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This commit introduces a new simulation capability for models that do not natively support tool/function calling, allowing them to act as tool-using agents through structured prompting. - `simulate_agent`: An iterative agent loop that automatically prompts the LLM with available tools, parses its requested tool calls, executes them locally, and feeds the results back until a final answer is reached (or `max_iterations` is hit). - `simulate_respond_with_tools`: A single-turn simulation that wraps the LLM call with instructions and a structured output schema built dynamically from the provided Python functions.
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This commit introduces a new simulation capability for models that do not natively support tool/function calling, allowing them to act as tool-using agents through structured prompting.
simulate_agent: An iterative agent loop that automatically prompts the LLM with available tools, parses its requested tool calls, executes them locally, and feeds the results back until a final answer is reached (ormax_iterationsis hit).simulate_respond_with_tools: A single-turn simulation that wraps the LLM call with instructions and a structured output schema built dynamically from the provided Python functions.