Build Multi Agent Systems with ADK, Streamlit, Autogen and CrewAI

Build Multi Agent Systems with ADK, Streamlit, Autogen and CrewAI teaches you how to design and build multi-agent AI applications using some of the most popular open-source agent frameworks. Multi-agent systems assign specialised AI agents to different parts of a complex task and coordinate them to deliver results that a single model cannot achieve on its own. The course is designed for developers, AI engineers, data scientists and technical professionals with Python experience who want hands-on experience with leading agent frameworks.

You will start with an introduction to large language model (LLM) AI agents, understanding how LLMs reason, use tools and keep memory, and comparing Google's Agent Development Kit (ADK), Microsoft AutoGen and CrewAI so you can choose the right framework for each problem. You will build simple agents in each framework and create user interfaces for them with Streamlit. Next you will implement retrieval-augmented generation (RAG), connecting agents to vector databases and your own documents so they answer accurately from trusted knowledge.

The final topic covers implementing a multi-agent AI workflow, designing agent roles and tasks, orchestrating collaboration and handoffs, and building an end-to-end application with a Streamlit front end. Throughout the course you will also learn how to evaluate agent output, handle errors, control costs and apply guardrails. Every topic is hands-on. By the end, you will be able to build, compare and deploy multi-agent systems using ADK, AutoGen, CrewAI and Streamlit with confidence.