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Documentation Index

Fetch the complete documentation index at: https://docs.pandaprobe.com/llms.txt

Use this file to discover all available pages before exploring further.

wrap_bedrock is currently in beta.

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Installation

pip install "pandaprobe[bedrock]"
The 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.

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Setup

import boto3
from pandaprobe.wrappers import wrap_bedrock

client = wrap_bedrock(
    boto3.client("bedrock-runtime", region_name="us-east-1")
)
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},
)
The Converse API is provider-agnostic — the same call shape works across Claude, Mistral, Llama, Titan and other Bedrock-hosted foundation models. Prefer Converse over InvokeModel for new integrations. What gets traced
  • 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

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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="")
The wrapper preserves the {"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.

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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",
)
InvokeModel bodies are provider-specific JSON; the wrapper parses the body on a best-effort basis and recognises:
  • 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.

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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(...)

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Token usage mapping

Bedrock FieldPandaProbe Field
usage.inputTokens (Converse)prompt_tokens
usage.outputTokens (Converse)completion_tokens
usage.totalTokens (Converse)total_tokens
usage.cacheReadInputTokenscache_read_tokens
usage.cacheWriteInputTokenscache_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