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OpenAI agents (temporalio#195)
Add samples using OpenAI Agents SDK --------- Co-authored-by: Maxim Fateev <mfateev@gmail.com> Co-authored-by: Dan Davison <dan.davison@temporal.io> Co-authored-by: Maxim Fateev <mfateev@users.noreply.github.com> Co-authored-by: Grant <14.gsmith.14@gmail.com> Co-authored-by: Chad Retz <chad@temporal.io>
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‎openai_agents/README.md‎

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# Temporal OpenAI Agents SDK Integration
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⚠️ **Experimental** - This module is not yet stable and may change in the future.
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This directory contains samples demonstrating how to use the [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) with Temporal's durable execution engine.
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These samples are adapted from the [OpenAI Agents SDK examples](https://github.com/openai/openai-agents-python/tree/main/examples) and extended with Temporal's durability and orchestration capabilities.
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See the [module documentation](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/openai_agents/README.md) for more information.
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## Overview
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The integration combines:
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- **Temporal workflows** for orchestrating agent control flow and state management
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- **OpenAI Agents SDK** for AI agent creation and tool interactions
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This approach ensures that AI agent workflows are durable, observable, and can handle failures gracefully.
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## Prerequisites
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- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
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- Required dependencies installed via `uv sync --group openai-agents`
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- OpenAI API key set as environment variable: `export OPENAI_API_KEY=your_key_here`
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## Running the Examples
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1. **Start the worker** (supports all samples):
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```bash
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uv run openai_agents/run_worker.py
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```
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2. **Run individual samples** in separate terminals:
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### Basic Agent Examples
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- **Hello World Agent** - Simple agent that responds in haikus:
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```bash
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uv run openai_agents/run_hello_world_workflow.py
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```
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- **Tools Agent** - Agent with access to external tools (weather API):
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```bash
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uv run openai_agents/run_tools_workflow.py
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```
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### Advanced Multi-Agent Examples
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- **Research Workflow** - Multi-agent research system with specialized roles:
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```bash
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uv run openai_agents/run_research_workflow.py
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```
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Features a planner agent, search agent, and writer agent working together.
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- **Customer Service Workflow** - Customer service agent with escalation capabilities (interactive):
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```bash
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uv run openai_agents/run_customer_service_client.py --conversation-id my-conversation-123
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```
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- **Agents as Tools** - Demonstrate using agents as tools within other agents:
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```bash
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uv run openai_agents/run_agents_as_tools_workflow.py
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```
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‎openai_agents/__init__.py‎

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‎openai_agents/run_agents_as_tools_workflow.py‎

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import asyncio
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from temporalio.client import Client
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from openai_agents.workflows.agents_as_tools_workflow import AgentsAsToolsWorkflow
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async def main():
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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# Execute a workflow
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result = await client.execute_workflow(
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AgentsAsToolsWorkflow.run,
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"Translate to English: '¿Cómo estás?'",
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id="my-workflow-id",
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task_queue="openai-agents-task-queue",
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)
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print(f"Result: {result}")
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/run_customer_service_client.py‎

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import argparse
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import asyncio
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from temporalio.client import (
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Client,
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WorkflowQueryRejectedError,
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WorkflowUpdateFailedError,
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)
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from temporalio.common import QueryRejectCondition
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from temporalio.service import RPCError, RPCStatusCode
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from openai_agents.workflows.customer_service_workflow import (
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CustomerServiceWorkflow,
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ProcessUserMessageInput,
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)
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async def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--conversation-id", type=str, required=True)
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args = parser.parse_args()
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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handle = client.get_workflow_handle(args.conversation_id)
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# Query the workflow for the chat history
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# If the workflow is not open, start a new one
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start = False
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try:
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history = await handle.query(
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CustomerServiceWorkflow.get_chat_history,
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reject_condition=QueryRejectCondition.NOT_OPEN,
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)
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except WorkflowQueryRejectedError as e:
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start = True
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except RPCError as e:
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if e.status == RPCStatusCode.NOT_FOUND:
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start = True
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else:
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raise e
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if start:
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await client.start_workflow(
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CustomerServiceWorkflow.run,
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id=args.conversation_id,
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task_queue="openai-agents-task-queue",
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)
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history = []
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print(*history, sep="\n")
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# Loop to send messages to the workflow
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while True:
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user_input = input("Enter your message: ")
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message_input = ProcessUserMessageInput(
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user_input=user_input, chat_length=len(history)
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)
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try:
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new_history = await handle.execute_update(
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CustomerServiceWorkflow.process_user_message, message_input
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)
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history.extend(new_history)
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print(*new_history, sep="\n")
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except WorkflowUpdateFailedError:
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print("** Stale conversation. Reloading...")
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length = len(history)
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history = await handle.query(
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CustomerServiceWorkflow.get_chat_history,
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reject_condition=QueryRejectCondition.NOT_OPEN,
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)
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print(*history[length:], sep="\n")
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/run_hello_world_workflow.py‎

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import asyncio
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from temporalio.client import Client
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from openai_agents.workflows.hello_world_workflow import HelloWorldAgent
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async def main():
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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# Execute a workflow
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result = await client.execute_workflow(
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HelloWorldAgent.run,
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"Tell me about recursion in programming.",
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id="my-workflow-id",
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task_queue="openai-agents-task-queue",
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)
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print(f"Result: {result}")
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/run_research_workflow.py‎

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import asyncio
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from temporalio.client import Client
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from openai_agents.workflows.research_bot_workflow import ResearchWorkflow
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async def main():
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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# Execute a workflow
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result = await client.execute_workflow(
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ResearchWorkflow.run,
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"Caribbean vacation spots in April, optimizing for surfing, hiking and water sports",
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id="research-workflow",
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task_queue="openai-agents-task-queue",
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)
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print(f"Result: {result}")
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/run_tools_workflow.py‎

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import asyncio
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from temporalio.client import Client
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from openai_agents.workflows.tools_workflow import ToolsWorkflow
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async def main():
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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# Execute a workflow
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result = await client.execute_workflow(
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ToolsWorkflow.run,
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"What is the weather in Tokio?",
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id="tools-workflow",
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task_queue="openai-agents-task-queue",
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)
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print(f"Result: {result}")
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/run_worker.py‎

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from __future__ import annotations
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import asyncio
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import concurrent.futures
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from datetime import timedelta
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from temporalio import workflow
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from temporalio.client import Client
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from temporalio.contrib.openai_agents.invoke_model_activity import ModelActivity
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from temporalio.contrib.openai_agents.open_ai_data_converter import (
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open_ai_data_converter,
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)
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from temporalio.contrib.openai_agents.temporal_openai_agents import (
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set_open_ai_agent_temporal_overrides,
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)
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from temporalio.worker import Worker
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from openai_agents.workflows.agents_as_tools_workflow import AgentsAsToolsWorkflow
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from openai_agents.workflows.customer_service_workflow import CustomerServiceWorkflow
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from openai_agents.workflows.get_weather_activity import get_weather
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from openai_agents.workflows.hello_world_workflow import HelloWorldAgent
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from openai_agents.workflows.research_bot_workflow import ResearchWorkflow
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from openai_agents.workflows.tools_workflow import ToolsWorkflow
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async def main():
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with set_open_ai_agent_temporal_overrides(
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start_to_close_timeout=timedelta(seconds=60),
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):
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# Create client connected to server at the given address
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client = await Client.connect(
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"localhost:7233",
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data_converter=open_ai_data_converter,
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)
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model_activity = ModelActivity(model_provider=None)
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worker = Worker(
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client,
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task_queue="openai-agents-task-queue",
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workflows=[
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HelloWorldAgent,
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ToolsWorkflow,
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ResearchWorkflow,
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CustomerServiceWorkflow,
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AgentsAsToolsWorkflow,
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],
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activities=[
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model_activity.invoke_model_activity,
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get_weather,
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],
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)
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await worker.run()
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/workflows/__init__.py‎

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