name: llm-evaluator description: LLM-as-a-Judge evaluation system using Langfuse. Score AI outputs on relevance, accuracy, hallucination, and helpfulness. Backfill scoring on historical traces. Uses GPT-5-nano for cost-efficient judging. Use when evaluating AI quality, building evals, or monitoring output accuracy. homepage: https://www.agxntsix.ai license: MIT compatibility: Python 3.10+, Langfuse instance, OpenRouter API key metadata: {"openclaw": {"emoji": "\u2696\ufe0f", "requires": {"env": ["OPENROUTER_API_KEY"]}, "primaryEnv": "OPENROUTER_API_KEY", "homepage": "https://www.agxntsix.ai"}}

LLM Evaluator ⚖️

LLM-as-a-Judge evaluation system powered by Langfuse. Uses GPT-5-nano to score AI outputs.

When to Use

• Evaluating quality of search results or AI responses
• Scoring traces for relevance, accuracy, hallucination detection
• Batch scoring recent unscored traces
• Quality assurance on agent outputs

Usage

# Test with sample cases
python3 {baseDir}/scripts/evaluator.py test

# Score a specific Langfuse trace
python3 {baseDir}/scripts/evaluator.py score <trace_id>

# Score with specific evaluator only
python3 {baseDir}/scripts/evaluator.py score <trace_id> --evaluators relevance

# Backfill scores on recent unscored traces
python3 {baseDir}/scripts/evaluator.py backfill --limit 20

Evaluators

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

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