Autogen, Building Multi-Agent agentic app, Beginner-to-Master | OpenAI Structured Output

Our highlight ⭐️ is the beginner guide to Autogen library, which is a framework that you can use to create agentic workflow and multi-agent collaboration. We will build AI agent group that help search the internet and summarizes the results! Also, we update about LLM price ware and OpenAI's Structured Output.

⭐️What You'll Learn:
- Overview of Microsoft’s AutoGen library for building agentic workflows and multi-agent collaboration
- Practical examples of agent roles, conversations, and human-in-the-loop interactions with AutoGen
- Coding walkthrough to demonstrate the concept, especially two-agent conversation and group-chat conversation
- LLM price war and how major players like OpenAI and Google are reducing prices
- Detailed cost analysis and comparisons for various LLM models from OpenAI, Google, Anthropic, and Meta
- Understanding OpenAI’s Structured Output, an update ensuring valid JSON schemas - great news for AI developers!
- Step-by-step explanation of OpenAI's two-step approach for deterministic JSON output

⛓️Connect with Us:
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🎬Quick navigation:
02:20 LLM Price War. OpenAI, Google, and other major providers dropped API pricing significantly. See how to estimate cost per user per month, and see the most cost effective models.
18:22 OpenAI's Structured Output. Why it is important and will help AI developers, and How OpenAI did to get here.
29:53 Autogen - Concept introduction: Agent, Role, Conversation, and Tools
52:05 Autogen - Coding Walkthrough 1: Two-agent conversations - AI agent that searches the internet
01:09:28 Autogen - Coding Walkthrough 2: Group-chat conversation - AI agent groups that help with internet search and summarization.

#genai #autogen #aiagents #microsoft #agentic #openai #google #chatgpt #gpt4o #gemini #codingwalkthrough