> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# Combining the SDK with OpenTelemetry Spans

> LangWatch receives traces from an existing OpenTelemetry setup in Python and TypeScript.

The LangWatch SDKs are built entirely on top of the [OpenTelemetry (OTel)](https://opentelemetry.io/) standard. They integrate with existing OTel setups and interoperate 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.
* **Reuse Existing Setup:** LangWatch integrates with your existing OTel `TracerProvider` and instrumentation.

**LangWatch also works with the standard OpenTelemetry auto-instrumentation libraries.** You can combine LangWatch's LLM-specific observability with insights from other parts of your application stack.

## Use the OpenTelemetry Ecosystem: Auto-Instrumentation

The OpenTelemetry foundation gives LangWatch **automatic compatibility with the 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

```python Basic Python Setup theme={null}
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 ...
```

```python Python with Existing OTel Setup theme={null}
import langwatch
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter

# Create your own TracerProvider
my_tracer_provider = TracerProvider()

# Add the ConsoleSpanExporter for debugging
my_tracer_provider.add_span_processor(
    SimpleSpanProcessor(ConsoleSpanExporter())
)

# Setup LangWatch with your pre-configured provider
langwatch.setup(
    api_key=os.getenv("LANGWATCH_API_KEY"),
    tracer_provider=my_tracer_provider,
    ignore_global_tracer_provider_override_warning=True
)
```

### TypeScript Setup

```typescript Basic TypeScript Setup theme={null}
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);
});
```

```typescript TypeScript with Custom Configuration theme={null}
import { setupObservability } from "langwatch/observability/node";
import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { ConsoleSpanExporter } from "@opentelemetry/sdk-trace-base";

const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
    processorType: "batch",
  },
  serviceName: "my-service",
  spanProcessors: [new BatchSpanProcessor(new ConsoleSpanExporter())],
});
```

## Manual Span Management

### Python Manual Span Control

```python Python Manual Span Management theme={null}
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()
```

```python Python Span Context Propagation theme={null}
import langwatch
import asyncio
from opentelemetry import context, trace

async def process_with_context(user_id: str):
    with langwatch.span(name="process-user") as span:
        span.set_attribute("user.id", user_id)

        # Propagate context to async operations
        ctx = trace.set_span(context.active(), span)
        await context.with_(ctx, process_user_data, user_id)
        await context.with_(ctx, update_user_profile, user_id)
```

### TypeScript Manual Span Control

```typescript TypeScript Manual Span Management theme={null}
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();
}
```

```typescript TypeScript Span Context Propagation theme={null}
import { context, trace } from "@opentelemetry/api";

async function processWithContext(userId: string) {
  const span = tracer.startSpan("process-user");
  const ctx = trace.setSpan(context.active(), span);

  try {
    // Propagate context to async operations
    await context.with(ctx, async () => {
      await processUserData(userId);
      await updateUserProfile(userId);
    });

    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

```python Python with Multiple Exporters theme={null}
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
)
```

```python Python with Auto-Instrumentation theme={null}
import langwatch
import os
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.celery import CeleryInstrumentor

# Setup auto-instrumentation
FastAPIInstrumentor().instrument()
RequestsInstrumentor().instrument()
CeleryInstrumentor().instrument()

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

### TypeScript Advanced Configuration

```typescript TypeScript with Multiple Exporters theme={null}
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/v1/traces",
      }),
    ),
    // Send to LangWatch for production monitoring
    new BatchSpanProcessor(
      new LangWatchExporter({
        apiKey: process.env.LANGWATCH_API_KEY,
      }),
    ),
  ],
});
```

```typescript TypeScript with Auto-Instrumentation theme={null}
import { setupObservability } from "langwatch/observability/node";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { ExpressInstrumentation } from "@opentelemetry/instrumentation-express";
import { MongoDBInstrumentation } from "@opentelemetry/instrumentation-mongodb";

const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
  },
  serviceName: "my-service",
  instrumentations: [
    new HttpInstrumentation({
      ignoreIncomingPaths: ["/health", "/metrics"],
      ignoreOutgoingUrls: ["https://external-service.com/health"],
    }),
    new ExpressInstrumentation(),
    new MongoDBInstrumentation(),
  ],
});
```

## Sampling and Performance Tuning

### Python Sampling Configuration

```python Python Sampling Configuration theme={null}
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
)
```

```python Python Performance Tuning theme={null}
import langwatch
import os
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.trace.sampling import TraceIdRatioBasedSampler

provider = TracerProvider(
    sampler=TraceIdRatioBasedSampler(0.05),  # 5% sampling for high volume
    span_limits={
        "attribute_count_limit": 64,
        "event_count_limit": 32,
        "link_count_limit": 32
    }
)

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

### TypeScript Sampling Configuration

```typescript TypeScript Sampling Configuration theme={null}
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
});
```

```typescript TypeScript Performance Tuning theme={null}
import { setupObservability } from "langwatch/observability/node";
import { TraceIdRatioBasedSampler } from "@opentelemetry/sdk-trace-base";

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

  // Performance tuning
  spanLimits: {
    attributeCountLimit: 64,
    eventCountLimit: 32,
    linkCountLimit: 32,
  },

  // Sampling for high volume
  sampler: new TraceIdRatioBasedSampler(0.05), // 5% sampling

  // Batch processing configuration
  spanProcessors: [
    new BatchSpanProcessor(
      new LangWatchExporter({
        apiKey: process.env.LANGWATCH_API_KEY,
      }),
      {
        maxQueueSize: 4096,
        maxExportBatchSize: 1024,
        scheduledDelayMillis: 1000,
        exportTimeoutMillis: 30000,
      },
    ),
  ],
});
```

## Complete Example: RAG with OpenAI and Background Tasks

### Python Complete Example

```python Python Complete Example theme={null}
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-6-luna",
            messages=[
                {"role": "system", "content": f"Use this context: {context_docs}"},
                {"role": "user", "content": user_query}
            ],
        )
        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

```typescript TypeScript Complete Example theme={null}
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-6-luna",
        messages: [
          { role: "system", content: `Use this context: ${contextDocs.join(" ")}` },
          { role: "user", content: userQuery },
        ],
      });

      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

```python Python Console Exporter for Debugging theme={null}
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.")
```

```python Python Accessing OTel Span API theme={null}
import langwatch
from opentelemetry.trace import Status, StatusCode

langwatch.setup()

with langwatch.span(name="MyInitialSpanName") as span:
    # Use standard OpenTelemetry Span API methods directly on span:
    span.set_attribute("my.custom.otel.attribute", "value")
    span.add_event("Specific OTel Event", {"detail": "more info"})
    span.set_status(Status(StatusCode.ERROR, description="Something went wrong"))
    span.update_name("MyUpdatedSpanName")  # Renaming the span

    print(f"Is Recording? {span.is_recording()}")
    print(f"OTel Span Context: {span.get_span_context()}")

    # You can still use LangWatch-specific methods like update()
    span.update(langwatch_info="extra data")
```

### TypeScript Debugging

```typescript TypeScript Console Exporter for Debugging theme={null}
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",
  },
});
```

```typescript TypeScript Custom Span Attributes theme={null}
const span = tracer.startSpan("custom-operation");

// Add custom attributes
span.setAttributes({
  "custom.business_unit": "marketing",
  "custom.campaign_id": "summer-2024",
  "custom.user_tier": "premium",
});

// Add events to the span
span.addEvent("user_action", {
  action: "button_click",
  button_id: "cta-primary",
});

span.end();
```

## Best Practices

### General Best Practices

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

### Language-Specific Best Practices

```python Python Best Practices theme={null}
import langwatch

# 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"}
)
```

```typescript TypeScript Best Practices theme={null}
// Use startActiveSpan for automatic span management
tracer.startActiveSpan("operation", (span) => {
  try {
    // Your code here
    span.setStatus({ code: SpanStatusCode.OK });
  } catch (error) {
    span.setStatus({
      code: SpanStatusCode.ERROR,
      message: error.message,
    });
    span.recordException(error);
    throw error;
  } finally {
    span.end();
  }
});

// Set meaningful attributes
span.setAttributes({
  "user.id": userId,
  "operation.type": "database_query",
});

// Use LangWatch-specific methods
span.setType("llm");
span.setInput({ query: userQuery });
span.setOutput({ result: result });
```

## Migration Checklist

When migrating from an existing OpenTelemetry setup:

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

## Troubleshooting Common Issues

### Common Migration Problems

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

### Debugging Migration

```python Python Debugging Migration theme={null}
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()))
```

```typescript TypeScript Debugging Migration theme={null}
import { setupObservability } from "langwatch/observability/node";

// Enable detailed logging during migration
const handle = setupObservability({
  langwatch: {
    apiKey: process.env.LANGWATCH_API_KEY,
  },
  serviceName: "my-service",
  debug: {
    consoleTracing: true,
    consoleLogging: true,
    logLevel: "debug",
  },
  advanced: {
    throwOnSetupError: true,
  },
});
```

## Performance Considerations

When using OpenTelemetry with LangWatch, consider these performance implications:

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


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