- 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": "\⚖\️", "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 20Evaluators
| Evaluator | Measures | Scale |
|---|---|---|
| relevance | Response relevance to query | 0–1 |
| accuracy | Factual correctness | 0–1 |
| hallucination | Made-up information detection | 0–1 |
| helpfulness | Overall usefulness | 0–1 |
Credits
Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.
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