@DanKornas: Most agents don’t need more prompts. They need memory that fits the job. Agent Memory Techniques is a hands-on GitHub g…

Most agents don’t need more prompts. They need memory that fits the job. Agent Memory Techniques is a hands-on GitHub guide to memory patterns for LLM agents, organized as 30 runnable Jupyter notebooks. It helps you choose and test memory designs by grouping techniques into short-term, long-term, cognitive architecture, retrieval/routing, framework, and evaluation/production sections, with a decision tree and comparison matrix for navigation. Key features: • 30 runnable notebooks – covers conversation buffers, vector stores, knowledge graphs, episodic/semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns • Six-family taxonomy – splits memory into short-term, long-term, cognitive architecture, retrieval, framework, and evaluation/production tracks • Decision tree for builders – points you toward the right technique based on current chat, cross-session memory, frameworks, or production/evaluation needs • Comparison matrix – helps you filter techniques by persistence, retrieval style, token cost, and best-fit use case • Notebook-first learning path – starts with Conversation Buffer Memory and links each technique to runnable notebooks and Colab It’s open-source under the Apache License 2.0. Link in the reply