Agent_Memory_Techniques
Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs.
Good fit when
- Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores
- Want hands-on examples on using MemGPT, Mem0, Letta, and other specialized technologies within the same package
Avoid when
- Looking for a lightweight solution with minimal setup; this has extensive notebooks and dependencies
- Require real-time memory management without heavy computational overhead, as some techniques are more geared toward detailed offline analysis
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/NirDiamant/Agent_Memory_TechniquesSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository offers thirty practical examples through Jupyter Notebooks focusing on the integration of advanced memory techniques with language models to store, retrieve, and use information effectively by AI agents. It explores conversation buffers, various vector stores, knowledge graph implementations, episodic and semantic memory designs, alongside benchmarking methods and production guidelines. Techniques like MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks are covered.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
pip install -r requirements.txtSource link
Tags
README
🚀 Quick Start 💡 Prefer not to install anything? Every notebook renders on GitHub directly. Click a technique in the table above to read it in your browser. Or use the Colab badges to run it in the cloud. ```bash Install dependencies pip install r requirements.txt 📄 License Thi...
For agents
This page has a .md twin and JSON over the API.