Amazon Bedrock Agents Tracing

Instrument LLM calls to AWS Bedrock via the boto3 client using the BedrockInstrumentor

Google Colab

Amazon Bedrock Agents allow you to easily define, deploy, and manage agents on your AWS infrastructure. Traces on invocations of these agents can be captured using OpenInference and viewed in Phoenix.

This instrumentation will capture data on LLM calls, action group invocations (as tools), knowledgebase lookups, and more.

Launch Phoenix

Phoenix Cloud

Sign up for Phoenix:

1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login

2. Click Create Space, then follow the prompts to create and launch your space.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint and API Key:

From your new Phoenix Space

1. Create your API key from the Settings page

2. Copy your Hostname from the Settings page

3. In your code, set your endpoint and API key:

import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"

Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

Command Line

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

See Terminal for more details.

Docker

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

For more info on using Phoenix with Docker, see Docker.

Notebook

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See self-hosting or use one of the other deployment options to retain traces.

Install

pip install openinference-instrumentation-bedrock

Setup

Connect to your Phoenix instance using the register function.

from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

After connecting to your Phoenix server, instrument boto3 prior to initializing a bedrock-runtime client. All clients created after instrumentation will send traces on all calls to invoke_model, invoke_agent, and their streaming variations.

import boto3

session = boto3.session.Session()
client = session.client("bedrock-runtime")

Run Bedrock Agents

From here you can run Bedrock as normal

session_id = f"default-session1_{int(time.time())}"

attributes = dict(
    inputText=input_text,
    agentId=AGENT_ID,
    agentAliasId=AGENT_ALIAS_ID,
    sessionId=session_id,
    enableTrace=True,
)
response = client.invoke_agent(**attributes)

Observe

Now that you have tracing setup, all calls will be streamed to your running Phoenix for observability and evaluation.

Bedrock Traces in Phoenix

Resources

• Tracing and Evals example

• OpenInference package