This folder contains examples demonstrating different ways to create and use agents with the OpenAI Assistants client from the agent_framework.openai package.
| File | Description |
|---|---|
openai_assistants_basic.py |
The simplest way to create an agent using ChatAgent with OpenAIAssistantsClient. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
openai_assistants_with_existing_assistant.py |
Shows how to work with a pre-existing assistant by providing the assistant ID to the OpenAI Assistants client. Demonstrates proper cleanup of manually created assistants. |
openai_assistants_with_explicit_settings.py |
Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including API key and model ID. |
openai_assistants_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_assistants_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_assistants_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_assistants_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_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_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_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_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_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. |
Make sure to set the following environment variables before running the examples:
OPENAI_API_KEY: Your OpenAI API keyOPENAI_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-4oorgpt-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)
Some examples require additional dependencies:
- Image Generation Example: The
openai_responses_client_image_generation.pyexample requires PIL (Pillow) for image display. Install with:# Using uv uv add pillow # Or using pip pip install pillow