How does this skill help with debugging?

It provides detailed OpenTelemetry-based traces of your LLM's execution flow, helping you identify exactly where failures or latency occur in your chain.

What are LLM-as-judge evaluators?

They are specialized models used to automatically grade the quality, relevance, or toxicity of your AI's outputs based on predefined criteria.

What is Phoenix AI Observability?

Phoenix is an open-source platform designed for monitoring, tracing, and evaluating Large Language Model (LLM) applications.

Does Phoenix support self-hosting?

Yes, Phoenix is open-source and can be self-hosted using Docker or PostgreSQL, offering a privacy-focused alternative to managed SaaS services.

Can I use Phoenix with LangChain or LlamaIndex?

Yes, Phoenix includes built-in instrumentation for popular frameworks like LangChain, LlamaIndex, OpenAI, and Anthropic.

Phoenix AI Observability

Name: Phoenix AI Observability
Author: zechenzhangAGI

byzechenzhangAGI

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384

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분석 및 모니터링

Monitors, traces, and evaluates LLM applications using an open-source, OpenTelemetry-based observability platform.

Phoenix provides a comprehensive toolkit for AI engineering, enabling developers to debug LLM application issues through detailed traces, run systematic evaluations on datasets, and monitor production AI systems in real-time. By leveraging OpenTelemetry standards, it offers a vendor-neutral approach to observability, allowing teams to compare prompts and models through experiment pipelines and ensure quality with LLM-as-judge evaluators. It is ideal for teams seeking a self-hosted alternative to managed platforms like LangSmith, providing deep insights into RAG pipelines, agentic workflows, and token usage.

주요 기능

01Experiment pipelines for prompt and model configuration comparison

02384 GitHub stars

03LLM-as-judge evaluators for automated quality assessment

04Versioned dataset management for rigorous regression testing

05OpenTelemetry-based trace collection for any LLM framework

06Real-time production monitoring and token usage tracking

사용 사례

01Implementing self-hosted AI observability for data privacy and security

02Benchmarking prompt performance across different model versions

03Debugging complex LLM application flows with nested span tracing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zechenzhangagi/ai-research-skills phoenix

For use in Claude.ai and ChatGPT

주요 기능

01Experiment pipelines for prompt and model configuration comparison

02384 GitHub stars

03LLM-as-judge evaluators for automated quality assessment

04Versioned dataset management for rigorous regression testing

05OpenTelemetry-based trace collection for any LLM framework

06Real-time production monitoring and token usage tracking

사용 사례

01Implementing self-hosted AI observability for data privacy and security

02Benchmarking prompt performance across different model versions

03Debugging complex LLM application flows with nested span tracing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zechenzhangagi/ai-research-skills phoenix

For use in Claude.ai and ChatGPT