chore: import upstream snapshot with attribution

2026-07-13 12:39:17 +08:00
commit 4ed4e9ff99
1368 changed files with 334957 additions and 0 deletions
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+from __future__ import annotations
+
+import asyncio
+from typing import Any, Literal
+
+from pydantic import BaseModel
+
+from agents import (
+    Agent,
+    FunctionToolResult,
+    ModelSettings,
+    RunContextWrapper,
+    Runner,
+    ToolsToFinalOutputFunction,
+    ToolsToFinalOutputResult,
+    function_tool,
+)
+from examples.auto_mode import is_auto_mode
+
+"""
+This example shows how to force the agent to use a tool. It uses `ModelSettings(tool_choice="required")`
+to force the agent to use any tool.
+
+You can run it with 3 options:
+1. `default`: The default behavior, which is to send the tool output to the LLM. In this case,
+    `tool_choice` is not set, because otherwise it would result in an infinite loop - the LLM would
+    call the tool, the tool would run and send the results to the LLM, and that would repeat
+    (because the model is forced to use a tool every time.)
+2. `first_tool_result`: The first tool result is used as the final output.
+3. `custom`: A custom tool use behavior function is used. The custom function receives all the tool
+    results, and chooses to use the first tool result to generate the final output.
+
+Usage:
+python examples/agent_patterns/forcing_tool_use.py -t default
+python examples/agent_patterns/forcing_tool_use.py -t first_tool
+python examples/agent_patterns/forcing_tool_use.py -t custom
+"""
+
+
+class Weather(BaseModel):
+    city: str
+    temperature_range: str
+    conditions: str
+
+
+@function_tool
+def get_weather(city: str) -> Weather:
+    print("[debug] get_weather called")
+    return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind")
+
+
+async def custom_tool_use_behavior(
+    context: RunContextWrapper[Any], results: list[FunctionToolResult]
+) -> ToolsToFinalOutputResult:
+    weather: Weather = results[0].output
+    return ToolsToFinalOutputResult(
+        is_final_output=True, final_output=f"{weather.city} is {weather.conditions}."
+    )
+
+
+async def main(tool_use_behavior: Literal["default", "first_tool", "custom"] = "default"):
+    if tool_use_behavior == "default":
+        behavior: Literal["run_llm_again", "stop_on_first_tool"] | ToolsToFinalOutputFunction = (
+            "run_llm_again"
+        )
+    elif tool_use_behavior == "first_tool":
+        behavior = "stop_on_first_tool"
+    elif tool_use_behavior == "custom":
+        behavior = custom_tool_use_behavior
+
+    agent = Agent(
+        name="Weather agent",
+        instructions="You are a helpful agent.",
+        tools=[get_weather],
+        tool_use_behavior=behavior,
+        model_settings=ModelSettings(
+            tool_choice="required" if tool_use_behavior != "default" else None
+        ),
+    )
+
+    result = await Runner.run(agent, input="What's the weather in Tokyo?")
+    print(result.final_output)
+
+
+async def auto_demo() -> None:
+    for behavior in ("default", "first_tool", "custom"):
+        print(f"=== {behavior} ===")
+        await main(behavior)
+        print()
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-t",
+        "--tool-use-behavior",
+        type=str,
+        default="default",
+        choices=["default", "first_tool", "custom"],
+        help=(
+            "The behavior to use for tool use. "
+            "default sends tool outputs back to the model, first_tool uses the first tool result as the final output, "
+            "custom runs a custom tool use behavior function."
+        ),
+    )
+    args = parser.parse_args()
+    if is_auto_mode():
+        asyncio.run(auto_demo())
+    else:
+        asyncio.run(main(args.tool_use_behavior))