Having established the core principles of LLM agent tooling in the previous chapter, we now turn to the practical implementation of custom tools using Python. This chapter focuses on equipping you with the skills to build these tools from the ground up.

You will learn to structure your tools as Python functions or classes, manage state for more complex interactions, and enable your tools to communicate with external services like APIs and databases. We will also cover important considerations such as validating inputs, formatting outputs effectively for LLM consumption, and implementing asynchronous operations for non-blocking tasks. By the end of this chapter, your ability to extend LLM agent capabilities through custom Python code will be significantly enhanced.