arizephoenix/phoenix repository overview

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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	Language	Description
arize-phoenix-otel⁠	Python	Provides a lightweight wrapper around OpenTelemetry primitives with Phoenix-aware defaults
arize-phoenix-client⁠	Python	Lightweight client for interacting with the Phoenix server via its OpenAPI REST interface
arize-phoenix-evals⁠	Python	Tooling to evaluate LLM applications including RAG relevance, answer relevance, and more
@arizeai/phoenix-client⁠	JavaScript	Client for the Arize Phoenix API
@arizeai/phoenix-evals⁠	TypeScript	TypeScript evaluation library for LLM applications (alpha release)
@arizeai/phoenix-mcp⁠	JavaScript	MCP server implementation for Arize Phoenix providing unified interface to Phoenix's capabilities

⁠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.