This tutorial video provides a comprehensive guide to utilizing "tools" within the OpenAI Agents SDK, a core feature for extending agent capabilities. It covers the different types of tools available, starting with the simple integration of pre-built tools like web search. The main focus is on creating custom tools, for which two methods are detailed: first, using the @FunctionTool decorator on a Python function, where the function's docstring is critically used as the description for the LLM; and second, a more advanced approach using Pydantic models to define complex input arguments with detailed descriptions and validation. The video also demonstrates how to implement structured output to receive complex JSON objects from the agent. Finally, it introduces the concept of "agents-as-tools" for building multi-agent systems, where one agent can be invoked as a tool by an orchestrator agent, while also noting the performance considerations of this token-heavy approach.
A Guide to Using Tools in the OpenAI Agents SDK
0:00:02 Introduction to Tools: The Agents SDK supports two types of tools: pre-built tools provided by OpenAI (e.g., web search, file search) and custom, user-defined tools. Pre-built tools require the use of OpenAI models to function correctly.
0:02:46 Creating Custom Tools: The primary way to create a custom tool is by defining a standard Python function and applying the @FunctionTool decorator. This decorator converts the function into a tool object that the agent can use.
0:05:54 Docstrings as Tool Descriptions: A critical aspect of custom tools is the function's docstring. The Agents SDK uses the docstring as the tool's description, which is passed to the LLM to help it understand when and how to use the tool. A well-written docstring is essential for reliable tool use.
0:07:09 Achieving Structured Output: You can force the agent to provide a structured response (e.g., a JSON object) instead of a simple string by defining a Pydantic class and passing it to the output_type parameter. This allows for more complex and predictable outputs with multiple fields, such as response, approach_taken, or a confidence score.
0:09:38 Advanced Tools with Pydantic for Inputs: For more complex tools with multiple parameters, you can use Pydantic to define the input arguments. This method generates a detailed params_json_schema that describes each parameter's purpose and specifies which are required or optional, giving the LLM more precise instructions.
0:13:51 Agents as Tools for Multi-Agent Systems: The SDK allows one agent to be used as a tool by another, creating a multi-agent system. An "orchestrator" agent can be given other specialized agents as tools, allowing it to delegate complex tasks.
0:16:47 Performance Considerations: The "agents-as-tools" approach is described as "token-heavy" because it involves a chain of LLM calls (orchestrator to sub-agent to tool and back). The video notes that this method may not be efficient for all use cases and that lighter alternatives like "handoffs" exist.