Memori – Open-Source Memory Engine for AI Agents
Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). 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…
In plain words
Memori is an open-source memory engine that gives AI agents the ability to retain information across sessions. It allows agents to recall past work, maintain consistency, and avoid repeating tasks or forgetting user preferences. Built for developers creating AI agents, Memori addresses the stateless nature of current large language models by enabling agents to learn and improve over time. The project includes demo applications demonstrating memory in action across diary assistance, research, and travel planning use cases.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). 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.
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