llm_rules
Benchmark for evaluating rule-following in language models
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is llm_rules?
RuLES is a benchmark designed to evaluate how well language models follow rules. It's crucial for assessing the reliability and safety of AI systems.
Key differentiator
“llm_rules provides a specialized benchmark for evaluating how well language models follow rules, focusing on the critical aspect of reliability and safety in AI systems.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Primary support is for Python, other language bindings are experimental and community-driven
GitHub activity shows few contributors and infrequent updates to non-Python SDKs
Fit analysis
Who is it for?
✓ Best for
Research teams looking to benchmark rule-following capabilities in language models
Developers assessing the safety and reliability of AI systems
Academics studying machine learning and natural language processing
✕ Not a fit for
Teams requiring real-time performance metrics (benchmark is not designed for real-time use)
Projects focused on other aspects of model evaluation beyond rule-following
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with llm_rules
Step-by-step setup guide with code examples and common gotchas.