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Google Colab

colab.research.google.com
 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.

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Launch Phoenix

  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
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

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Install

pip install openinference-instrumentation-bedrock

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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")

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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)

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Observe

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

Bedrock Traces in Phoenix

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