Python SDK Overview

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/Arize-ai/phoenix/llms.txt

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

​

Packages

​

arize-phoenix-client

• Manage projects, datasets, and experiments
• Query and annotate traces and spans
• Create and version prompts
• Run evaluations and experiments

​

arize-phoenix-otel

• Enable OpenTelemetry-based instrumentation
• Auto-instrument popular frameworks (OpenAI, LangChain, LlamaIndex, etc.)
• Configure trace export to Phoenix
• Set up batching and performance optimization

​

arize-phoenix-evals

• Run LLM-based evaluations (hallucination, relevance, toxicity, etc.)
• Create custom evaluators
• Compute metrics on datasets
• Integrate evaluations into your workflow

​

Installation

pip install arize-phoenix-client

​

Quick Start

# 1. Set up tracing with OTEL
from phoenix.otel import register

tracer_provider = register(
    project_name="my-llm-app",
    auto_instrument=True
)

# 2. Your LLM application runs and generates traces automatically
import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# 3. Use the client to query traces and run evaluations
from phoenix.client import Client

client = Client()
project = client.projects.list()[0]
traces = list(client.traces.list(project.id))

# 4. Run evaluations on your traces
from phoenix.evals import evaluate_dataframe
from phoenix.evals.metrics import hallucination
import pandas as pd

df = pd.DataFrame([{
    "output": response.choices[0].message.content,
    "input": "Hello!",
    "context": "System context"
}])

results = evaluate_dataframe(
    df,
    evaluators=[hallucination()]
)

​

Common Workflows

​

Development Workflow

1. Instrument your app with phoenix.otel.register() to capture traces
2. Run your application and generate traces automatically
3. Review traces in the Phoenix UI or via the client
4. Run evaluations to assess quality using phoenix.evals
5. Iterate on prompts and configuration

​

Production Workflow

1. Configure OTEL with batching for performance
2. Set up continuous evaluation using the evals package
3. Monitor metrics via the Phoenix UI
4. Use the client for programmatic access to data
5. Create datasets from production traces for testing

​

Environment Variables

​

Next Steps