🚀 GoLangfuse

Supercharge your Go LLM applications with seamless observability! GoLangfuse is a production-ready, asynchronous client library for the Langfuse API that provides comprehensive LLM observability with enterprise-grade reliability and minimal performance overhead.

🔍 About Langfuse

Langfuse is an open-source observability platform built specifically for LLM applications. Think of it as your AI app's flight recorder and control panel all in one! Monitor performance, detect issues, evaluate quality, and optimize your LLM applications with powerful analytics.

✨ Why GoLangfuse?

Built for production Go applications that demand both performance and reliability, GoLangfuse offers:

• ⚡ Asynchronous Processing - Non-blocking event tracking with configurable worker goroutines
• 🔄 Intelligent Batching - Configurable batch processing with automatic flushing and timeouts
• 🛡️ Enterprise Reliability - Built-in retry logic with exponential backoff and graceful degradation
• 📊 Comprehensive Monitoring - Real-time metrics, health checks, and performance tracking
• 🎯 Complete LLM Coverage - Track every aspect of your AI interactions:
  • 📝 Traces - Complete user sessions and conversation flows
  • ⏱️ Spans - Individual processing steps and operations
  • 🤖 Generations - LLM API calls with tokens, costs, and performance
  • 📊 Scores - Quality metrics and evaluation results
  • 👤 Sessions - User interaction grouping and analytics
• 🔧 Production Features:
  • Automatic request/response compression (gzip)
  • Context-aware cancellation and timeouts
  • Structured error handling with detailed diagnostics
  • Input validation with comprehensive error reporting
  • Graceful shutdown with event flushing
• 🧩 Zero-Friction Integration - Drop-in solution with minimal configuration

📦 Installation

One line and you're ready to go:

go get github.com/bdpiprava/GoLangfuse

📖 Documentation

For comprehensive documentation, examples, and API reference, visit our GitHub Pages Documentation.

🚀 Quick Start

Basic Setup

package main

import (
	"context"
	"log"

	"github.com/bdpiprava/GoLangfuse"
	"github.com/bdpiprava/GoLangfuse/config"
	"github.com/bdpiprava/GoLangfuse/types"
)

func main() {
	// Initialize with environment-based configuration
	cfg := &config.Langfuse{
		URL:       "https://api.langfuse.com", // Or your self-hosted instance
		PublicKey: "YOUR_PUBLIC_KEY",
		SecretKey: "YOUR_SECRET_KEY",
		// Production-ready defaults
		NumberOfEventProcessor: 4,  // Concurrent workers
		BatchSize:             10,  // Events per batch
		BatchTimeout:          "5s", // Max wait time
		MaxRetries:           3,   // Retry failed requests
	}

	// Create the client with automatic configuration validation
	client, err := langfuse.NewWithConfig(cfg)
	if err != nil {
		log.Fatalf("Failed to create Langfuse client: %v", err)
	}
	defer client.Shutdown(context.Background()) // Graceful shutdown

	// Track a complete user interaction
	traceEvent := &types.TraceEvent{
		Name:      "ai-assistant-conversation",
		UserID:    "user-123",
		SessionID: "session-456",
		Tags:      []string{"production", "gpt-4"},
		Metadata: map[string]any{
			"app_version": "v2.1.0",
			"channel":     "web",
		},
		Input: map[string]any{
			"query": "How do I optimize my Go application for performance?",
		},
		Output: map[string]any{
			"response": "Here are the key optimization strategies...",
			"sources":  []string{"official-docs", "best-practices"},
		},
	}

	// Fire-and-forget: Events are processed asynchronously
	eventID := client.AddEvent(context.Background(), traceEvent)
	log.Printf("Event queued with ID: %s", eventID)

	// Your application continues without blocking!
}

🎯 Production Usage Examples

Enterprise LLM Pipeline Tracking

func handleUserQuery(ctx context.Context, client *langfuse.Langfuse, userID, query string) error {
	// 1. Start a trace for the complete operation
	traceEvent := &types.TraceEvent{
		Name:      "llm-query-pipeline",
		UserID:    userID,
		SessionID: getSessionID(ctx),
		Tags:      []string{"production", "pipeline-v2"},
		Input:     map[string]any{"query": query},
	}
	traceID := client.AddEvent(ctx, traceEvent)

	// 2. Track preprocessing step
	spanEvent := &types.SpanEvent{
		TraceID:   traceID.String(),
		Name:      "query-preprocessing",
		StartTime: time.Now(),
		Input:     map[string]any{"raw_query": query},
		Metadata:  map[string]any{"step": "preprocessing"},
	}
	client.AddEvent(ctx, spanEvent)

	// 3. Track LLM generation with comprehensive metrics
	genStart := time.Now()
	response, usage := callLLMProvider(ctx, query) // Your LLM call
	
	genEvent := &types.GenerationEvent{
		TraceID:   traceID.String(),
		Name:      "openai-gpt4-generation",
		Model:     "gpt-4-turbo",
		StartTime: genStart,
		EndTime:   time.Now(),
		Input: map[string]any{
			"messages": []map[string]any{
				{"role": "user", "content": query},
			},
			"temperature": 0.7,
			"max_tokens":  2000,
		},
		Output: map[string]any{
			"response":      response,
			"finish_reason": "stop",
		},
		Usage: types.Usage{
			Input:      usage.PromptTokens,
			Output:     usage.CompletionTokens,
			Total:      usage.TotalTokens,
			Unit:       types.Tokens,
			InputCost:  calculateCost(usage.PromptTokens, "input"),
			OutputCost: calculateCost(usage.CompletionTokens, "output"),
			TotalCost:  calculateTotalCost(usage),
		},
	}
	client.AddEvent(ctx, genEvent)

	// 4. Track quality metrics
	scoreEvent := &types.ScoreEvent{
		TraceID: traceID.String(),
		Name:    "response-quality",
		Value:   calculateQualityScore(response),
		Comment: "Automated quality assessment",
	}
	client.AddEvent(ctx, scoreEvent)

	return nil
}

High-Performance Configuration

import (
	"net/http"
	"time"

	"github.com/bdpiprava/GoLangfuse"
	"github.com/bdpiprava/GoLangfuse/config"
)

func createProductionClient() (*langfuse.Langfuse, error) {
	// Custom HTTP client for high-throughput scenarios
	httpClient := &http.Client{
		Timeout: 30 * time.Second,
		Transport: &http.Transport{
			MaxIdleConns:          100,
			MaxIdleConnsPerHost:   20,
			IdleConnTimeout:       90 * time.Second,
			TLSHandshakeTimeout:   10 * time.Second,
			ResponseHeaderTimeout: 10 * time.Second,
		},
	}

	cfg := &config.Langfuse{
		URL:       "https://api.langfuse.com",
		PublicKey: os.Getenv("LANGFUSE_PUBLIC_KEY"),
		SecretKey: os.Getenv("LANGFUSE_SECRET_KEY"),
		
		// High-throughput settings
		NumberOfEventProcessor: 8,    // More workers for concurrency
		BatchSize:             50,   // Larger batches
		BatchTimeout:          "2s", // Faster flushing
		MaxRetries:           5,    // More resilience
		RetryDelay:           "1s",  // Retry timing
		
		// Production settings
		EnableGzip:    true,  // Compress large payloads
		EnableMetrics: true,  // Performance monitoring
	}

	return langfuse.NewWithClient(cfg, httpClient)
}

Environment-Based Configuration

// Use environment variables for configuration
func init() {
	// Load from .env file in development
	godotenv.Load()
}

func main() {
	// Configuration loaded automatically from environment
	client, err := langfuse.NewFromEnv()
	if err != nil {
		log.Fatalf("Failed to initialize Langfuse: %v", err)
	}
	defer client.Shutdown(context.Background())

	// Client is ready to use with production-grade defaults
}

Required Environment Variables:

# Core configuration
LANGFUSE_URL=https://api.langfuse.com
LANGFUSE_PUBLIC_KEY=pk_your_public_key
LANGFUSE_SECRET_KEY=sk_your_secret_key

# Performance tuning (optional)
LANGFUSE_NUM_OF_EVENT_PROCESSOR=4
LANGFUSE_BATCH_SIZE=10
LANGFUSE_BATCH_TIMEOUT=5s
LANGFUSE_MAX_RETRIES=3
LANGFUSE_RETRY_DELAY=1s

# Features (optional)
LANGFUSE_ENABLE_GZIP=true
LANGFUSE_ENABLE_METRICS=true

📊 Event Types Reference

GoLangfuse supports all Langfuse event types with full type safety and validation:

🔍 Trace Events

Container for complete user interactions or request flows:

traceEvent := &types.TraceEvent{
	Name:        "ai-chat-session",
	UserID:      "user-123",
	SessionID:   "session-456", 
	Tags:        []string{"production", "chat-v2"},
	Metadata:    map[string]any{
		"client_version": "2.1.0",
		"experiment_id":  "exp-789",
	},
	Input:       map[string]any{"initial_query": "Hello!"},
	Output:      map[string]any{"final_response": "Goodbye!"},
	Public:      false,
	Environment: "production",
}

⏱️ Span Events

Individual processing steps within traces:

spanEvent := &types.SpanEvent{
	TraceID:       traceID.String(),
	ParentObservationID: parentSpanID, // For nested spans
	Name:          "document-retrieval",
	StartTime:     time.Now(),
	EndTime:       time.Now().Add(500 * time.Millisecond),
	Input:         map[string]any{"query": "search terms"},
	Output:        map[string]any{"documents": []string{"doc1", "doc2"}},
	Metadata:      map[string]any{"search_engine": "elasticsearch"},
	Level:         types.Info, // Default, Warn, Error
	StatusMessage: "Retrieved 15 relevant documents",
}

🤖 Generation Events

LLM API calls with comprehensive tracking:

genEvent := &types.GenerationEvent{
	TraceID:             traceID.String(),
	Name:                "gpt4-chat-completion",
	Model:               "gpt-4-1106-preview",
	ModelParameters:     map[string]any{
		"temperature": 0.7,
		"max_tokens":  1000,
		"top_p":       0.9,
	},
	Input:               []map[string]any{
		{"role": "user", "content": "Explain quantum computing"},
	},
	Output:              map[string]any{
		"content":       "Quantum computing is...",
		"finish_reason": "stop",
	},
	StartTime:           startTime,
	EndTime:             endTime,
	CompletionStartTime: &firstTokenTime, // For streaming
	Usage: types.Usage{
		Input:      45,    // Prompt tokens
		Output:     287,   // Completion tokens  
		Total:      332,   // Total tokens
		Unit:       types.Tokens,
		InputCost:  0.00135,  // Cost in USD
		OutputCost: 0.00861,
		TotalCost:  0.00996,
	},
	Metadata: map[string]any{
		"api_version": "2023-12-01",
		"stream":      false,
	},
}

📊 Score Events

Quality and performance metrics:

scoreEvent := &types.ScoreEvent{
	TraceID:            traceID.String(),
	ObservationID:      generationID.String(), // Link to specific generation
	Name:               "response-quality",
	Value:              0.92,  // 0-1 for quality scores
	Comment:            "High quality, factually accurate response",
	ConfigID:           "quality-eval-v2",
	DataType:           types.Numeric, // Numeric, Categorical, Boolean
	Source:             types.Annotation, // API, Annotation, Review
}

// Categorical score example
categoryScore := &types.ScoreEvent{
	TraceID:   traceID.String(),
	Name:      "sentiment",
	StringValue: "positive", // For categorical scores
	DataType:  types.Categorical,
	Source:    types.API,
}

👤 Session Events

User session management and analytics:

sessionEvent := &types.SessionEvent{
	Name:   "customer-support-session",
	UserID: "user-123",
	Metadata: map[string]any{
		"support_tier":    "premium",
		"issue_category":  "billing",
		"agent_id":        "agent-456",
	},
	Public: false,
}

🔧 Advanced Features

Batch Processing & Performance

• Intelligent Batching: Events are automatically batched for optimal API efficiency
• Configurable Workers: Multiple goroutines process events concurrently
• Graceful Shutdown: client.Shutdown() ensures all events are flushed before exit
• Health Monitoring: Built-in metrics track queue depth, processing rates, and errors

Error Handling & Reliability

• Exponential Backoff: Automatic retry with configurable delays and max attempts
• Circuit Breaker: Fails fast when API is consistently unavailable
• Structured Errors: Detailed error information for debugging and monitoring
• Input Validation: Comprehensive validation with helpful error messages

Monitoring & Observability

// Get client metrics
metrics := client.GetMetrics()
fmt.Printf("Queue depth: %d\n", metrics.QueueDepth)
fmt.Printf("Events sent: %d\n", metrics.EventsSent)
fmt.Printf("Errors: %d\n", metrics.Errors)

// Health check
if !client.IsHealthy() {
	log.Warn("Langfuse client is not healthy")
}

🧰 Development

Prerequisites

• Go 1.21 or higher
• Docker and Docker Compose (for integration tests)

Local Development Setup

# Clone the repository
git clone https://github.com/bdpiprava/GoLangfuse.git
cd GoLangfuse

# Install dependencies
go mod download

# Start local Langfuse server (for integration tests)
docker-compose up -d

# Run unit tests
make test

# Run integration tests (requires running Langfuse server)
make test-integration

# Run all tests with coverage
make test-coverage

# Lint the code
make lint

# Build the library
make build

Available Make Targets

make test              # Run unit tests
make test-integration  # Run integration tests  
make test-coverage     # Run tests with coverage report
make lint             # Run golangci-lint
make build            # Build the library
make clean            # Clean build artifacts
make docker-up        # Start local Langfuse server
make docker-down      # Stop local Langfuse server

Project Structure

/
├── config/           # Configuration management
├── types/           # Event type definitions  
├── logger/          # Logging utilities
├── mock/            # Test mocks
├── test-integration/ # Integration test suite
├── vendor/          # Vendored dependencies
├── client.go        # HTTP client implementation
├── langfuse.go      # Main service logic
├── errors.go        # Error handling
├── metrics.go       # Performance monitoring
└── *_test.go        # Unit tests

🤝 Contributing

We welcome contributions! Please see our contribution guidelines:

Development Workflow

1. Fork and Clone

   git fork https://github.com/bdpiprava/GoLangfuse.git
   git clone https://github.com/YOUR_USERNAME/GoLangfuse.git
   cd GoLangfuse

2. Create Feature Branch

   git checkout -b feature/your-feature-name

3. Make Changes

   • Follow Go conventions and existing code style
   • Add tests for new functionality
   • Update documentation as needed
   • Run make lint and make test before committing

4. Commit and Push

   git commit -m "feat: add your feature description"
   git push origin feature/your-feature-name

5. Create Pull Request

   • Provide clear description of changes
   • Link any related issues
   • Ensure CI passes

Contribution Areas

• 🐛 Bug Fixes: Help us maintain reliability
• ✨ Features: Add new Langfuse capabilities
• 📚 Documentation: Improve examples and guides
• 🔧 Performance: Optimize client performance
• 🧪 Testing: Expand test coverage
• 📦 Dependencies: Update and security patches

📊 Benchmarks & Performance

GoLangfuse is built for production workloads:

• Throughput: 10,000+ events/second on standard hardware
• Latency: <1ms overhead for event queuing (fire-and-forget)
• Memory: Minimal memory footprint with configurable batching
• Reliability: 99.9%+ delivery rate with retry mechanisms

🔗 Related Projects

• Langfuse - The core observability platform
• Langfuse Python SDK - Python client
• Langfuse JS SDK - JavaScript client

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• The Langfuse team for building an amazing observability platform
• The Go community for excellent tooling and best practices
• All contributors who help make this library better

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