Detailed Introduction
PowerMem (Intelligent Memory System) is an open-source memory infrastructure that combines vector, full-text and graph retrieval to support long-term context retention for AI systems. It incorporates time-decay weighting inspired by the Ebbinghaus forgetting curve to prioritise recent and relevant memories, supports multimodal data (text, image, audio), and is suitable for dialogue memory layers, customer history retrieval, and multi-agent collaboration.
Main Features
- Intelligent memory extraction: LLM-based extraction, deduplication and merging to keep memories consistent.
- Hybrid retrieval: joint vector + text + graph queries with multi-hop traversal and fine-grained filtering.
- Multi-agent support: isolated or shared memory spaces with scope and permission control.
- Time-decay weighting: retention strategy based on forgetting curve to prioritise relevant recent memories.
Use Cases
Ideal for conversation memory layers, customer history retrieval, fact extraction from documents, and multi-agent systems requiring coordinated memory sharing.
Technical Features
PowerMem provides a lightweight Python SDK, sub-store partitioning for large-scale performance, and integrations with LangChain and LangGraph. The project focuses on engineering metrics (accuracy, latency, token cost) and offers comprehensive guides and examples.