openai

OpenAI Agent Framework Examples

This folder contains examples demonstrating different ways to create and use agents with the OpenAI clients 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 OpenAIAssistantsClient.get_code_interpreter_tool() 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 OpenAIAssistantsClient.get_file_search_tool() 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_session.py	Session management with OpenAIAssistantProvider for conversation context persistence.
openai_chat_client_basic.py	The simplest way to create an agent using Agent 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_session.py	Demonstrates session management with OpenAI agents, including automatic session creation for stateless conversations and explicit session management for maintaining conversation context across multiple interactions.
openai_chat_client_with_web_search.py	Shows how to use OpenAIChatClient.get_web_search_tool() for web search capabilities with OpenAI agents.
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 Agent 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 OpenAIResponsesClient.get_image_generation_tool() to create images based on text descriptions.
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 OpenAIResponsesClient.get_code_interpreter_tool() to write and execute Python code.
openai_responses_client_with_code_interpreter_files.py	Shows how to use code interpreter with uploaded files for data analysis.
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 OpenAIResponsesClient.get_file_search_tool() for searching through uploaded files.
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 use OpenAIResponsesClient.get_mcp_tool() for hosted MCP servers, including approval workflows.
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_session.py	Demonstrates session management with OpenAI agents, including automatic session creation for stateless conversations and explicit session management for maintaining conversation context across multiple interactions.
openai_responses_client_with_web_search.py	Shows how to use OpenAIResponsesClient.get_web_search_tool() for web search capabilities.

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