Tool calling, also known as function calling, is a structured way to give LLMs the ability to make requests back to the application that called it. You define the tools you want to make available to the model, and the model will make tool requests to your app as necessary to fulfill the prompts you give it.
The use cases of tool calling generally fall into a few themes:
Giving an LLM access to information it wasn't trained with
- Frequently changing information, such as a restaurant's daily menu or a store's inventory status.
- Information specific to your app domain, such as product information.
Note the overlap with retrieval augmented generation (RAG), which is also a way to let an LLM integrate factual information into its generations. RAG is a heavier solution that is most suited when you have a large amount of information or the information that's most relevant to a prompt is ambiguous. On the other hand, if retrieving the information the LLM needs is a simple function call or database lookup, tool calling is more appropriate.
Introducing a degree of determinism into an LLM workflow
- Performing calculations that the LLM cannot reliably complete itself.
- Forcing an LLM to generate verbatim text under certain circumstances, such as when responding to a question about an app's terms of service.
Performing an action when initiated by an LLM
- Turning on and off lights in an LLM-powered home assistant
- Reserving table reservations in an LLM-powered restaurant agent
Before you begin
If you want to run the code examples on this page, first complete the steps in the Getting started guide. All of the examples assume that you have already set up a project with Genkit dependencies installed.
This page discusses one of the advanced features of Genkit model abstraction and generate() function, so before you dive too deeply, you should be familiar with the content on the Generating content with AI models page. You should also be familiar with Genkit's system for defining input and output schemas, which is discussed on the Flows page.
Overview of tool calling
At a high level, this is what a typical tool-calling interaction with an LLM looks like:
- The calling application prompts the LLM with a request and also includes in the prompt a list of tools the LLM can use to generate a response.
- The LLM either generates a complete response or generates a tool call request in a specific format.
- If the caller receives a complete response, the request is fulfilled and the interaction ends; but if the caller receives a tool call, it performs whatever logic is appropriate and sends a new request to the LLM containing the original prompt or some variation of it as well as the result of the tool call.
- The LLM handles the new prompt as in Step 2.
For this to work, several requirements must be met:
- The model must be trained to make tool requests when it's needed to complete a prompt. Most of the larger models provided through web APIs, such as Gemini and Claude, can do this, but smaller and more specialized models often cannot. Genkit will throw an error if you try to provide tools to a model that doesn't support it.
- The calling application must provide tool definitions to the model in the format it expects.
- The calling application must prompt the model to generate tool calling requests in the format the application expects.
Tool calling with Genkit
Genkit provides a single interface for tool calling with models that support it.
Each model plugin ensures that the last two of the above criteria are met, and
the generate() function automatically carries out the tool calling loop
described earlier.
Model support
Tool calling support depends on the model, the model API, and the Genkit plugin. Consult the relevant documentation to determine if tool calling is likely to be supported. In addition:
- Genkit will throw an error if you try to provide tools to a model that doesn't support it.
- If the plugin exports model references, the
info.supports.toolsproperty will indicate if it supports tool calling.
Defining tools
Use the defineTool() function to write tool definitions:
const specialToolInputSchema = z.object({ meal: z.enum(["breakfast", "lunch", "dinner"]) });
const specialTool = defineTool(
{
name: "specialTool",
description: "Retrieves today's special for the given meal",
inputSchema: specialToolInputSchema,
outputSchema: z.string(),
},
async ({ meal }): Promise<string> => {
// Retrieve up-to-date information and return it. Here, we just return a
// fixed value.
return "Baked beans on toast";
}
);
The syntax here looks just like the defineFlow() syntax; however, all four of
the name, description, inputSchema, and outputSchema parameters are
required.
When writing a tool definition, take special care with the wording and
descriptiveness of these parameters, as they are vital for the LLM to
effectively make use of the available tools.
Including tools with your prompts
After you've defined your tools, specify them in the tools parameter of
generate():
const llmResponse = await generate({
model: gemini15Flash,
prompt,
tools: [specialTool],
});
You can make multiple tools available; the LLM will call the tools as necessary to complete the prompt.
Explicitly handling tool calls
By default, Genkit repeatedly calls the LLM until every tool call has been
resolved. If you want more control over this tool calling loop, for example to
apply more complicated logic, set the returnToolRequests parameter to true.
Now it's your responsibility to ensure all of the tool requests are fulfilled:
let generateOptions: GenerateOptions = {
model: gemini15Flash,
prompt,
tools: [specialTool],
returnToolRequests: true,
};
let llmResponse;
while (true) {
llmResponse = await generate(generateOptions);
const toolRequests = llmResponse.toolRequests();
if (toolRequests.length < 1) {
break;
}
const toolResponses: ToolResponsePart[] = await Promise.all(
toolRequests.map(async (part) => {
switch (part.toolRequest.name) {
case "specialTool":
return {
toolResponse: {
name: part.toolRequest.name,
ref: part.toolRequest.ref,
output: await specialTool(specialToolInputSchema.parse(part.toolRequest?.input)),
},
};
default:
throw Error('Tool not found');
}
}));
generateOptions.history = llmResponse.toHistory();
generateOptions.prompt = toolResponses;
}