Memori is an open source SQL-native memory engine designed to add persistent memory capabilities to AI applications, large language models, and multi-agent systems. It provides a memory layer that automatically captures conversations and interactions between users and AI models, allowing systems to retain knowledge across sessions instead of operating statelessly. It extracts structured information such as facts, preferences, rules, and summaries from interactions and stores them in standard SQL databases for later retrieval. By recalling relevant context during future model calls, Memori helps AI agents produce more consistent and context-aware responses while reducing the need to repeatedly provide background information. Memori is designed to work with multiple LLM providers, data stores, and AI frameworks, allowing it to integrate into existing software architectures without requiring major changes.
Features
- SQL-native memory storage using standard relational databases
- Persistent conversational memory for AI agents and LLM applications
- Automatic extraction of facts, preferences, and contextual knowledge
- Integration with multiple LLM frameworks and providers
- Context recall that injects relevant memories into future prompts
- Asynchronous processing for enrichment and memory analysis