Calling the OpenAI API

The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most.

40 min · Reviewed 2026

Two APIs, One Client

OpenAI ships chat.completions (classic) and responses (modern). New code should prefer responses — it unifies text, tools, and structured output.

from openai import OpenAI client = OpenAI() def ask(prompt: str) -> str: try: r = client.responses.create( model="gpt-5", input=[ {"role": "system", "content": "Be concise."}, {"role": "user", "content": prompt}, ], ) return r.output_text except Exception as e: print(f"OpenAI call failed: {e}") raise print(ask("Explain recursion in one sentence."))output_text is a convenience accessor that concatenates all text in the response.

Streaming

def ask_stream(prompt: str) -> None: with client.responses.stream( model="gpt-5", input=[{"role": "user", "content": prompt}], ) as stream: for event in stream: if event.type == "response.output_text.delta": print(event.delta, end="", flush=True) stream.until_done() print()Context manager ensures the stream closes. Event types are strings — filter for the text delta.

Understanding "Calling the OpenAI API" in practice: AI-assisted coding shifts work from syntax recall to design thinking — models handle boilerplate so you focus on architecture. The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most — and knowing how to apply this gives you a concrete advantage.

Apply Responses API in your ai-coding workflow to get better results
Apply chat completions in your ai-coding workflow to get better results
Apply streaming in your ai-coding workflow to get better results
Apply model in your ai-coding workflow to get better results

Use AI to generate unit tests for an existing function
Ask AI to refactor a messy function and explain the changes
Have AI suggest a code review for a recent pull request

The big idea: responses.create for the modern path, stream for UIs, and centralize model ids so provider swaps are painless.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-progx-openai-api-creators

What is the main idea of "Calling the OpenAI API"?
1. The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Calling the OpenAI API"?
1. chat completions
2. Responses API
3. streaming
4. model
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. Apply Responses API in your ai-coding workflow to get better results
4. Treat the AI output as automatically correct
What should a careful learner remember about "Model ids change"?
1. Don't hard-code old model names in many files. Put the id in one config constant. Swaps become a one-line PR.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use AI for drafting and comparison, but verify before publishing or relying on it.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about Responses API be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about Responses API.
Which action would help you apply "Calling the OpenAI API" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Treat the AI output as automatically correct
4. Apply chat completions in your ai-coding workflow to get better results

← Back to interactive lesson

Tendril · Creators · AI-Assisted Coding

Calling the OpenAI API

The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most.

40 min · Reviewed 2026

Two APIs, One Client

OpenAI ships chat.completions (classic) and responses (modern). New code should prefer responses — it unifies text, tools, and structured output.

from openai import OpenAI client = OpenAI() def ask(prompt: str) -> str: try: r = client.responses.create( model="gpt-5", input=[ {"role": "system", "content": "Be concise."}, {"role": "user", "content": prompt}, ], ) return r.output_text except Exception as e: print(f"OpenAI call failed: {e}") raise print(ask("Explain recursion in one sentence."))output_text is a convenience accessor that concatenates all text in the response.

Streaming

def ask_stream(prompt: str) -> None: with client.responses.stream( model="gpt-5", input=[{"role": "user", "content": prompt}], ) as stream: for event in stream: if event.type == "response.output_text.delta": print(event.delta, end="", flush=True) stream.until_done() print()Context manager ensures the stream closes. Event types are strings — filter for the text delta.

Apply Responses API in your ai-coding workflow to get better results
Apply chat completions in your ai-coding workflow to get better results
Apply streaming in your ai-coding workflow to get better results
Apply model in your ai-coding workflow to get better results

Use AI to generate unit tests for an existing function
Ask AI to refactor a messy function and explain the changes
Have AI suggest a code review for a recent pull request

The big idea: responses.create for the modern path, stream for UIs, and centralize model ids so provider swaps are painless.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-progx-openai-api-creators

What is the main idea of "Calling the OpenAI API"?
1. The Responses API is OpenAI's modern surface. One call, text and tools. Learn the shape you'll use most.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "Calling the OpenAI API"?
1. chat completions
2. Responses API
3. streaming
4. model
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. Apply Responses API in your ai-coding workflow to get better results
4. Treat the AI output as automatically correct
What should a careful learner remember about "Model ids change"?
1. Don't hard-code old model names in many files. Put the id in one config constant. Swaps become a one-line PR.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use AI for drafting and comparison, but verify before publishing or relying on it.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about Responses API be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about Responses API.
Which action would help you apply "Calling the OpenAI API" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Treat the AI output as automatically correct
4. Apply chat completions in your ai-coding workflow to get better results

← Back to interactive lesson