Smolagents - Lightweight Tool-Calling AI Agent Framework

4 Steps

1. 1

   Define a Custom Tool: Create a Python class for your tool, inheriting from a base `Tool` class. Implement the `__init__` method to define its `name`, `description`, and `parameters`. Crucially, implement an `execute` method that contains the tool's specific logic, like making an API call or performing a calculation.

2. 2

   Implement a Mock LLM for Tool Calling: Develop a `MockLLM` class that holds a list of your defined tools. Its `generate_response` method will simulate an LLM's decision-making process, parsing the input prompt to determine if a tool should be called and, if so, which one and with what arguments. It then returns the tool's output.

3. 3

   Assemble Your Agent: Instantiate your custom tools (e.g., `SearchTool`) and then create an instance of your `MockLLM`, passing the list of available tools to it. Finally, create your `Agent` instance, providing it with the configured `MockLLM`.

4. 4

   Chat with Your Agent: Use the `agent.chat()` method to interact with your newly created agent. Provide different prompts to observe how it processes your input, potentially invokes the defined `SearchTool`, and returns a response based on the tool's output or its own general knowledge.