Frameworks

Now, we’ve mastered our basic understanding of agents, we can dive into the most popular frameworks that are used to build them!

In this second unit, You will learn how to build agents and agentic workflows with various frameworks including:

smolagents: a barebones library for agents. Agents write python code to call tools and orchestrate other agents.

LangGraph: a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.

LlamaIndex: a data framework for your LLM application. Building with LlamaIndex typically involves working with LlamaIndex core and a chosen set of integrations (or plugins).

Sounds more or less similar, right?

Don’t worry. After this unit, you will have a solid understanding of each one of these frameworks and how and when to use each framework. You will also have hands-on experience with building agents using each framework.

You will for example, create an agent, called Alfred, which can help you with agentic Retrieval Augmented Generation (RAG) tasks. Not only focussing on a single vector database, but also offering Alfred the tools to do query rewriting, web search, and everything else you need to build an optimal RAG system.

Ready? Let’s get started! 🚀

< > Update on GitHub