Working with the OpenAI Responses API
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Learn to call the OpenAI Responses API in Python, add web search and custom tools, and produce structured JSON, streamed events and image prompts.
The OpenAI Responses API gives Python developers one interface for prompting models, keeping a multi-turn chat going and calling tools, and this course builds working code against it step by step. You send requests with model instructions, read the items each reply returns and chain messages into a conversation. Later lessons connect built-in web search and your own function-calling tools, then produce Pydantic-validated JSON, stream semantic events and prompt GPT-5 models with images.
What You Will Achieve
- Send Responses API requests with instructions and pull the generated text out of each reply
- Keep a back-and-forth chat going by passing earlier turns into each new request
- Define a Python function, describe it as an OpenAI tool and let the model call it, such as a timezone converter
- Return consistent JSON shaped by Pydantic classes and stream updates to users as output arrives
Program Overview
Getting Started with the Responses API
First calls to the API, shaping replies with instructions and parameters, and carrying one conversation across several turns.
- First API calls and model instructions
- Response parameters and more powerful models
- Prompting reasoning models
- Conversation chaining for chat
Building Tools for LLMs
Reading the items a response contains and giving models extra abilities through built-in web search and custom functions.
- Response items and their output contents
- Web search and checking its sources
- Function definitions and the tool workflow
- A timezone conversion tool
Advanced Patterns for Production
Three design patterns for production systems: structured outputs, streaming semantic events and multimodal input with text and images.
- Structured outputs with nested Pydantic classes
- Streaming semantic events, including with function calls
- Role-based inputs
- Prompting with image URLs and local image files
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever developers build applications on OpenAI models, such as chat applications, assistants that call web search or custom functions, and services that return structured JSON or stream replies to users.
Good for: Python developers building applications on OpenAI models who want tool calls, structured JSON and streamed replies in their own code.
Keep in mind: Every lesson is written for the Responses API and OpenAI’s GPT-5 models, so the code and tools, such as the built-in web search, are OpenAI’s own.
Bottom line: A hands-on run through the Responses API that ends with three production patterns: Pydantic-checked JSON, streamed semantic events and image prompts.
Frequently Asked Questions
How does the Responses API differ from Chat Completions?
It is a simpler interface for prompting OpenAI models than the older Chat Completions API, and it streamlines building conversations, integrating tools and generating structured outputs. Every lesson works with the Responses API.
Which tool does the function-calling example build?
The worked example defines a Python function that converts timezones, turns it into an OpenAI tool definition and then runs the complete function-calling workflow around it, so the model can use the function when a request needs it.
Does the built-in web search need a separate API key?
No. The web search tool works with the model without extra API keys or credentials, and one lesson looks at the sources the model returns so you can judge how far to trust an answer.
Which OpenAI models do the lessons use?
The lessons use OpenAI's GPT-5 models, including reasoning models, with a lesson on prompting practices for reasoning and another on trying more powerful models. The final chapter sends these models images from URLs and from local files.
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