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Rekal

Long-term memory for LLMs in a single SQLite file

Details

External ID
47744683
Source
HN
Company
—
Product
Rekal
Website domain
github.com
Launched
April 12, 2026
Cohort
—
Upvotes
9
Upvotes percentile
0.5501285347043702
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

I got tired of repeating myself to my LLM every session. rekal is an MCP server that stores memories in SQLite and retrieves them with hybrid search (BM25 + vectors + recency decay). One file, local embeddings, no API keys.

Enrichment

Theme
database infrastructure and developer tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
persistent memory for llms
Manually corrected
False

Could you build this?

Yes Rekal is an MCP server written on top of SQLite, sqlite-vec / FTS5 for hybrid BM25 and vector search, and an embedded local model (e.g. fastembed/onnx). This is a well-scoped weekend developer tool that an AI assistant can easily generate.

Discussion

10 comments analyzed.

Concerns raised: Python 3.14 requirement may limit adoption, Effectiveness unclear - requires significant intervention to keep agents using guardrails, Fixed weights (0.4/0.4/0.2) may not suit different use cases

Feature requests: Make hybrid search weights configurable, Add config file support (pyproject.toml or .rekal/config.yml), Different decay rates for different memory types

Competitors

Other products that read as similar to this one — 31 launches clear the similarity bar, closest 8 shown.

Attention rank: #16 of 32 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 136 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.