Agent_Memory_Techniques
A collection of 30 runnable Jupyter notebooks teaching LLM agent memory techniques, from basic conversation buffers to advanced frameworks like MemGPT, Mem0, and Zep. Covers how to build systems that let AI agents remember information across conversations and sessions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | NirDiamant/Agent_Memory_Techniques |
| Owner | NirDiamant |
| Primary language | Jupyter Notebook |
| License | Apache-2.0 — OSI-approved |
| Stars | 762 |
| Forks | 98 |
| Open issues | 2 |
| Latest release | v1.0.0 (2026-05-30) |
| Last updated | 2026-07-04 |
| Source | https://github.com/NirDiamant/Agent_Memory_Techniques |
What Agent_Memory_Techniques is
Educational resource documenting memory patterns for LLM agents across six families: short-term context management (buffers, summaries), long-term storage (vector DBs, knowledge graphs), cognitive architectures (working/episodic/semantic memory), retrieval strategies, production frameworks (Mem0, Letta, Zep, Graphiti), and evaluation/deployment patterns.
Get the Agent_Memory_Techniques source
Clone the repository and explore it locally.
git clone https://github.com/NirDiamant/Agent_Memory_Techniques.gitcd Agent_Memory_Techniques# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Each notebook is standalone; integrating multiple patterns requires custom glue code and careful design of handoffs between memory layers (no single orchestration framework provided).
- Notebooks assume access to external services (OpenAI, Anthropic, vector DBs) with valid API keys; costs and API rate limits not documented.
- Memory persistence, user management, and multi-agent coordination are addressed in 'production patterns' notebooks but require significant additional engineering.
- Techniques rely on external dependencies (langchain, mem0, zep SDKs); verify compatibility across versions before production use.
- Benchmark notebooks (LoCoMo) demonstrate evaluation methods but don't provide pre-built metrics or dashboards; you must adapt them to your system.
When to avoid it — and what to weigh
- Need production memory system off-the-shelf — This is educational material, not a production framework. For deployed systems, use Mem0, Letta, or Zep directly rather than re-implementing from notebooks.
- Require advanced personalization without engineering — Notebooks teach patterns but don't handle user management, persistence, or multi-tenant concerns required for SaaS deployments.
- No Python/Jupyter expertise available — Repository assumes familiarity with Jupyter notebooks, Python 3.10+, and basic LLM/RAG concepts; not suitable as intro-to-AI material.
- Building closed-source proprietary systems — Apache 2.0 license requires attribution and applies to derivative works; verify compliance with your IP strategy.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved open-source license. Allows commercial use, modification, and distribution with attribution and liability disclaimer.
Apache 2.0 permits commercial use. You may use, modify, and redistribute notebooks and any code derived from them in commercial products, provided you include a copy of the license and attribute the original author (NirDiamant). No royalty or permission required. However, this is educational material—frameworks it documents (Mem0, Letta, Zep) have their own separate licenses and commercial terms.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Notebooks require external API keys (OpenAI, Anthropic, etc.) and database credentials; ensure secure handling of secrets in your environment. Vector stores and knowledge graphs store user conversation data; verify your implementations meet privacy/compliance requirements (GDPR, HIPAA, etc.). No formal security audit or vulnerability disclosure process documented. Dependency versions should be pinned in production; notebooks may lag on security patches. Evaluation notebooks expose agent reasoning traces; sanitize before sharing logs.
Alternatives to consider
Mem0 official docs + API reference
If you want a production-ready managed memory layer, Mem0's official docs are more concise and tie directly to their API. Use this repo to *understand* memory first, then Mem0 docs to *deploy*.
LangChain memory module + cookbook
LangChain offers built-in memory abstractions (conversation buffers, vector stores, entity memory) with less setup than notebooks. Better for rapid prototyping if you're already using LangChain.
Letta (MemGPT) tutorials + playground
Letta's interactive playground and docs focus on self-editing memory patterns. Use this repo for breadth across 30 techniques; use Letta docs for depth on one specific approach.
Build on Agent_Memory_Techniques with DEV.co software developers
Clone the repository, pick notebook 01 or your learning path, and run hands-on examples. Apache 2.0 licensed. No signup required.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
Agent_Memory_Techniques FAQ
Can I use these notebooks directly in production?
Do I need to know RAG to understand these notebooks?
Which memory technique should I start with?
Are the frameworks (Mem0, Letta, Zep) compared fairly?
Custom software development services
Need help beyond evaluating Agent_Memory_Techniques? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.
Start Learning Agent Memory Today
Clone the repository, pick notebook 01 or your learning path, and run hands-on examples. Apache 2.0 licensed. No signup required.