telemetry-dev-bedrock 0.1.0

AWS Bedrock integration for telemetry.dev Python SDK

telemetry-dev-bedrock

AWS Bedrock instrumentation for the telemetry.dev Python SDK.

This package instruments boto3/botocore clients for:

• bedrock-runtime
  • Converse
  • ConverseStream
  • InvokeModel
  • InvokeModelWithResponseStream
  • ApplyGuardrail
• bedrock-agent-runtime
  • InvokeAgent
  • InvokeInlineAgent
  • Retrieve
  • RetrieveAndGenerate
  • RetrieveAndGenerateStream
  • InvokeFlow

Telemetry is emitted through telemetry_dev.start_span. No AWS request parameters are mutated.

Install

uv add telemetry-dev telemetry-dev-bedrock boto3

or with pip:

pip install telemetry-dev telemetry-dev-bedrock boto3

Quickstart

import os
import boto3
import telemetry_dev
from telemetry_dev_bedrock import wrap_bedrock

telemetry_dev.init(
    api_key=os.getenv("TELEMETRY_DEV_API_KEY"),
    service_name="bedrock-app",
    environment="production",
)

bedrock = wrap_bedrock(boto3.client("bedrock-runtime", region_name="us-east-1"))

bedrock.converse(
    modelId="anthropic.claude-3-5-haiku-20241022-v1:0",
    messages=[{"role": "user", "content": [{"text": "Hello"}]}],
)

telemetry_dev.shutdown()

wrap_bedrock accepts either a bedrock-runtime or bedrock-agent-runtime boto3 client. It patches the operation methods on that instance and is idempotent.

Global instrumentation

from telemetry_dev_bedrock import instrument_bedrock, uninstrument_bedrock

instrument_bedrock()
# bedrock-runtime and bedrock-agent-runtime clients created before or after this point are covered.
uninstrument_bedrock()

Global instrumentation wraps botocore.client.BaseClient._make_api_call and filters by service name, so unrelated boto3 clients pass through untouched. Per-client wrap_bedrock() clients are skipped by the global wrapper to avoid double spans.

Options

Option	Default	Applies to	Notes
capture_agent_trace	False	Agent Runtime streams	Aggregates trace usage and counts by default. When enabled, also attaches raw trace events as td.metadata.agent_trace after SDK masking/truncation.

agent = wrap_bedrock(
    boto3.client("bedrock-agent-runtime"),
    capture_agent_trace=True,
)

Signal coverage

All instrumented AWS responses/errors include gen_ai.response.id from the AWS request id, plus aws.http.status_code, aws.request.attempts when retries occurred, and aws.request.total_retry_delay_ms when the SDK exposes them.

Operation	Span type	Span name	Captured fields
Converse	generation	chat {modelId}	normalized input/output messages, system instructions, sampling params, usage/cache usage, finish reason, request id, retry attempts, prompt-router response model, server latency, guardrail metadata
ConverseStream	generation	chat {modelId}	pull-through stream output, time-to-first-chunk, usage/latency from metadata, partial output on early break/error
InvokeModel	generation or embedding	chat {modelId} / embeddings {modelId}	native JSON request/response bodies, provider-native sampling, provider-native usage, embedding output type, HTTP-header token fallback
InvokeModelWithResponseStream	generation or embedding	chat {modelId} / embeddings {modelId}	provider-native chunk text where known, optional final amazon-bedrock-invocationMetrics usage
ApplyGuardrail	span	apply_guardrail {guardrailIdentifier}	guardrail input/output, action, action reason, request id
InvokeAgent / InvokeInlineAgent	agent	invoke_agent {agentId} / invoke_agent {agentName}	user input, streamed answer, session/memory ids, alias id, trace usage aggregation, trace event count, return-control output
Retrieve	span	retrieve {knowledgeBaseId}	query, retrieval results, guardrail action, result count metadata
RetrieveAndGenerate / stream	generation	retrieve_and_generate {modelArn basename}	input text, generated text, citations count, session id, guardrail action
InvokeFlow	agent	invoke_flow {flowIdentifier}	inputs, flow output events, completion reason

StreamingBody behavior

InvokeModel returns a botocore StreamingBody. The integration reads it once to capture output and token usage, then replaces it with a new StreamingBody over the same bytes. Caller code can still call response["body"].read() normally.

Event streams are wrapped lazily. The wrapper does not pre-read events, preserves backpressure, forwards unknown attributes to the inner stream, and calls close() on the inner stream when closed.

Message normalization

Converse messages follow the OpenTelemetry GenAI non-normative LLM-call examples:

• text blocks become { "type": "text", "content": ... }
• toolUse becomes { "type": "tool_call", "id", "name", "arguments" }
• toolResult becomes { "type": "tool_call_response", "id", "response" }
• reasoning text becomes { "type": "reasoning", "content" }
• image/document/video/audio bytes become blob parts without byte content
• S3/URI sources become { "type": "uri", "uri", "modality" }

InvokeModel captures provider-native JSON bodies verbatim. Non-JSON bodies are not captured.

Semantics and guarantees

• Provider is emitted as amazon-bedrock. This intentionally differs from the OpenTelemetry registry value aws.bedrock so telemetry.dev pricing keys match Bedrock model IDs exactly. The raw caller modelId, modelArn, or foundationModel is emitted as the model.
• Instrumentation is fail-open. Normalization, span updates, and span endings are guarded so telemetry failures do not break caller code.
• Requests are never modified.
• Streams end spans exactly once. Exhaustion records full output and finish reason; early break, close(), GeneratorExit, or stream errors record partial output.
• boto3 has no async client. aiobotocore is a separate package and is out of scope.

Coverage gaps

The following calls pass through unless a supported operation above is used:

• CountTokens
• InvokeGuardrailChecks
• InvokeModelWithBidirectionalStream
• StartAsyncInvoke, GetAsyncInvoke, ListAsyncInvokes
• Agent Runtime session CRUD
• Rerank
• GenerateQuery
• OptimizePrompt
• AgenticRetrieveStream

Provider-native InvokeModel usage is best-effort. Anthropic Claude, Amazon Titan/Nova, Meta Llama, and Titan embeddings expose known token fields. Cohere and Mistral text models do not consistently include usage in the JSON body; spans still capture request/response bodies and finish reasons where present.