Anthropic

Instrument LLM calls made using Anthropic's SDK

Anthropic is a leading provider for state-of-the-art LLMs. The Anthropic SDK can be instrumented using the openinference-instrumentation-anthropic package.

Phoenix Cloud

Sign up for Phoenix:

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

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint and API Key:

import os

# Add Phoenix API Key for tracing
PHOENIX_API_KEY = "ADD YOUR API KEY"
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com"

Your Phoenix API key can be found on the Keys section of your dashboard.

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 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 or use one of the other deployment options to retain traces.

Install

pip install openinference-instrumentation-anthropic anthropic

Setup

Use the register function to connect your application to Phoenix:

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
)

Run Anthropic

A simple Anthropic application that is now instrumented

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1000,
    temperature=0,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Why is the ocean salty?"
                }
            ]
        }
    ]
)
print(message.content)

Observe

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

Resources:

• Example Messages

• Example Tool Calling

• OpenInference package