MemoriLabs/Memori

Memori is agent-native memory infrastructure that captures structured, persistent state from agent execution and conversation for production systems.

The problem Memori solves is that agents need to retain and efficiently access information across conversations and executions, but naive approaches either bloat context windows or lose important details. Memori works by converting agent interactions into structured memory that persists independently of the LLM, allowing agents to query and update state without reloading full conversation history. The tool is LLM-agnostic and datastore-agnostic, integrating into existing infrastructure without requiring replacement of current systems or databases.

Memori suits teams building production agent systems who need memory that scales beyond single-session contexts. It works across managed cloud, single-tenant cloud, VPC, and on-premises deployments. The tool offers both a managed cloud option requiring only an API key for immediate use, and a bring-your-own-database option for teams with existing data infrastructure. The README benchmarks Memori against other retrieval-based memory systems, showing it achieves higher accuracy on long-conversation tasks while using significantly fewer tokens per query than alternatives.

The maintainers respond to issues and pull requests within a few days. Development activity centers on bug fixes, feature enhancements, and community contributions.