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.

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
@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

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 | | | 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 | |

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

Java Integrations

Integration	Package	Version Badge
LangChain4j	`openinference-instrumentation-langchain4j`
SpringAI	`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

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.
🗺️ 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

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.

phoenix

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Arize-ai

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