30 FREE Tutorials to Build AI Agents With Real Memory Fast!

This is a must-open newsletter for anyone building AI agents that need to remember

Hey dear readers and community,

I’m thrilled to share this blog post, and I’m choosing my words carefully so you can truly understand the value of the content I’m sharing today!

This is something I’ve spent the last few weeks pulling together: a hands-on guide to every major agent memory technique, with a runnable notebook for each one.

It isn’t tied to a single library or framework. It walks through the full landscape, from the simplest conversation buffer to production-grade tiered systems, so you can compare patterns side by side and pick the right one for what you’re building.

This comes together in a new open-source GitHub repository called Agent Memory Techniques. It currently contains 30 individual tutorials, organized into 11 essential categories:

🚀 Explore the repository on GitHub – Agent Memory Techniques:

Short-Term Memory

Manage what your agent remembers inside a single conversation. Master conversation buffers, sliding windows, summary memory, and token budgets so your agent stays coherent without blowing up the context window.

Long-Term Memory

Save knowledge that survives across sessions, users, and time. Learn the storage patterns that turn a one-shot chatbot into an agent that builds on past interactions instead of starting from zero every time.

Vector Stores & Embeddings

Turn past messages and documents into vectors and search them by meaning instead of keywords. Build a retrieval layer that finds the right memory at the right moment, even when the user phrases the question in a totally new way.

Knowledge Graphs

Build a graph of how entities, people, and projects connect. Walk the graph to reason over what your agent has learned and surface insights a flat memory store would miss.

Episodic & Semantic Memory

Borrow two of the brain’s most powerful patterns. Store complete interactions with when-and-where context, then distill general facts on top so your agent can recall both what happened and what it learned from it.

Cognitive Architectures

Build human-inspired memory systems with working memory, hierarchical layers, consolidation, and self-reflection. Your agent learns to prioritize, forget, and rewrite its own memory the way a person does.

Memory Retrieval & Routing

Pick the right memory at the right moment. Compare semantic search, recency, hybrid scoring, diversity, and re-ranking, then route reads and writes by content type and intent.

Cross-Session & Multi-Agent Memory

Save and reload state across sessions so users pick up where they left off. Then share memory across multi-agent teams with namespaces and conflict resolution baked in.

Memory Frameworks (Mem0, Letta, Zep, Graphiti)

Get hands-on with the leading production memory libraries. Learn when managed services like Mem0 and Zep win, when self-editing memory like Letta/MemGPT shines, and when Graphiti’s time-aware graphs are the right tool.

Memory Evaluation & Benchmarks

Measure your memory the way researchers do. Run against LoCoMo and LongMemEval, check retrieval precision and recall, catch staleness and contradictions, and prove your system actually works.

Production Memory Patterns

Ship memory at real scale. Learn caching, TTLs, sharding, backups, GDPR right-to-forget, and observability so your agent’s memory survives the messy reality of production traffic.

---

I truly believe this is already the best educational resource available for agent memory, and I’ll make sure it keeps improving and stays up to date over time.

If you find value in it, please make sure to star the repo and bookmark it for easy access.

I hope you enjoy it and that it helps you build agents that actually remember what matters!

Yours, Nir