🔧 AI ProductivityOpen SourceFree

Phoenix (Arize)

AI observability — tracing, evaluation, experimentation

4.4/ 5⭐ 10,000 GitHub starsPythonApache-2.0

📋 Overview

Phoenix is an open-source AI observability platform that provides tracing, evaluation, and experimentation for LLM applications. Unlike Langfuse (which focuses on tracing and evals), Phoenix emphasizes experimentation — A/B testing prompts, comparing model versions, and analyzing output quality at scale. It integrates with OpenTelemetry, LangChain, LlamaIndex, and OpenAI SDK. The platform provides trace exploration, span analysis, and built-in evaluation frameworks for common tasks (hallucination detection, relevance scoring).

✨ Key Features

•10K+ GitHub stars — AI observability with experimentation
•Tracing: capture prompts, responses, latency, token usage
•Evaluation: LLM-as-judge, heuristic, human evals
•Experimentation: A/B test prompts and models
•OpenTelemetry integration
•LangChain, LlamaIndex, OpenAI SDK support
•Trace exploration and span analysis
•Statistical significance testing
•Span analysis

🎯 The Problem It Solves

Teams need to compare prompt versions, evaluate model outputs, and debug production LLM behavior. Phoenix provides a unified platform for tracing, evaluation, and experimentation — going beyond observability to active improvement.

🔧 How It Works

Phoenix integrates via OpenTelemetry or SDK — wrap your LLM calls to automatically capture traces. The evaluation framework runs LLM-as-judge, heuristic, and human evaluations against your traces. The experimentation module lets you A/B test prompts and models, with statistical significance testing. The dashboard provides trace exploration, span analysis, and quality metrics.

🚀 Installation & Quick Start

Installation

See website

Quick Start

See documentation

✅ Pros

•Strong experimentation features
•OpenTelemetry integration
•Multiple evaluation methods
•Active development
•Apache 2.0 licensed
•Statistical significance testing
•Span analysis

❌ Cons

•Smaller community than Langfuse
•No cloud tier
•Evaluation framework less mature
•Documentation lags
•Limited deployment options

💬 Practitioner Verdict

“Phoenix is a strong Langfuse alternative — the experimentation features are genuinely differentiated, and the OpenTelemetry integration is excellent. The trade-off: smaller community than Langfuse, no cloud tier, and the evaluation framework is less mature. For teams that want open-source AI observability with experimentation, Phoenix is the default.”