ReMe, developed by agentscope-ai, is a memory management kit for AI agents. The framework aims to solve two primary challenges in agent memory: the limited context window, which often leads to the truncation or loss of early information in long conversations, and stateless sessions, where agents cannot inherit historical data and must start fresh with each new interaction. ReMe achieves this by compacting old conversations, persistently storing important information, and automatically recalling relevant context for future interactions.
The kit offers both file-based and RAG-based memory systems. The file-based system, ReMeLight, treats memory as readable, editable, and copyable files. The RAG-based system integrates long-term memory and context management by building upon ReMeLight. Key capabilities include context management, which checks context size and splits messages, and support for various applications such as personal assistants, coding assistants, customer service bots, task automation, knowledge Q&A, and multi-turn dialogues.
ReMe is implemented in Python and is available under the Apache 2.0 License. The project has garnered over 3,000 stars and 240 forks on GitHub, indicating active community interest. Its latest release is v0.3.1.9.