> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Amazon Bedrock

> Trace AWS Bedrock invoke_model and converse calls with OpenInference and send spans to Arize AX for LLM observability.

[Amazon Bedrock](https://aws.amazon.com/bedrock/) is AWS's managed foundation-model service — Claude, Llama, Mistral, Titan, and others are reachable through a single `boto3` client. Arize AX captures every Bedrock model call (`invoke_model`, `converse`, `converse_stream`) via the [`openinference-instrumentation-bedrock`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-bedrock) package.

<CardGroup>
  <Card horizontal icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico" href="https://colab.research.google.com/github/Arize-ai/tutorials/blob/35f5d3557b13188be4addb37baf3aa492b43b9b3/python/llm/tracing/bedrock/bedrock-tracing.ipynb#L23" title="Bedrock Tracing Tutorial (Google Colab)" />
</CardGroup>

## Prerequisites

* Python 3.10+
* An Arize AX account ([sign up](https://arize.com/sign-up/))
* An AWS account with [Bedrock model access enabled](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) for the model you want to call (the example below uses Anthropic Claude Sonnet 4.6 — request access from the Bedrock console under **Model access** if you haven't already)

## Launch Arize AX

1. Sign in to your [Arize AX account](https://app.arize.com/).
2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below.

## Install

```bash theme={null}
pip install arize-otel openinference-instrumentation-bedrock boto3
```

## Configure credentials

```bash theme={null}
export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="amazon-bedrock-tracing-example"

# AWS credentials — long-lived or SSO/STS temporary.
export AWS_ACCESS_KEY_ID="<your-aws-access-key-id>"
export AWS_SECRET_ACCESS_KEY="<your-aws-secret-access-key>"
export AWS_REGION="us-east-1"

# Only required when using SSO / STS / federated logins. Leave unset
# for long-lived IAM user keys.
export AWS_SESSION_TOKEN=""  # optional
```

## Setup tracing

```python theme={null}
# instrumentation.py
import os

from arize.otel import register
from openinference.instrumentation.bedrock import BedrockInstrumentor

tracer_provider = register(
    space_id=os.environ["ARIZE_SPACE_ID"],
    api_key=os.environ["ARIZE_API_KEY"],
    project_name=os.environ["ARIZE_PROJECT_NAME"],
)

BedrockInstrumentor().instrument(tracer_provider=tracer_provider)
print("Arize AX tracing initialized for Amazon Bedrock.")
```

## Run Amazon Bedrock

```python theme={null}
# example.py

# Importing instrumentation first ensures BedrockInstrumentor patches
# boto3 before the bedrock-runtime client is created.
from instrumentation import tracer_provider

import os

import boto3

# boto3 reads AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY /
# AWS_SESSION_TOKEN (optional) / AWS_REGION from the environment.
client = boto3.client(
    "bedrock-runtime",
    region_name=os.environ.get("AWS_REGION", "us-east-1"),
)

# Cross-region inference profile for Claude Sonnet 4.6. The `us.` prefix
# tells Bedrock to route across US regions automatically. Drop the
# prefix and use `anthropic.claude-sonnet-4-6` for region-pinned calls.
response = client.converse(
    modelId="us.anthropic.claude-sonnet-4-6",
    messages=[
        {
            "role": "user",
            "content": [
                {"text": "Why is the ocean salty? Answer in two sentences."}
            ],
        }
    ],
    inferenceConfig={"maxTokens": 256, "temperature": 0.0},
)

print(response["output"]["message"]["content"][0]["text"])
```

### Expected output

```text wrap theme={null}
Arize AX tracing initialized for Amazon Bedrock.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.
```

## Verify in Arize AX

1. Open your Arize AX space and select project **`amazon-bedrock-tracing-example`**.
2. You should see a new trace within \~30 seconds containing a `bedrock.converse` LLM span with the prompt, response, and token usage attached. The span's `llm.model_name` is the model id you called (e.g. `us.anthropic.claude-sonnet-4-6`).
3. If no traces appear, see [Troubleshooting](#troubleshooting).

## Troubleshooting

* **No traces in Arize AX.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run.
* **Bedrock spans missing but other spans present.** `BedrockInstrumentor().instrument(...)` must run before `boto3.client("bedrock-runtime", ...)` is called. Make sure `instrumentation.py` is the first import in your entry point — `boto3` clients created before instrumentation aren't patched.
* **`AccessDeniedException` / `Could not assume role`.** Your IAM principal doesn't have `bedrock:InvokeModel` permission, or model access isn't enabled for the model id in the example. Enable access in the Bedrock console under **Model access** and confirm your IAM policy grants `bedrock:InvokeModel` on `arn:aws:bedrock:*::foundation-model/*`.
* **`ValidationException: Invocation of model ID anthropic.claude-sonnet-4-6 ... isn't supported`.** Some Claude models on Bedrock are only available through [cross-region inference profiles](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html). Prefix the model id with your geography slug — `us.anthropic.claude-sonnet-4-6` (the example uses this) or `eu.anthropic.claude-sonnet-4-6`.
* **`ExpiredTokenException`.** Your `AWS_SESSION_TOKEN` (SSO / STS temporary credentials) has expired. Re-run the SSO login and re-export the new triple of `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`.
* **Meta Llama spans missing with `invoke_model`.** The instrumentor doesn't currently capture Llama responses via the `invoke_model` API — use `converse` (which the example above already does) for any non-Anthropic model.

## Resources

<CardGroup>
  <Card icon="book-open" href="https://docs.aws.amazon.com/bedrock/" title="Amazon Bedrock Documentation" horizontal />

  <Card icon="terminal" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-bedrock" title="OpenInference Bedrock Instrumentor" horizontal />

  <Card icon="github" href="https://github.com/boto/boto3" title="boto3 (AWS SDK for Python)" horizontal />

  <Card icon="code" href="https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-bedrock/examples" title="Bedrock Examples (Converse, Streaming, Tools, Agents)" horizontal />
</CardGroup>