Combining the SDK with OpenTelemetry Spans

The LangWatch SDKs are built entirely on top of the robust OpenTelemetry (OTel) standard. This means seamless integration with existing OTel setups and interoperability with the wider OTel ecosystem across both Python and TypeScript environments.

LangWatch Spans are OpenTelemetry Spans

It’s important to understand that LangWatch traces and spans are standard OpenTelemetry traces and spans. LangWatch adds specific semantic attributes (like langwatch.span.type, langwatch.inputs, langwatch.outputs, langwatch.metadata) to these standard spans to power its observability features. This foundation provides several benefits:

Interoperability: Traces generated with LangWatch can be sent to any OTel-compatible backend (Jaeger, Tempo, Datadog, etc.) alongside your other application traces.
Familiar API: If you’re already familiar with OpenTelemetry concepts and APIs, working with LangWatch’s manual instrumentation will feel natural.
Leverage Existing Setup: LangWatch integrates smoothly with your existing OTel TracerProvider and instrumentation.

Perhaps the most significant advantage is that LangWatch seamlessly integrates with the vast ecosystem of standard OpenTelemetry auto-instrumentation libraries. This means you can easily combine LangWatch’s LLM-specific observability with insights from other parts of your application stack.

Leverage the OpenTelemetry Ecosystem: Auto-Instrumentation

One of the most powerful benefits of LangWatch’s OpenTelemetry foundation is its automatic compatibility with the extensive ecosystem of OpenTelemetry auto-instrumentation libraries. When you use standard OTel auto-instrumentation for libraries like web frameworks, databases, or task queues alongside LangWatch, you gain complete end-to-end visibility into your LLM application’s requests. Because LangWatch and these auto-instrumentors use the same underlying OpenTelemetry tracing system and context propagation mechanisms, spans generated across different parts of your application are automatically linked together into a single, unified trace.

Examples of Auto-Instrumentation Integration

Here are common scenarios where combining LangWatch with OTel auto-instrumentation provides significant value:

Web Frameworks: Using libraries like opentelemetry-instrumentation-fastapi (Python) or @opentelemetry/instrumentation-express (TypeScript), an incoming HTTP request automatically starts a trace. When your request handler calls a function instrumented with LangWatch, those LangWatch spans become children of the incoming request span.
HTTP Clients: If your LLM application makes outbound API calls using libraries instrumented by opentelemetry-instrumentation-requests (Python) or @opentelemetry/instrumentation-http (TypeScript), these HTTP request spans will automatically appear within your LangWatch trace.
Task Queues: When a request handled by your web server (and traced by LangWatch) enqueues a background job using opentelemetry-instrumentation-celery (Python) or similar task queue instrumentations, the trace context is automatically propagated.
Databases & ORMs: Using libraries like opentelemetry-instrumentation-sqlalchemy (Python) or @opentelemetry/instrumentation-mongodb (TypeScript), any database queries executed during your LLM processing will appear as spans within the relevant LangWatch trace.

Basic Setup and Configuration

Python Setup

import langwatch
import os

# Basic setup - LangWatch will create its own TracerProvider
langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY")
)

# Your LangWatch spans are now standard OpenTelemetry spans
with langwatch.span(name="my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # ... your logic ...

TypeScript Setup

import { setupObservability } from "langwatch/observability/node";

const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY
  },
  serviceName: "my-service"
});

// Graceful shutdown
process.on('SIGTERM', async () => {
  await handle.shutdown();
  process.exit(0);
});

Manual Span Management

Python Manual Span Control

import langwatch
from opentelemetry.trace import Status, StatusCode

# Using context manager (recommended)
with langwatch.span(name="my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    span.add_event("operation_started", {"detail": "more info"})
    
    try:
        # ... your logic ...
        span.set_status(Status(StatusCode.OK))
    except Exception as e:
        span.set_status(Status(StatusCode.ERROR, description=str(e)))
        span.record_exception(e)
        raise

# Using manual control
span = langwatch.span(name="my-operation")
try:
    span.set_attribute("custom.attribute", "value")
    # ... your logic ...
    span.set_status(Status(StatusCode.OK))
except Exception as e:
    span.set_status(Status(StatusCode.ERROR, description=str(e)))
    span.record_exception(e)
    raise
finally:
    span.end()

TypeScript Manual Span Control

import { getLangWatchTracer } from "langwatch";
import { SpanStatusCode } from "@opentelemetry/api";

const tracer = getLangWatchTracer("my-service");

// Using startActiveSpan (recommended)
tracer.startActiveSpan("my-operation", (span) => {
  try {
    span.setType("llm");
    span.setInput("Hello, world!");
    span.setAttributes({
      "custom.business_unit": "marketing",
      "custom.campaign_id": "summer-2024"
    });
    
    // ... your logic ...
    
    span.setOutput("Hello! How can I help you?");
    span.setStatus({ code: SpanStatusCode.OK });
  } catch (error) {
    span.setStatus({
      code: SpanStatusCode.ERROR,
      message: error.message
    });
    span.recordException(error);
    throw error;
  } finally {
    span.end();
  }
});

// Using startSpan (complete manual control)
const span = tracer.startSpan("my-operation");
try {
  span.setType("llm");
  span.setInput("Hello, world!");
  // ... your logic ...
  span.setOutput("Hello! How can I help you?");
  span.setStatus({ code: SpanStatusCode.OK });
} catch (error) {
  span.setStatus({
    code: SpanStatusCode.ERROR,
    message: error.message
  });
  span.recordException(error);
  throw error;
} finally {
  span.end();
}

Advanced Configuration

Python Advanced Configuration

import langwatch
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from langwatch.domain import SpanProcessingExcludeRule

# Create TracerProvider
provider = TracerProvider()

# Add Jaeger exporter for debugging
provider.add_span_processor(
    BatchSpanProcessor(JaegerExporter(
        agent_host_name="localhost",
        agent_port=6831
    ))
)

# Define exclude rules for LangWatch
exclude_rules = [
    SpanProcessingExcludeRule(
        field_name="span_name",
        match_value="GET /health_check",
        match_operation="exact_match"
    ),
    SpanProcessingExcludeRule(
        field_name="attribute",
        attribute_name="http.method",
        match_value="OPTIONS",
        match_operation="exact_match"
    ),
]

# Setup LangWatch with existing provider
langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    tracer_provider=provider,
    span_exclude_rules=exclude_rules,
    ignore_global_tracer_provider_override_warning=True
)

TypeScript Advanced Configuration

import { setupObservability } from "langwatch/observability/node";
import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { JaegerExporter } from "@opentelemetry/exporter-jaeger";
import { LangWatchExporter } from "langwatch";

const handle = setupObservability({
  langwatch: 'disabled', // Disable default LangWatch integration
  serviceName: "my-service",
  spanProcessors: [
    // Send to Jaeger for debugging
    new BatchSpanProcessor(new JaegerExporter({
      endpoint: "http://localhost:14268/api/traces"
    })),
    // Send to LangWatch for production monitoring
    new BatchSpanProcessor(new LangWatchExporter({
      apiKey: process.env.LANGWATCH_API_KEY
    }))
  ]
});

Sampling and Performance Tuning

Python Sampling Configuration

import langwatch
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import TraceIdRatioBasedSampler

# Create provider with sampling
provider = TracerProvider(
    sampler=TraceIdRatioBasedSampler(0.1)  # Sample 10% of traces
)

langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    tracer_provider=provider,
    ignore_global_tracer_provider_override_warning=True
)

TypeScript Sampling Configuration

import { setupObservability } from "langwatch/observability/node";
import { TraceIdRatioBasedSampler, ParentBasedSampler } from "@opentelemetry/sdk-trace-base";

const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY
  },
  serviceName: "my-service",
  sampler: new TraceIdRatioBasedSampler(0.1) // 10% sampling
});

Complete Example: RAG with OpenAI and Background Tasks

Python Complete Example

import langwatch
import os
import time
import asyncio
from celery import Celery
from openai import OpenAI
from langwatch.types import RAGChunk
from opentelemetry_instrumentation.celery import CeleryInstrumentor

# 1. Configure Celery App
celery_app = Celery('tasks', broker=os.getenv('CELERY_BROKER_URL', 'redis://localhost:6379/0'))

# 2. Setup Auto-Instrumentation
CeleryInstrumentor().instrument()

# 3. Setup LangWatch
langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    ignore_global_tracer_provider_override_warning=True
)

client = OpenAI()

# 4. Define the Celery Task
@celery_app.task
def process_result_background(result_id: str, llm_output: str):
    # This task execution will be automatically linked to the trace
    # that enqueued it, thanks to CeleryInstrumentor.
    print(f"[Celery Worker] Processing result {result_id}...")
    time.sleep(1)
    print(f"[Celery Worker] Finished processing {result_id}")
    return f"Processed: {llm_output[:10]}..."

# 5. Define RAG and Main Processing Logic
@langwatch.span(type="rag")
def retrieve_documents(query: str) -> list:
    print(f"Retrieving documents for: {query}")
    chunks = [
        RAGChunk(document_id="doc-abc", content="LangWatch uses OpenTelemetry."),
        RAGChunk(document_id="doc-def", content="Celery integrates with OpenTelemetry."),
    ]
    langwatch.get_current_span().update(contexts=chunks)
    time.sleep(0.1)
    return [c.content for c in chunks]

@langwatch.trace(name="Handle User Query with Celery")
def handle_request(user_query: str):
    # This is the root span for the request
    langwatch.get_current_trace().autotrack_openai_calls(client)
    langwatch.get_current_trace().update(metadata={"user_query": user_query})

    context_docs = retrieve_documents(user_query)

    try:
        completion = client.chat.completions.create(
            model="gpt-5-mini",
            messages=[
                {"role": "system", "content": f"Use this context: {context_docs}"},
                {"role": "user", "content": user_query}
            ],
            temperature=0.5,
        )
        llm_result = completion.choices[0].message.content
    except Exception as e:
        langwatch.get_current_trace().record_exception(e)
        llm_result = "Error calling OpenAI"

    result_id = f"res_{int(time.time())}"
    # The current trace context is automatically propagated
    process_result_background.delay(result_id, llm_result)
    print(f"Enqueued background processing task {result_id}")

    return llm_result

# 6. Simulate Triggering the Request
if __name__ == "__main__":
    print("Simulating web request...")
    final_answer = handle_request("How does LangWatch work with Celery?")
    print(f"\nFinal Answer returned to user: {final_answer}")
    time.sleep(3)  # Allow time for task to be processed

TypeScript Complete Example

import { setupObservability } from "langwatch/observability/node";
import { getLangWatchTracer } from "langwatch";
import { SpanStatusCode } from "@opentelemetry/api";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { ExpressInstrumentation } from "@opentelemetry/instrumentation-express";
import OpenAI from "openai";

// 1. Setup Observability
const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY
  },
  serviceName: "rag-service",
  instrumentations: [
    new HttpInstrumentation(),
    new ExpressInstrumentation()
  ]
});

const tracer = getLangWatchTracer("rag-service");
const client = new OpenAI();

// 2. Define RAG Function
async function retrieveDocuments(query: string): Promise<string[]> {
  return tracer.startActiveSpan("rag", async (span) => {
    try {
      span.setType("rag");
      span.setInput({ query });
      
      console.log(`Retrieving documents for: ${query}`);
      
      // Simulate RAG retrieval
      const chunks = [
        { document_id: "doc-abc", content: "LangWatch uses OpenTelemetry." },
        { document_id: "doc-def", content: "Express integrates with OpenTelemetry." }
      ];
      
      span.setAttributes({
        "rag.chunks_count": chunks.length,
        "rag.query": query
      });
      
      // Simulate processing time
      await new Promise(resolve => setTimeout(resolve, 100));
      
      const results = chunks.map(c => c.content);
      span.setOutput({ documents: results });
      span.setStatus({ code: SpanStatusCode.OK });
      
      return results;
    } catch (error) {
      span.setStatus({
        code: SpanStatusCode.ERROR,
        message: error.message
      });
      span.recordException(error);
      throw error;
    }
  });
}

// 3. Define Background Task
async function processResultBackground(resultId: string, llmOutput: string): Promise<string> {
  return tracer.startActiveSpan("background-processing", async (span) => {
    try {
      span.setType("background_job");
      span.setInput({ resultId, llmOutput });
      
      console.log(`[Background] Processing result ${resultId}...`);
      
      // Simulate background processing
      await new Promise(resolve => setTimeout(resolve, 1000));
      
      const result = `Processed: ${llmOutput.substring(0, 10)}...`;
      
      span.setOutput({ result });
      span.setStatus({ code: SpanStatusCode.OK });
      
      console.log(`[Background] Finished processing ${resultId}`);
      return result;
    } catch (error) {
      span.setStatus({
        code: SpanStatusCode.ERROR,
        message: error.message
      });
      span.recordException(error);
      throw error;
    }
  });
}

// 4. Define Main Request Handler
async function handleRequest(userQuery: string): Promise<string> {
  return tracer.startActiveSpan("handle-user-query", async (span) => {
    try {
      span.setType("request");
      span.setInput({ userQuery });
      
      // Get context documents
      const contextDocs = await retrieveDocuments(userQuery);
      
      // Call OpenAI
      const completion = await client.chat.completions.create({
        model: "gpt-5-mini",
        messages: [
          { role: "system", content: `Use this context: ${contextDocs.join(" ")}` },
          { role: "user", content: userQuery }
        ],
        temperature: 0.5,
      });
      
      const llmResult = completion.choices[0].message.content || "No response";
      
      // Trigger background processing
      const resultId = `res_${Date.now()}`;
      processResultBackground(resultId, llmResult).catch(console.error);
      
      console.log(`Enqueued background processing task ${resultId}`);
      
      span.setOutput({ result: llmResult });
      span.setStatus({ code: SpanStatusCode.OK });
      
      return llmResult;
    } catch (error) {
      span.setStatus({
        code: SpanStatusCode.ERROR,
        message: error.message
      });
      span.recordException(error);
      throw error;
    }
  });
}

// 5. Simulate Request
async function main() {
  console.log("Simulating web request...");
  const finalAnswer = await handleRequest("How does LangWatch work with Express?");
  console.log(`\nFinal Answer returned to user: ${finalAnswer}`);
  
  // Allow time for background task
  await new Promise(resolve => setTimeout(resolve, 2000));
  
  // Graceful shutdown
  await handle.shutdown();
}

main().catch(console.error);

Debugging and Troubleshooting

Python Debugging

import langwatch
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter

# Create TracerProvider with console exporter
provider = TracerProvider()
provider.add_span_processor(
    SimpleSpanProcessor(ConsoleSpanExporter())
)

langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    tracer_provider=provider,
    ignore_global_tracer_provider_override_warning=True
)

# Test span creation
with langwatch.span(name="test-span") as span:
    span.set_attribute("test.attribute", "value")
    print("This span should appear in the console.")

TypeScript Debugging

import { setupObservability } from "langwatch/observability/node";
import { ConsoleSpanExporter } from "@opentelemetry/sdk-trace-base";

const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY
  },
  serviceName: "my-service",
  spanProcessors: [
    new ConsoleSpanExporter()
  ],
  debug: {
    consoleTracing: true,
    consoleLogging: true,
    logLevel: 'debug'
  }
});

Best Practices

General Best Practices

Always End Spans: Use try-finally blocks or context managers to ensure spans are ended
Set Appropriate Types: Use meaningful span types for better categorization
Add Context: Include relevant attributes and events
Handle Errors: Properly record exceptions and set error status
Use Async Context: Propagate span context across async boundaries
Monitor Performance: Track the impact of span management on your application

Language-Specific Best Practices

# Use context managers for automatic span management
with langwatch.span(name="operation") as span:
    # Your code here
    pass

# Set meaningful attributes
span.set_attribute("user.id", user_id)
span.set_attribute("operation.type", "database_query")

# Record exceptions properly
try:
    # Your code
    pass
except Exception as e:
    span.record_exception(e)
    span.set_status(Status(StatusCode.ERROR, description=str(e)))
    raise

# Use span.update() for LangWatch-specific data
span.update(
    inputs={"query": user_query},
    outputs={"result": result},
    metadata={"custom": "data"}
)

Migration Checklist

When migrating from an existing OpenTelemetry setup:

Inventory Current Setup: Document all current instrumentations, exporters, and configurations
Test in Development: Start with development environment migration
Verify Data Flow: Ensure traces are appearing in LangWatch dashboard
Performance Testing: Monitor application performance impact
Gradual Rollout: Migrate environments one at a time
Fallback Plan: Keep existing setup as backup during transition
Documentation: Update team documentation and runbooks

Troubleshooting Common Issues

Common Migration Problems

Duplicate Spans: Ensure only one observability setup is running
Missing Traces: Check API key and endpoint configuration
Performance Degradation: Adjust sampling and batch processing settings
Context Loss: Verify context propagation configuration
Instrumentation Conflicts: Check for conflicting instrumentations

Debugging Migration

import langwatch
import os
from opentelemetry.sdk.trace.export import ConsoleSpanExporter

# Enable detailed logging during migration
langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    tracer_provider=TracerProvider(),
    span_exclude_rules=[],  # No exclusions during debugging
    ignore_global_tracer_provider_override_warning=True
)

# Add console exporter for debugging
provider = langwatch.get_current_trace().get_span_context().trace_id
provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

Performance Considerations

When using OpenTelemetry with LangWatch, consider these performance implications:

Memory Usage: Spans consume memory until explicitly ended
Context Propagation: Context management can be error-prone in complex async scenarios
Error Handling: Ensure spans are always ended, even when exceptions occur
Batch Processing: Use batch processors for high-volume applications
Sampling: Implement sampling to reduce overhead in production

By following these guidelines and leveraging the power of OpenTelemetry’s ecosystem, you can achieve comprehensive observability of your LLM applications while maintaining compatibility with existing monitoring infrastructure.

Overview

SDKs

Frameworks

Model Providers

Tools

No-Code Platforms

Direct Integrations

Combining the SDK with OpenTelemetry Spans

LangWatch Spans are OpenTelemetry Spans

Leverage the OpenTelemetry Ecosystem: Auto-Instrumentation

Examples of Auto-Instrumentation Integration

Basic Setup and Configuration

Python Setup

TypeScript Setup

Manual Span Management

Python Manual Span Control

TypeScript Manual Span Control

Advanced Configuration

Python Advanced Configuration

TypeScript Advanced Configuration

Sampling and Performance Tuning

Python Sampling Configuration

TypeScript Sampling Configuration

Complete Example: RAG with OpenAI and Background Tasks

Python Complete Example

TypeScript Complete Example

Debugging and Troubleshooting

Python Debugging

TypeScript Debugging

Best Practices

General Best Practices

Language-Specific Best Practices

Migration Checklist

Troubleshooting Common Issues

Common Migration Problems

Debugging Migration

Performance Considerations

Overview

SDKs

Frameworks

Model Providers

Tools

No-Code Platforms

Direct Integrations

Documentation Index

​LangWatch Spans are OpenTelemetry Spans

​Leverage the OpenTelemetry Ecosystem: Auto-Instrumentation

​Examples of Auto-Instrumentation Integration

​Basic Setup and Configuration

​Python Setup

​TypeScript Setup

​Manual Span Management

​Python Manual Span Control

​TypeScript Manual Span Control

​Advanced Configuration

​Python Advanced Configuration

​TypeScript Advanced Configuration

​Sampling and Performance Tuning

​Python Sampling Configuration

​TypeScript Sampling Configuration

​Complete Example: RAG with OpenAI and Background Tasks

​Python Complete Example

​TypeScript Complete Example

​Debugging and Troubleshooting

​Python Debugging

​TypeScript Debugging

​Best Practices

​General Best Practices

​Language-Specific Best Practices

​Migration Checklist

​Troubleshooting Common Issues

​Common Migration Problems

​Debugging Migration

​Performance Considerations

LangWatch Spans are OpenTelemetry Spans

Leverage the OpenTelemetry Ecosystem: Auto-Instrumentation

Examples of Auto-Instrumentation Integration

Basic Setup and Configuration

Python Setup

TypeScript Setup

Manual Span Management

Python Manual Span Control

TypeScript Manual Span Control

Advanced Configuration

Python Advanced Configuration

TypeScript Advanced Configuration

Sampling and Performance Tuning

Python Sampling Configuration

TypeScript Sampling Configuration

Complete Example: RAG with OpenAI and Background Tasks

Python Complete Example

TypeScript Complete Example

Debugging and Troubleshooting

Python Debugging

TypeScript Debugging

Best Practices

General Best Practices

Language-Specific Best Practices

Migration Checklist

Troubleshooting Common Issues

Common Migration Problems

Debugging Migration

Performance Considerations