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phoenix

极光（Phoenix）是一个开源的AI可观测性平台，专为实验、评估和故障排除而设计。它支持多种框架和LLM供应商，提供追踪、评估、数据集管理、实验和提示优化等功能。极光基于OpenTelemetry构建，与供应商和语言无关，可以在本地机器、Jupyter笔记本、容器化部署或云端运行。通过`pip`或`conda`安装，极光的容器镜像可通过Docker Hub获取，并支持Docker或Kubernetes部署。极光的MCP服务器实现提供统一接口访问其功能，适用于复杂的AI应用场景。

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README

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Add Arize Phoenix MCP server to Cursor

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 (🦙LlamaIndex, 🦜⛓LangChain, Haystack, 🧩DSPy, 🤗smolagents) and LLM providers (OpenAI, Bedrock, MistralAI, VertexAI, LiteLLM, Google GenAI 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 platfom. However if you have deployed the Phoenix platform, there are light-weight Python sub-packages and TypeScript packages that can be used in conjunction with the platfrom.

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 | MCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities | | @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.

Python Integrations | Integration | Package | Version Badge | |------------------|-----------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------| | 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 | | 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 | | 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 | | 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 |

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 Badge | | ------------------------------------------------------------------------------------------ | -------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | OpenAI | @arizeai/openinference-instrumentation-openai | NPM Version | | LangChain.js | @arizeai/openinference-instrumentation-langchain | NPM Version | | Vercel AI SDK | @arizeai/openinference-vercel | NPM Version | | BeeAI | @arizeai/openinference-instrumentation-beeai | NPM Version | | Mastra | @mastra/arize | NPM Version |

Java Integrations

| Integration | Package | Version Badge | | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | LangChain4j | openinference-instrumentation-langchain4j | Maven Central | | SpringAI | openinference-instrumentation-springAI | Maven Central |

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 |

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.

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.

help

Runtime guide

cloud

Hosted runtime

Hosted servers run from a provider-managed environment. You usually connect the MCP client to the hosted endpoint or follow the provider's authorization flow, without keeping a local process alive

  1. Open provider connection page
  2. Authorize or copy endpoint
  3. Connect from your MCP client
terminal

Local runtime / other methods

Local servers run on your own machine or infrastructure. You normally copy the server_config into your MCP client, install the required package, and provide env variables from env_schema when needed

  1. Copy server_config
  2. Install required package
  3. Fill env variables and restart client