telemetry-dev-anthropic 0.1.0

Anthropic integration for telemetry.dev Python SDK

telemetry-dev-anthropic

Anthropic Claude SDK instrumentation for telemetry.dev. It wraps the official anthropic Python SDK and emits telemetry.dev generation spans through telemetry-dev.

Install

pip install telemetry-dev-anthropic

Initialize the core SDK first:

import telemetry_dev

telemetry_dev.init(
    api_key="td_live_...",
    service_name="my-service",
)

Per-client wrapping

from anthropic import Anthropic
from telemetry_dev_anthropic import wrap_anthropic

client = wrap_anthropic(Anthropic())
message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Tell me a joke about OpenTelemetry"}],
)

wrap_anthropic also supports AsyncAnthropic, AnthropicBedrock, AsyncAnthropicBedrock, AnthropicVertex, and AsyncAnthropicVertex clients.

Global instrumentation

from anthropic import Anthropic
from telemetry_dev_anthropic import instrument_anthropic, uninstrument_anthropic

instrument_anthropic()
try:
    client = Anthropic()
    client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello"}],
    )
finally:
    uninstrument_anthropic()

Instrumented surfaces

• client.messages.create(...)
• client.messages.create(..., stream=True)
• client.messages.stream(...) context managers, sync and async

The integration maps native Anthropic request and response shapes directly into telemetry.dev fields. It does not normalize messages into another schema.

Streaming

Native Anthropic stream events pass through unmodified. The span records time to first chunk on the first received event, merges usage from message_start and message_delta, aggregates text, tool-use JSON, and thinking blocks, and ends on stream exhaustion, close, context-manager exit, or error.

messages.stream() starts the span when the context manager is entered, because that is when the Anthropic SDK opens the HTTP stream.

Bedrock and Vertex

Class instrumentation covers Bedrock and Vertex clients because the Anthropic SDK reuses the same Messages and AsyncMessages resource classes. Provider attribution is recorded as aws.bedrock or gcp.vertex_ai when the client class identifies those runtimes.

Limitations

• client.beta.messages is not instrumented.
• messages.parse() and messages.count_tokens() are not instrumented.
• with_raw_response snapshots bound methods; wrap or instrument clients before creating raw-response wrappers.
• Unconsumed streams end spans only on exhaustion, close, context-manager exit, or error.