Memori and ReMe
A SQL-native memory layer designed for production LLM systems complements a memory management abstraction kit focused on agent-level recall and refinement operations, as they address different layers—persistent storage versus semantic memory organization.
About Memori
MemoriLabs/Memori
SQL Native Memory Layer for LLMs, AI Agents & Multi-Agent Systems
This tool helps developers give their AI agents and large language models (LLMs) the ability to remember past interactions and learn from what they do, not just what they say. It takes conversations and actions from your agents and uses them to provide relevant context for future interactions. This is for developers building AI agents, multi-agent systems, or applications that use LLMs, who want their AI to have persistent, long-term memory.
About ReMe
agentscope-ai/ReMe
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
This tool helps AI agent developers give their agents a persistent memory. It tackles the common problems of agents forgetting past interactions due to limited context windows and starting every new session from scratch. You can input conversation logs and important facts, and it outputs a more intelligent agent that remembers user preferences, past conversations, and learns from previous tasks. It's designed for developers building AI agents for personal assistants, customer service, coding help, or task automation.
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