Go SDK - Untrace

Overview

The Untrace Go SDK provides zero-latency LLM observability with automatic instrumentation for all major LLM providers. Built on OpenTelemetry standards, it captures comprehensive trace data and routes it to your chosen observability platforms.

Quick Start

Start tracing LLM calls in minutes

Framework Support

Gin, Echo, Fiber, and microservices

Performance

High-performance, low-overhead tracing

Examples

Real-world examples and best practices

Installation

Install the Untrace Go SDK using go get:

go get github.com/untrace-dev/untrace-sdk-go

Quick Start

Basic Setup

package main

import (
    "context"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    // Initialize the SDK
    client, err := untrace.Init(untrace.Config{
        APIKey: "your-api-key",
        ServiceName: "my-llm-app",
        Environment: "production",
    })
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Create a span for an LLM operation
    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "chat-completion", untrace.LLMSpanOptions{
        Provider: "openai",
        Model: "gpt-3.5-turbo",
        Operation: "chat",
    })
    defer span.End()

    // Your LLM code here
    // The span will automatically capture timing and context
}

Manual Instrumentation

package main

import (
    "context"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, err := untrace.Init(untrace.Config{
        APIKey: "your-api-key",
        ServiceName: "my-api",
    })
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Create custom spans for your LLM workflows
    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "custom-llm-call", untrace.LLMSpanOptions{
        Provider: "custom",
        Model: "my-model",
        Operation: "chat",
    })
    defer span.End()

    // Add custom attributes
    span.SetAttributes(
        untrace.String("custom.metric", "42"),
        untrace.Int("llm.prompt.tokens", 100),
    )

    // Your LLM logic here
}

Configuration

Configuration Options

type Config struct {
    // Required
    APIKey string

    // Optional
    ServiceName         string
    Environment         string
    Version             string
    BaseURL             string
    Debug               bool
    SamplingRate        float64
    MaxBatchSize        int
    ExportInterval      time.Duration
    Headers             map[string]string
    ResourceAttributes  map[string]interface{}
}

Environment Variables

The SDK supports configuration via environment variables:

# Core settings
export UNTRACE_API_KEY="your-api-key"
export UNTRACE_BASE_URL="https://api.untrace.dev"
export UNTRACE_DEBUG="true"

# OpenTelemetry settings
export OTEL_SERVICE_NAME="my-service"
export OTEL_RESOURCE_ATTRIBUTES="environment=production,version=1.0.0"

Configuration from Environment

package main

import (
    "context"
    "log"
    "os"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    // Initialize with environment variables
    config := untrace.Config{
        APIKey: os.Getenv("UNTRACE_API_KEY"),
        ServiceName: os.Getenv("OTEL_SERVICE_NAME"),
        Environment: os.Getenv("ENVIRONMENT"),
        Debug: os.Getenv("UNTRACE_DEBUG") == "true",
    }

    client, err := untrace.Init(config)
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())
}

Framework Integration

Gin Framework

package main

import (
    "net/http"

    "github.com/gin-gonic/gin"
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    r := gin.Default()

    r.POST("/chat", func(c *gin.Context) {
        ctx, span := client.Tracer().StartLLMSpan(c.Request.Context(), "chat", untrace.LLMSpanOptions{
            Provider: "openai",
            Model: "gpt-4",
        })
        defer span.End()

        // Your LLM logic here
        c.JSON(http.StatusOK, gin.H{"message": "success"})
    })

    r.Run(":8080")
}

Echo Framework

package main

import (
    "net/http"

    "github.com/labstack/echo/v4"
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    e := echo.New()

    e.POST("/chat", func(c echo.Context) error {
        ctx, span := client.Tracer().StartLLMSpan(c.Request().Context(), "chat", untrace.LLMSpanOptions{
            Provider: "openai",
            Model: "gpt-4",
        })
        defer span.End()

        // Your LLM logic here
        return c.JSON(http.StatusOK, map[string]string{"message": "success"})
    })

    e.Logger.Fatal(e.Start(":8080"))
}

Fiber Framework

package main

import (
    "github.com/gofiber/fiber/v2"
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    app := fiber.New()

    app.Post("/chat", func(c *fiber.Ctx) error {
        ctx, span := client.Tracer().StartLLMSpan(c.Context(), "chat", untrace.LLMSpanOptions{
            Provider: "openai",
            Model: "gpt-4",
        })
        defer span.End()

        // Your LLM logic here
        return c.JSON(fiber.Map{"message": "success"})
    })

    app.Listen(":8080")
}

gRPC Integration

package main

import (
    "context"

    "google.golang.org/grpc"
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    // Create gRPC server with tracing
    s := grpc.NewServer(
        grpc.UnaryInterceptor(untrace.UnaryServerInterceptor()),
        grpc.StreamInterceptor(untrace.StreamServerInterceptor()),
    )

    // Register your services
    // ...

    // Start server
    s.Serve(lis)
}

Advanced Usage

Workflow Tracking

package main

import (
    "context"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, err := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Start a workflow
    workflow := client.Context().StartWorkflow("customer-support-chat", untrace.WorkflowOptions{
        UserID: "user-123",
        SessionID: "session-456",
        Metadata: map[string]interface{}{
            "tier": "premium",
        },
    })
    defer workflow.End()

    // Your LLM calls are automatically associated with this workflow
    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "llm-call", untrace.LLMSpanOptions{
        Provider: "openai",
        Model: "gpt-4",
    })
    defer span.End()

    // Your LLM logic here
}

Metrics Collection

package main

import (
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    // Record custom metrics
    client.Metrics().RecordTokenUsage(untrace.TokenUsage{
        PromptTokens: 150,
        CompletionTokens: 50,
        TotalTokens: 200,
        Model: "gpt-3.5-turbo",
        Provider: "openai",
    })

    client.Metrics().RecordLatency(1234, map[string]interface{}{
        "provider": "openai",
        "operation": "chat",
    })

    client.Metrics().RecordCost(untrace.Cost{
        Prompt: 0.0015,
        Completion: 0.002,
        Total: 0.0035,
        Model: "gpt-4",
        Provider: "openai",
    })
}

Custom Attributes

package main

import (
    "context"
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "custom-operation", untrace.LLMSpanOptions{
        Provider: "openai",
        Model: "gpt-4",
    })
    defer span.End()

    // Add custom attributes
    span.SetAttributes(
        untrace.String("user.id", "user-123"),
        untrace.String("user.subscription_tier", "premium"),
        untrace.String("feature.name", "advanced-search"),
        untrace.String("feature.version", "2.0"),
        untrace.Int("custom.metric", 42),
        untrace.Bool("experimental", true),
    )

    // Your LLM logic here
}

Error Handling

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
    "github.com/untrace-dev/untrace-sdk-go/errors"
)

func main() {
    client, err := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "risky-operation", untrace.LLMSpanOptions{
        Provider: "openai",
        Model: "gpt-4",
    })
    defer span.End()

    // Check for specific error types
    if err := riskyOperation(); err != nil {
        switch e := err.(type) {
        case *errors.APIError:
            log.Printf("API error: %v", e)
            span.SetStatus(codes.Error, e.Error())
        case *errors.ValidationError:
            log.Printf("Validation error: %v", e)
            span.SetStatus(codes.Error, e.Error())
        case *errors.UntraceError:
            log.Printf("Untrace error: %v", e)
            span.SetStatus(codes.Error, e.Error())
        default:
            log.Printf("Unknown error: %v", e)
            span.SetStatus(codes.Error, e.Error())
        }
        span.RecordError(err)
    }
}

Examples

OpenAI Integration

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, err := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Chat with tracing
    response, err := chatWithTracing(client, "What is the meaning of life?")
    if err != nil {
        log.Fatal(err)
    }

    fmt.Printf("Response: %s\n", response)
}

func chatWithTracing(client *untrace.Client, prompt string) (string, error) {
    ctx, span := client.Tracer().StartLLMSpan(context.Background(), "openai-chat", untrace.LLMSpanOptions{
        Provider: "openai",
        Model: "gpt-4",
        Operation: "chat",
    })
    defer span.End()

    // Set prompt attribute
    span.SetAttributes(untrace.String("llm.prompt", prompt))

    // Make OpenAI API call (pseudo-code)
    response, err := callOpenAI(prompt)
    if err != nil {
        span.RecordError(err)
        span.SetStatus(codes.Error, err.Error())
        return "", err
    }

    // Set response attributes
    span.SetAttributes(
        untrace.String("llm.response", response),
        untrace.Int("llm.tokens", 100),
    )

    return response, nil
}

Batch Processing

package main

import (
    "context"
    "log"

    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    client, err := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Process multiple prompts
    prompts := []string{
        "What is AI?",
        "Explain machine learning",
        "How does deep learning work?",
    }

    for i, prompt := range prompts {
        ctx, span := client.Tracer().StartLLMSpan(context.Background(), "batch-processing", untrace.LLMSpanOptions{
            Provider: "openai",
            Model: "gpt-4",
        })
        defer span.End()

        span.SetAttributes(
            untrace.String("llm.prompt", prompt),
            untrace.Int("batch.index", i),
            untrace.Int("batch.total", len(prompts)),
        )

        // Process prompt
        response, err := processPrompt(prompt)
        if err != nil {
            span.RecordError(err)
            continue
        }

        span.SetAttributes(untrace.String("llm.response", response))
    }
}

Middleware for HTTP Handlers

package main

import (
    "net/http"

    "github.com/gin-gonic/gin"
    "github.com/untrace-dev/untrace-sdk-go"
)

// TracingMiddleware creates a middleware that traces HTTP requests
func TracingMiddleware(client *untrace.Client) gin.HandlerFunc {
    return func(c *gin.Context) {
        ctx, span := client.Tracer().StartSpan(c.Request.Context(), "http-request")
        defer span.End()

        // Set HTTP attributes
        span.SetAttributes(
            untrace.String("http.method", c.Request.Method),
            untrace.String("http.url", c.Request.URL.String()),
            untrace.String("http.user_agent", c.Request.UserAgent()),
        )

        // Add span to context
        c.Request = c.Request.WithContext(ctx)

        // Process request
        c.Next()

        // Set response attributes
        span.SetAttributes(
            untrace.Int("http.status_code", c.Writer.Status()),
        )
    }
}

func main() {
    client, _ := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    defer client.Shutdown(context.Background())

    r := gin.Default()
    r.Use(TracingMiddleware(client))

    r.POST("/chat", func(c *gin.Context) {
        // Your handler logic here
        c.JSON(http.StatusOK, gin.H{"message": "success"})
    })

    r.Run(":8080")
}

Performance

Sampling

package main

import (
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    // Configure sampling for high-volume applications
    client, err := untrace.Init(untrace.Config{
        APIKey: "your-api-key",
        SamplingRate: 0.1, // Sample 10% of requests
    })
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())
}

Batching

package main

import (
    "github.com/untrace-dev/untrace-sdk-go"
)

func main() {
    // Configure batching for better performance
    client, err := untrace.Init(untrace.Config{
        APIKey: "your-api-key",
        MaxBatchSize: 100,        // Batch up to 100 spans
        ExportInterval: 5 * time.Second, // Export every 5 seconds
    })
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())
}

Best Practices

1. Use Context Propagation

// Good: Pass context through the call chain
func processRequest(ctx context.Context, client *untrace.Client, data string) error {
    ctx, span := client.Tracer().StartSpan(ctx, "process-request")
    defer span.End()

    return processData(ctx, data)
}

func processData(ctx context.Context, data string) error {
    // Context is automatically propagated
    // Child spans will be linked to parent
    return nil
}

2. Handle Errors Properly

func safeOperation(ctx context.Context, client *untrace.Client) error {
    ctx, span := client.Tracer().StartSpan(ctx, "safe-operation")
    defer span.End()

    if err := riskyOperation(); err != nil {
        span.RecordError(err)
        span.SetStatus(codes.Error, err.Error())
        return err
    }

    span.SetStatus(codes.Ok, "")
    return nil
}

3. Use Semantic Attributes

span.SetAttributes(
    untrace.String("user.id", userID),
    untrace.String("user.subscription_tier", "premium"),
    untrace.String("feature.name", "advanced-search"),
    untrace.String("feature.version", "2.0"),
)

4. Initialize Early

// Good: Initialize in main or init function
func main() {
    client, err := untrace.Init(untrace.Config{APIKey: "your-api-key"})
    if err != nil {
        log.Fatal(err)
    }
    defer client.Shutdown(context.Background())

    // Rest of your application
}

Troubleshooting

Common Issues

No traces appearing

High memory usage

Context propagation issues

// Make sure to pass context through call chain
func handler(ctx context.Context, client *untrace.Client) {
    ctx, span := client.Tracer().StartSpan(ctx, "handler")
    defer span.End()

    // Pass ctx to child functions
    processData(ctx, data)
}

Goroutine leaks

API Reference

Core Types

type Config struct {
    APIKey             string
    ServiceName        string
    Environment        string
    Version            string
    BaseURL            string
    Debug              bool
    SamplingRate       float64
    MaxBatchSize       int
    ExportInterval     time.Duration
    Headers            map[string]string
    ResourceAttributes map[string]interface{}
}

type LLMSpanOptions struct {
    Provider         string
    Model            string
    Operation        string
    PromptTokens     int
    CompletionTokens int
    TotalTokens      int
    Temperature      float64
    MaxTokens        int
}

type WorkflowOptions struct {
    UserID    string
    SessionID string
    Metadata  map[string]interface{}
}

Client Methods

type Client interface {
    Tracer() Tracer
    Context() Context
    Metrics() Metrics
    Shutdown(ctx context.Context) error
}

type Tracer interface {
    StartSpan(ctx context.Context, name string) (context.Context, Span)
    StartLLMSpan(ctx context.Context, name string, options LLMSpanOptions) (context.Context, Span)
}

type Span interface {
    SetAttributes(attrs ...Attribute)
    SetStatus(code codes.Code, description string)
    RecordError(err error)
    End()
}

Support

Documentation: https://docs.untrace.dev
GitHub Issues: https://github.com/untrace-dev/untrace/issues
Discord Community: Join our Discord
Email Support: support@untrace.dev

Next Steps

Dashboard Guide

Learn to use the Untrace dashboard

Configuration

Configure your observability setup

Examples

Browse Go example implementations

API Reference

Complete API documentation

Get Started

Core Features

SDK Reference

Advanced

Reference

Development

​Overview

Quick Start

Framework Support

Performance

Examples

​Installation

​Quick Start

​Basic Setup

​Manual Instrumentation

​Configuration

​Configuration Options

​Environment Variables

​Configuration from Environment

​Framework Integration

​Gin Framework

​Echo Framework

​Fiber Framework

​gRPC Integration

​Advanced Usage

​Workflow Tracking

​Metrics Collection

​Custom Attributes

​Error Handling

​Examples

​OpenAI Integration

​Batch Processing

​Middleware for HTTP Handlers

​Performance

​Sampling

​Batching

​Best Practices

​1. Use Context Propagation

​2. Handle Errors Properly

​3. Use Semantic Attributes

​4. Initialize Early

​Troubleshooting

​Common Issues

​API Reference

​Core Types

​Client Methods

​Support

​Next Steps

Dashboard Guide

Configuration

Examples

API Reference

Overview

Installation

Quick Start

Basic Setup

Manual Instrumentation

Configuration

Configuration Options

Environment Variables

Configuration from Environment

Framework Integration

Gin Framework

Echo Framework

Fiber Framework

gRPC Integration

Advanced Usage

Workflow Tracking

Metrics Collection

Custom Attributes

Error Handling

Examples

OpenAI Integration

Batch Processing

Middleware for HTTP Handlers

Performance

Sampling

Batching

Best Practices

1. Use Context Propagation

2. Handle Errors Properly

3. Use Semantic Attributes

4. Initialize Early

Troubleshooting

Common Issues

API Reference

Core Types

Client Methods

Support

Next Steps