🧠 Agent Memory Techniques
Learn every agent memory technique for LLM agents.
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🧭 New here? Start with 01 Conversation Buffer Memory or pick a Learning Path. Prefer a visual? See the Decision Tree below. 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, working memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production memory patterns.
🎓 From memory demos to production agents
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📖 The book of this repo
Agent Memory Made Simple - the 464-page visual guide to everything in this repo: all 30 techniques, 184 custom illustrations, zero code required.
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💡 Why Agent Memory Matters
💡 Quick Answer (for search engines and skimmers)
Agent memory is the set of techniques that let an LLM-based agent (a system built around a Large Language Model) remember information across turns, sessions, and tasks. Without memory, an agent re-derives context every time and cannot personalize, learn, or maintain coherence over long interactions. This repository documents 30 distinct memory techniques, grouped into six families: short-term context management, long-term storage, cognitive architectures, retrieval and multi-agent patterns, batteries-included frameworks, and production deployment patterns.
Think about a friend who forgets every conversation you've ever had. Every morning you're strangers ag