NirDiamant/Agent_Memory_Techniques
FreeLearn every agent memory technique for LLM agents.
About NirDiamant/Agent_Memory_Techniques
Agent memory is the set of techniques that let an LLM-based agent 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. It includes 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.
Key Features
Pros & Cons
- Comprehensive coverage of 30 distinct memory techniques
- Runnable notebooks allow hands-on experimentation
- Free and open source with no licensing costs
- Integrates with popular frameworks like MemGPT, Letta, Zep, etc.
- Structured learning paths for beginners and advanced users
- Requires Jupyter environment to run notebooks
- Educational resource, not a plug-and-play library
- Assumes existing knowledge of LLM agent concepts
- No direct API or hosted service; self-hosted execution needed