OpenAI Agents Third-Party model providers

openai_agents/model_providers/README.md

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+# Model Providers Examples
+
+Custom LLM provider integration examples for OpenAI Agents SDK with Temporal workflows.
+
+*Adapted from [OpenAI Agents SDK model providers examples](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers)*
+
+Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md).
+
+## Running the Examples
+
+### Currently Implemented
+
+#### LiteLLM Auto
+Uses built-in LiteLLM support to connect to various model providers.
+
+Start the LiteLLM provider worker:
+```bash
+# Set the required environment variable for your chosen provider
+export ANTHROPIC_API_KEY="your_anthropic_api_key"  # For Anthropic
+
+uv run openai_agents/model_providers/run_worker_litellm_provider.py
+```
+
+Then run the example in a separate terminal:
+```bash
+uv run openai_agents/model_providers/run_litellm_auto_workflow.py
+```
+
+The example uses Anthropic Claude by default but can be modified to use other LiteLLM-supported providers.
+
+Find more LiteLLM providers at: https://docs.litellm.ai/docs/providers
+
+## Not Yet Implemented
+
+- **Custom Example Agent** - Custom OpenAI client integration
+- **Custom Example Global** - Global default client configuration  
+- **Custom Example Provider** - Custom ModelProvider pattern
+- **LiteLLM Provider** - Interactive model/API key input

openai_agents/model_providers/run_litellm_auto_workflow.py

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+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.openai_agents import OpenAIAgentsPlugin
+
+from openai_agents.model_providers.workflows.litellm_auto_workflow import (
+    LitellmAutoWorkflow,
+)
+
+
+async def main():
+    client = await Client.connect(
+        "localhost:7233",
+        plugins=[
+            OpenAIAgentsPlugin(),
+        ],
+    )
+
+    result = await client.execute_workflow(
+        LitellmAutoWorkflow.run,
+        "What's the weather in Tokyo?",
+        id="litellm-auto-workflow-id",
+        task_queue="openai-agents-model-providers-task-queue",
+    )
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

openai_agents/model_providers/run_worker_litellm_provider.py

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+import asyncio
+from datetime import timedelta
+
+from agents.extensions.models.litellm_provider import LitellmProvider
+from temporalio.client import Client
+from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin
+from temporalio.worker import Worker
+
+from openai_agents.model_providers.workflows.litellm_auto_workflow import (
+    LitellmAutoWorkflow,
+)
+
+
+async def main():
+    # Create client connected to server at the given address
+    client = await Client.connect(
+        "localhost:7233",
+        plugins=[
+            OpenAIAgentsPlugin(
+                model_params=ModelActivityParameters(
+                    start_to_close_timeout=timedelta(seconds=30)
+                ),
+                model_provider=LitellmProvider(),
+            ),
+        ],
+    )
+
+    worker = Worker(
+        client,
+        task_queue="openai-agents-model-providers-task-queue",
+        workflows=[
+            LitellmAutoWorkflow,
+        ],
+    )
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

openai_agents/model_providers/workflows/litellm_auto_workflow.py

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+from __future__ import annotations
+
+from agents import Agent, Runner, function_tool, set_tracing_disabled
+from temporalio import workflow
+
+
+@workflow.defn
+class LitellmAutoWorkflow:
+    @workflow.run
+    async def run(self, prompt: str) -> str:
+        set_tracing_disabled(disabled=True)
+
+        @function_tool
+        def get_weather(city: str):
+            return f"The weather in {city} is sunny."
+
+        agent = Agent(
+            name="Assistant",
+            instructions="You only respond in haikus.",
+            model="anthropic/claude-3-5-sonnet-20240620",
+            tools=[get_weather],
+        )
+
+        result = await Runner.run(agent, prompt)
+        return result.final_output