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🧠 Agent Memory Techniques
Learn every agent memory technique for LLM agents.
⭐ If you find this useful, please star the repo so more learners can discover it.
🧭 New here? Start with 01 Conversation Buffer Memory or pick a Learning Path. Prefer a visual? See the Decision Tree below. 30 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.
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Agent Memory Made Simple - the 464-page visual guide to everything in this repo: all 30 techniques, 184 custom illustrations, zero code required.
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💡 Why Agent Memory Matters
💡 Quick Answer (for search engines and skimmers)
Agent memory is the set of techniques that let an LLM-based agent (a system built around a Large Language Model) 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.
Think about a friend who forgets every conversation you've ever had. Every morning you're strangers again. That's what most AI agents are like today.
Every AI agent eventually hits the same wall: it forgets.
In 2026, AI agents are everywhere. But most of them still forget what you told them yesterday. Without strong memory, an agent can't keep context across conversations. It can't learn from past chats. It can't build a lasting relationship with you.
The landscape is shifting fast:
- Anthropic's 7 Layers of Memory (March 2026): from conversation context to cross-project knowledge, defining the memory hierarchy for Claude Code
- Mem0: managed memory layer gaining rapid adoption for personalized AI
- Letta (MemGPT): self-editing memory with inner/outer monologue architecture
- Zep: temporal knowledge graphs for long-term agent memory
- Graphiti: episodic-to-semantic knowledge graph extraction
- MemOS & Memori: memory-as-infrastructure platforms for production agents
But there's no single hands-on guide that teaches you how each technique works, when to use it, and how to build it yourself.
That's why this repository exists. 30 techniques. Runnable notebooks. Real code you can use today.
🗺️ Taxonomy of Agent Memory Techniques
The 30 techniques fall into six families. Each family solves a different memory problem. Each technique lives in its own notebook.
| Family | What it solves | Techniques |
|---|
| Short-term | Keep recent turns in memory without filling up the context window. | 01 - 05 |
| Long-term | Save knowledge across sessions, users, and time. | 06 - 11 |
| Cognitive architectures | Working, hierarchical, and reflective memory systems. | 12 - 19 |
| Retrieval & routing | Choose what to recall and when. | 20 - 23 |
| Frameworks | Production-ready memory libraries (Mem0, Letta, Zep, Graphiti). | 24 - 27 |
| Evaluation & production | Measure, benchmark, and deploy memory. | 28 - 30 |
🧭 Which Technique Do I Need?
30 techniques grouped by what you are building. Pick the group that matches your goal, then open the technique inside it.
Quick text version:
- Need to manage the current chat? Start with 01-05 (short-term memory).
- Need to persist across sessions? Start with 06 Vector Store or 21 Cross-Session Memory.
- Building a cognitive architecture with multiple stores? See 12-19.
- Using a framework? Go straight to 24 Graphiti, 25 Mem0, 26 Letta, or 27 Zep.
- Evaluating or shipping to production? See 28-30.
Still not sure? Start with 01 Conversation Buffer. Almost every other technique builds on it.
📐 Compare Techniques at a Glance
Looking to filter by constraint (persistence, retrieval style, token cost, best-for use case)? See the side-by-side comparison matrix covering all 30 techniques in one table.
📚 All 30 Techniques
🔄 Short-Term Memory (Techniques 1-5)
Manage the conversation inside a single chat.
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