LLM Agents and Operating Systems

[wassfila]

Introduction

Traditional LLMs, while robust in general tasks, often struggle with domain-specific applications due to their pre-trained, generalized nature. The introduction of LLM Agents and dedicated Operating Systems helps bridge this gap. These systems allow LLMs to dynamically adapt to user-specific, domain-specific, and context-specific data, significantly enhancing their applicability in specialized fields. This adaptability is crucial as it addresses the limitations of LLMs in handling precise or niche queries where generalized knowledge falls short.

When it comes to facts sensitive analysis, relying on user approved consistent data can mitigate the statistical approach and hallucinations bias inherent to Large Language Models.

Overview

Large Language Models offer capabilities in a stateless manner.
Retrieval Augmented Generation expands the context capabilities by filtering relevant data to fit in the context query.
Agents add a State that runs planing capabilities for a muti steps LLMs invocations.
LLMs Operating Systems unify Inputs and Outputs to standardise Agents capabilities.
Capabilities include

• Context episodic memory
• Short term, long term planning and scheduling
• Data Storage
  • LLM Embeddings Vectors
  • Text instant search indexing for hybrid retriaval
  • General Knowledge Graph and data specific Graph representations
• Function calling for real time update of the knowledge base
• Function calling for targets achievements

Projects

For instance, the 'AutoGPT' project focuses on automating repetitive tasks using GPT models, illustrating practical task automation. Meanwhile, 'Semantic Kernel' aims to provide a deeper integration of semantic understanding in OS operations, enhancing the OS's ability to manage and retrieve knowledge effectively.

LLM tools and OS

https://github.com/Significant-Gravitas/AutoGPT
https://github.com/microsoft/semantic-kernel
https://github.com/agiresearch/AIOS

LLM Agents

https://github.com/geekan/MetaGPT
https://github.com/microsoft/autogen
https://github.com/OpenBMB/ChatDev
https://github.com/yoheinakajima/babyagi

https://github.com/crewAIInc/crewAI

https://www.langchain.com/langgraph

Other

Combine LLM with ML for task execution https://github.com/microsoft/JARVIS

LLMs and Knowledge Graph

a list of references, mainly papers but also some tools about Graph related LLMs https://github.com/XiaoxinHe/Awesome-Graph-LLM

Frameworks

Transformers framework https://github.com/huggingface/transformers
Application pipeline https://github.com/langchain-ai/langchain
RAG and search documents pipeline https://haystack.deepset.ai/

AI initiatives

• https://ai-infrastructure.org/
• tools capabilities clasification https://ai-infrastructure.org/ai-infrastructure-landscape/
• https://thealliance.ai/

Challenges

Hype often leads to inflated expectations. LLMs Agents and OS face the risk of being perceived as 'silver bullets' capable of solving complex problems effortlessly. The reality is more nuanced, as these systems require careful integration, oversight, and continuous learning to perform as expected.

Transformers architecture combined with huge training datasets has brought a step forward in AI. The decoupling of the Agents mechanisms from the Model itself is a problem that limits the scaling capabilities.

Expanding planning to a long term or a high number of iterations without loosing consistency is also a common stability issue for Autonomous Agents.

Conclusion

This exploration into the realm of Large Language Models (LLMs), augmented by dedicated operating systems and agent-based frameworks, underscores the significant enhancements these integrations offer over traditional, stateless models. By incorporating user-specific, domain-specific, and scoped data, these systems address the inherent limitations of pre-trained models, particularly their tendency towards generality and bias. The capabilities of episodic memory, adaptive planning, and sophisticated data management not only refine the LLMs' responses but also extend their applicability across various specialized sectors.

Looking ahead, the convergence of LLMs with Graph-based knowledge learning presents a promising frontier. Graph knowledge structures, integrated within the training phase of LLMs, could revolutionize how these models understand and interact with complex data relationships and dependencies. This integration promises to enhance the models' reasoning capabilities and decision-making processes, making them more intuitive and contextually aware.

see #7 for a "Graph based LLMs Architecture" Discussion