ai-mldeveloper-tools

Arize Phoenix▌

by arize-ai

Arize Phoenix — unified interface for managing prompts, exploring datasets, and running LLM experiments across providers

Provides a unified interface to Arize Phoenix's capabilities for managing prompts, exploring datasets, and running experiments across different LLM providers

github stars

★ 8.8K

GitHub npm

Multi-provider LLM supportUnified experiment management

best for

/ ML engineers comparing model performance
/ AI developers managing prompt workflows
/ Data scientists evaluating LLM outputs
/ Teams running A/B tests on AI models

capabilities

/ 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

what it does

Connects 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.

about

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

NOASSERTION

Arize Phoenix is released under the NOASSERTION license.

readme

Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides: - [**_Tracing_**](https://arize.com/docs/phoenix/tracing/llm-traces) - Trace your LLM application's runtime using OpenTelemetry-based instrumentation. - [**_Evaluation_**](https://arize.com/docs/phoenix/evaluation/llm-evals) - Leverage LLMs to benchmark your application's performance using response and retrieval evals. - [**_Datasets_**](https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets) - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning. - [**_Experiments_**](https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets#experiments) - Track and evaluate changes to prompts, LLMs, and retrieval. - [**_Playground_**](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts)- Optimize prompts, compare models, adjust parameters, and replay traced LLM calls. - [**_Prompt Management_**](https://arize.com/docs/phoenix/prompt-engineering/overview-prompts/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](https://arize.com/docs/phoenix/tracing/integrations-tracing/openai-agents-sdk), [Claude Agent SDK](https://arize.com/docs/phoenix/integrations/python/claude-agent-sdk), [LangGraph](https://arize.com/docs/phoenix/tracing/integrations-tracing/langchain), [Vercel AI SDK](https://arize.com/docs/phoenix/tracing/integrations-tracing/vercel-ai-sdk), [Mastra](https://arize.com/docs/phoenix/integrations/typescript/mastra), [CrewAI](https://arize.com/docs/phoenix/tracing/integrations-tracing/crewai), [LlamaIndex](https://arize.com/docs/phoenix/tracing/integrations-tracing/llamaindex), [DSPy](https://arize.com/docs/phoenix/tracing/integrations-tracing/dspy)) and LLM providers ([OpenAI](https://arize.com/docs/phoenix/tracing/integrations-tracing/openai), [Anthropic](https://arize.com/docs/phoenix/tracing/integrations-tracing/anthropic), [Google GenAI](https://arize.com/docs/phoenix/tracing/integrations-tracing/google-genai), [Google ADK](https://arize.com/docs/phoenix/integrations/llm-providers/google-gen-ai/google-adk-tracing), [AWS Bedrock](https://arize.com/docs/phoenix/tracing/integrations-tracing/bedrock), [OpenRouter](https://arize.com/docs/phoenix/integrations/python/openrouter), [LiteLLM](https://arize.com/docs/phoenix/tracing/integrations-tracing/litellm), and more). For details on auto-instrumentation, check out the [OpenInference](https://github.com/Arize-ai/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](https://hub.docker.com/r/arizephoenix/phoenix) and can be deployed using Docker or Kubernetes. Arize AI also provides cloud instances at [app.phoenix.arize.com](https://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](https://github.com/Arize-ai/phoenix/tree/main/packages/phoenix-otel) | [![PyPI Version](https://img.shields.io/pypi/v/arize-phoenix-otel)](https://pypi.org/project/arize-phoenix-otel/) [![Docs](https://img.shields.io/badge/docs-blue?logo=readthedocs&logoColor=white)](https://arize-phoenix.readthedocs.io/projects/otel/en/latest/index.html) | Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aw ---