A self-evolving RAG system that enables AI agents to autonomously read and write memory, continuously learning and adapting user preferences, daily logs, and knowledge graphs across applications.
Enables AI agents to store, retrieve, and self-improve procedural memories (lessons learned) based on relevance to the current task, pruning unused memories to reduce context load and prevent repetition of past mistakes.
Long-term memory system for AI agents that accumulates domain expertise through mentorship, automatically recalls relevant knowledge, and supports memory decay and growth with anti-fabrication.
A persistent semantic memory system for LLMs with time-based decay, automatic memory capture, and spatial navigation tools for exploring knowledge graphs.
A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.
An agentic memory system that enables AI assistants to store, search, and manage persistent memories with semantic understanding using natural language instructions.