Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.
NirDiamant/Agent_Memory_Techniques is a collection of runnable Jupyter notebooks that teaches how LLM agents retain and retrieve information, including conversation history, vector stores, knowledge graphs, and episodic or semantic memory. It is for learners and developers studying agent-memory designs and production patterns. The catalogue instruction supports working through these techniques with a coding agent.
Latest release v1.0.0 — v1.0.0 — Agent Memory Techniques · 30 May 2026
1 file for Codex and OpenCode: Agent_Memory_Techniques AGENTS.md — 975 tokens loaded in every session.
AGENTS.md A 975 tok These files are NirDiamant/Agent_Memory_Techniques's own configuration — they tell Codex and OpenCode how to work on this repository, so they are not mods to install elsewhere. Copy one as a starting point and replace the parts that are about this project.