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Agent Memory

kensavebykensave
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3
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Developer Tools
Data Science & ML
Productivity & Workflow

Manages comprehensive memory for AI agents, implementing episodic, semantic, and procedural memory with automatic consolidation, intelligent decay, and hierarchical retrieval.

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Agent Memory RS offers a sophisticated memory management system designed for LLM agents, built on modern AI research and cognitive science. It provides three distinct memory types: episodic memory for interaction events, semantic memory for consolidated knowledge, and procedural memory for learned workflows. Key capabilities include auto-consolidation of memories, intelligent decay based on composite scoring, hybrid search, and hierarchical retrieval, ensuring agents have efficient access to relevant information through its MCP server support and CLI tools.

Key Features

01Intelligent Decay (composite scoring for memory archival)
02Hierarchical Retrieval (multi-level memory access from synopsis to archived)
03MCP Server (Model Context Protocol integration for AI assistants)
04Hybrid Search (BM25 keyword + vector semantic search with RRF fusion)
05Auto-Consolidation (nightly pattern extraction, daily synopsis generation)
060 GitHub stars

Use Cases

01Enabling LLM agents to learn and adapt workflows over time
02Providing persistent, context-aware memory for long-running AI applications
03Integrating advanced memory capabilities into custom AI assistants