divagr18/memlayer
🏗️ Frameworkdivagr18
Add persistent, human-like memory and intelligent recall to any LLM with just three lines of code.
Memlayer simplifies the complex task of implementing long-term memory for LLMs by abstracting the infrastructure behind a simple, three-line API. Unlike standard RAG implementations that rely solely on vector search, Memlayer leverages a graph-based architecture to store and retrieve information, allowing for more nuanced, relationship-aware context management. This approach enables agents to 'remember' user preferences, historical facts, and complex entity relationships over extended sessions. The library is built for seamless integration, supporting various LLM backends and providing a persistent storage layer that ensures data survives across application restarts. By handling the heavy lifting of indexing, retrieval, and context injection, Memlayer allows developers to focus on building agentic workflows rather than managing database schemas or embedding pipelines. It is an essential tool for those looking to bridge the gap between simple chatbots and sophisticated, stateful AI agents.
💡Highlights
- ├─3-line integration API
- ├─Graph-based memory storage
- └─Persistent context recall
🎯For
- ├─AI Application Developers
- └─Agentic Workflow Engineers