wrap_bedrock is currently in beta.
Installation
pip install "pandaprobe[bedrock]"
uv add "pandaprobe[bedrock]"
bedrock extra installs boto3>=1.34.0. For async support install aioboto3 separately — wrap_bedrock detects it at runtime and instruments async methods automatically without making it a hard dependency.
Setup
import boto3
from pandaprobe.wrappers import wrap_bedrock
client = wrap_bedrock(
boto3.client("bedrock-runtime", region_name="us-east-1")
)
Converse API (recommended)
Span name: "bedrock-converse", SpanKind: LLM
response = client.converse(
modelId="anthropic.claude-haiku-4-5-20251001-v1:0",
system=[{"text": "You are a concise assistant."}],
messages=[
{"role": "user", "content": [{"text": "Explain recursion in one sentence."}]},
],
inferenceConfig={"temperature": 0.5, "maxTokens": 200},
)
- Input: top-level
system blocks hoisted into the messages list as a role="system" entry, followed by the messages array. Text-only content blocks are flattened into a single string; mixed-block content (images, tool use/results) round-trips as structured JSON.
- Output: assistant
content text blocks joined together
- Model:
modelId from the request kwargs
- Token usage (see mapping table below)
- Model parameters:
temperature, topP, maxTokens, stopSequences from inferenceConfig, plus guardrailConfig, additionalModelRequestFields, toolConfig
reasoningContent blocks (when models emit them) are stored in span metadata as reasoning_summary
Streaming
response = client.converse_stream(
modelId="anthropic.claude-haiku-4-5-20251001-v1:0",
messages=[{"role": "user", "content": [{"text": "Hello!"}]}],
inferenceConfig={"temperature": 0.5, "maxTokens": 200},
)
for event in response["stream"]:
delta = event.get("contentBlockDelta", {}).get("delta", {})
if delta.get("text"):
print(delta["text"], end="")
{"stream": ..., "ResponseMetadata": ...} response shape — only the inner iterator is replaced with a tracing-aware reducer. User code accesses response["stream"] exactly as before. Time-to-first-token is captured on the first contentBlockDelta; final token usage is read from the trailing metadata event.
InvokeModel API (legacy fallback)
Span name: "bedrock-invoke-model" (or "bedrock-invoke-model-stream"), SpanKind: LLM
import json
response = client.invoke_model(
modelId="anthropic.claude-haiku-4-5-20251001-v1:0",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 200,
"messages": [{"role": "user", "content": "Hi"}],
}),
contentType="application/json",
accept="application/json",
)
- Anthropic Claude on Bedrock —
{"messages": [...], "system": "..."}, output content blocks, usage as input_tokens / output_tokens
- Mistral on Bedrock —
{"messages": [...]}
- Amazon Titan —
{"inputText": "..."}, output via results[0].outputText, usage via inputTextTokenCount + results[0].tokenCount
- Cohere / Meta Llama —
{"prompt": "..."} and provider-specific generation fields
Unknown body shapes still produce an LLM span containing the serialised request body as input.
Async (aioboto3)
aioboto3 is supported but not required. When wrap_bedrock is given an aioboto3 client (its module path starts with aioboto3 / aiobotocore, or its methods are coroutine functions), the wrapper installs async-shaped patches for converse, converse_stream, invoke_model, and invoke_model_with_response_stream.
import aioboto3
from pandaprobe.wrappers import wrap_bedrock
session = aioboto3.Session()
async with session.client("bedrock-runtime", region_name="us-east-1") as client:
wrap_bedrock(client)
response = await client.converse(...)
Token usage mapping
| Bedrock Field | PandaProbe Field |
|---|
usage.inputTokens (Converse) | prompt_tokens |
usage.outputTokens (Converse) | completion_tokens |
usage.totalTokens (Converse) | total_tokens |
usage.cacheReadInputTokens | cache_read_tokens |
usage.cacheWriteInputTokens | cache_creation_tokens |
usage.input_tokens (InvokeModel/Anthropic) | prompt_tokens |
usage.output_tokens (InvokeModel/Anthropic) | completion_tokens |
inputTextTokenCount (Titan) | prompt_tokens |
results[0].tokenCount (Titan) | completion_tokens |
meta.billed_units.input_tokens (Cohere) | prompt_tokens |
meta.billed_units.output_tokens (Cohere) | completion_tokens |
