AI Observability & Evaluation for the Model Context Protocol. Enhance AI assistants with powerful integrations. Jupyter Notebook-based implementation.
Documentation
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.
- **_PXI (Phoenix Intelligence)_** - An AI engineering agent built into Phoenix for debugging traces, iterating on prompts, and navigating the product.
- **_Remote MCP Server_** - Connect Claude Code, Cursor, and other MCP clients directly to your Phoenix instance's
/mcpendpoint to query traces, datasets, experiments, and more.
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 containerized deployment, or in the cloud. See Environments for a walkthrough of each option, or jump straight into the Tracing Quickstart.
Table of Contents
- Run Locally
- Trace Your Application
- Deploy
- Packages
- Tracing Integrations
- Sandboxes
- For Humans and Coding Agents
- Security & Privacy
- Community
Run Locally
Install Phoenix via pip or conda and have a fully functional Phoenix. For all installation and hosting options, see the install guide.
pip install arize-phoenix
phoenix serveOr run it with no install using [uvx](https://docs.astral.sh/uv/):
uvx arize-phoenix serveTrace Your Application
The fastest way to send traces is to let your coding agent (Claude Code, Codex, Cursor, and others) instrument your app. From your project directory, run:
npx @arizeai/phoenix-cli setup
# or, with Phoenix installed: px setupSetup detects your framework and LLM provider, installs the right OpenInference instrumentation, and wires up trace export. Prefer to wire it up in code? See the tracing documentation.
Deploy
Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes via the Helm chart.
For Docker Compose, Kubernetes/Helm, and other deployment options, see the self-hosting documentation.
[!NOTE]
The Google Cloud button builds Phoenix from source in Cloud Shell rather than deploying the prebuilt Docker Hub image. The Azure template serves plain HTTP (Azure Container Instances does not terminate TLS) — front it with a TLS proxy such as an Application Gateway before production use.
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 |   | 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 |   | Standalone stdio MCP server for older Phoenix versions (maintenance mode — superseded by the remote MCP server built into Phoenix) |
| @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 and the integrations documentation.
Python Integrations
| Integration | Package | Version |
|---|---|---|
| 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 |  |
| LangGraph | openinference-instrumentation-langchain | |
| MistralAI | openinference-instrumentation-mistralai |  |
| Cohere | openinference-instrumentation-cohere |  |
| Together AI | openinference-instrumentation-together |  |
| Ollama | openinference-instrumentation-ollama |  |
| 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 |  |
| OpenRouter | openinference-instrumentation-openai | |
| OrcaRouter | openinference-instrumentation-openai | |
| Groq | openinference-instrumentation-groq |  |
| Instructor | openinference-instrumentation-instructor |  |
| Anthropic | openinference-instrumentation-anthropic |  |
| Smolagents | openinference-instrumentation-smolagents |  |
| Agno | openinference-instrumentation-agno |  |
| BeeAI | openinference-instrumentation-beeai |  |
| Strands Agents | openinference-instrumentation-strands-agents |  |
| Restate | openinference-instrumentation-openai-agents | |
| MCP | openinference-instrumentation-mcp |  |
| Pydantic AI | openinference-instrumentation-pydantic-ai |  |
| AG2 | openinference-instrumentation-ag2 |  |
| Autogen AgentChat | openinference-instrumentation-autogen-agentchat |  |
| Portkey | openinference-instrumentation-portkey |  |
| Agent Spec | openinference-instrumentation-agentspec |  |
| Claude Agent SDK | openinference-instrumentation-claude-agent-sdk |  |
Span Processors
Normalize and convert data across other instrumentation libraries by adding span processors that unify data.
| Package | Description | Version |
|---|---|---|
[openinference-instrumentation-openlit](./python/instrumentation/openinference-instrumentation-openlit) | OpenInference Span Processor for OpenLIT traces. |  |
[openinference-instrumentation-openllmetry](./python/instrumentation/openinference-instrumentation-openllmetry) | OpenInference Span Processor for OpenLLMetry (Traceloop) traces. |  |
JavaScript Integrations
| Integration | Package | Version |
|---|---|---|
| OpenAI | @arizeai/openinference-instrumentation-openai |  |
| OpenAI Agents | @arizeai/openinference-instrumentation-openai-agents |  |
| LangChain.js | @arizeai/openinference-instrumentation-langchain |  |
| TanStack AI | @arizeai/openinference-tanstack-ai |  |
| Vercel AI SDK | @arizeai/openinference-vercel |  |
| BeeAI | @arizeai/openinference-instrumentation-beeai | |
| Claude Agent SDK | @arizeai/openinference-instrumentation-claude-agent-sdk |  |
| Mastra | @mastra/arize |  |
| MCP | @arizeai/openinference-instrumentation-mcp |  |
Java Integrations
| Integration | Package | Version |
|---|---|---|
| LangChain4j | openinference-instrumentation-langchain4j |  |
| SpringAI | openinference-instrumentation-springAI |  |
| Arconia for Spring AI | io.arconia:arconia-openinference-semantic-conventions |  |
Go Integrations
| Integration | Package | Version |
|---|---|---|
| OpenAI | github.com/Arize-ai/openinference/go/openinference-instrumentation-openai-go |  |
| Anthropic | github.com/Arize-ai/openinference/go/openinference-instrumentation-anthropic-sdk-go |  |
Platforms
| Platform | Description | Docs |
|---|---|---|
| BeeAI | AI agent framework with built-in observability | Integration Guide |
| Dify | Open-source LLM app development platform | Integration Guide |
| Envoy AI Gateway | AI Gateway built on Envoy Proxy for AI workloads | Integration Guide |
| LangFlow | Visual framework for building multi-agent and RAG applications | Integration Guide |
| LiteLLM Proxy | Proxy server for LLMs | Integration Guide |
| Flowise | Visual framework for building LLM applications | Integration Guide |
| Prompt Flow | Microsoft's prompt flow orchestration tool | Integration Guide |
| NVIDIA NeMo | NVIDIA NeMo Agent Toolkit for enterprise agents | Integration Guide |
| Graphite | Multi-agent LLM workflow framework with visual builder | Integration Guide |
Sandboxes
Run Phoenix code evaluators in hosted sandbox providers for kernel-level isolation and runtime dependency installation.
| Integration | Description | Docs |
|---|---|---|
| E2B | Hosted micro-VM sandboxes for AI-generated code | Integration Guide |
| Daytona | Managed development sandboxes with snapshot startup | Integration Guide |
| Vercel Sandbox | Ephemeral compute on Vercel's infrastructure | Integration Guide |
| Modal | Serverless, Python-first container platform | Integration Guide |
For Humans and Coding Agents
Phoenix is built to be driven by people and by AI coding agents alike. Three surfaces let agents (Claude Code, Codex, Cursor, and others) work with your traces, datasets, and experiments:
- **CLI** —
npx @arizeai/phoenix-clifetches traces, datasets, and experiments and instruments your app (setup), so an agent can pull context and act on it from the terminal. - **Skills** — [
.agents/skills/](.agents/skills/) packages workflows that teach agents how to debug, evaluate, and trace with Phoenix. - **Remote MCP Server** — connect any MCP client to your instance's
/mcpendpoint to query Phoenix directly.
See the coding agents documentation for setup and usage.
| Skill | Description |
|---|---|
| phoenix-cli | Debug LLM applications using the Phoenix CLI — fetch traces, analyze errors, review experiments, and query the GraphQL API |
| phoenix-evals | Build and run evaluators for AI/LLM applications using Phoenix |
| phoenix-tracing | OpenInference 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.
- 🌍 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.
- 💼 Follow us on LinkedIn.
- 🗺️ 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.
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