Memori has matured into a full-featured, open-source memory system designed to give AI agents long-term, structured, and queryable memory using standard databases rather than proprietary vector stores. Instead of relying on ad-hoc prompts or ephemeral session state, Memori continuously extracts entities, facts, relationships, and context from interactions and stores them in SQL or MongoDB backends, enabling agents to recall and reuse information across sessions with no manual orchestration.
The system offers a database-agnostic architecture, enabling developers to use SQLite for local projects, PostgreSQL or MySQL for scalability, or MongoDB for document-oriented needs. Memori automatically detects the backend in use and manages data ingestion, search, and retrieval through specific adapters, all while maintaining a consistent external API. This approach makes it appealing for production workloads where reliability and portability are key.
Memori’s memory engine automatically extracts and categorizes entities into facts, preferences, rules, identities, and relationships. It prioritizes interpretable storage, saving memories in a human-readable format for easy inspection, export, or migration without vendor lock-in. Agents can retrieve information without needing to create SQL queries, as the process is fully abstracted. As Sumanth P explained in response to a community question:
Memori handles the storage internally, and the agent can retrieve info through its API without generating SQL directly.
Framework compatibility has also been a recurring question. In a community thread, Anand Trimbake asked whether Memori integrates with LangChain, a common requirement for agent developers. Sumanth P confirmed that support is available, noting that Memori can be used directly within LangChain-powered pipelines without additional adapters.
This emphasis on broad ecosystem support, covering OpenAI, Anthropic, LiteLLM, Azure OpenAI, Ollama, LM Studio, LangChain, and any OpenAI-compatible stack, positions Memori as a drop-in memory layer for both lightweight assistants and complex autonomous agents.
Beyond retrieval, Memori separates short-term conscious context from long-term accumulated knowledge. Short-term context is injected directly into prompts, while long-term memory grows automatically via auto-ingest mechanisms. This ensures that identity-related information is clearly separated from general knowledge, helping to prevent uncontrolled memory expansion.
Memori has a modular architecture, SQL-native storage, and multi-database support, positioning itself as a core component for next-generation agentic systems. It provides developers with a reliable, cost-effective, and open-source memory infrastructure that integrates seamlessly within the LLM ecosystem.
For those interested in experimenting, the full codebase is available on GitHub.
