Everyone's trying vectors and graphs for AI memory. We went back to SQL

1. Memori

   1 10 15,713 9.6 Python

   Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems. Built for enterprise, Memori works with the data infrastructure you already run, no rip-and-replace, and deploys across managed cloud, single-tenant cloud, VPC, and on-premises.

   

   Relational databases! Yes, the tech that’s been running banks and social media for decades is looking like one of the most practical ways to give AI persistent memory.

   Instead of exotic stores, you can:

   - Keep short-term vs long-term memory in SQL tables

   - Store entities, rules, and preferences as structured records

   - Promote important facts into permanent memory

   - Use joins and indexes for retrieval

   This is the approach we’ve been working on at Gibson. We built an open-source project called Memori( https://github.com/gibsonai/memori ) , a multi-agent memory engine that gives your AI agents human-like memory.

   It’s kind of ironic, after all the hype around vectors and graphs, one of the best answers to AI memory might be the tech we’ve trusted for 50+ years.

   I would love to know your thoughts about our approach!

2. SaaSHub

   www.saashub.com featured

   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

3. PixlieAI

   2 5 48 9.8 Rust

   Discontinued Please check our new project with similar targets: https://github.com/pixlie/Pixlie

   

   I tried a graph based approach in my previous product (1). I am on a new product now and I came back to SQLite. Initially it was because I just wanted a simple DB to enable creating cross-platform desktop apps.

   I realized LLMs are really good at using sqlite3 and SQL statements. So in my current product (2) I am planning to keep all project data in SQLite. I am creating a self-hosted AI coding platform and I debated where to keep project state for LLMs. I thought of JSON/NDJSON files (3) but I am gravitating toward SQLite and figuring out the models at the moment (4).

     1. Previous project with a graph data approach https://github.com/pixlie/PixlieAI

4. nocodo

   3 25 26 9.2 Rust

   Coding agent built around small (<10B) models for local development with a Rust (Actix Web, Diesel)/Typescript (Solid, Tailwind) opinionated stack. No coding knowledge needed. Multiple agents and roles - Product Owner, PM, Engineering Manager, Rust Engineer, SolidJS Engineer, etc. work together to build full-stack apps.

   

   3. Github issue on JSON/NDJSON based data for project state for LLMs: https://github.com/brainless/nocodo/issues/114

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