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add more examples
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‎openai_agents/mcp/README.md‎

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@@ -6,6 +6,23 @@ Integration with hosted MCP (Model Context Protocol) servers using OpenAI agents
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Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md).
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## MCP Server Dependencies
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All examples use self-contained MCP servers in the `servers/` directory. The servers require these dependencies:
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```bash
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pip install mcp requests
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# or
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uv add mcp requests
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```
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## Server Implementations
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The `servers/` directory contains self-contained MCP server implementations:
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- **`tools_server.py`** - Multi-transport MCP server with tools for arithmetic, weather, and random words. Supports both `--transport=streamable-http` and `--transport=sse`
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- **`prompt_server.py`** - MCP server that provides prompts for generating agent instructions (code review example)
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## Running the Examples
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### File System MCP - Stateless
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Connect to a hosted MCP server for file system operations:
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```bash
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uv run openai_agents/mcp/run_file_system_stateful_workflow.py
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```
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### Streamable HTTP MCP - Stateless
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First, start the MCP server:
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```bash
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uv run openai_agents/mcp/servers/tools_server.py --transport=streamable-http
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```
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Then start the worker:
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```bash
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uv run openai_agents/mcp/run_streamable_http_stateless_worker.py
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```
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Finally, run the workflow:
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```bash
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uv run openai_agents/mcp/run_streamable_http_stateless_workflow.py
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```
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### SSE MCP - Stateless
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First, start the MCP server:
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```bash
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uv run openai_agents/mcp/servers/tools_server.py --transport=sse
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```
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Then start the worker:
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```bash
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uv run openai_agents/mcp/run_sse_stateless_worker.py
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```
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Finally, run the workflow:
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```bash
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uv run openai_agents/mcp/run_sse_stateless_workflow.py
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```
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### Prompt Server MCP - Stateless
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First, start the MCP server:
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```bash
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uv run openai_agents/mcp/servers/prompt_server.py
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```
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Then start the worker:
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```bash
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uv run openai_agents/mcp/run_prompt_server_stateless_worker.py
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```
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Finally, run the workflow:
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```bash
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uv run openai_agents/mcp/run_prompt_server_stateless_workflow.py
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```

‎openai_agents/mcp/run_file_system_stateful_workflow.py‎

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import asyncio
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import os
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from temporalio.client import Client
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from temporalio.contrib.openai_agents import OpenAIAgentsPlugin

‎openai_agents/mcp/run_file_system_stateless_worker.py‎

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import asyncio
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import os
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from datetime import timedelta
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import uuid
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from agents.mcp import MCPServerStdio
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from temporalio.client import Client
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from temporalio.contrib.openai_agents import (
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ModelActivityParameters,
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OpenAIAgentsPlugin,
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StatelessMCPServer,
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StatelessMCPServerProvider,
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)
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from temporalio.worker import Worker
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samples_dir = os.path.join(current_dir, "sample_files")
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# TODO: StatelessMCPServer will switch to StatelessMCPServerProvider instead
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file_system_server = StatelessMCPServer(
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MCPServerStdio(
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file_system_server = StatelessMCPServerProvider(
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lambda: MCPServerStdio(
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name="FileSystemServer",
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params={
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"command": "npx",
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worker = Worker(
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client,
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task_queue="openai-agents-mcp-stateless-task-queue",
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task_queue=f"openai-agents-mcp-stateless-task-queue-{uuid.uuid4()}",
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workflows=[
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FileSystemWorkflow,
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],

‎openai_agents/mcp/run_file_system_stateless_workflow.py‎

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import asyncio
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import os
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from temporalio.client import Client
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from temporalio.contrib.openai_agents import OpenAIAgentsPlugin
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from openai_agents.mcp.workflows.file_system_stateless_workflow import (
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FileSystemWorkflow,
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)
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import uuid
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async def main():
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# Create client connected to server at the given address
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# Execute a workflow
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result = await client.execute_workflow(
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FileSystemWorkflow.run,
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id="file-system-workflow",
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id=f"file-system-workflow-{uuid.uuid4()}",
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task_queue="openai-agents-mcp-stateless-task-queue",
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)
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‎openai_agents/mcp/run_prompt_server_stateless_worker.py‎

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from __future__ import annotations
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import asyncio
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import shutil
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from datetime import timedelta
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from agents.mcp import MCPServerStreamableHttp
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from temporalio.client import Client
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from temporalio.contrib.openai_agents import (
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ModelActivityParameters,
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OpenAIAgentsPlugin,
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StatelessMCPServerProvider,
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)
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import logging
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from temporalio.worker import Worker
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from openai_agents.mcp.workflows.prompt_server_stateless_workflow import PromptServerWorkflow
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async def main():
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logging.basicConfig(level=logging.DEBUG)
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print("Setting up worker...\n")
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try:
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prompt_server_provider = StatelessMCPServerProvider(
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lambda: MCPServerStreamableHttp(
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name="PromptServer",
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params={
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"url": "http://localhost:8000/mcp",
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},
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)
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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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plugins=[
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OpenAIAgentsPlugin(
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model_params=ModelActivityParameters(
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start_to_close_timeout=timedelta(seconds=120)
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),
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mcp_servers=[prompt_server_provider],
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),
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],
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)
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worker = Worker(
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client,
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task_queue="openai-agents-mcp-stateless-task-queue",
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workflows=[
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PromptServerWorkflow,
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],
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activities=[
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# No custom activities needed for these workflows
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],
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)
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logging.debug("STARTED WORKER")
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await worker.run()
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except Exception as e:
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print(f"Worker failed: {e}")
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raise
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/mcp/run_prompt_server_stateless_workflow.py‎

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from __future__ import annotations
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import asyncio
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from temporalio.client import Client
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from openai_agents.mcp.workflows.prompt_server_stateless_workflow import PromptServerWorkflow
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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("localhost:7233")
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# Execute a workflow
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result = await client.execute_workflow(
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PromptServerWorkflow.run,
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id="prompt-server-workflow",
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task_queue="openai-agents-mcp-stateless-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/mcp/run_sse_stateless_worker.py‎

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from __future__ import annotations
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import asyncio
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import shutil
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from datetime import timedelta
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from agents.mcp import MCPServerSse
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from temporalio.client import Client
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from temporalio.contrib.openai_agents import (
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ModelActivityParameters,
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OpenAIAgentsPlugin,
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StatelessMCPServerProvider,
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)
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import logging
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from temporalio.worker import Worker
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from openai_agents.mcp.workflows.sse_stateless_workflow import SseWorkflow
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async def main():
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logging.basicConfig(level=logging.DEBUG)
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print("Setting up worker...\n")
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try:
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sse_server_provider = StatelessMCPServerProvider(
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lambda: MCPServerSse(
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name="SseServer",
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params={
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"url": "http://localhost:8000/sse",
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},
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)
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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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plugins=[
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OpenAIAgentsPlugin(
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model_params=ModelActivityParameters(
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start_to_close_timeout=timedelta(seconds=60)
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),
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mcp_servers=[sse_server_provider],
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),
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],
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)
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worker = Worker(
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client,
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task_queue="openai-agents-mcp-stateless-task-queue",
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workflows=[
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SseWorkflow,
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],
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activities=[
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# No custom activities needed for these workflows
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],
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)
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logging.debug("STARTED WORKER")
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await worker.run()
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except Exception as e:
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print(f"Worker failed: {e}")
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raise
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if __name__ == "__main__":
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asyncio.run(main())

‎openai_agents/mcp/run_sse_stateless_workflow.py‎

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from __future__ import annotations
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import asyncio
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from temporalio.client import Client
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from openai_agents.mcp.workflows.sse_stateless_workflow import SseWorkflow
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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("localhost:7233")
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# Execute a workflow
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result = await client.execute_workflow(
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SseWorkflow.run,
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id="sse-stateless-workflow",
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task_queue="openai-agents-mcp-stateless-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())

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