update_settings

Update AI runtime settings dynamically during a call. Changes take effect for subsequent LLM turns.

Parameters

settings

dict[str, Any]Required

Dictionary of settings to update. Supported keys:

Key	Type	Range
`frequency-penalty`	`float`	-2.0 to 2.0
`presence-penalty`	`float`	-2.0 to 2.0
`max-tokens`	`int`	0 to 4096
`top-p`	`float`	0.0 to 1.0
`confidence`	`float`	0.0 to 1.0
`barge-confidence`	`float`	0.0 to 1.0
`temperature`	`float`	0.0 to 2.0 (clamped to 1.5)

Returns

FunctionResult — self, for chaining.

Example

from signalwire import AgentBase
from signalwire import FunctionResult
agent = AgentBase(name="my-agent", route="/agent")
agent.set_prompt_text("You are a helpful assistant.")
@agent.tool(name="adjust_for_technical_discussion", description="Adjust AI settings for technical topics")
def adjust_for_technical_discussion(args, raw_data):
    return (
        FunctionResult("Adjusting response parameters.")
        .update_settings({
            "temperature": 0.3,
            "confidence": 0.9,
            "barge-confidence": 0.8
        })
    )
agent.serve()