OpenMemory and memlayer
These two tools are competitors, with OpenMemory providing a more fundamental, low-level local persistent memory store for various LLM applications, while Memlayer offers a higher-level, plug-and-play solution specifically focused on adding intelligent, human-like memory and recall to LLMs with minimal code.
About OpenMemory
CaviraOSS/OpenMemory
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
Provides multi-sector memory (episodic, semantic, procedural) with temporal reasoning and composite scoring—not just vector retrieval—via self-hosted SQLite/Postgres backends. Offers both embedded SDKs (Python/Node) and a centralized server exposing HTTP API, MCP protocol, and dashboard, with source connectors for GitHub, Notion, Google Drive, and web crawling to populate long-term agent context.
About memlayer
divagr18/memlayer
Plug-and-play memory for LLMs in 3 lines of code. Add persistent, intelligent, human-like memory and recall to any model in minutes.
Implements a hybrid vector + knowledge graph architecture using ChromaDB and NetworkX to enable fast semantic search combined with entity relationship traversal. Supports three operation modes (LOCAL/ONLINE/LIGHTWEIGHT) that trade off accuracy, startup time, and cost by varying the salience filtering approach—from ML-based sentence transformers to LLM embeddings to lightweight keyword extraction. Works across all major LLM providers (OpenAI, Claude, Gemini, Ollama, LMStudio) with intelligent multi-tier search (Fast/Balanced/Deep) that automatically adjusts retrieval depth based on query complexity.
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