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Why Use Function Calling?

Function calling lets you extend LLM capabilities by allowing models to invoke external functions and tools. This enables your applications to perform actions like fetching real-time data, interacting with APIs, or executing computations based on the model’s reasoning.

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Prerequisites

  • An API key from Prem Studio
  • Python ≥ 3.8 or Node.js ≥ 18
  • premai or openai SDK installed

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Supported Models

The list of supported models is constantly evolving. For the most up-to-date list of models that support function calling, please visit the models list page. Models that support function calling are clearly marked with a tools tag on the models page.

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Generate Function Calls with Prem

This quick guide shows you how to implement a weather assistant that can fetch current weather information using function calling. You’ll define a weather function, let the model decide when to call it, and handle the tool responses. Why it’s useful: It enables your AI assistant to perform real-world actions and access live data — not just generate text responses. When to use it: Perfect for building AI assistants, chatbots, or automation tools that need to interact with external APIs, databases, or perform calculations based on user queries.
1

Setup Environment

npm install premai
npm install --save-dev @types/node
export PREMAI_API_KEY=your_api_key
2

Define Function Schema and Mock Implementation


const tools = [
    {
        type: "function",
        function: {
            name: "get_current_weather",
            description: "Get the current weather in a given location",
            parameters: {
                type: "object",
                properties: {
                    location: {
                        type: "string",
                        description: "The city and state, e.g. San Francisco, CA",
                    },
                    unit: {
                        type: "string",
                        enum: ["celsius", "fahrenheit"]
                    },
                },
                required: ["location"],
            },
        },
    }
];

// Mock function implementation
function getCurrentWeather(location: string, unit: string = "celsius"): object {
    // In a real app, you'd call a weather API here
    return {
        location: location,
        temperature: unit === "celsius" ? "15°C" : "59°F",
        condition: "Partly cloudy",
        humidity: "65%"
    };
}
3

Initialize Client

import PremAI from "premai";

const client = new PremAI({
    apiKey: process.env.PREMAI_API_KEY || "",
});
4

Send Initial Request with Tools

const messages: ChatCompletionsParams.Message[] = [
    { role: "user", content: "What's the weather like in Boston today?" }
] as any;

const response = await client.chat.completions({
    model: "claude-4-sonnet",
    messages,
    tools,
    tool_choice: "auto"
});
5

Handle Tool Calls and Execute Functions

// Check if the model wants to call a function
if (response.choices[0].message.tool_calls) {
    // Add the assistant's response to conversation
    messages.push({
        role: "assistant",
        content: response.choices[0].message.content,
        tool_calls: response.choices[0].message.tool_calls
    });

    // Execute each tool call
    for (const toolCall of response.choices[0].message.tool_calls) {
        const functionName = toolCall.function.name;
        const functionArgs = JSON.parse(toolCall.function.arguments);

        let result: object;
        if (functionName === "get_current_weather") {
            result = getCurrentWeather(functionArgs.location, functionArgs.unit);
        } else {
            result = { error: "Unknown function" };
        }

        // Add the function result to conversation
        messages.push({
            role: "tool",
            tool_call_id: toolCall.id,
            content: JSON.stringify(result),
            name: functionName
        });
    }

    // Get final response with function results
    const finalResponse = await client.chat.completions({
        model: "claude-4-sonnet",
        messages,
        tools
    });

    console.log(finalResponse.choices[0].message.content);
} else {
    console.log(response.choices[0].message.content);
}
The json_repair.loads() function automatically handles malformed JSON in function arguments, which is more robust than using JSON.parse() or json.loads() directly, as models sometimes generate slightly malformed JSON.

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Full Copy-Paste Example

import { ChatCompletionsParams } from "premai/resources/chat";
import PremAI from "premai";

const tools = [
    {
        type: "function",
        function: {
            name: "get_current_weather",
            description: "Get the current weather in a given location",
            parameters: {
                type: "object",
                properties: {
                    location: {
                        type: "string",
                        description: "The city and state, e.g. San Francisco, CA",
                    },
                    unit: {
                        type: "string",
                        enum: ["celsius", "fahrenheit"]
                    },
                },
                required: ["location"],
            },
        },
    }
];

// Mock function implementation
function getCurrentWeather(location: string, unit: string = "celsius"): object {
    // In a real app, you'd call a weather API here
    return {
        location: location,
        temperature: unit === "celsius" ? "15°C" : "59°F",
        condition: "Partly cloudy",
        humidity: "65%"
    };
}

const client = new PremAI({
    apiKey: process.env.PREMAI_API_KEY || "",
});

const messages: ChatCompletionsParams.Message[] = [
    { role: "user", content: "What's the weather like in Boston today?" }
] as any;

const response = await client.chat.completions({
    model: "claude-4-sonnet",
    messages,
    tools,
    tool_choice: "auto"
});

// Check if the model wants to call a function
if (response.choices[0].message.tool_calls) {
    // Add the assistant's response to conversation
    messages.push({
        role: "assistant",
        content: response.choices[0].message.content,
        tool_calls: response.choices[0].message.tool_calls
    });

    // Execute each tool call
    for (const toolCall of response.choices[0].message.tool_calls) {
        const functionName = toolCall.function.name;
        const functionArgs = JSON.parse(toolCall.function.arguments);

        let result: object;
        if (functionName === "get_current_weather") {
            result = getCurrentWeather(functionArgs.location, functionArgs.unit);
        } else {
            result = { error: "Unknown function" };
        }

        // Add the function result to conversation
        messages.push({
            role: "tool",
            tool_call_id: toolCall.id,
            content: JSON.stringify(result),
            name: functionName
        });
    }

    // Get final response with function results
    const finalResponse = await client.chat.completions({
        model: "claude-4-sonnet",
        messages,
        tools
    });

    console.log(finalResponse.choices[0].message.content);
} else {
    console.log(response.choices[0].message.content);
}

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Pro Tips

  • We use the json-repair package to automatically handle malformed JSON in function arguments, making the parsing much more robust than using json.loads().
  • Always validate function arguments before executing functions to prevent errors or security issues.
  • Use descriptive function names and detailed descriptions to help the model understand when to call each function.
  • Set tool_choice: "auto" to let the model decide when to use functions, or set it to "none" to disable function calling for a specific request.
  • For production applications, implement proper error handling and logging for function executions.
  • Consider implementing rate limiting and authentication for external API calls within your functions.

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Common Use-Cases

  • Building AI assistants that can fetch real-time data (weather, stock prices, news)
  • Creating chatbots that can interact with databases or APIs
  • Implementing AI agents that can perform calculations or data processing
  • Developing automation tools that can execute actions based on natural language commands

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Tool Choice Options

You can control when and how the model uses functions with the tool_choice parameter:
  • "auto" (default): Model decides whether to call functions
  • "none": Model will not call any functions
  • {"type": "function", "function": {"name": "function_name"}}: Force the model to call a specific function
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