{"repo":"divagr18/memlayer","free":true,"listed":false,"github":"https://github.com/divagr18/memlayer","clone":"git clone https://github.com/divagr18/memlayer.git","description":"Plug-and-play memory for LLMs in 3 lines of code. Add persistent, intelligent, human-like memory and recall to any model in minutes.","language":"Python","stars":290,"topics":["agent","ai","ai-infrastructure","context-management","developer-tools","embedded","graph-database","knowledge-graph","llm","llm-memory"],"license":"MIT","category":"ai-agents","readme_excerpt":"Memlayer The plug-and-play memory layer for smart, contextual agents Memlayer adds persistent, intelligent memory to any LLM in just 3 lines of code, enabling agents that recall context across conversations, extract structured knowledge, and surface relevant information when it matters. Contents - Features - Quick Start - Key Concepts - Memory Modes - Search Tiers - Providers - Advanced Features - Examples - Performance - Documentation - Contributing Features - Universal LLM Support : Works with OpenAI, Claude, Gemini, Ollama models - Plug-and-play : Install with pip install memlayer and get started in minutes — minimal setup required. - Intelligent Memory Filtering : Three operation modes (LOCAL/ONLINE/LIGHTWEIGHT) automatically filter important information - Hybrid Search : Combines vector similarity + knowledge graph traversal for accurate retrieval - Three Search Tiers : Fast (<100ms), Balanced (<500ms), Deep (<2s) optimized for different use cases - Knowledge Graph : Automatically extracts entities, relationships, and facts from conversations - Proactive Reminders : Schedule tasks and get automatic reminders when they're due - Built-in Observability : Trace every search operation with detailed performance metrics - Flexible Storage : ChromaDB (vector) + NetworkX (graph) or graph-only mode - Production Ready : Serverless-friendly with fast cold starts using online mode Quick Start Installation Basic Usage That's it! Memlayer automatically: 1. ✅ Filters salient information","default_branch":null,"files":null,"tree":[],"storefront":"/r/divagr18","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/divagr18/memlayer/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}