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.
Install Phoenix via pip or conda
pip install arize-phoenixPhoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes.
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.
| 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 |
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 |  |
| 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 |  |
| Integration | Package | Version Badge |
|---|---|---|
| LangChain4j | openinference-instrumentation-langchain4j |  |
| Platform | Description | Docs |
|---|---|---|
| Dify | Open-source LLM app development platform | Integration Guide |
| LangFlow | Visual framework for building multi-agent and RAG applications | Integration Guide |
| BeeAI | AI agent framework with built-in observability | Integration Guide |
| LiteLLM Proxy | Proxy server for LLMs | Integration Guide |
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.
See the migration guide for a list of breaking changes.
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.