"Iโve been tracking llm-tool-use for a while. I first saw the trick described in the ReAcT paper, first published in October 2022 (a month before the initial release of ChatGPT). I built a simple implementation of that in a few dozen lines of Python. It was clearly a very neat pattern!
Over the past few years it has become very apparent that tool use is the single most effective way to extend the abilities of language models. Itโs such a simple trick: you tell the model that there are tools it can use, and have it output special syntax (JSON or XML or tool_name(arguments), it doesnโt matter which) requesting a tool action, then stop.
Your code parses that output, runs the requested tools and then starts a new prompt to the model with the results.
This works with almost every model now. Most of them are specifically trained for tool usage, and there are leaderboards like the Berkeley Function-Calling Leaderboard dedicated to tracking which models do the best job of it.
All of the big model vendorsโOpenAI, Anthropic, Google, Mistral, Metaโhave a version of this baked into their API, either called tool usage or function calling. Itโs all the same underlying pattern.
The models you can run locally are getting good at this too. Ollama added tool support last year, and itโs baked into the llama.cpp server as well.
Itโs been clear for a while that LLM absolutely needed to grow support for tools. I released LLM schema support back in February as a stepping stone towards this. Iโm glad to finally have it over the line.
As always with LLM, the challenge was designing an abstraction layer that could work across as many different models as possible. A year ago I didnโt feel that model tool support was mature enough to figure this out. Today thereโs a very definite consensus among vendors about how this should work, which finally gave me the confidence to implement it."
https://simonwillison.net/2025/May/27/llm-tools/
#AI #GenerativeAI #LLMs #CLI #Python #OpenAI #Anthropic #Google #Programming
