temporalio.contrib.openai_agents.workflow

Convert a single Temporal activity function to an OpenAI agent tool.

This function takes a Temporal activity function and converts it into an OpenAI agent tool that can be used by the agent to execute the activity during workflow execution. The tool will automatically handle the conversion of inputs and outputs between the agent and the activity. Note that if you take a context, mutation will not be persisted, as the activity may not be running in the same location.

>>> @activity.defn
>>> def process_data(input: str) -> str:
...     return f"Processed: {input}"
>>>
>>> # Create tool with custom activity options
>>> tool = activity_as_tool(
...     process_data,
...     start_to_close_timeout=timedelta(seconds=30),
...     retry_policy=RetryPolicy(maximum_attempts=3),
...     heartbeat_timeout=timedelta(seconds=10)
... )
>>> # Use tool with an OpenAI agent

Convert a Nexus operation into an OpenAI agent tool.

This function takes a Nexus operation and converts it into an OpenAI agent tool that can be used by the agent to execute the operation during workflow execution. The tool will automatically handle the conversion of inputs and outputs between the agent and the operation.

>>> @nexusrpc.service
... class WeatherService:
...     get_weather_object_nexus_operation: nexusrpc.Operation[WeatherInput, Weather]
>>>
>>> # Create tool with custom activity options
>>> tool = nexus_operation_as_tool(
...     WeatherService.get_weather_object_nexus_operation,
...     service=WeatherService,
...     endpoint="weather-service",
... )
>>> # Use tool with an OpenAI agent

A stateful MCP server implementation for Temporal workflows.

This wraps an MCP server to maintain a persistent connection throughout the workflow execution. It creates a dedicated worker that stays connected to the MCP server and processes operations on a dedicated task queue.

This approach is more efficient for workflows that make multiple MCP calls, as it avoids connection overhead, but requires more resources to maintain the persistent connection and worker.

The caller will have to handle cases where the dedicated worker fails, as Temporal is unable to seamlessly recreate any lost state in that case.

A stateless MCP server implementation for Temporal workflows.

This uses a TemporalMCPServer of the same name registered with the OpenAIAgents plugin to implement durable MCP operations statelessly.

This approach is suitable for simple use cases where connection overhead is acceptable and you don't need to maintain state between operations. It should be preferred to stateful when possible due to its superior durability guarantees.