> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140-feat-new-brand-design-system.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Python SDK
> Lightweight Python packages for tracing, evaluation, and platform interaction
Phoenix's Python SDK is modular by design, allowing you to install only what you need. Each package serves a specific purpose and can be used independently or together.
API for the Phoenix platform
OpenTelemetry tracing with Phoenix defaults
LLM evaluation and metrics toolkit
Instrumentation and tracing helpers
## Installation
Install all packages together or individually based on your needs:
```shell theme={"theme":{"light":"github-light-default","dark":"github-dark-default"}}
# Install everything
pip install arize-phoenix-client arize-phoenix-otel arize-phoenix-evals
# Or install individually
pip install arize-phoenix-client # REST API client
pip install arize-phoenix-otel # Tracing
pip install arize-phoenix-evals # Evaluations
pip install openinference-instrumentation # Instrumentation helpers
```
## Environment Variables
All packages respect common Phoenix environment variables for seamless configuration:
| Variable | Description | Used By |
| ---------------------------- | ------------------------------------------------------------------- | -------------------------------------------------------------------- |
| `PHOENIX_ENDPOINT` | Phoenix base URL — canonical for everything except trace export | Client, CLI |
| `PHOENIX_COLLECTOR_ENDPOINT` | Base Phoenix collector URL or full OTLP trace endpoint | OTEL (Client and CLI infer from it when `PHOENIX_ENDPOINT` is unset) |
| `PHOENIX_API_KEY` | API key for authentication | Client, OTEL |
| `PHOENIX_PROJECT` | Default project name (canonical) | Client, OTEL, CLI |
| `PHOENIX_PROJECT_NAME` | Alias for `PHOENIX_PROJECT`; `PHOENIX_PROJECT` wins if both are set | Client, OTEL |
| `PHOENIX_CLIENT_HEADERS` | Custom HTTP headers | Client, OTEL |
| `PHOENIX_DISCOVER_CONFIG` | Set to `false` to disable `.env.phoenix` credential-file discovery | Client, OTEL, CLI |
Phoenix SDKs and the CLI also auto-load `PHOENIX_`-prefixed settings from a `.env.phoenix` file, discovered by walking up from the current directory. Process environment variables always take precedence. See [Environments](/docs/phoenix/environments).
***
## Phoenix Client
[](https://pypi.org/project/arize-phoenix-client/)
The Phoenix Client provides a programmatic interface to the Phoenix platform via its REST API. Use it to manage datasets, run experiments, analyze traces, and collect feedback.
* **Prompts** — Create, version, and invoke prompt templates with variable substitution
* **Datasets** — Build evaluation datasets from DataFrames, CSV files, or dictionaries
* **Experiments** — Run evaluations and track experiment results over time
* **Spans** — Query and analyze traces with powerful filtering capabilities
* **Annotations** — Add human feedback and automated evaluations to spans
* **Projects** — Organize your work across multiple AI applications
Examples and getting started
Full API documentation
***
## Phoenix OTEL
[](https://pypi.org/project/arize-phoenix-otel/)
Phoenix OTEL provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults. It simplifies tracing setup and provides decorators for common GenAI patterns.
* **Zero-config tracing** — Enable `auto_instrument=True` to automatically trace AI libraries
* **Phoenix-aware defaults** — Reads `PHOENIX_COLLECTOR_ENDPOINT`, `PHOENIX_API_KEY`, and other environment variables
* **Production ready** — Built-in batching and authentication support
* **Tracing decorators** — `@tracer.chain`, `@tracer.tool`, and more for manual instrumentation
* **OpenTelemetry compatible** — Works with existing OTel infrastructure
Examples and getting started
Full API documentation
***
## Phoenix Evals
[](https://pypi.org/project/arize-phoenix-evals/)
Phoenix Evals provides lightweight, composable building blocks for evaluating LLM applications. It includes tools for relevance scoring, faithfulness detection, toxicity checks, and custom metrics.
* **Model adapters** — Works with OpenAI, LiteLLM, LangChain, and other providers
* **Pre-built metrics** — Faithfulness detection, relevance, toxicity, and more
* **Input mapping** — Powerful binding for complex data structures
* **Native instrumentation** — OpenTelemetry tracing for observability
* **High performance** — Up to 20x speedup with built-in concurrency and batching
Examples and getting started
Full API documentation
***
## OpenInference
[](https://pypi.org/project/openinference-instrumentation/)
OpenInference provides instrumentation utilities and helpers for tracing AI applications. Use it alongside Phoenix OTEL for decorators, context managers, and data masking capabilities.
* **Decorators** — Use `@tracer.agent`, `@tracer.chain`, `@tracer.tool` to trace custom functions
* **Context managers** — Wrap code blocks with `using_` helpers for fine-grained control
* **Data masking** — Redact sensitive information from traces with built-in masking utilities
* **Framework instrumentors** — Auto-trace OpenAI, LangChain, LlamaIndex, Anthropic, and more
Examples and getting started
Source code and documentation