Mastering Multi-Agent Workflows in OpenAI's Agents SDK

- Authors
- Published on
- Published on
In this thrilling exploration of OpenAI's agents SDK, we delve into the world of multi-agent workflows with the swagger of a seasoned race car driver. OpenAI's agents SDK, the successor to the groundbreaking Swarm package, offers a robust platform for building dynamic agent systems. The orchestrator sub-agent pattern takes center stage, where a main orchestrator agent calls the shots, deciding whether to consult sub-agents for additional info or respond directly to queries. It's like having a team of expert advisors at your beck and call, ready to assist in navigating the complex landscape of information retrieval.
The web search sub-agent revs its engines, utilizing the LinkUp API to scour the web for data and deliver concise text responses. Meanwhile, the internal docs sub-agent steps into the ring, providing access to private company information through a clever RAG tool. This sub-agent is like a top-secret vault, unlocking hidden gems of knowledge that are off-limits to the general public. And let's not forget the code execution agent, a precision tool designed to handle simple calculations with the finesse of a skilled mechanic.
As we hurtle through the twists and turns of this high-octane journey, it becomes clear that the orchestrator sub-agent pattern is the glue that holds this multi-agent system together. Each sub-agent plays a crucial role in the orchestra, following the orchestrator's lead and executing tasks with precision. It's a symphony of AI prowess, orchestrated by the human touch that guides the flow of information. So buckle up, because in the world of OpenAI's agents SDK, the possibilities are as vast and thrilling as an open road stretching into the horizon.

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube
Watch Multi-Agent Systems in OpenAI's Agents SDK | Full Tutorial on Youtube
Viewer Reactions for Multi-Agent Systems in OpenAI's Agents SDK | Full Tutorial
Code and article links provided for further reference
Positive feedback on the tutorial
Interest in exploring the openai framework further
Request for discussion on other agentic frameworks like autogen, langgraph, lano
Interest in using multi-agents with multiple llms
Related Articles

Exploring AI Agents and Tools in Lang Chain: A Deep Dive
Lang Chain explores AI agents and tools, crucial for enhancing language models. The video showcases creating tools, agent construction, and parallel tool execution, offering insights into the intricate world of AI development.

Mastering Conversational Memory in Chatbots with Langchain 0.3
Langchain explores conversational memory in chatbots, covering core components and memory types like buffer and summary memory. They transition to a modern approach, "runnable with message history," ensuring seamless integration of chat history for enhanced conversational experiences.

Mastering AI Prompts: Lang Chain's Guide to Optimal Model Performance
Lang Chain explores the crucial role of prompts in AI models, guiding users through the process of structuring effective prompts and invoking models for optimal performance. The video also touches on future prompting for smaller models, enhancing adaptability and efficiency.

Enhancing AI Observability with Langmith and Linesmith
Langmith, part of Lang Chain, offers AI observability for LMS and agents. Linesmith simplifies setup, tracks activities, and provides valuable insights with minimal effort. Obtain an API key for access to tracing projects and detailed information. Enhance observability by making functions traceable and utilizing filtering options in Linesmith.