Summary

This repository serves as a curated, continuously evolving knowledge base addressing the critical limitations of Large Language Model (LLM) context windows. It provides researchers and practitioners with a centralized resource on AI memory systems, covering long-term memory, reasoning, retrieval, and memory-native system design for LLMs and intelligent agents. The primary benefit is accelerating the development of AI systems capable of sustained reasoning and adaptive evolution.

How It Works

The project systematically curates research papers, framework tools, and practical implementations related to AI memory for LLMs. Its scope includes memory mechanisms that extend LLM context capabilities, encompassing explicit external memory, various memory types (short-term, long-term, episodic, semantic), Retrieval-Augmented Generation (RAG), and memory management strategies like writing, updating, forgetting, and compression. It details core components such as Memory Storage, Processing, Retrieval, and Control Layers, aiming to map the rapidly evolving research landscape across NLP, information retrieval, and cognitive science.

Quick Start & Requirements

This repository is a curated knowledge base and does not have a direct installation or execution process. It serves as a reference for existing research and tools.

Highlighted Details

• Comprehensive catalog of AI memory concepts, including explicit/implicit memory, RAG, memory operations (write, read, update, delete, compress), management strategies, and classification systems.
• Extensive collection of recent research papers, frameworks, benchmarks, and open-source systems, with content updated through late 2025 and early 2026.
• Detailed exploration of memory types (short-term, long-term, episodic, semantic) and mechanisms, alongside a breakdown of memory system components (storage, processing, retrieval, control).
• Lists numerous specialized AI memory systems and evaluation benchmarks, providing practical resources for adoption.

Maintenance & Community

Community support is available through GitHub Issues, Pull Requests, and Discussions. A WeChat group is also provided for latest research information and project promotion. The repository is presented as "continuously evolving," indicating ongoing curation.

Licensing & Compatibility

Licensing information for the curated resources is not explicitly detailed within the README.

Limitations & Caveats

As a curated knowledge base, this repository does not offer runnable code or direct installation instructions. It serves solely as a reference for external research and tools. Consolidated licensing information for all listed resources is not provided.