Arize Phoenix logo

Arize Phoenix

OrganizationPopular
arize-ai

AI Observability & Evaluation

Publisherarize-ai
Repositoryphoenix
LanguagePython
Forks
869
Stars
9.7K
Available tools
0
Transport typestdio
Categories
AI MlDeveloper Tools
LicenseNOASSERTION
Links
GitHubHomepage
  • Connect tools to AI workflows

    Arize Phoenix exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    9.7K stars and 869 forks from the linked repository.

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

shell
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

PackageVersion & DocsDescription
arize-phoenix-otelPyPI Version DocsProvides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
arize-phoenix-clientPyPI Version DocsLightweight client for interacting with the Phoenix server via its OpenAPI REST interface
arize-phoenix-evalsPyPI Version DocsTooling to evaluate LLM applications including RAG relevance, answer relevance, and more

TypeScript Subpackages

PackageVersion & DocsDescription
@arizeai/phoenix-otelNPM Version DocsProvides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
@arizeai/phoenix-clientNPM Version DocsClient for the Arize Phoenix API
@arizeai/phoenix-evalsNPM Version DocsTypeScript evaluation library for LLM applications (alpha release)
@arizeai/phoenix-mcpNPM Version DocsMCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities
@arizeai/phoenix-cliNPM Version DocsCLI 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

IntegrationPackageVersion
OpenAIopeninference-instrumentation-openaiPyPI Version
OpenAI Agentsopeninference-instrumentation-openai-agentsPyPI Version
LlamaIndexopeninference-instrumentation-llama-indexPyPI Version
DSPyopeninference-instrumentation-dspyPyPI Version
AWS Bedrockopeninference-instrumentation-bedrockPyPI Version
LangChainopeninference-instrumentation-langchainPyPI Version
MistralAIopeninference-instrumentation-mistralaiPyPI Version
Google GenAIopeninference-instrumentation-google-genaiPyPI Version
Google ADKopeninference-instrumentation-google-adkPyPI Version
Guardrailsopeninference-instrumentation-guardrailsPyPI Version
VertexAIopeninference-instrumentation-vertexaiPyPI Version
CrewAIopeninference-instrumentation-crewaiPyPI Version
Haystackopeninference-instrumentation-haystackPyPI Version
LiteLLMopeninference-instrumentation-litellmPyPI Version
Groqopeninference-instrumentation-groqPyPI Version
Instructoropeninference-instrumentation-instructorPyPI Version
Anthropicopeninference-instrumentation-anthropicPyPI Version
Smolagentsopeninference-instrumentation-smolagentsPyPI Version
Agnoopeninference-instrumentation-agnoPyPI Version
MCPopeninference-instrumentation-mcpPyPI Version
Pydantic AIopeninference-instrumentation-pydantic-aiPyPI Version
Autogen AgentChatopeninference-instrumentation-autogen-agentchatPyPI Version
Portkeyopeninference-instrumentation-portkeyPyPI Version
Agent Specopeninference-instrumentation-agentspecPyPI Version
Claude Agent SDKopeninference-instrumentation-claude-agent-sdkPyPI Version

Span Processors

Normalize and convert data across other instrumentation libraries by adding span processors that unify data.

PackageDescriptionVersion
openinference-instrumentation-openlitOpenInference Span Processor for OpenLIT traces.PyPI Version
openinference-instrumentation-openllmetryOpenInference Span Processor for OpenLLMetry (Traceloop) traces.PyPI Version

JavaScript Integrations

IntegrationPackageVersion
OpenAI@arizeai/openinference-instrumentation-openaiNPM Version
LangChain.js@arizeai/openinference-instrumentation-langchainNPM Version
Vercel AI SDK@arizeai/openinference-vercelNPM Version
BeeAI@arizeai/openinference-instrumentation-beeaiNPM Version
Claude Agent SDK@arizeai/openinference-instrumentation-claude-agent-sdkNPM Version
Mastra@mastra/arizeNPM Version
MCP@arizeai/openinference-instrumentation-mcpNPM Version

Java Integrations

IntegrationPackageVersion
LangChain4jopeninference-instrumentation-langchain4jMaven Central
SpringAIopeninference-instrumentation-springAIMaven Central
Arconiaopeninference-instrumentation-springAIMaven Central

Platforms

PlatformDescriptionDocs
BeeAIAI agent framework with built-in observabilityIntegration Guide
DifyOpen-source LLM app development platformIntegration Guide
Envoy AI GatewayAI Gateway built on Envoy Proxy for AI workloadsIntegration Guide
LangFlowVisual framework for building multi-agent and RAG applicationsIntegration Guide
LiteLLM ProxyProxy server for LLMsIntegration Guide
FlowiseVisual framework for building LLM applicationsIntegration Guide
Prompt FlowMicrosoft's prompt flow orchestration toolIntegration Guide
NVIDIA NeMoNVIDIA NeMo Agent Toolkit for enterprise agentsIntegration Guide
GraphiteMulti-agent LLM workflow framework with visual builderIntegration 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.

SkillDescription
phoenix-cliDebug LLM applications using the Phoenix CLI — fetch traces, analyze errors, review experiments, and query the GraphQL API
phoenix-evalsBuild and run evaluators for AI/LLM applications using Phoenix
phoenix-tracingOpenInference 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.

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.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "phoenix": {
      "command": "npx",
      "args": [
        "-y",
        "@arizeai/phoenix-mcp@latest"
      ],
      "env": {
        "PHOENIX_API_KEY": "your-api-key",
        "PHOENIX_BASE_URL": "https://my-phoenix.com"
      }
    }
  }
}

Use Arize Phoenix MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Arize Phoenix is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Arize Phoenix as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Arize Phoenix MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "arize-phoenix": {
      "command": "npx",
      "args": [
        "-y",
        "@arizeai/phoenix-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Arize Phoenix MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Arize Phoenix MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Arize Phoenix to help me with this task?
Arize Phoenix
Sure. I read it.
Here is what I found using Arize Phoenix.

Frequently asked questions

What is the Arize Phoenix MCP server used for?

Arize Phoenix is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Arize Phoenix MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Arize Phoenix with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Arize Phoenix MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Arize Phoenix connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Arize Phoenix MCP to TypingMind?

Arize Phoenix runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Arize Phoenix MCP provide in TypingMind?

Arize Phoenix exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Arize Phoenix MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Arize Phoenix requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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