Metadata and Labels

Metadata enriches your traces with contextual information, who made the request, which conversation it belongs to, and any custom data relevant to your application. Labels help you categorize and filter traces in the dashboard. The reference below covers every integration method. For SDK-specific details, see the tutorials linked below.

Quick Reference

Concept	OTEL Attribute	REST API	Description
Thread/Conversation	`gen_ai.conversation.id`	`metadata.thread_id`	Groups messages in a conversation
User ID	`langwatch.user.id`	`metadata.user_id`	Identifies the end user
Customer ID	`langwatch.customer.id`	`metadata.customer_id`	Your platform’s customer/tenant
Labels	`langwatch.labels`	`metadata.labels`	Categorization tags
Custom Metadata	`metadata` attribute	`metadata.*`	Any additional context

For OTEL, gen_ai.conversation.id follows the OpenTelemetry GenAI semantic conventions. The legacy langwatch.thread.id attribute is also supported.

SDK Examples

For detailed SDK-specific tutorials, see:

TypeScript: Capturing Metadata · Tracking Conversations · Full example
Python: Capturing Metadata · Tracking Conversations · Full example

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

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

async function handleUserMessage(userId: string, conversationId: string) {
  return await tracer.withActiveSpan("HandleMessage", async (span) => {
    // Thread/conversation ID (OTEL semconv)
    span.setAttribute("gen_ai.conversation.id", conversationId);

    // User and customer identification
    span.setAttribute("langwatch.user.id", userId);
    span.setAttribute("langwatch.customer.id", "tenant-123");

    // Labels for filtering (JSON array)
    span.setAttribute("langwatch.labels", JSON.stringify(["production", "premium-user"]));

    // Custom metadata (JSON object)
    span.setAttribute("metadata", JSON.stringify({
      feature_flags: ["new-ui", "beta-model"],
      request_source: "mobile-ios"
    }));

    // Your application logic...
  });
}

import langwatch

@langwatch.trace()
def handle_request(user_id: str, thread_id: str):
    langwatch.get_current_trace().update(
        metadata={
            "user_id": user_id,
            "thread_id": thread_id,
            "labels": ["production", "premium"],
            "custom_field": "any value"
        }
    )

    # Your logic here...

Raw OpenTelemetry

If you’re using vanilla OpenTelemetry without the LangWatch SDK:

import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("my-service");

tracer.startActiveSpan("operation", (span) => {
  // OTEL semconv for conversation/thread
  span.setAttribute("gen_ai.conversation.id", "conv-456");

  // LangWatch-specific attributes
  span.setAttribute("langwatch.user.id", "user-123");
  span.setAttribute("langwatch.customer.id", "customer-789");
  span.setAttribute("langwatch.labels", JSON.stringify(["urgent", "support"]));

  // Custom metadata as JSON string
  span.setAttribute("metadata", JSON.stringify({
    priority: "high",
    department: "engineering"
  }));

  // ... your code ...
  span.end();
});

import json
from opentelemetry import trace

tracer = trace.get_tracer("my-service")

with tracer.start_as_current_span("operation") as span:
    # OTEL semconv for conversation/thread
    span.set_attribute("gen_ai.conversation.id", "conv-456")

    # LangWatch-specific attributes
    span.set_attribute("langwatch.user.id", "user-123")
    span.set_attribute("langwatch.customer.id", "customer-789")
    span.set_attribute("langwatch.labels", '["urgent", "support"]')

    # Custom metadata as JSON string
    span.set_attribute("metadata", json.dumps({
        "priority": "high",
        "department": "engineering"
    }))

    # ... your code ...

Exporter configuration:

import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";

const exporter = new OTLPTraceExporter({
  url: "https://app.langwatch.ai/api/otel/v1/traces",
  headers: {
    Authorization: `Bearer ${process.env.LANGWATCH_API_KEY}`,
  },
});

from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

exporter = OTLPSpanExporter(
    endpoint="https://app.langwatch.ai/api/otel/v1/traces",
    headers={"Authorization": f"Bearer {os.environ['LANGWATCH_API_KEY']}"},
)

The OTEL endpoint is /api/otel/v1/traces (not /v1/traces).

REST API

Send traces directly via HTTP. See REST API for full details.

curl -X POST "https://app.langwatch.ai/api/collector" \
  -H "X-Auth-Token: $LANGWATCH_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "trace_id": "trace-123",
    "spans": [
      {
        "type": "llm",
        "span_id": "span-456",
        "name": "chat-completion",
        "model": "gpt-5-mini",
        "input": {"type": "text", "value": "Hello"},
        "output": {"type": "text", "value": "Hi there!"},
        "timestamps": {
          "started_at": 1699900000000,
          "finished_at": 1699900001000
        }
      }
    ],
    "metadata": {
      "user_id": "user-123",
      "thread_id": "conversation-456",
      "customer_id": "customer-789",
      "labels": ["production", "premium"],
      "any_custom_field": "any value"
    }
  }'

import os
import requests

requests.post(
    "https://app.langwatch.ai/api/collector",
    headers={
        "X-Auth-Token": os.environ["LANGWATCH_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "trace_id": "trace-123",
        "spans": [
            {
                "type": "llm",
                "span_id": "span-456",
                "name": "chat-completion",
                "model": "gpt-5-mini",
                "input": {"type": "text", "value": "Hello"},
                "output": {"type": "text", "value": "Hi there!"},
                "timestamps": {
                    "started_at": 1699900000000,
                    "finished_at": 1699900001000,
                },
            }
        ],
        "metadata": {
            "user_id": "user-123",
            "thread_id": "conversation-456",
            "customer_id": "customer-789",
            "labels": ["production", "premium"],
            "any_custom_field": "any value",
        },
    },
)

const response = await fetch("https://app.langwatch.ai/api/collector", {
  method: "POST",
  headers: {
    "X-Auth-Token": process.env.LANGWATCH_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    trace_id: "trace-123",
    spans: [
      {
        type: "llm",
        span_id: "span-456",
        name: "chat-completion",
        model: "gpt-5-mini",
        input: { type: "text", value: "Hello" },
        output: { type: "text", value: "Hi there!" },
        timestamps: {
          started_at: 1699900000000,
          finished_at: 1699900001000,
        },
      },
    ],
    metadata: {
      user_id: "user-123",
      thread_id: "conversation-456",
      customer_id: "customer-789",
      labels: ["production", "premium"],
      any_custom_field: "any value",
    },
  }),
});

Reserved vs Custom Fields

In the REST API metadata object:

Field	Type	Description
`user_id`	string	End user identifier
`thread_id`	string	Conversation/session ID
`customer_id`	string	Your tenant/customer ID
`labels`	string[]	Categorization tags
other keys	any	Stored as custom metadata

Timings

Beyond metadata, spans carry timing information that powers the latency views: the Duration column, the Time to First Token (TTFT) column, and the latency analytics.

Field	Where	Description
`timestamps.started_at`	Span	When the operation started (epoch ms). Set automatically by the SDKs
`timestamps.first_token_at`	Span	When the first streamed token arrived (epoch ms). Powers TTFT
`timestamps.finished_at`	Span	When the operation finished (epoch ms). Set automatically by the SDKs
`gen_ai.server.time_to_first_token`	OTEL attribute	TTFT as a duration in milliseconds, alternative to `first_token_at`

Most streaming instrumentations report TTFT automatically. To set it manually, or to understand how it rolls up to the trace level, see the dedicated tutorials:

Python: Tracking Time to First Token
TypeScript: Tracking Time to First Token

Best Practices

Always set user_id

Required for user-level analytics and filtering by specific users.

Use thread_id for conversations

Groups related messages together. Essential for chatbots and multi-turn interactions.

Labels for categorization

Use consistent labels like production, staging, support for filtering.

Custom metadata for context

Add any relevant context: feature flags, A/B variants, request sources.

What You Get

Once traces include metadata:

Filter by user: Find all traces for a specific user
View conversations: See all messages in a thread grouped together
Filter by labels: Quickly filter to specific categories
Search custom fields: Find traces by any custom metadata value
User analytics: View per-user metrics and patterns

Overview

SDKs

Frameworks

Model Providers

Tools

No-Code Platforms

Direct Integrations

Metadata and Labels

Quick Reference

SDK Examples

Raw OpenTelemetry

REST API

Reserved vs Custom Fields

Timings

Best Practices

Always set user_id

Use thread_id for conversations

Labels for categorization

Custom metadata for context

What You Get

​Quick Reference

​SDK Examples

​Raw OpenTelemetry

​REST API

​Reserved vs Custom Fields

​Timings

​Best Practices

Always set user_id

Use thread_id for conversations

Labels for categorization

Custom metadata for context

​What You Get

Quick Reference

SDK Examples

Raw OpenTelemetry

REST API

Reserved vs Custom Fields

Timings

Best Practices

What You Get