Detailed Introduction
memlayer is a plug-and-play memory layer designed to give large language models (LLMs) persistent, intelligent, human-like memory and recall. Through an abstract memory interface and semantic retrieval mechanisms, it enables models to gain history-aware context, conversation rewind, and external knowledge augmentation in minutes. memlayer supports multiple storage backends and retrieval strategies, making it easy to integrate with existing retrieval-augmented generation (RAG) workflows and vector databases.
Main Features
- Pluggable memory interface for fast integration with different models.
- Persistent storage with semantic retrieval, supporting write, update, and expiry policies.
- Compatibility with vector databases and retrieval engines for RAG pipelines.
- Lightweight Python implementation suitable for production deployments.
Use Cases
- Conversational agents and assistants that retain historical context across multi-turn dialogues.
- Building long-term user or entity profiles for personalization and memory-aware recommendations.
- Combining with knowledge bases to provide long-term factual memory and retrieval-augmented generation (RAG).
Technical Features
- Semantic embedding–based retrieval and recall to reduce hallucination risk.
- Unified storage abstraction supporting SQL and vector DB backends.
- Engineering-oriented lightweight design for quick integration into existing model pipelines and SDKs.