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Skool of AI and Meta Brains

Transform your ideas into powerful AI applications without writing a single line of code. This comprehensive Flowise course takes you from complete beginner to advanced AI workflow creator, teaching you to build sophisticated chatbots, document retrieval systems, and autonomous agents using visual programming.

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Transform your ideas into powerful AI applications without writing a single line of code. This comprehensive Flowise course takes you from complete beginner to advanced AI workflow creator, teaching you to build sophisticated chatbots, document retrieval systems, and autonomous agents using visual programming.

Flowise is revolutionizing how we create AI applications by providing an intuitive, drag-and-drop interface for building complex LLM workflows. Whether you're a business owner looking to automate customer service, a content creator wanting to streamline research, or an entrepreneur building AI-powered products, this course provides everything you need.

You'll start with the fundamentals—installing Flowise, understanding its interface, and creating your first simple workflows. Then dive deep into advanced features like conversational agents, document retrieval systems, and multi-prompt chains. Through hands-on projects, you'll build a PDF Q&A chatbot, create API endpoints for external integration, and develop sophisticated agentic systems.

The course covers both Flowise Agents V1 and the latest V2 features, including advanced capabilities like branch-out merge-in patterns, portfolio management agents, and financial research systems. You'll master text summarization, conditional logic, and state management while building real-world applications.

By the end, you'll have created multiple AI agents capable of complex reasoning, multi-tool usage, and autonomous task execution—all through Flowise's powerful no-code platform.

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What's inside

Learning objectives

  • Build sophisticated ai chatbots using flowise's visual drag-and-drop interface
  • Create document retrieval systems that can answer questions from pdf files
  • Develop conversational agents with memory and context understanding capabilities
  • Master llm chains and multi-prompt workflows for complex ai applications
  • Deploy ai workflows as api endpoints for integration with external applications
  • Build autonomous agents capable of multi-tool usage and complex reasoning
  • Implement advanced agentic systems with state management and conditional logic
  • Create real-world ai solutions including research agents and customer service bots

Syllabus

PART I: INTRODUCTION TO FLOWISE
Course Overview
What is Flowise and Why Use It?
Flowise Installation
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Activities

Coming soon We're preparing activities for AI Agent Builder Bootcamp: Flowise, LangFlow, RAG & more!. These are activities you can do either before, during, or after a course.

Career center

Learners who complete AI Agent Builder Bootcamp: Flowise, LangFlow, RAG & more! will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Provides a gentle introduction to machine learning, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It must-read for anyone who wants to learn about deep learning.
Classic introduction to reinforcement learning, covering topics such as Markov decision processes, value functions, and Q-learning. It valuable resource for anyone who wants to learn about reinforcement learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as Bayesian inference, neural networks, and support vector machines.
Provides a gentle introduction to AI, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Dives deep into the complexities of systems with multiple interacting agents. It covers algorithmic, game-theoretic, and logical foundations, which are crucial for understanding how agents behave and coordinate in complex environments. It valuable reference for those looking to deepen their understanding beyond single-agent systems.
Reinforcement learning key paradigm for developing intelligent agents that can learn to make sequential decisions by interacting with their environment. is the classic text on the subject, providing a comprehensive introduction to the core concepts and algorithms used in training agents. It must-read for anyone focusing on learning agents.
Provides a solid introduction to the field of multiagent systems, covering key concepts, architectures, and applications. It's more accessible than some of the deeper theoretical texts and serves as an excellent starting point for understanding the principles behind multiple interacting intelligent agents.
Delves into the logical foundations for reasoning about the properties and behavior of rational agents, particularly focusing on the Belief-Desire-Intention (BDI) model. It is more theoretical and suited for those who want to understand the formal underpinnings of agent systems.
This textbook presents AI as the study of intelligent computational agents, providing a unified vision of the field's foundations. It covers a wide range of AI topics through the lens of agents, making it highly relevant for understanding the subject broadly. The latest edition includes updates on recent AI advances like deep learning.
Offers a practical approach to designing and implementing single and multi-agent systems, particularly in the context of generative AI. It helps bridge the gap between theoretical concepts and real-world deployment of AI agents. It is highly relevant for understanding contemporary applications.
Focusing on building LLM-powered autonomous agents, this book provides a practical framework for developing agents that can handle real-world tasks. It covers using tools like the OpenAI Assistants API and LangChain, making it very relevant for contemporary agent development.
Provides a comprehensive overview of AI, covering topics such as machine learning, natural language processing, and computer vision. It is also written in a clear and concise style, making it accessible to readers of all levels.

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