chore: import upstream snapshot with attribution
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wehub-resource-sync
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import asyncio
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from pydantic import BaseModel
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from agents import Agent, AgentBase, ModelSettings, RunContextWrapper, Runner, trace
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from agents.tool import function_tool
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from examples.auto_mode import confirm_with_fallback, input_with_fallback, is_auto_mode
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"""
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This example demonstrates the agents-as-tools pattern with conditional tool enabling.
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Agent tools are dynamically enabled/disabled based on user access levels using the
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is_enabled parameter.
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"""
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class AppContext(BaseModel):
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language_preference: str = "spanish_only" # "spanish_only", "french_spanish", "european"
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def french_spanish_enabled(ctx: RunContextWrapper[AppContext], agent: AgentBase) -> bool:
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"""Enable for French+Spanish and European preferences."""
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return ctx.context.language_preference in ["french_spanish", "european"]
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def european_enabled(ctx: RunContextWrapper[AppContext], agent: AgentBase) -> bool:
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"""Only enable for European preference."""
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return ctx.context.language_preference == "european"
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@function_tool(needs_approval=True)
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async def get_user_name() -> str:
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print("Getting the user's name...")
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return "Kaz"
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# Create specialized agents
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spanish_agent = Agent(
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name="spanish_agent",
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instructions=(
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"Respond in Spanish. Call get_user_name exactly once before replying, then greet that "
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"user by name and answer the user's question in a non-empty final response."
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),
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model_settings=ModelSettings(tool_choice="required"),
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tools=[get_user_name],
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)
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french_agent = Agent(
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name="french_agent",
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instructions="You respond in French. Always reply to the user's question in French.",
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)
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italian_agent = Agent(
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name="italian_agent",
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instructions="You respond in Italian. Always reply to the user's question in Italian.",
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)
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# Create orchestrator with conditional tools
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orchestrator = Agent(
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name="orchestrator",
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instructions=(
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"You are a multilingual assistant. Call each available language tool requested by the "
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"user exactly once, including every requested tool when multiple languages are requested. "
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"Wait for all tool calls to finish, then combine their responses into a non-empty final "
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"response. Never translate the user's request yourself."
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),
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tools=[
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spanish_agent.as_tool(
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tool_name="respond_spanish",
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tool_description="Respond to the user's question in Spanish",
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is_enabled=True, # Always enabled
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needs_approval=True, # HITL
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),
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french_agent.as_tool(
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tool_name="respond_french",
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tool_description="Respond to the user's question in French",
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is_enabled=french_spanish_enabled,
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),
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italian_agent.as_tool(
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tool_name="respond_italian",
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tool_description="Respond to the user's question in Italian",
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is_enabled=european_enabled,
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),
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],
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)
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async def main():
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"""Interactive demo with LLM interaction."""
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print("Agents-as-Tools with Conditional Enabling\n")
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print(
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"This demonstrates how language response tools are dynamically enabled based on user preferences.\n"
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)
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print("Choose language preference:")
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print("1. Spanish only (1 tool)")
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print("2. French and Spanish (2 tools)")
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print("3. European languages (3 tools)")
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choice = input_with_fallback("\nSelect option (1-3): ", "2").strip()
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preference_map = {"1": "spanish_only", "2": "french_spanish", "3": "european"}
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language_preference = preference_map.get(choice, "spanish_only")
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# Create context and show available tools
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context = RunContextWrapper(AppContext(language_preference=language_preference))
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available_tools = await orchestrator.get_all_tools(context)
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tool_names = [tool.name for tool in available_tools]
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print(f"\nLanguage preference: {language_preference}")
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print(f"Available tools: {', '.join(tool_names)}")
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print(f"The LLM will only see and can use these {len(available_tools)} tools\n")
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# Get user request
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user_request = input_with_fallback(
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"Ask a question and see responses in available languages:\n",
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"Answer in Spanish and French: How do you say good morning?",
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)
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# Run with LLM interaction
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print("\nProcessing request...")
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with trace("Conditional tool access"):
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result = await Runner.run(
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starting_agent=orchestrator,
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input=user_request,
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context=context.context,
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)
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while result.interruptions:
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async def confirm(question: str) -> bool:
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return confirm_with_fallback(f"{question} (y/n): ", default=True)
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state = result.to_state()
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for interruption in result.interruptions:
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prompt = f"\nDo you approve this tool call: {interruption.name} with arguments {interruption.arguments}?"
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confirmed = await confirm(prompt)
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if confirmed:
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state.approve(interruption)
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print(f"✓ Approved: {interruption.name}")
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else:
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state.reject(interruption)
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print(f"✗ Rejected: {interruption.name}")
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result = await Runner.run(orchestrator, state)
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if is_auto_mode():
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called_tools: list[str] = []
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for item in result.new_items:
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tool_name = getattr(item.raw_item, "name", None)
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if item.type == "tool_call_item" and isinstance(tool_name, str):
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if tool_name.startswith("respond_"):
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called_tools.append(tool_name)
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if sorted(called_tools) != ["respond_french", "respond_spanish"]:
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raise RuntimeError(f"Expected Spanish and French responses once, got {called_tools}")
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if not result.final_output:
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raise RuntimeError("Expected a non-empty multilingual response")
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print(f"\nResponse:\n{result.final_output}")
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if __name__ == "__main__":
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asyncio.run(main())
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