What's a Function?

Learn about functions in Modus

Functions in Modus

Functions in Modus are stateless, request-response operations that handle specific tasks with precision and speed. They get in, complete their objective, and report back—no unnecessary complications, no lingering presence, just pure operational efficiency.

You can think of a function as an endpoint—each function you write automatically becomes a callable API endpoint that external systems can access.

Functions are the backbone of your app, handling everything from data gathering to AI-powered analysis, all while maintaining clean, predictable behavior.

Core characteristics

Functions are stateless, request-response operations designed for fast, predictable execution. They’re your go-to choice when you need quick data processing, external API integration, or computational tasks without maintaining state between requests.

Key capabilities

• Stateless: Each operation is independent with no memory of previous requests
• Lightning fast: Optimized for sub-second response times
• Infinitely scalable: Deploy as many instances as needed
• Clean operations: Straightforward request-in, response-out pattern
• Auto-deployed: Exposed automatically as GraphQL queries or mutations

How functions become endpoints

When you deploy your functions, Modus automatically exposes them through a GraphQL API. Your functions become either queries (for data retrieval) or mutations (for data modifications) based on their operation type.

Data retrieval queries

Most functions become GraphQL queries—perfect for fetching and processing data:

Go

// This function becomes a GraphQL query
func GatherThreatIntelligence(source string) (*ThreatReport, error) {
  // Data gathering and processing operation
  return fetchThreatData(source)
}

Your functions are now accessible via GraphQL:

query {
  gatherThreatIntelligence(source: "network_logs") {
    threatLevel
    indicators
    recommendations
  }
}

Response:

{
  "data": {
    "gatherThreatIntelligence": {
      "threatLevel": "HIGH",
      "indicators": ["unusual_traffic", "failed_auth_attempts"],
      "recommendations": ["immediate_investigation", "block_suspicious_ips"]
    }
  }
}

Data modification mutations

Functions that modify data automatically become GraphQL mutations. Modus detects these by their operation prefixes:

Go

// This becomes a GraphQL mutation
func CreateSecurityAlert(data AlertInput) (*SecurityAlert, error) {
    // Create new security alert
    return deploySecurityAlert(data)
}

Now you can execute data modifications:

mutation {
  createSecurityAlert(
    data: {
      type: "INTRUSION_ATTEMPT"
      severity: "CRITICAL"
      source: "firewall_logs"
    }
  ) {
    alertId
    status
    timestamp
  }
}

Response:

{
  "data": {
    "createSecurityAlert": {
      "alertId": "alert_20250115_001",
      "status": "ACTIVE",
      "timestamp": "2025-01-15T14:30:00Z"
    }
  }
}

Functions starting with create, update, delete, and similar action words automatically become mutations.

Example: Weather intelligence analysis

Here’s a complete example that demonstrates how functions integrate external APIs with AI models for intelligent data processing:

Go

package main

import ( "fmt" "strings" "github.com/hypermodeinc/modus/sdk/go/pkg/http"
"github.com/hypermodeinc/modus/sdk/go/pkg/models"
"github.com/hypermodeinc/modus/sdk/go/pkg/models/openai" )

type WeatherIntel struct { City string `json:"city"` Temperature float64
`json:"temperature"` Conditions string `json:"conditions"` Analysis string
`json:"analysis"` }

const modelName = "text-generator"

// Function: Gather weather data and provide tactical analysis func
GatherWeatherIntelligence(city string) (\*WeatherIntel, error) { // Fetch
weather data from OpenWeatherMap API url := fmt.Sprintf(
"https://api.openweathermap.org/data/2.5/weather?q=%s&appid={{API_KEY}}&units=metric",
city, )

    response, err := http.Fetch(url)
    if err != nil {
        return nil, err
    }
    if !response.Ok() {
        return nil, fmt.Errorf(
            "weather data retrieval failed: %d %s",
            response.Status,
            response.StatusText,
        )
    }

    // Parse weather data
    var weatherData struct {
        Name string `json:"name"`
        Main struct {
            Temp float64 `json:"temp"`
        } `json:"main"`
        Weather []struct {
            Description string `json:"description"`
        } `json:"weather"`
    }

    response.JSON(&weatherData)

    conditions := "unknown"
    if len(weatherData.Weather) > 0 {
        conditions = weatherData.Weather[0].Description
    }

    // Generate tactical analysis
    analysis, err := analyzeTacticalConditions(
        weatherData.Name,
        weatherData.Main.Temp,
        conditions,
    )
    if err != nil {
        fmt.Printf("Analysis failed for %s: %v\n", weatherData.Name, err)
        analysis = "Analysis unavailable - proceed with standard protocols"
    }

    return &WeatherIntel{
        City:        weatherData.Name,
        Temperature: weatherData.Main.Temp,
        Conditions:  conditions,
        Analysis:    analysis,
    }, nil

}

// Analyze weather conditions for tactical implications func
analyzeTacticalConditions(city string, temp float64, conditions string) (string,
error) { model, err := models.GetModel[openai.ChatModel](modelName) if err !=
nil { return "", err }

    prompt := `You are a tactical analyst evaluating weather conditions for field operations.
    Provide a brief tactical assessment of how these weather conditions might impact
    outdoor activities, visibility, and operational considerations in 1-2 sentences.`

    content := fmt.Sprintf(
        "Location: %s, Temperature: %.1f°C, Conditions: %s",
        city,
        temp,
        conditions,
    )

    input, err := model.CreateInput(
        openai.NewSystemMessage(prompt),
        openai.NewUserMessage(content),
    )
    if err != nil {
        return "", err
    }

    input.Temperature = 0.7

    output, err := model.Invoke(input)
    if err != nil {
        return "", err
    }

    return strings.TrimSpace(output.Choices[0].Message.Content), nil

}

This function automatically becomes available as a GraphQL query:

query {
  gatherWeatherIntelligence(city: "London") {
    city
    temperature
    conditions
    analysis
  }
}

You’ll receive an intelligence report like:

{
  "data": {
    "gatherWeatherIntelligence": {
      "city": "London",
      "temperature": 12.3,
      "conditions": "light rain",
      "analysis": "
        Light rain conditions will reduce visibility for surveillance operations and may impact equipment performance.
        Recommend waterproof gear and consider delayed outdoor activities requiring clear sight lines."
    }
  }
}

Setting up the function

To use this function, you’ll need to:

Sign up for a free API key at OpenWeatherMap

Add the connections and models to your modus.json:

{
  "models": {
    "text-generator": {
      "sourceModel": "meta-llama/Llama-3.2-3B-Instruct",
      "provider": "hugging-face",
      "connection": "hypermode"
    }
  },
  "connections": {
    "weather-api": {
      "type": "http",
      "baseUrl": "https://api.openweathermap.org/",
      "queryParameters": {
        "appid": "{{API_KEY}}"
      }
    }
  }
}

Set your API key in .env.dev.local:

MODUS_WEATHER_API_API_KEY=your_openweathermap_api_key_here

When to use agents instead

Functions are perfect for most computational tasks, but when you need persistent state, complex multi-step workflows, or processes that remember details across multiple interactions, it’s time to consider agents.

Consider upgrading to agents when your use case requires:

• Persistent memory across multiple requests
• Conversation context that builds over time
• Multi-step workflows spanning extended periods
• Recovery from failures with state preservation
• Continuous monitoring with context retention

Your functions form the API backbone of your app—fast, reliable, and always ready for the next request. They handle the immediate computational needs while your agents manage the long-term stateful processes.

Assistant

Responses are generated using AI and may contain mistakes.