Arize Phoenix
OfficialConnects to Arize Phoenix for managing AI prompts, exploring ML datasets, and running LLM experiments. Provides a unified interface to work with different language model providers.
Provides a unified interface to Arize Phoenix's capabilities for managing prompts, exploring datasets, and running experiments across different LLM providers
What it does
- Manage prompts and prompt templates
- Explore machine learning datasets
- Run experiments across multiple LLM providers
- Track model performance and metrics
- Access Phoenix's evaluation tools
- Query experimental results
Best for
About Arize Phoenix
Arize Phoenix is an official MCP server published by arize-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Arize Phoenix — unified interface for managing prompts, exploring datasets, and running LLM experiments across providers It is categorized under ai ml, developer tools.
How to install
You can install Arize Phoenix in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Arize Phoenix is released under the NOASSERTION license.
Link to bsky.app
Link to x.com
Link to anaconda.org
Link to github.com
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-aw |
README truncated. View full README on GitHub.
Alternatives
Related Skills
Browse all skillsUI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.
Open-source AI observability platform for tracing, evaluating, and improving LLM applications with OpenTelemetry integration
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".
Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
Guide for building TypeScript CLIs with Bun. Use when creating command-line tools, adding subcommands to existing CLIs, or building developer tooling. Covers argument parsing, subcommand patterns, output formatting, and distribution.