SIMD instructions your CPU has. Turns out this might be all you need. No need for memory a mem
Read the rest
ory hungry search index (like HNSW) or writing a huge index to disk (like DiskANN).
In Vector database that can index 1B vectors in 48M

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