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Dmytro Struk
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python/samples/getting_started/agents/openai/README.md
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| [`openai_responses_client_image_analysis.py`](openai_responses_client_image_analysis.py) | Demonstrates how to use vision capabilities with agents to analyze images. |
| [`openai_responses_client_image_generation.py`](openai_responses_client_image_generation.py) | Demonstrates how to use image generation capabilities with OpenAI agents to create images based on text descriptions. Requires PIL (Pillow) for image display. |
| [`openai_responses_client_reasoning.py`](openai_responses_client_reasoning.py) | Demonstrates how to use reasoning capabilities with OpenAI agents, showing how the agent can provide detailed reasoning for its responses. |
| [`openai_responses_client_streaming_image_generation.py`](openai_responses_client_streaming_image_generation.py) | Demonstrates streaming image generation with partial images for real-time image creation feedback and improved user experience. |
| [`openai_responses_client_with_code_interpreter.py`](openai_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with OpenAI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
| [`openai_responses_client_with_explicit_settings.py`](openai_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including API key and model ID. |
| [`openai_responses_client_with_file_search.py`](openai_responses_client_with_file_search.py) | Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions. |
python/samples/getting_started/agents/openai/openai_responses_client_streaming_image_generation.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import base64
import anyio
from agent_framework import DataContent
from agent_framework.openai import OpenAIResponsesClient
"""OpenAI Responses Client Streaming Image Generation Example
Demonstrates streaming partial image generation using OpenAI's image generation tool.
Shows progressive image rendering with partial images for improved user experience.
Note: The number of partial images received depends on generation speed:
- High quality/complex images: More partials (generation takes longer)
- Low quality/simple images: Fewer partials (generation completes quickly)
- You may receive fewer partial images than requested if generation is fast
Important: The final partial image IS the complete, full-quality image. Each partial
represents a progressive refinement, with the last one being the finished result.
"""
async def save_image_from_data_uri(data_uri: str, filename: str) -> None:
"""Save an image from a data URI to a file."""
try:
if data_uri.startswith("data:image/"):
# Extract base64 data
base64_data = data_uri.split(",", 1)[1]
image_bytes = base64.b64decode(base64_data)
# Save to file
await anyio.Path(filename).write_bytes(image_bytes)
print(f" Saved: {filename} ({len(image_bytes) / 1024:.1f} KB)")
except Exception as e:
print(f" Error saving {filename}: {e}")
async def main():
"""Demonstrate streaming image generation with partial images."""
print("=== OpenAI Streaming Image Generation Example ===\n")
# Create agent with streaming image generation enabled
agent = OpenAIResponsesClient().create_agent(
instructions="You are a helpful agent that can generate images.",
tools=[
{
"type": "image_generation",
"size": "1024x1024",
"quality": "high",
"partial_images": 3,
}
],
)
query = "Draw a beautiful sunset over a calm ocean with sailboats"
print(f" User: {query}")
print()
# Track partial images
image_count = 0
# Create output directory
output_dir = anyio.Path("generated_images")
await output_dir.mkdir(exist_ok=True)
print(" Streaming response:")
async for update in agent.run_stream(query):
for content in update.contents:
# Handle partial images
# The final partial image IS the complete, full-quality image. Each partial
# represents a progressive refinement, with the last one being the finished result.
if isinstance(content, DataContent) and content.additional_properties.get("is_partial_image"):
print(f" Image {image_count} received")
# Extract file extension from media_type (e.g., "image/png" -> "png")
extension = "png" # Default fallback
if content.media_type and "/" in content.media_type:
extension = content.media_type.split("/")[-1]
# Save images with correct extension
filename = output_dir / f"image{image_count}.{extension}"
await save_image_from_data_uri(content.uri, str(filename))
image_count += 1
# Summary
print("\n Summary:")
print(f" Images received: {image_count}")
print(" Output directory: generated_images")
print("\n Streaming image generation completed!")
if __name__ == "__main__":
asyncio.run(main())