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OpenInference provides auto-instrumentation packages for popular Java AI frameworks, allowing you to capture traces without manual instrumentation code.

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Available Instrumentations

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LangChain4j

Auto-instrumentation for LangChain4j applications. Package: com.arize.instrumentation.langchain4j
Maven Artifact: com.arize:openinference-instrumentation-langchain4j:0.1.1

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Spring AI

Instrumentation for Spring AI applications using Micrometer observation handlers. Package: com.arize.instrumentation.springAI
Maven Artifact: com.arize:openinference-instrumentation-springAI:0.1.1

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LangChain4j Instrumentation

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Installation

dependencies {
    implementation 'com.arize:openinference-instrumentation-langchain4j:0.1.1'
    implementation 'dev.langchain4j:langchain4j:1.0.0'
}

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Basic Usage

import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;
import dev.langchain4j.model.openai.OpenAiChatModel;

// Initialize OpenTelemetry first
// (see OpenTelemetry Java docs for setup)

// Auto-instrument LangChain4j
LangChain4jInstrumentor.instrument();

// Use LangChain4j as normal - traces are automatically created
OpenAiChatModel model = OpenAiChatModel.builder()
    .apiKey("your-api-key")
    .modelName("gpt-4")
    .build();

String response = model.generate("What is the capital of France?");

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With Custom TracerProvider

import io.opentelemetry.api.trace.TracerProvider;
import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;

TracerProvider tracerProvider = // your custom TracerProvider
LangChain4jInstrumentor.instrument(tracerProvider);

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With Custom TraceConfig

import com.arize.instrumentation.TraceConfig;
import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;

// Configure what to hide in traces
TraceConfig config = TraceConfig.builder()
    .hideInputMessages(true)  // Hide input messages for privacy
    .hideOutputMessages(false) // Keep output messages visible
    .build();

// Instrument with custom config
LangChain4jInstrumentor.instrument(config);

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Manual Model Listener

For finer control, you can manually create and attach model listeners: 
import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;
import com.arize.instrumentation.langchain4j.LangChain4jModelListener;

// Initialize instrumentor
LangChain4jInstrumentor instrumentor = LangChain4jInstrumentor.instrument();

// Create a model listener
LangChain4jModelListener listener = instrumentor.createModelListener();

// Attach to specific models or chains
// (Implementation depends on LangChain4j's listener mechanism)

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Uninstrumenting

import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;

LangChain4jInstrumentor instrumentor = LangChain4jInstrumentor.instrument();

// Later, when you want to remove instrumentation
instrumentor.uninstrument();

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Spring AI Instrumentation

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Installation

dependencies {
    implementation 'com.arize:openinference-instrumentation-springAI:0.1.1'
    implementation 'org.springframework.ai:spring-ai-core:1.0.0'
}

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Configuration

Spring AI instrumentation uses Micrometer’s observation API. Register the instrumentor as an observation handler: 
import io.micrometer.observation.ObservationRegistry;
import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.api.trace.Tracer;
import com.arize.instrumentation.OITracer;
import com.arize.instrumentation.springAI.SpringAIInstrumentor;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class ObservabilityConfig {
    
    @Bean
    public SpringAIInstrumentor springAIInstrumentor(
            ObservationRegistry observationRegistry) {
        
        // Create OITracer
        Tracer otelTracer = GlobalOpenTelemetry.getTracer("spring-ai");
        OITracer tracer = new OITracer(otelTracer);
        
        // Create and register instrumentor
        SpringAIInstrumentor instrumentor = new SpringAIInstrumentor(tracer);
        observationRegistry.observationConfig()
            .observationHandler(instrumentor);
        
        return instrumentor;
    }
}

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With Custom TraceConfig

import com.arize.instrumentation.TraceConfig;
import com.arize.instrumentation.OITracer;
import com.arize.instrumentation.springAI.SpringAIInstrumentor;

@Bean
public SpringAIInstrumentor springAIInstrumentor(
        ObservationRegistry observationRegistry) {
    
    // Configure trace privacy settings
    TraceConfig config = TraceConfig.builder()
        .hideInputMessages(true)
        .hideOutputMessages(false)
        .build();
    
    Tracer otelTracer = GlobalOpenTelemetry.getTracer("spring-ai");
    OITracer tracer = new OITracer(otelTracer, config);
    
    SpringAIInstrumentor instrumentor = new SpringAIInstrumentor(tracer);
    observationRegistry.observationConfig()
        .observationHandler(instrumentor);
    
    return instrumentor;
}

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Usage with Spring AI

Once configured, Spring AI automatically creates observations that the instrumentor converts to OpenInference traces: 
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.stereotype.Service;

@Service
public class ChatService {
    
    private final ChatClient chatClient;
    
    public ChatService(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder.build();
    }
    
    public String chat(String message) {
        // Automatically traced with OpenInference spans
        return chatClient.prompt()
            .user(message)
            .call()
            .content();
    }
}

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Captured Information

The Spring AI instrumentor automatically captures:
  • Model name and provider
  • Input/output messages
  • Token counts
  • Tool calls
  • Invocation parameters (temperature, max_tokens, etc.)
  • Error information

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Environment Variables

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LangChain4j

  • OTEL_INSTRUMENTATION_LANGCHAIN4J_ENABLED: Enable/disable LangChain4j auto-instrumentation (default: true)

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Captured Span Attributes

Both instrumentations automatically set the following OpenInference attributes:

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Core Attributes

  • openinference.span.kind: Set to LLM
  • llm.model_name: Model identifier
  • llm.provider: Provider name (e.g., “openai”)
  • llm.system: System name (e.g., “openai”, “spring-ai”)

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Input/Output

  • input.value: Input messages as JSON
  • input.mime_type: “application/json”
  • output.value: Output messages as JSON
  • output.mime_type: “application/json”

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Token Counts

  • llm.token_count.prompt: Input token count
  • llm.token_count.completion: Output token count
  • llm.token_count.total: Total token count

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Invocation Parameters

  • llm.invocation_parameters: JSON string of model parameters (temperature, max_tokens, etc.)

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Tool Calls

  • llm.input_messages.{i}.message.tool_calls: Tool calls in messages
  • tool_call.id: Tool call identifier
  • tool_call.function.name: Function name
  • tool_call.function.arguments: Function arguments

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Privacy Controls

Use TraceConfig to control what data is captured: 
TraceConfig privacyConfig = TraceConfig.builder()
    // Don't capture message content
    .hideInputMessages(true)
    .hideOutputMessages(true)
    
    // Don't capture images
    .hideInputImages(true)
    .hideOutputImages(true)
    
    // Don't capture prompt variables
    .hidePromptTemplateVariables(true)
    
    .build();

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Dependencies

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LangChain4j Instrumentation

  • OpenInference Base Instrumentation
  • OpenInference Semantic Conventions
  • LangChain4j (1.0.0+)
  • OpenTelemetry API and SDK

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Spring AI Instrumentation

  • OpenInference Base Instrumentation
  • OpenInference Semantic Conventions
  • Spring AI Core
  • Micrometer Observation API
  • OpenTelemetry API and SDK

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Complete Example

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LangChain4j Application

import io.opentelemetry.api.GlobalOpenTelemetry;
import io.opentelemetry.sdk.OpenTelemetrySdk;
import io.opentelemetry.sdk.trace.SdkTracerProvider;
import io.opentelemetry.sdk.trace.export.BatchSpanProcessor;
import io.opentelemetry.exporter.otlp.trace.OtlpGrpcSpanExporter;
import com.arize.instrumentation.langchain4j.LangChain4jInstrumentor;
import com.arize.instrumentation.TraceConfig;
import dev.langchain4j.model.openai.OpenAiChatModel;

public class LangChain4jApp {
    public static void main(String[] args) {
        // 1. Setup OpenTelemetry
        OtlpGrpcSpanExporter spanExporter = OtlpGrpcSpanExporter.builder()
            .setEndpoint("http://localhost:4317")
            .build();
        
        SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
            .addSpanProcessor(BatchSpanProcessor.builder(spanExporter).build())
            .build();
        
        OpenTelemetrySdk sdk = OpenTelemetrySdk.builder()
            .setTracerProvider(tracerProvider)
            .buildAndRegisterGlobal();
        
        // 2. Configure and instrument LangChain4j
        TraceConfig config = TraceConfig.builder()
            .hideInputMessages(false)
            .hideOutputMessages(false)
            .build();
        
        LangChain4jInstrumentor.instrument(config);
        
        // 3. Use LangChain4j - traces are automatically created
        OpenAiChatModel model = OpenAiChatModel.builder()
            .apiKey(System.getenv("OPENAI_API_KEY"))
            .modelName("gpt-4")
            .temperature(0.7)
            .build();
        
        String response = model.generate("Explain quantum computing in simple terms");
        System.out.println("Response: " + response);
        
        // 4. Cleanup
        sdk.close();
    }
}

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Spring AI Application

import io.micrometer.observation.ObservationRegistry;
import io.opentelemetry.api.GlobalOpenTelemetry;
import com.arize.instrumentation.OITracer;
import com.arize.instrumentation.springAI.SpringAIInstrumentor;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;

@SpringBootApplication
public class SpringAIApp {
    
    public static void main(String[] args) {
        SpringApplication.run(SpringAIApp.class, args);
    }
    
    @Bean
    public SpringAIInstrumentor springAIInstrumentor(
            ObservationRegistry observationRegistry) {
        
        OITracer tracer = new OITracer(
            GlobalOpenTelemetry.getTracer("spring-ai")
        );
        
        SpringAIInstrumentor instrumentor = new SpringAIInstrumentor(tracer);
        observationRegistry.observationConfig()
            .observationHandler(instrumentor);
        
        return instrumentor;
    }
}

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Next Steps