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AutoGen in 60 Minutes

TL;DR: Learn how to build agentic AI systems using AutoGen in 60 minutes with hands-on examples including live market data debates and SEC filing analysis.

Tutorial in 30 Seconds#

AutoGen is an open-source framework from Microsoft for building agentic AI systems made of multiple collaborating agents.

Key capabilities:

  • Layered APIs: Message-passing agents, tool usage, and human-in-the-loop workflows
  • Autonomous planning: Reasoning and execution of tasks such as coding, browsing, and data processing
  • Multi-language support: Python and other languages, with extensible integrations for LLM providers using OpenAI-style clients
  • Rapid prototyping: Helps developers quickly build applications from simple assistants to coordinated teams of specialized AI agents

This tutorial's goal is to show you in 60 minutes:

  • The basic API of AutoGen (an open-source framework for building agentic AI systems)
  • Concrete examples of using AutoGen to build agents that can debate and reason investment strategies based on market data and SEC filings

Official References#

Tutorial Content#

This tutorial includes all the code, notebooks, and Docker containers in tutorials/Autogen

  • README.md: Instructions and setup for the tutorial environment
  • A Docker system to build and run the environment using our standardized approach
  • autogen.API.ipynb: Tutorial notebook focusing on API configurations and basic agent setup
  • autogen.example1.ipynb: Advanced end-to-end agentic workflow example Part 1
    • Fetches real-time stock data from Yahoo Finance
    • Bull and Bear strategist agents debate market trends
    • Selector agent dynamically decides which expert to call at each step
    • Generates stock charts and financial summaries
  • autogen.example2.ipynb: Advanced end-to-end agentic workflow example Part 2
    • Pulls 10-K filings from SEC EDGAR and cleans them
    • Embeds documents into a ChromaDB vector database
    • Senior Quant Analyst agent queries the database to extract revenue splits, risk factors, and other insights
    • Quant Runtime agent executes Python code locally to transform raw tables into structured visualizations
  • autogen_utils.py: Utility functions required by the example notebooks