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phoenix
AI Observability & Evaluation
git clone https://github.com/Arize-ai/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.
- PXI (Phoenix Intelligence) - An AI engineering agent built into Phoenix for debugging traces, iterating on prompts, and navigating the product.
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 | |
| OpenAI Agents | @arizeai/openinference-instrumentation-openai-agents | |
| 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 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 |
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.
// compatibility
| Platforms | cli, api, web |
|---|---|
| Operating systems | — |
| AI compatibility | claude |
| License | NOASSERTION |
| Pricing | open-source |
| Language | Python |
// faq
What is phoenix?
AI Observability & Evaluation. It is open-source on GitHub.
Is phoenix free to use?
phoenix is open-source under the NOASSERTION license, so it is free to use.
What category does phoenix belong to?
phoenix is listed under data in the Claudeers registry of Claude-compatible tools.
// embed badge
[](https://claudeers.com/phoenix)
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[](https://claudeers.com/phoenix)
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3 of its contributors also build on official projects — claude-agent-sdk-python, claude-code, claude-cookbooks, claude-plugins-official
→ see how phoenix connects across the ecosystem