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10 Must Know Memory AI Libraries on GitHub, Including SuperMemory

Memory mechanisms play a crucial role in enhancing the capabilities of machine learning models. This article explores 10 Memory AI libraries available on GitHub. Whether looking to improve your AI applications or dive into cutting-edge research, these libraries offer valuable resources to elevate your work.

1. LangChain

Langchain is a framework for building applications with language models. It provides tools to manage the memory in AI workflows, which can be used for chatbots or more complex AI assistants with persistent memory. LangChain can be found on GitHub: here.

To install you can use pip 

pip install langchain

Key Features

  • Chain different LLM calls together
  • Handle state between calls
  • Implement memory
  • Interact with external APIs

2. Haystack

Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search, and more. It offers memory features to support AI models in retrieving information from large document sets. The Haystack library can be found on GitHub here.

To install you can use pip 

pip install haystack-ai

Key Features

  • Neural search
  • Document-based memory management
  • QA systems with persistent memory

3. Hugging Face Transformers

Hugging Face’s Transformers library supports various LLMs like GPT, BERT, and more. It can persist information across sessions by integrating retrieval-augmented generation and memory systems. The Hugging Face library can be found on GitHub here.

You can clone the repo

https://github.com/huggingface/transformers.git

Key Features

  • A wide variety of models
  • Memory augmentation support
  • Integration with custom memory systems like LangChain

4. MemN2N (End-to-End Memory Networks)

MemN2N, or End-to-End Memory Networks, is an advanced deep learning model introduced by Facebook AI, designed to store past inputs explicitly in memory. The model uses this memory to reason and answer questions based on previous knowledge. MemN2N library can be found on GitHub here.

You can clone the repo

https://github.com/domluna/memn2n.git

Key Features

  • Sequence modeling
  • Memory-based tasks
  • End-to-end learning for remembering context

5. LlamaIndex (formerly GPT Index)

It is a project designed to manage large collections of documents and effectively index them for AI models, thereby allowing for memory over large datasets. LlamaIndex can be found on GitHub here.

You can clone the repo 

https://github.com/run-llama/llama_index.git

Key Features

  • Data integration
  • Large-scale document memory
  • Retrieval-augmented generation (RAG) integration with LLMs

6. EleutherAI GPT-NeoX

EleutherAI’s GPT-NeoX library is a GPT-like large language model that can be fine-tuned for memory-based tasks, where retaining context over long conversations is crucial. It can be found on GitHub here.

You can clone the repo using 

https://github.com/EleutherAI/gpt-neox.git

Key Features

  • Large-scale language models with memory-based applications
  • Model customization
  • Open-source accessibility

7. OpenAI Baselines

Though primarily for reinforcement learning, OpenAI Baselines can be adapted to store past states, enabling memory functions within RL environments. It can be found on GitHub here.

You can clone the repo using 

https://github.com/openai/baselines.git

Key Features

  • Reinforcement learning environments
  • State memory
  • Policy optimization.

8. Transformer-XL

Enhanced transformer model designed for language tasks that require long-term memory retention, such as text generation and NLP tasks. It can be found on GitHub here.

You can clone the repo using 

https://github.com/kimiyoung/transformer-xl.git

Key Features

  • Memory mechanism for long sequence handling
  • Segment-level recurrence
  • Performance improvements over traditional transformers

9. Neural Turing Machines (NTM)

A model with external memory capabilities for tasks involving reasoning and long-term decision-making, mimicking the memory of Turing machines. It can be found on GitHub here.

You can clone the repo using

https://github.com/loudinthecloud/pytorch-ntm.git

Key Features

  • Differentiable external memory
  • Read/write capabilities
  • Flexible architecture for algorithmic problem-solving and complex AI tasks

10. SuperMemory AI

SuperMemory AI is a cutting-edge memory-augmented system designed to enhance AI applications by allowing models to retain and recall information across multiple interactions. SuperMemory AI can be found on GitHub here.

You can clone the repo using

https://github.com/supermemoryai/supermemory.git

Key Features

  • Persistent memory
  • Modularity
  • Scalability

Wrapping up

These libraries can be used to build AI systems with memory persistence, enhance chatbot capabilities, improve document retrieval, or extend the capacity of models to maintain and utilize information over time.