Beta: Firebase Genkit is in Beta, which means that it is not subject to any SLA or deprecation policy and could change in backwards-incompatible ways. Throughout the Beta period, Firebase Genkit and its documentation will be updated and improved.

Tool calling

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 stock price or the current weather.
Information specific to your app domain, such as product information or user profiles.

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, 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 Genkit instance's 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.tools property will indicate if it supports tool calling.

Defining tools

Use the Genkit instance's defineTool() function to write tool definitions:

import { genkit, z } from 'genkit';
import { googleAI, gemini15Flash } from '@genkit-ai/google-ai';

const ai = genkit({
  plugins: [googleAI()],
  model: gemini15Flash,
});

const getWeather = ai.defineTool(
  {
    name: 'getWeather',
    description: 'Gets the current weather in a given location',
    inputSchema: z.object({ 
      location: z.string().describe('The location to get the current weather for')
    }),
    outputSchema: z.string(),
  },
  async (input) => {
    // Here, we would typically make an API call or database query. For this
    // example, we just return a fixed value.
    return 'The current weather in ${input.location} is 63°F and sunny.';
  }
);

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.

Using tools

Include defined tools in your prompts to generate content.

Generate

const response = await ai.generate({
  prompt: 'What is the weather in Baltimore?',
  tools: [getWeather],
});

definePrompt

const weatherPrompt = ai.definePrompt(
  {
    name: 'weatherPrompt',
    tools: [getWeather],
  },
  'What is the weather in {{location}}?'
);

const response = await weatherPrompt({ location: 'Baltimore' });

Prompt file

---
system: "Answer questions using the tools you have."
tools: [getWeather]
input:
  schema:
    location: string
---

What is the weather in {{location}}?

Then you can execute the prompt in your code as follows:

// assuming prompt file is named weatherPrompt.prompt
const weatherPrompt = ai.prompt('weatherPrompt');

const response = await weatherPrompt({ location: 'Baltimore' });

Chat

const chat = ai.chat({
  system: 'Answer questions using the tools you have.',
  tools: [getWeather],
});

const response = await chat.send('What is the weather in Baltimore?');

// Or, specify tools that are message-specific 
const response = await chat.send({
  prompt: 'What is the weather in Baltimore?',
  tools: [getWeather],
});

Genkit will automatically handle the tool call if the LLM needs to use the getWeather tool to answer 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:

const getWeather = ai.defineTool(
  {
    // ... tool definition ...
  },
  async ({ location }) => {
    // ... tool implementation ...
  },
);

const generateOptions: GenerateOptions = {
  prompt: "What's the weather like in Baltimore?",
  tools: [getWeather],
  returnToolRequests: true,
};

let llmResponse;
while (true) {
  llmResponse = await ai.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 getWeather(part.toolRequest.input),
            },
          };
        default:
          throw Error('Tool not found');
      }
    })
  );
  generateOptions.messages = llmResponse.messages;
  generateOptions.prompt = toolResponses;
}