Awesome AI Agents › Courses and Tutorials
Agent Memory Techniques is an educational repository that teaches memory design for LLM agents through 30 runnable Jupyter notebooks. Each technique lives in its own numbered directory under all_techniques, starting with conversation buffer memory and progressing through more elaborate approaches. The material covers conversation buffers and windowed histories, vector store backed retrieval, knowledge graph memory, episodic and semantic memory, working memory, and production oriented memory patterns for real deployments. Several established memory systems and libraries are demonstrated directly, including MemGPT, Mem0, Letta, Zep and Graphiti, and evaluation is addressed through the LoCoMo long-conversation benchmark. The repository is organised for self-directed study: readers can start at the first notebook, follow one of the suggested learning paths, or use a decision tree that maps a stated requirement onto the technique that fits it, which makes it usable both as a course and as a reference when choosing a memory architecture for a specific agent. The notebooks are Python and target Python 3.10 or newer, and the code is licensed under Apache 2.0. The project is maintained by Nir Diamant, who publishes a related family of technique collections, and it links out to a companion 464-page illustrated book covering the same 30 techniques without code, to a paid course on building production software with AI, and to community channels on Discord and Reddit. The author also tracks notebook views through instrumented links in the README. In terms of audience, the repository suits engineers who already build agents with frameworks such as LangChain and need a structured survey of how state is retained across turns and sessions, as well as learners who want hands-on exposure to the trade-offs between buffer, vector, graph and hybrid memory before committing to one in production.
https://github.com/NirDiamant/Agent_Memory_Techniques
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