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phoenix
AI Observability & Evaluation
10kstars
906forks
Python
agentsai-monitoringai-observabilityaiengineeringanthropicdatasetsevalslangchainllamaindexllm-evalllm-evaluationllmops
Quality signals
StaleNo testsNo CIThin docsNo licensePopular
AI summary
Auto-generated overview of what this skill does and when to reach for it.
Open-source toolkit for monitoring, debugging, and evaluating AI applications in production.
Use this when
- Debugging why an AI model is giving bad answers
- Tracking AI performance drift over time
- Comparing multiple AI models side-by-side
- Detecting hallucinations or toxic outputs
- Logging and analyzing AI interactions at scale
Capabilities
- Traces AI model inputs and outputs for debugging
- Evaluates AI responses with built-in metrics
- Compares model versions for performance changes
- Detects anomalies in AI behavior automatically
- Supports LLM, vision, and structured data models
- Integrates with popular AI frameworks like LangChain
InstallSKILLInstall as a Claude Code skill
Universal installANY TOOL
$ npx skillcity@latest Arize-ai/phoenixAuto-detects the skill or MCP server in this repo and installs it for Claude Code, Codex, Cursor, Cline, OpenCode, Gemini CLI, Anti-Gravity, OpenClaw and Hermes.
Requires Node.js 18+ (ships with npx).
Or install manually
# Install into ~/.claude/skills/ mkdir -p ~/.claude/skills/phoenix curl -fsSL https://raw.githubusercontent.com/Arize-ai/phoenix/main/SKILL.md -o ~/.claude/skills/phoenix/SKILL.md
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Ask a simulated agent to run this skill. It loads the real SKILL.md and reasons over your task with NVIDIA.
phoenix — sandboxAI sandbox
Pick a quick prompt below or type your own — watch the agent execute this skill step by step.
guest@skillcity:~$Try a prompt