Agent frameworks
Build & orchestrate your own custom AI agents.
This track teaches you how to build and orchestrate your own custom AI agents. It is for developers who want to move beyond pre‑made bots and control the logic of each agent in code or visually. After completing the chapters you will be able to create a team of agents with defined roles, assign them tasks, and connect tools using CrewAI; you will also be able to design chatbot, RAG bot, or multi‑agent flows by dragging boxes on a canvas with Flowise; finally you will know how to use Langflow’s visual canvas while keeping the underlying Python editable. In short, you will have practical skills to build agents from scratch and choose the right framework for any project. Start with the introductory chapter that explains what an agent framework is and compares the three options. Then follow the Build agents chapter to see the overall process, move on to CrewAI for code‑first development, continue with Flowise for a drag‑and‑drop approach, and finish with Langflow to combine visual design with editable Python. If you are short on time, you can skip the detailed comparison in the intro after reading the overview, but do not skip the Build agents chapter because it ties all later sections together.
An agent framework is scaffolding for a program that decides its own next step. You give it a goal, a set of tools it may call and a model to think with; it loops — plan, call a tool, read the result, plan again — until the goal is met or it gives up. That loop is the whole difference from a chatbot: a chatbot answers you, an agent goes and does something and comes back. → The three tools in this section build the same thing three ways. Flowise and Langflow are canvases: you drag nodes, connect them, and watch data move through the graph — Langflow additionally lets you open a node and edit the Python inside it. CrewAI has no canvas at all; you declare each agent, its role and its task list in Python, and the structure lives in your repository like any other code. → That is the trade-off to make deliberately, because it is hard to undo later. A canvas is faster to a working demo and much easier to show someone. Code is what you can diff, review, test and roll back. Pick the canvas when the value is in the idea and the audience is human; pick code when the agent is going to run unattended and somebody will have to debug it at 2am. → All three are free and self-hostable, and none of them includes a model. You bring an API key (see LLM models and EU-sovereign inference) and you pay per token for every loop the agent takes — which is why agents cost more than they look like they should. The three chapters after this one take each tool in turn.
- 7.1 Agent frameworks - Intro What an agent framework actually is, and which of the three to reach for. 2026-08-13 150
- 7.2 Langflow A visual canvas whose blocks stay editable Python underneath 2026-08-13 4
- 7.3 Flowise Drag boxes on a canvas to build a chatbot, a RAG bot or a multi-agent flow 2026-08-13 3
- 7.4 CrewAI A Python framework for a team of agents with roles, tasks and tools 2026-08-13 35
- 7.5 Build agents Build & orchestrate your own custom AI agents 2026-08-13 150