Memori

Memori turns agent execution traces and conversation history into structured, persistent memory for production AI systems. Rather than agents relying only on conversation context, Memori captures what agents actually do during execution and stores it as queryable, reusable state. This works across any LLM, making it straightforward to integrate memory into production agents without vendor lock-in. The tool is aimed at teams building production AI agents, particularly those managing multiple agents or long-running systems where maintaining context across sessions matters. It bridges a common gap in agent frameworks: conversations get logged, but the agent's internal decision-making and actions fade away. Memori makes this actionable. For production systems, this means agents can learn from their own trace data, avoid repeating mistakes, and maintain consistent behaviour across sessions. It's especially useful when you need memory that survives beyond a single chat window, or when multiple agents need shared context about previous interactions.