Streaming with Function Calls

You've seen already how many use cases are unlocked with function-calling LLMs, which have their own event types. This is useful for providing real-time feedback to users when the model is preparing to call a tool, or for logging to track tool usage.

The convert_timezone() function you defined earlier to convert datetimes between timezones, and a tools list containing the function definition for the Responses API have been defined for you.

This exercise is part of the course

Working with the OpenAI Responses API

Exercise instructions

Complete the streaming context manager by calling client.responses.create() with the model "gpt-5-mini", the prompt, and the tools list.
Inside the loop, check if the "function_call_arguments.delta" events.
Add a condition to check for "function_call_arguments.done" events.
Add a final condition to check if the event type is "response.completed" and print a final completion message.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

prompt = "What time is 2:30pm on January 20th in New York in Tokyo time?"

# Open the streaming connection and enable tool-calling
with ____ as stream:
    for event in stream:
        # Filter for function call arguments delta events
        if ____:
            print(f"\nTool args streaming: {event.delta}")
        # Filter for function call arguments complete events
        elif ____:
            print("Tool call args complete.")
        # Filter for response completed events
        elif ____:
            print("\n--- Completed ---")

Edit and Run Code

This exercise is part of the course

Working with the OpenAI Responses API

IntermediateSkill Level

5.0+

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Building AI applications has never been easier with the Responses API! Learn how to prompt models using a simple interface, best practices for working with reasoning models, and how to build back-and-forth conversations with LLMs.

Exercise 1: The Responses API: a Developer's Best Friend Exercise 2: Your First Responses API Call Exercise 3: Adding Model Instructions Exercise 4: Extracting Information from the Response Exercise 5: Mastering Response Parameters Exercise 6: Experimenting with More Powerful Models Exercise 7: Reasoning About Reasoning Exercise 8: Conversation Chaining Exercise 9: From One Message to Another Exercise 10: Enabling Chat Conversations

LLMs are powerful, but they're even more powerful once tools are introduced! Learn how to integrate web search with your LLM systems with no additional API keys or credentials. Discover that tools can be, well... pretty much anything! Build your own tools for LLMs to call to go beyond the model's base capabilities.

Exercise 1: Response Items: The Building Blocks Exercise 2: Extracting Output Items and Their Contents Exercise 3: Using Items for Custom Handling Exercise 4: Web Search That Just Works Exercise 5: Combining LLMs and Web Search Exercise 6: Building Confidence with LLM Sources Exercise 7: Function-Calling Tools: Defining the Function Exercise 8: The Function-Calling Tool Workflow Exercise 9: Defining a Function for Converting Timezones Exercise 10: Function-Calling Tools: The Full Workflow Exercise 11: Building the OpenAI Tool Definition Exercise 12: Integrating Function-Calling Tools

Move your LLM systems from proof-of-concept to production with three key design paradigms. Learn how to generate consistent and reliable structured outputs, like JSON, using LLMs. You'll also use semantic events to stream updates to your users, creating interactive and enjoyable experiences. Finally, go multi-modal and learn how to combine text and images with OpenAI's GPT-5 models.

Exercise 1: Generating Structured Outputs Exercise 2: Consistent Outputs, Every Time!Exercise 3: Nesting Pydantic Classes Exercise 4: Streaming with Semantic Events Exercise 5: Streaming OpenAI Responses Exercise 6: Streaming Semantic Events Exercise 7: Streaming with Function Calls

Current Exercise

Exercise 8: Input Flexibility and Multimodality Exercise 9: Role-Based Inputs Exercise 10: Prompting with Images from URLs Exercise 11: Prompting with Local Image Files Exercise 12: Congratulations!