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Hydrate vs Memori (GibsonAI)
agent memory infraGibsonAI's open-source SQL-native memory engine (memori.enable()) — shares Hydrate's 'plain SQL, no vector lock-in' instinct, but is infra for app builders with no team-canon, cross-vendor-live, or orchestration.
What Memori (GibsonAI) is
| Stack / runtime | SQL-native: SQLite / PostgreSQL / MySQL; Python SDK |
| Licence | Open source |
| Storage | Standard SQL with full-text search + versioning; export as SQLite file |
| Hooks | None (memori.enable() SDK call) |
| Injection | 3-agent pipeline selects + injects |
| Scope | General LLM/agent memory infra |
| Maturity | GibsonAI-backed; memorilabs.ai |
| Price | Free OSS; enterprise deployments on Postgres/MySQL |
| Version referenced | GA 2025-09 onward |
| Source | github.com/GibsonAI/Memori |
Where Memori (GibsonAI) leads
- General-purpose drop-in memory API for any LLM app
- SQL-native portability + audit/compliance story (data residency, audit trails)
- 80-90% cost efficiency vs vector DBs at scale (claimed)
Where Hydrate leads
- Automatic for coding agents (no app code)
- Team canon propagation with attribution
- Cross-vendor live sessions + orchestration
- Decision-centric capture + three-layer bootstrap
Verification notes
CONVERGENCE: Memori independently chose SQL-first over vector DB, same as Hydrate (validation, not threat). Landscape doc's MongoDB/CockroachDB/DigitalOcean testimonials + PCI/SOC2 NOT confirmed (GibsonAI Memori is SQL-first) — verify before publishing. Confirm 80-90% cost figure.
Sources
- Primary repo https://github.com/GibsonAI/Memori
- Vendor blog https://memorilabs.ai/blog/introducing-memori-the-open-source-memory-engine-for-ai-agents