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Author: Arize AI

Requires Python: <3.15,>=3.10

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Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides:

Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Playground- Optimize prompts, compare models, adjust parameters, and replay traced LLM calls.
Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation.

Phoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (OpenAI Agents SDK, Claude Agent SDK, LangGraph, Vercel AI SDK, Mastra, CrewAI, LlamaIndex, DSPy) and LLM providers (OpenAI, Anthropic, Google GenAI, Google ADK, AWS Bedrock, OpenRouter, LiteLLM, and more). For details on auto-instrumentation, check out the OpenInference project.

Phoenix runs practically anywhere, including your local machine, a Jupyter notebook, a containerized deployment, or in the cloud.

Installation

Install Phoenix via pip or conda

pip install arize-phoenix

Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes. Arize AI also provides cloud instances at app.phoenix.arize.com.

Packages

The arize-phoenix package includes the entire Phoenix platform. However, if you have deployed the Phoenix platform, there are lightweight Python sub-packages and TypeScript packages that can be used in conjunction with the platform.

Python Subpackages

Package	Version & Docs	Description
arize-phoenix-otel		Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
arize-phoenix-client		Lightweight client for interacting with the Phoenix server via its OpenAPI REST interface
arize-phoenix-evals		Tooling to evaluate LLM applications including RAG relevance, answer relevance, and more

TypeScript Subpackages

Package	Version & Docs	Description
@arizeai/phoenix-otel		Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
@arizeai/phoenix-client		Client for the Arize Phoenix API
@arizeai/phoenix-evals		TypeScript evaluation library for LLM applications (alpha release)
@arizeai/phoenix-mcp		MCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities
@arizeai/phoenix-cli		CLI for fetching traces, datasets, and experiments for use with Claude Code, Cursor, and other coding agents

Tracing Integrations

Phoenix is built on top of OpenTelemetry and is vendor, language, and framework agnostic. For details about tracing integrations and example applications, see the OpenInference project.

Python Integrations

	Integration	Package	Version
	OpenAI	`openinference-instrumentation-openai`
	OpenAI Agents	`openinference-instrumentation-openai-agents`
	LlamaIndex	`openinference-instrumentation-llama-index`
	DSPy	`openinference-instrumentation-dspy`
	AWS Bedrock	`openinference-instrumentation-bedrock`
	LangChain	`openinference-instrumentation-langchain`
	MistralAI	`openinference-instrumentation-mistralai`
	Google GenAI	`openinference-instrumentation-google-genai`
	Google ADK	`openinference-instrumentation-google-adk`
	Guardrails	`openinference-instrumentation-guardrails`
	VertexAI	`openinference-instrumentation-vertexai`
	CrewAI	`openinference-instrumentation-crewai`
	Haystack	`openinference-instrumentation-haystack`
	LiteLLM	`openinference-instrumentation-litellm`
	Groq	`openinference-instrumentation-groq`
	Instructor	`openinference-instrumentation-instructor`
	Anthropic	`openinference-instrumentation-anthropic`
	Smolagents	`openinference-instrumentation-smolagents`
	Agno	`openinference-instrumentation-agno`
	MCP	`openinference-instrumentation-mcp`
	Pydantic AI	`openinference-instrumentation-pydantic-ai`
	Autogen AgentChat	`openinference-instrumentation-autogen-agentchat`
	Portkey	`openinference-instrumentation-portkey`
	Agent Spec	`openinference-instrumentation-agentspec`
	Claude Agent SDK	`openinference-instrumentation-claude-agent-sdk`

Span Processors

Normalize and convert data across other instrumentation libraries by adding span processors that unify data.

Package	Description	Version
`openinference-instrumentation-openlit`	OpenInference Span Processor for OpenLIT traces.
`openinference-instrumentation-openllmetry`	OpenInference Span Processor for OpenLLMetry (Traceloop) traces.

JavaScript Integrations

	Integration	Package	Version
	OpenAI	`@arizeai/openinference-instrumentation-openai`
	LangChain.js	`@arizeai/openinference-instrumentation-langchain`
	Vercel AI SDK	`@arizeai/openinference-vercel`
	BeeAI	`@arizeai/openinference-instrumentation-beeai`
	Claude Agent SDK	`@arizeai/openinference-instrumentation-claude-agent-sdk`
	Mastra	`@mastra/arize`
	MCP	`@arizeai/openinference-instrumentation-mcp`

Java Integrations

	Integration	Package	Version
	LangChain4j	`openinference-instrumentation-langchain4j`
	SpringAI	`openinference-instrumentation-springAI`
	Arconia	`openinference-instrumentation-springAI`

Platforms

Platform	Description	Docs
BeeAI	AI agent framework with built-in observability	Integration Guide
Dify	Open-source LLM app development platform	Integration Guide
Envoy AI Gateway	AI Gateway built on Envoy Proxy for AI workloads	Integration Guide
LangFlow	Visual framework for building multi-agent and RAG applications	Integration Guide
LiteLLM Proxy	Proxy server for LLMs	Integration Guide
Flowise	Visual framework for building LLM applications	Integration Guide
Prompt Flow	Microsoft's prompt flow orchestration tool	Integration Guide
NVIDIA NeMo	NVIDIA NeMo Agent Toolkit for enterprise agents	Integration Guide
Graphite	Multi-agent LLM workflow framework with visual builder	Integration Guide

Security & Privacy

We take data security and privacy very seriously. For more details, see our Security and Privacy documentation.

Telemetry

By default, Phoenix collects basic web analytics (e.g., page views, UI interactions) to help us understand how Phoenix is used and improve the product. None of your trace data, evaluation results, or any sensitive information is ever collected.

arize-phoenix 13.23.0