Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions.

We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action.

Current LLMs are stateless — they forget everything between sessions. This leads to repetitive interactions, wasted tokens, and inconsistent results. When building AI agents, this problem gets even worse: without memory, they can’t recover from failures, coordinate across steps, or apply simple rules like “always write tests.”

We realized that for AI agents to work in production, they need memory. That’s why we built Memori.

Memori uses a multi-agent architecture to capture conversations, analyze them, and decide which memories to keep active. It supports three modes:

- Conscious Mode: short-term memory for recent, essential context. - Auto Mode: dynamic search across long-term memory. - Combined Mode: blends both for fast recall and deep retrieval.

Under the hood, Memori is SQL-first. You can use SQLite, PostgreSQL, or MySQL to store memory with built-in full-text search, versioning, and optimization. This makes it simple to deploy, production-ready, and extensible.

Memori is backed by GibsonAI’s database infrastructure, which supports:

- Instant provisioning - Autoscaling on demand - Database branching & versioning - Query optimization - Point of recovery

This means memory isn’t just stored, it’s reliable, efficient, and scales with real-world workloads.

We’ve open-sourced Memori under the Apache 2.0 license so anyone can build with it. You can check out the GitHub repo here: https://github.com/GibsonAI/memori, explore the docs, and join our community on Discord.

We’d love to hear your thoughts. Please dive into the code, try out the demos, and share feedback, your input will help shape where we take Memori from here.