@arizeai/phoenix-mcp

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		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 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	@mastra/arize

Java Integrations

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

Platforms

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.
• 🗺️ 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.