tools

Concept

Having proper tool abstractions is at the core of building agentic systems in LlamaIndex. Defining a set of Tools is similar to defining any API interface, with the exception that these Tools are meant for agent rather than human use. We allow users to define both a Tool as well as a ToolSpec containing a series of functions under the hood.

When using an agent or LLM with function calling, the tool selected (and the arguments written for that tool) rely strongly on the tool name and description of the tools purpose and arguments. Spending time tuning these parameters can result in larges changes in how the LLM calls these tools.

A Tool implements a very generic interface - simply define __call__ and also return some basic metadata (name, description, function schema).

We offer a few different types of Tools:

• FunctionTool: A function tool allows users to easily convert any user-defined function into a Tool. It can also auto-infer the function schema, or let you customize various aspects.
• QueryEngineTool: A tool that wraps an existing query engine. Note: since our agent abstractions inherit from BaseQueryEngine, these tools can also wrap other agents.
• Community contributed ToolSpecs that define one or more tools around a single service (like Gmail)
• Utility tools for wrapping other tools to handle returning large amounts of data from a tool

FunctionTool

A function tool is a simple wrapper around any existing function (both sync and async are supported!).

from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.tools import FunctionTool


def get_weather(location: str) -> str:
    """Usfeful for getting the weather for a given location."""
    ...


tool = FunctionTool.from_defaults(
    get_weather,
    # async_fn=aget_weather,  # optional!
)

agent = ReActAgent(llm=llm, tools=tools)