Building AI agents from scratch can quickly become complex. You need to handle prompting, tool integration, memory management, task planning, execution loops, and error handling. Implementing all of this manually can slow development and make systems difficult to maintain.

This is where agent frameworks come in.

Agent frameworks provide structured tools and abstractions for building AI agents. Instead of writing the entire agent logic yourself, these frameworks offer reusable components for common tasks such as tool calling, memory management, workflow orchestration, and agent communication.

In this chapter, we will explore the most popular agent frameworks, how they work internally, and when it makes sense to use them when building AI-powered systems.