In the previous chapter, I partially made an LLM Agent. One that is missing the agent loop. In this chapter, I use ipython to manually tell the LLM Agent to write it’s own agent loop. It succeeded (GPT-4.1). There are two sections: Write Your Own Agent Loop This is where I define the task. Note: [ ]
To this end, I have shown the circular context, the toolkit, and the evaluator. Now is the time to use these three components. In this chapter, I show infer, a function that uses the circular context, toolkit and evaluator. I do not automate everything. I still manually manage the messages, but I introduce infer to [ ]
In the previous chapter, I have shown a Toolkit component. The Toolkit contains definitions for function tools for the LLM API. But it does not explicitly perform a tool call (that will be done in later chapters). In this chapter, I show the evaluator component. The evaluator is a program to which you can send [ ]
In the previous chapter, I have shown a Python program to manually exchange messages with an LLM API. I introduced a data structure that contains inputs and outputs to interact with the LLM. In this chapter, I introduce the Toolkit component. The Toolkit contains definitions for function tools for the LLM API. But it does [ ]
In the previous chapter, I have shown how to exchange data with an LLM API. I used CURL as the HTTP client. In this chapter, I replace CURL with a Python program. That does not mean that everything is automated. No, I still manually manage the messages, but I introduce a data structure that contains [ ]
This is a tutorial on using the OpenAI LLM API, focusing on: messages and function calls. But, without Python, TypeScript, or some other programming language. The only requirements are CURL (an HTTP client) and an OpenAI API key. Why Bother? “Why waste my time, when I can just import an API package?” Sure, that works, [ ]