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

分析与监控

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

关于

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.

主要功能

Experiment pipelines for prompt and model configuration comparison
384 GitHub stars
LLM-as-judge evaluators for automated quality assessment
Versioned dataset management for rigorous regression testing
OpenTelemetry-based trace collection for any LLM framework
Real-time production monitoring and token usage tracking

使用场景

Implementing self-hosted AI observability for data privacy and security
Benchmarking prompt performance across different model versions
Debugging complex LLM application flows with nested span tracing

关于

主要功能

Experiment pipelines for prompt and model configuration comparison
384 GitHub stars
LLM-as-judge evaluators for automated quality assessment
Versioned dataset management for rigorous regression testing
OpenTelemetry-based trace collection for any LLM framework
Real-time production monitoring and token usage tracking

使用场景

Implementing self-hosted AI observability for data privacy and security
Benchmarking prompt performance across different model versions
Debugging complex LLM application flows with nested span tracing