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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.

Table of Contents

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

TypeScript Subpackages

Package	Version & Docs	Description
@arizeai/phoenix-otel	NPM Version Docs	Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
@arizeai/phoenix-client	NPM Version Docs	Client for the Arize Phoenix API
@arizeai/phoenix-evals	NPM Version Docs	TypeScript evaluation library for LLM applications (alpha release)
@arizeai/phoenix-mcp	NPM Version Docs	Standalone stdio MCP server for older Phoenix versions (maintenance mode — superseded by the remote MCP server built into Phoenix)
@arizeai/phoenix-cli	NPM Version Docs	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	PyPI Version
	OpenAI Agents	openinference-instrumentation-openai-agents	PyPI Version
	LlamaIndex	openinference-instrumentation-llama-index	PyPI Version
	DSPy	openinference-instrumentation-dspy	PyPI Version
	AWS Bedrock	openinference-instrumentation-bedrock	PyPI Version
	LangChain	openinference-instrumentation-langchain	PyPI Version
	LangGraph	openinference-instrumentation-langchain	PyPI Version
	MistralAI	openinference-instrumentation-mistralai	PyPI Version
	Google GenAI	openinference-instrumentation-google-genai	PyPI Version
	Google ADK	openinference-instrumentation-google-adk	PyPI Version
	Guardrails	openinference-instrumentation-guardrails	PyPI Version
	VertexAI	openinference-instrumentation-vertexai	PyPI Version
	CrewAI	openinference-instrumentation-crewai	PyPI Version
	Haystack	openinference-instrumentation-haystack	PyPI Version
	LiteLLM	openinference-instrumentation-litellm	PyPI Version
	OpenRouter	openinference-instrumentation-openai	PyPI Version
	OrcaRouter	openinference-instrumentation-openai	PyPI Version
	Groq	openinference-instrumentation-groq	PyPI Version
	Instructor	openinference-instrumentation-instructor	PyPI Version
	Anthropic	openinference-instrumentation-anthropic	PyPI Version
	Smolagents	openinference-instrumentation-smolagents	PyPI Version
	Agno	openinference-instrumentation-agno	PyPI Version
	BeeAI	openinference-instrumentation-beeai	PyPI Version
	Strands Agents	openinference-instrumentation-strands-agents	PyPI Version
	Restate	openinference-instrumentation-openai-agents	PyPI Version
	MCP	openinference-instrumentation-mcp	PyPI Version
	Pydantic AI	openinference-instrumentation-pydantic-ai	PyPI Version
	Autogen AgentChat	openinference-instrumentation-autogen-agentchat	PyPI Version
	Portkey	openinference-instrumentation-portkey	PyPI Version
	Agent Spec	openinference-instrumentation-agentspec	PyPI Version
	Claude Agent SDK	openinference-instrumentation-claude-agent-sdk	PyPI Version

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.	PyPI Version
openinference-instrumentation-openllmetry	OpenInference Span Processor for OpenLLMetry (Traceloop) traces.	PyPI Version

JavaScript Integrations

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

Java Integrations

	Integration	Package	Version
	LangChain4j	openinference-instrumentation-langchain4j	Maven Central
	SpringAI	openinference-instrumentation-springAI	Maven Central
	Arconia for Spring AI	io.arconia:arconia-openinference-semantic-conventions	Maven Central

Go Integrations

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

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.

You can opt-out of telemetry by setting the environment variable: PHOENIX_TELEMETRY_ENABLED=false

Community

Join our community to connect with thousands of AI builders.

• 🌍 Join our Slack community.
• 📚 Read our documentation.
• 💡 Ask questions and provide feedback in the #phoenix-support channel.
• 🌟 Leave a star on our GitHub.
• 🐞 Report bugs with GitHub Issues.
• 𝕏 Follow us on 𝕏.
• 💼 Follow us on LinkedIn.
• 🗺️ Check out our roadmap to see where we're heading next.
• 🧑‍🏫 Deep dive into everything Agents and LLM Evaluations on Arize's Learning Hubs.

Breaking Changes

See the migration guide for a list of breaking changes.

Copyright, Patent, and License

Copyright 2025 Arize AI, Inc. All Rights Reserved.

Portions of this code are patent protected by one or more U.S. Patents. See the IP_NOTICE.

This software is licensed under the terms of the Elastic License 2.0 (ELv2). See LICENSE.