Building Multi-Agent AI Systems From Scratch: OpenAI vs. Ollama
Last Updated on November 17, 2024 by Editorial Team
Author(s): Isuru Lakshan Ekanayaka
Originally published on Towards AI.
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In the dynamic realm of artificial intelligence, multi-agent systems have emerged as a transformative approach for addressing complex tasks through collaboration and specialization. By distributing responsibilities among distinct agents such as summarizing texts, generating content, and ensuring data privacy these systems enhance efficiency, accuracy, and reliability. This comprehensive guide explores the creation of two robust multi-agent AI systems from scratch using Python: one leveraging OpenAI’s GPT-4 model and the other utilizing Ollama’s open-source LLaMA 3.2:3b model. Both implementations are designed without relying on existing agent frameworks, offering a foundational understanding for developers eager to master AI agent architectures.
IntroductionOpenAI-Based Multi-Agent SystemOllama-Based Multi-Agent SystemConclusionGitHub Repositories and Installation
Multi-agent systems in AI involve multiple specialized agents working collaboratively to achieve intricate objectives. By distributing tasks among agents with distinct roles — such as summarizing texts, generating content, and ensuring data privacy — these systems enhance efficiency, accuracy, and reliability. This guide delves into the development of two such systems: one powered by OpenAI’s GPT-4 and the other by Ollama’s LLaMA 3.2:3b model. Both implementations prioritize transparency and educational value, enabling beginners to grasp the fundamentals of AI agent construction without relying on… Read the full blog for free on Medium.
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