agent-framework/python/samples/getting_started/agents/openai

OpenAI Agent Framework Examples

This folder contains examples demonstrating different ways to create and use agents with the OpenAI Assistants client from the agent_framework.openai package.

Examples

File	Description
openai_assistants_basic.py	Basic usage of OpenAIAssistantProvider with streaming and non-streaming responses.
openai_assistants_provider_methods.py	Demonstrates all OpenAIAssistantProvider methods: create_agent(), get_agent(), and as_agent().
openai_assistants_with_code_interpreter.py	Using HostedCodeInterpreterTool with OpenAIAssistantProvider to execute Python code.
openai_assistants_with_existing_assistant.py	Working with pre-existing assistants using get_agent() and as_agent() methods.
openai_assistants_with_explicit_settings.py	Configuring OpenAIAssistantProvider with explicit settings including API key and model ID.
openai_assistants_with_file_search.py	Using HostedFileSearchTool with OpenAIAssistantProvider for file search capabilities.
openai_assistants_with_function_tools.py	Function tools with OpenAIAssistantProvider at both agent-level and query-level.
openai_assistants_with_response_format.py	Structured outputs with OpenAIAssistantProvider using Pydantic models.
openai_assistants_with_thread.py	Thread management with OpenAIAssistantProvider for conversation context persistence.
openai_chat_client_basic.py	The simplest way to create an agent using ChatAgent with OpenAIChatClient. Shows both streaming and non-streaming responses for chat-based interactions with OpenAI models.
openai_chat_client_with_explicit_settings.py	Shows how to initialize an agent with a specific chat client, configuring settings explicitly including API key and model ID.
openai_chat_client_with_function_tools.py	Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries).
openai_chat_client_with_local_mcp.py	Shows how to integrate OpenAI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration.
openai_chat_client_with_thread.py	Demonstrates thread management with OpenAI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions.
openai_chat_client_with_web_search.py	Shows how to use web search capabilities with OpenAI agents to retrieve and use information from the internet in responses.
openai_chat_client_with_runtime_json_schema.py	Shows how to supply a runtime JSON Schema via additional_chat_options for structured output without defining a Pydantic model.
openai_responses_client_basic.py	The simplest way to create an agent using ChatAgent with OpenAIResponsesClient. Shows both streaming and non-streaming responses for structured response generation with OpenAI models.
openai_responses_client_image_analysis.py	Demonstrates how to use vision capabilities with agents to analyze images.
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	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	Demonstrates streaming image generation with partial images for real-time image creation feedback and improved user experience.
openai_responses_client_with_agent_as_tool.py	Shows how to use the agent-as-tool pattern with OpenAI Responses Client, where one agent delegates work to specialized sub-agents wrapped as tools using as_tool(). Demonstrates hierarchical agent architectures.
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	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	Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions.
openai_responses_client_with_function_tools.py	Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and run-level tools (provided with specific queries).
openai_responses_client_with_hosted_mcp.py	Shows how to integrate OpenAI agents with hosted Model Context Protocol (MCP) servers, including approval workflows and tool management for remote MCP services.
openai_responses_client_with_local_mcp.py	Shows how to integrate OpenAI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration.
openai_responses_client_with_runtime_json_schema.py	Shows how to supply a runtime JSON Schema via additional_chat_options for structured output without defining a Pydantic model.
openai_responses_client_with_structured_output.py	Demonstrates how to use structured outputs with OpenAI agents to get structured data responses in predefined formats.
openai_responses_client_with_thread.py	Demonstrates thread management with OpenAI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions.
openai_responses_client_with_web_search.py	Shows how to use web search capabilities with OpenAI agents to retrieve and use information from the internet in responses.

Environment Variables

Make sure to set the following environment variables before running the examples:

• OPENAI_API_KEY: Your OpenAI API key
• OPENAI_CHAT_MODEL_ID: The OpenAI model to use (e.g., gpt-4o, gpt-4o-mini, gpt-3.5-turbo)
• OPENAI_RESPONSES_MODEL_ID: The OpenAI model to use (e.g., gpt-4o, gpt-4o-mini, gpt-3.5-turbo)
• For image processing examples, use a vision-capable model like gpt-4o or gpt-4o-mini

Optionally, you can set:

• OPENAI_ORG_ID: Your OpenAI organization ID (if applicable)
• OPENAI_API_BASE_URL: Your OpenAI base URL (if using a different base URL)

Optional Dependencies

Some examples require additional dependencies:

• Image Generation Example: The openai_responses_client_image_generation.py example requires PIL (Pillow) for image display. Install with:

  # Using uv
  uv add pillow
  
  # Or using pip
  pip install pillow