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

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.

At a high level, this is what a typical tool-calling interaction with an LLM looks like:

1. 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.
2. The LLM either generates a complete response or generates a tool call request in a specific format.
3. 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.
4. 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.

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.

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.

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

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

const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-2.0-flash'),
});

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, name, description, and inputSchema parameters are required. When writing a tool definition, take special care with the wording and descriptiveness of these parameters. They are vital for the LLM to make effective use of the available tools.

Include defined tools in your prompts to generate content.

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

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

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

---
tools: [getWeather]
input:
  schema:
    location: string
---

What is the weather in {{location}}?

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

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

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],
});

When working with tools that might trigger multiple sequential calls, you can control resource usage and prevent runaway execution using the maxTurns parameter. This sets a hard limit on how many back-and-forth interactions the model can have with your tools in a single generation cycle.

Why use maxTurns?

• Cost Control: Prevents unexpected API usage charges from excessive tool calls
• Performance: Ensures responses complete within reasonable timeframes
• Safety: Guards against infinite loops in complex tool interactions
• Predictability: Makes your application behavior more deterministic

The default value is 5 turns, which works well for most scenarios. Each “turn” represents one complete cycle where the model can make tool calls and receive responses.

Example: Web Research Agent

Consider a research agent that might need to search multiple times to find comprehensive information:

const webSearch = ai.defineTool(
  {
    name: 'webSearch',
    description: 'Search the web for current information',
    inputSchema: z.object({
      query: z.string().describe('Search query'),
    }),
    outputSchema: z.string(),
  },
  async (input) => {
    // Simulate web search API call
    return `Search results for "${input.query}": [relevant information here]`;
  },
);

const response = await ai.generate({
  prompt: 'Research the latest developments in quantum computing, including recent breakthroughs, key companies, and future applications.',
  tools: [webSearch],
  maxTurns: 8, // Allow up to 8 research iterations
});

Example: Financial Calculator

Here’s a more complex scenario where an agent might need multiple calculation steps:

const calculator = ai.defineTool(
  {
    name: 'calculator',
    description: 'Perform mathematical calculations',
    inputSchema: z.object({
      expression: z.string().describe('Mathematical expression to evaluate'),
    }),
    outputSchema: z.number(),
  },
  async (input) => {
    // Safe evaluation of mathematical expressions
    return eval(input.expression); // In production, use a safe math parser
  },
);

const stockAnalyzer = ai.defineTool(
  {
    name: 'stockAnalyzer',
    description: 'Get current stock price and basic metrics',
    inputSchema: z.object({
      symbol: z.string().describe('Stock symbol (e.g., AAPL)'),
    }),
    outputSchema: z.object({
      price: z.number(),
      change: z.number(),
      volume: z.number(),
    }),
  },
  async (input) => {
    // Simulate stock API call
    return {
      price: 150.25,
      change: 2.50,
      volume: 45000000
    };
  },
);

const response = await ai.generate({
  prompt: 'Calculate the total value of my portfolio: 100 shares of AAPL, 50 shares of GOOGL, and 200 shares of MSFT. Also calculate what percentage each holding represents.',
  tools: [calculator, stockAnalyzer],
  maxTurns: 12, // Multiple stock lookups + calculations needed
});

const portfolioAnalysisPrompt = ai.definePrompt(
  {
    name: "portfolioAnalysis",
    tools: [calculator, stockAnalyzer],
    maxTurns: 12,
  },
  "Calculate the total value of my portfolio: {{holdings}}. Also calculate what percentage each holding represents."
);

const response = await portfolioAnalysisPrompt({
  holdings: "100 shares of AAPL, 50 shares of GOOGL, and 200 shares of MSFT"
});

---
tools: [calculator, stockAnalyzer]
maxTurns: 12
input:
  schema:
    holdings: string
---

Calculate the total value of my portfolio: {{holdings}}. Also calculate what percentage each holding represents.

const portfolioAnalysisPrompt = ai.prompt("portfolioAnalysis");

const response = await portfolioAnalysisPrompt({
  holdings: "100 shares of AAPL, 50 shares of GOOGL, and 200 shares of MSFT"
});

const chat = ai.chat({
  system: "You are a financial analysis assistant. Use the available tools to provide accurate calculations and current market data.",
  tools: [calculator, stockAnalyzer],
  maxTurns: 12,
});

const response = await chat.send("Calculate the total value of my portfolio: 100 shares of AAPL, 50 shares of GOOGL, and 200 shares of MSFT. Also calculate what percentage each holding represents.");

What happens when maxTurns is reached?

When the limit is hit, Genkit stops the tool-calling loop and returns the model’s current response, even if it was in the middle of using tools. The model will typically provide a partial answer or explain that it couldn’t complete all the requested operations.

As most things in Genkit tools need to be predefined during your app’s initialization. This is necessary so that you would be able interact with your tools from the Genkit Dev UI. This is typically the recommended way. However there are scenarios when the tool must be defined dynamically per user request.

You can dynamically define tools using ai.dynamicTool function. It is very similar to ai.defineTool method, however dynamic tools are not tracked by Genkit runtime, so cannot be interacted with from Genkit Dev UI and must be passed to the ai.generate call by reference (for regular tools you can also use a string tool name).

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

const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-2.0-flash'),
});

ai.defineFlow('weatherFlow', async () => {
  const getWeather = ai.dynamicTool(
    {
      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) => {
      return `The current weather in ${input.location} is 63°F and sunny.`;
    },
  );

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

  return text;
});

When defining dynamic tools, to specify input and output schemas you can either use Zod as shown in the previous example, or you can pass in manually constructed JSON Schema.

const getWeather = ai.dynamicTool(
  {
    name: 'getWeather',
    description: 'Gets the current weather in a given location',
    inputJsonSchema: myInputJsonSchema,
    outputJsonSchema: myOutputJsonSchema,
  },
  async (input) => {
    /* ... */
  },
);

Dynamic tools don’t require the implementation function. If you don’t pass in the function the tool will behave like an interrupt and you can do manual tool call handling:

const getWeather = ai.dynamicTool({
  name: 'getWeather',
  description: 'Gets the current weather in a given location',
  inputJsonSchema: myInputJsonSchema,
  outputJsonSchema: myOutputJsonSchema,
});

By default, Genkit repeatedly calls the LLM until every tool call has been resolved. You can conditionally pause execution in situations where you want to, for example:

• Ask the user a question or display UI.
• Confirm a potentially risky action with the user.
• Request out-of-band approval for an action.

Interrupts are special tools that can halt the loop and return control to your code so that you can handle more advanced scenarios. Visit the interrupts guide to learn how to use them.

If you want full 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;
}