The unreasonable effectiveness of an LLM agent loop with tool use

I built this very same thing today! The only difference is that i pushed the tool call outputs into the conversation history and resent it back to the LLM for it to summarize, or perform further tool calls, if necessary, automagically.

I used ollama to build this and ollama supports tool calling natively, by passing a `tools=[...]` in the Python SDK. The tools can be regular Python functions with docstrings that describe the tool use. The SDK handles converting the docstrings into a format the LLM can recognize, so my tool's code documentation becomes the model's source of truth. I can also include usage examples right in the docstring to guide the LLM to work closely with all my available tools. No system prompt needed!

Moreover, I wrote all my tools in a separate module, and just use `inspect.getmembers` to construct the `tools` list that i pass to Ollama. So when I need to write a new tool, I just write another function in the tools module and it Just Works™

Paired with qwen 32b running locally, i was fairly satisfied with the output.

> The only difference is that i pushed the tool call outputs into the conversation history and resent it back to the LLM for it to summarize, or perform further tool calls, if necessary, automagically.

It looks like this one does that too.

     msg = [ handle_tool_call(tc) for tc in tool_calls ]