Memory in plain SQL — no vector DB, fully inspectable, portable. LoCoMo benchmark: 81.95% accuracy at ~1,294 tokens/query (self-reported). Paper: arxiv.org/abs/2603.19935.
- Storage
- Any SQLite / Postgres / MySQL; BYODB option supported for self-hosted databases
- Retrieval
- Recalls memories via plain SQL queries over relational tables — no vector database required; works with any LLM + datastore combination.
- Self-host
- Self-host: trivial
- License
- Apache-2.0
- Pricing
- Free OSS; managed cloud Free/Paid · Free + paid
- GitHub stars
- 15,486
- Last release
- 2026-05-28
- Last commit
- 2026-06-15
- First catalogued
- 2026-06-28
Strengths
- SQL-queryable / fully debuggable memory
- ~1,294 tokens/query on LoCoMo (self-reported) — ~5% of full-context cost
- One-line SDK integration: .llm.register(client)
- BYODB: bring your own SQLite / Postgres / MySQL
- Captures agent actions (tool calls, decisions) not just conversation text
- OpenClaw plugin available
Watch out
- Simpler retrieval than graph/hybrid systems — may miss relational or temporal queries
- Self-reported benchmark (81.95% LoCoMo); backbone LLM not stated in source
Best for
- Cost-sensitive production: skip the vector DB and run on the SQL infra you already have
- Inspectable, debuggable memory you can query directly
- Agents where what the agent *did* (tool calls, decisions) matters as much as what the user *said*
Benchmark results
| Benchmark | Value | Backbone | Trust | Source |
|---|---|---|---|---|
| locomo | 81.95 accuracy | — | Self-reported | Memori (MemoriLabs) ↗ |
Sources
- Memori repo (GitHub now resolves GibsonAI/Memori → MemoriLabs/Memori) (vendor)
- MemoriLabs — product home (vendor)
- Memori paper (MemoriLabs) (paper)
Last verified 2026-06-29 · updated by seo-article-writer