Evaluation & Testing

llm_rules

Benchmark for evaluating rule-following in language models

Status
UnmaintainedNo commits in 559 days.
Last checked
Checked · Oct 12, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

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.

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.

When it fits — and when it doesn’t

A good fit 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

Look elsewhere if

  • 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

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited integrations with non-Python ecosystems

    Primary support is for Python, other language bindings are experimental and community-driven

  2. 02

    Small community and limited third-party contributions

    GitHub activity shows few contributors and infrequent updates to non-Python SDKs

What it does well

  • Comprehensive benchmark for rule-following in language models

  • Open-source and freely available under Apache-2.0 license

  • Designed to assess the reliability of AI systems

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up llm_rules

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →