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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.
PXI (Phoenix Intelligence) - An AI engineering agent built into Phoenix for debugging traces, iterating on prompts, and navigating the product.
Remote MCP Server - Connect Claude Code, Cursor, and other MCP clients directly to your Phoenix instance's /mcp endpoint to query traces, datasets, experiments, and more.

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 containerized deployment, or in the cloud. See Environments for a walkthrough of each option, or jump straight into the Tracing Quickstart.

Run Locally
Trace Your Application
Deploy
Packages
Tracing Integrations
Sandboxes
For Humans and Coding Agents
Security & Privacy
Community

Run Locally

Install Phoenix via pip or conda and have a fully functional Phoenix. For all installation and hosting options, see the install guide.

pip install arize-phoenix
phoenix serve

Or run it with no install using uvx:

uvx arize-phoenix serve

Trace Your Application

The fastest way to send traces is to let your coding agent (Claude Code, Codex, Cursor, and others) instrument your app. From your project directory, run:

npx @arizeai/phoenix-cli setup
# or, with Phoenix installed: px setup

Setup detects your framework and LLM provider, installs the right OpenInference instrumentation, and wires up trace export. Prefer to wire it up in code? See the tracing documentation.

Deploy

Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes via the Helm chart.

For Docker Compose, Kubernetes/Helm, and other deployment options, see the self-hosting documentation.

[!NOTE] The Google Cloud button builds Phoenix from source in Cloud Shell rather than deploying the prebuilt Docker Hub image. The Azure template serves plain HTTP (Azure Container Instances does not terminate TLS) — front it with a TLS proxy such as an Application Gateway before production use.

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		Standalone stdio MCP server for older Phoenix versions (maintenance mode — superseded by the remote MCP server built into Phoenix)
@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 and the integrations documentation.

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`
	LangGraph	`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`
	OpenRouter	`openinference-instrumentation-openai`
	OrcaRouter	`openinference-instrumentation-openai`
	Groq	`openinference-instrumentation-groq`
	Instructor	`openinference-instrumentation-instructor`
	Anthropic	`openinference-instrumentation-anthropic`
	Smolagents	`openinference-instrumentation-smolagents`
	Agno	`openinference-instrumentation-agno`
	BeeAI	`openinference-instrumentation-beeai`
	Strands Agents	`openinference-instrumentation-strands-agents`
	Restate	`openinference-instrumentation-openai-agents`
	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`
	OpenAI Agents	`@arizeai/openinference-instrumentation-openai-agents`
	LangChain.js	`@arizeai/openinference-instrumentation-langchain`
	TanStack AI	`@arizeai/openinference-tanstack-ai`
	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 for Spring AI	`io.arconia:arconia-openinference-semantic-conventions`

Go Integrations

	Integration	Package	Version
	OpenAI	`github.com/Arize-ai/openinference/go/openinference-instrumentation-openai-go`
	Anthropic	`github.com/Arize-ai/openinference/go/openinference-instrumentation-anthropic-sdk-go`

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

Sandboxes

Run Phoenix code evaluators in hosted sandbox providers for kernel-level isolation and runtime dependency installation.

Integration	Description	Docs
E2B	Hosted micro-VM sandboxes for AI-generated code	Integration Guide
Daytona	Managed development sandboxes with snapshot startup	Integration Guide
Vercel Sandbox	Ephemeral compute on Vercel's infrastructure	Integration Guide
Modal	Serverless, Python-first container platform	Integration Guide

For Humans and Coding Agents

Phoenix is built to be driven by people and by AI coding agents alike. Three surfaces let agents (Claude Code, Codex, Cursor, and others) work with your traces, datasets, and experiments:

CLI — npx @arizeai/phoenix-cli fetches traces, datasets, and experiments and instruments your app (setup), so an agent can pull context and act on it from the terminal.
Skills — .agents/skills/ packages workflows that teach agents how to debug, evaluate, and trace with Phoenix.
Remote MCP Server — connect any MCP client to your instance's /mcp endpoint to query Phoenix directly.

See the coding agents documentation for setup and usage.

Skill	Description
phoenix-cli	Debug LLM applications using the Phoenix CLI — fetch traces, analyze errors, review experiments, and query the GraphQL API
phoenix-evals	Build and run evaluators for AI/LLM applications using Phoenix
phoenix-tracing	OpenInference semantic conventions and instrumentation for tracing LLM applications

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 19.11.0