phoenix

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

Coding Agent Skills

This repository includes skills that teach coding agents how to work with Phoenix. They are located in .agents/skills/ and can be used with Claude Code, Cursor, and other compatible tools.

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.