Claude Platform (Claude API OpenTelemetry)

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What gets traced

• Messages API requests: one anthropic.messages.create span per request, with an anthropic.model_request span for each model run inside it.
• Managed Agents sessions: a span per Sessions API request and an anthropic.session.turn span per agent turn, with the model requests, tool uses and outcome evaluations of that turn under it.
• Server tools and MCP tools: a tool span (anthropic.tool_use, anthropic.mcp_tool_use) for each tool the API runs, such as web search, code execution or a tool on an MCP server you connect with the MCP connector.
• Token usage on the request and model request spans.

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How it works

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Add a LangWatch destination

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Self-hosted LangWatch

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Test it

1. On the OpenTelemetry settings page, open the destination’s More actions menu and click Test. The Console sends an anthropic.test span and shows whether it was delivered.
2. Send a Messages API request in the Console playground or from your application, in a workspace the destination covers.
3. Check that the Delivery column shows Healthy. This can take about a minute.
4. In LangWatch, open your project’s traces and filter on service.name = claude-platform, the service name of every API span.

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Read your trace

• Input and output are on anthropic.messages.create. The system prompt is in gen_ai.system_instructions, the messages in gen_ai.input.messages and the response in gen_ai.output.messages.
• On anthropic.model_request spans you see no input or output, by design. These spans hold the model, timing and token usage of each model run. An empty input there does not mean content export failed: open the parent anthropic.messages.create span to read the content.
• Tool spans hold the tool arguments in gen_ai.tool.call.arguments and the result in anthropic.tool_result.content.

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Counting tokens

• Messages API request: the anthropic.model_request spans, plus any anthropic.compaction spans.
• Managed Agents session: the anthropic.model_request and anthropic.session.outcome_evaluation spans.

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Connect to your own app traces

import langwatch
from anthropic import Anthropic

# Registers the global tracer provider; reads LANGWATCH_API_KEY.
langwatch.setup()

client = Anthropic(open_telemetry={"traces": {"content_mode": "content"}})


@langwatch.trace(name="answer_question")
def answer_question(question: str) -> str:
    message = client.messages.create(
        model="claude-sonnet-5-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": question}],
    )
    return message.content[0].text


print(answer_question("What is OpenTelemetry?"))

import Anthropic from "@anthropic-ai/sdk";
import { getLangWatchTracer } from "langwatch";
import { setupObservability } from "langwatch/observability/node";

// Registers the global tracer provider; reads LANGWATCH_API_KEY.
setupObservability({ serviceName: "my-agent" });

const tracer = getLangWatchTracer("my-agent");
const client = new Anthropic({ openTelemetry: { traces: { contentMode: "content" } } });

const answer = await tracer.withActiveSpan("answer_question", async () => {
  const message = await client.messages.create({
    model: "claude-sonnet-5-5",
    max_tokens: 1024,
    messages: [{ role: "user", content: "What is OpenTelemetry?" }],
  });
  return message.content;
});

console.log(answer);

export LANGWATCH_API_KEY="<your project API key>"
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL="http/protobuf"
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT="https://app.langwatch.ai/api/otel/v1/traces"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer ${LANGWATCH_API_KEY}"
export ANTHROPIC_OPEN_TELEMETRY_TRACES_CONTENT_MODE="content"

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Turn traces into evaluations and datasets

• Run online evaluations on incoming traces, for example to score answer quality or catch PII.
• Add traces to a dataset and use it in experiments when you change a prompt or a model.
• Group a Managed Agents session’s turns as one conversation and add threads to datasets.

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Troubleshooting

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Related

• OpenTelemetry integration guide: send any OTLP traces to LangWatch.
• Anthropic Python instrumentation and Anthropic Go instrumentation.
• Anthropic’s API tracing setup, SDK tracing setup and span reference.