A Guardrails Library That Publishes Its Misses
Ask a guardrails library how often it is wrong and you will usually get silence, or a benchmark of how fast it is. That is the gap jamjet-guardrails was built into. Nine deterministic checks for LLM input and output, zero runtime dependencies, and a published precision and recall figure for every one of them, measured on a corpus committed to the repository and gated in CI so a change that moves…
Jamjet-guardrails emerges as a guardrails library that takes pride in openly acknowledging its limitations. This innovative tool offers nine deterministic checks for both LLM input and output, ensuring transparency and accountability. Each check comes with a published precision and recall figure, measured on a carefully curated corpus committed to the repository.
The repository also gates these metrics during CI, ensuring that any change that compromises the numbers triggers a failed build. One of the standout features of jamjet-guardrails is its explicit naming of cases it gets wrong, which is conveniently documented in the README using case IDs. This approach offers several advantages over traditional scanners, which often only provide a boolean result and a score, without any contextual information.
With jamjet-guardrails, users can pinpoint exactly what the system found, where, and even redact the offending content if necessary. Moreover, the library provides typed findings with character spans, enabling useful audit records that clearly outline what was removed from where. Every check in jamjet-guardrails also declares the specific directions it applies to, ensuring that rules tailored for model outputs do not inadvertently affect user inputs.
With these nine checks, no runtime dependencies, and a comprehensive corpus dedicated to measuring their precision and recall, jamjet-guardrails sets a new standard for transparency and reliability in guardrails libraries.
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