What is Phoenix for LLM observability?

Phoenix is an open-source platform for tracing, evaluating, and monitoring LLM applications, helping developers find and fix issues in AI pipelines through detailed visual traces.

Can I use Phoenix for hallucination detection?

Yes, Phoenix includes built-in 'LLM-as-judge' evaluators specifically designed to detect hallucinations, relevance, and toxicity in AI-generated responses.

Is Phoenix a self-hosted solution?

Yes, Phoenix is fully open-source and can be self-hosted using Docker or Python with PostgreSQL or SQLite, ensuring you maintain full control over your observability data.

How does Phoenix integrate with LangChain or LlamaIndex?

Phoenix uses OpenTelemetry-based instrumentation to automatically capture traces from popular frameworks like LangChain, LlamaIndex, and the OpenAI SDK with minimal configuration.

Phoenix AI Observability

Name: Phoenix AI Observability
Author: Orchestra-Research

byOrchestra-Research

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3,983

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Analytics & Monitoring

Provides comprehensive LLM observability, tracing, and evaluation tools to debug and monitor AI applications in real-time.

Phoenix is an open-source platform designed to bring transparency to LLM applications through advanced tracing, systematic evaluation, and production monitoring. It enables developers to visualize complex execution flows with OpenTelemetry-based traces, run 'LLM-as-judge' evaluations to ensure output quality, and manage datasets for rigorous regression testing. Whether you are debugging RAG pipelines, comparing model performance in an interactive playground, or monitoring live production systems for hallucinations and toxicity, Phoenix provides the necessary infrastructure to build reliable AI systems without vendor lock-in.

Key Features

01OpenTelemetry-based trace collection for LangChain, LlamaIndex, and OpenAI

02LLM-as-judge evaluators for hallucination, relevance, and toxicity detection

03Interactive playground for side-by-side prompt and model comparisons

04Production-ready monitoring with self-hosted PostgreSQL or SQLite support

05Versioned dataset management for experiments and regression testing

063,983 GitHub stars

Use Cases

01Monitoring live LLM deployments for performance regressions and output quality issues

02Debugging complex RAG applications by visualizing multi-step retrieval and generation traces

03Running automated quality benchmarks on new model versions or prompt templates

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add orchestra-research/ai-research-skills phoenix

For use in Claude.ai and ChatGPT

Download Skill