chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Model provider examples
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The examples in this directory show how to route models through adapter layers such as LiteLLM and
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any-llm. The default examples all use OpenRouter so you only need one API key:
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```bash
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export OPENROUTER_API_KEY="..."
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```
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Run one of the adapter examples:
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```bash
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uv run examples/model_providers/any_llm_provider.py
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uv run examples/model_providers/any_llm_auto.py
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uv run examples/model_providers/litellm_provider.py
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uv run examples/model_providers/litellm_auto.py
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```
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Direct-model examples let you override the target model:
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```bash
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uv run examples/model_providers/any_llm_provider.py --model openrouter/openai/gpt-5.4-mini
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uv run examples/model_providers/litellm_provider.py --model openrouter/openai/gpt-5.4-mini
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```
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from __future__ import annotations
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import asyncio
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from pydantic import BaseModel
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from agents import Agent, ModelSettings, Runner, function_tool, set_tracing_disabled
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"""This example uses the built-in any-llm routing through OpenRouter.
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Set OPENROUTER_API_KEY before running it.
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"""
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set_tracing_disabled(disabled=True)
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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class Result(BaseModel):
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output_text: str
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tool_results: list[str]
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async def main():
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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model="any-llm/openrouter/openai/gpt-5.4-mini",
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tools=[get_weather],
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model_settings=ModelSettings(tool_choice="required"),
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output_type=Result,
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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import os
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if os.getenv("OPENROUTER_API_KEY") is None:
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raise ValueError(
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"OPENROUTER_API_KEY is not set. Please set the environment variable and try again."
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)
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asyncio.run(main())
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from __future__ import annotations
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import asyncio
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import os
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from agents import Agent, Runner, function_tool, set_tracing_disabled
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from agents.extensions.models.any_llm_model import AnyLLMModel
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"""This example uses the AnyLLMModel directly.
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You can run it like this:
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uv run examples/model_providers/any_llm_provider.py --model openrouter/openai/gpt-5.4-mini
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or
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uv run examples/model_providers/any_llm_provider.py --model openrouter/anthropic/claude-4.5-sonnet
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"""
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set_tracing_disabled(disabled=True)
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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async def main(model: str, api_key: str):
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if api_key == "dummy":
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print("Skipping run because no valid OPENROUTER_API_KEY was provided.")
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return
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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model=AnyLLMModel(model=model, api_key=api_key),
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tools=[get_weather],
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, required=False)
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parser.add_argument("--api-key", type=str, required=False)
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args = parser.parse_args()
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model = args.model or os.environ.get("ANY_LLM_MODEL", "openrouter/openai/gpt-5.4-mini")
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api_key = args.api_key or os.environ.get("OPENROUTER_API_KEY", "dummy")
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if not args.model:
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print(f"Using default model: {model}")
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if not args.api_key:
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print("Using OPENROUTER_API_KEY from environment (or dummy placeholder).")
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asyncio.run(main(model, api_key))
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import asyncio
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import os
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from openai import AsyncOpenAI
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from agents import Agent, OpenAIChatCompletionsModel, Runner, function_tool, set_tracing_disabled
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BASE_URL = os.getenv("EXAMPLE_BASE_URL") or ""
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API_KEY = os.getenv("EXAMPLE_API_KEY") or ""
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MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or ""
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if not BASE_URL or not API_KEY or not MODEL_NAME:
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raise ValueError(
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"Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code."
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)
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"""This example uses a custom provider for a specific agent. Steps:
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1. Create a custom OpenAI client.
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2. Create a `Model` that uses the custom client.
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3. Set the `model` on the Agent.
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Note that in this example, we disable tracing under the assumption that you don't have an API key
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from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var
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or call set_tracing_export_api_key() to set a tracing specific key.
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"""
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client = AsyncOpenAI(base_url=BASE_URL, api_key=API_KEY)
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set_tracing_disabled(disabled=True)
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# An alternate approach that would also work:
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# PROVIDER = OpenAIProvider(openai_client=client)
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# agent = Agent(..., model="some-custom-model")
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# Runner.run(agent, ..., run_config=RunConfig(model_provider=PROVIDER))
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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async def main():
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# This agent will use the custom LLM provider
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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model=OpenAIChatCompletionsModel(model=MODEL_NAME, openai_client=client),
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tools=[get_weather],
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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asyncio.run(main())
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import asyncio
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import os
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from openai import AsyncOpenAI
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from agents import (
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Agent,
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Runner,
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function_tool,
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set_default_openai_api,
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set_default_openai_client,
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set_tracing_disabled,
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)
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BASE_URL = os.getenv("EXAMPLE_BASE_URL") or ""
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API_KEY = os.getenv("EXAMPLE_API_KEY") or ""
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MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or ""
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if not BASE_URL or not API_KEY or not MODEL_NAME:
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raise ValueError(
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"Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code."
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)
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"""This example uses a custom provider for all requests by default. We do three things:
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1. Create a custom client.
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2. Set it as the default OpenAI client, and don't use it for tracing.
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3. Set the default API as Chat Completions, as most LLM providers don't yet support Responses API.
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Note that in this example, we disable tracing under the assumption that you don't have an API key
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from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var
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or call set_tracing_export_api_key() to set a tracing specific key.
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"""
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client = AsyncOpenAI(
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base_url=BASE_URL,
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api_key=API_KEY,
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)
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set_default_openai_client(client=client, use_for_tracing=False)
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set_default_openai_api("chat_completions")
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set_tracing_disabled(disabled=True)
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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async def main():
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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model=MODEL_NAME,
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tools=[get_weather],
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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asyncio.run(main())
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from __future__ import annotations
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import asyncio
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import os
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from openai import AsyncOpenAI
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from agents import (
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Agent,
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Model,
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ModelProvider,
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OpenAIChatCompletionsModel,
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RunConfig,
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Runner,
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function_tool,
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set_tracing_disabled,
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)
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BASE_URL = os.getenv("EXAMPLE_BASE_URL") or ""
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API_KEY = os.getenv("EXAMPLE_API_KEY") or ""
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MODEL_NAME = os.getenv("EXAMPLE_MODEL_NAME") or ""
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if not BASE_URL or not API_KEY or not MODEL_NAME:
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raise ValueError(
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"Please set EXAMPLE_BASE_URL, EXAMPLE_API_KEY, EXAMPLE_MODEL_NAME via env var or code."
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)
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"""This example uses a custom provider for some calls to Runner.run(), and direct calls to OpenAI for
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others. Steps:
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1. Create a custom OpenAI client.
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2. Create a ModelProvider that uses the custom client.
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3. Use the ModelProvider in calls to Runner.run(), only when we want to use the custom LLM provider.
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Note that in this example, we disable tracing under the assumption that you don't have an API key
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from platform.openai.com. If you do have one, you can either set the `OPENAI_API_KEY` env var
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or call set_tracing_export_api_key() to set a tracing specific key.
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"""
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client = AsyncOpenAI(base_url=BASE_URL, api_key=API_KEY)
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set_tracing_disabled(disabled=True)
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class CustomModelProvider(ModelProvider):
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def get_model(self, model_name: str | None) -> Model:
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return OpenAIChatCompletionsModel(model=model_name or MODEL_NAME, openai_client=client)
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CUSTOM_MODEL_PROVIDER = CustomModelProvider()
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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async def main():
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agent = Agent(name="Assistant", instructions="You only respond in haikus.", tools=[get_weather])
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# This will use the custom model provider
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result = await Runner.run(
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agent,
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"What's the weather in Tokyo?",
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run_config=RunConfig(model_provider=CUSTOM_MODEL_PROVIDER),
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)
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print(result.final_output)
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# If you uncomment this, it will use OpenAI directly, not the custom provider
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# result = await Runner.run(
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# agent,
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# "What's the weather in Tokyo?",
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# )
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# print(result.final_output)
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,54 @@
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from __future__ import annotations
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import asyncio
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from pydantic import BaseModel
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from agents import Agent, ModelSettings, Runner, function_tool, set_tracing_disabled
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"""This example uses the built-in support for LiteLLM through OpenRouter.
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Set OPENROUTER_API_KEY before running it.
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"""
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set_tracing_disabled(disabled=True)
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# import logging
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# logging.basicConfig(level=logging.DEBUG)
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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class Result(BaseModel):
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output_text: str
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tool_results: list[str]
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async def main():
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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# We prefix with litellm/ to tell the Runner to use the LitellmModel
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model="litellm/openrouter/openai/gpt-5.4-mini",
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tools=[get_weather],
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model_settings=ModelSettings(tool_choice="required"),
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output_type=Result,
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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import os
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if os.getenv("OPENROUTER_API_KEY") is None:
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raise ValueError(
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"OPENROUTER_API_KEY is not set. Please set the environment variable and try again."
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)
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asyncio.run(main())
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@@ -0,0 +1,59 @@
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from __future__ import annotations
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import asyncio
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import os
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from agents import Agent, Runner, function_tool, set_tracing_disabled
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from agents.extensions.models.litellm_model import LitellmModel
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"""This example uses the LitellmModel directly, to hit any model provider.
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You can run it like this:
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uv run examples/model_providers/litellm_provider.py --model openrouter/openai/gpt-5.4-mini
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or
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uv run examples/model_providers/litellm_provider.py --model openrouter/anthropic/claude-4.5-sonnet
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Find more providers here: https://docs.litellm.ai/docs/providers
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"""
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set_tracing_disabled(disabled=True)
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@function_tool
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def get_weather(city: str):
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print(f"[debug] getting weather for {city}")
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return f"The weather in {city} is sunny."
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async def main(model: str, api_key: str):
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if api_key == "dummy":
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print("Skipping run because no valid OPENROUTER_API_KEY was provided.")
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return
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agent = Agent(
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name="Assistant",
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instructions="You only respond in haikus.",
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model=LitellmModel(model=model, api_key=api_key),
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tools=[get_weather],
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)
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result = await Runner.run(agent, "What's the weather in Tokyo?")
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print(result.final_output)
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if __name__ == "__main__":
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# Prefer non-interactive defaults in auto mode to avoid blocking.
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, required=False)
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parser.add_argument("--api-key", type=str, required=False)
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args = parser.parse_args()
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model = args.model or os.environ.get("LITELLM_MODEL", "openrouter/openai/gpt-5.4-mini")
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api_key = args.api_key or os.environ.get("OPENROUTER_API_KEY", "dummy")
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if not args.model:
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print(f"Using default model: {model}")
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if not args.api_key:
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print("Using OPENROUTER_API_KEY from environment (or dummy placeholder).")
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asyncio.run(main(model, api_key))
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Reference in New Issue
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