Phoenix：追踪与评估 LLM 应用

AI Observability & Evaluation

🇺🇸 English • 🇨🇳 中文 • 🇹🇼 繁體中文 • 🇯🇵 日本語 • 🇵🇹 Português • 🇧🇷 Português • 🇰🇷 한국어 • 🇪🇸 Español • 🇩🇪 Deutsch • 🇫🇷 Français • 🇮🇱 עברית • 🇸🇦 العربية • 🇷🇺 Русский • 🇵🇱 Polski • 🇨🇿 Čeština • 🇳🇱 Nederlands • 🇹🇷 Türkçe • 🇺🇦 Українська • 🇻🇳 Tiếng Việt • 🇵🇭 Tagalog • 🇮🇩 Indonesia • 🇹🇭 ไทย • 🇮🇳 हिन्दी • 🇧🇩 বাংলা • 🇵🇰 اردو • 🇷🇴 Română • 🇸🇪 Svenska • 🇮🇹 Italiano • 🇬🇷 Ελληνικά • 🇭🇺 Magyar • 🇫🇮 Suomi • 🇩🇰 Dansk • 🇳🇴 Norsk Arize Phoenix is Arize's open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. For managed production workflows, Arize also offers Arize AX. Phoenix 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 /mcp endpoint 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. Note Phoenix is open-source and self-hosted. For a managed platform, Arize offers Arize AX — the same OpenTelemetry/OpenInference instrumentation works with both. See the full comparison. 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 serve Or run it with no install using uvx: uvx arize-phoenix serve Trace 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 setup Setup 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 o