guardrails-ai/guardrails is a Python framework that enforces constraints on the inputs and outputs of large language models (LLMs). It runs Input/Output Guards before a request is passed to the model and after the answer comes back, detecting risks, quantifying them, and then mitigating them. It is distributed under the Apache-2.0 license, and the latest release in the repository is v0.11.0.
The purpose of this framework is to build trustworthy AI applications. Instead of passing model responses straight through to the service, it applies format constraints and content constraints in the middle of the execution path. It also provides handling paths for validation failures. Multiple validators from Guardrails Hub can be combined, and it helps to generate structured data from LLM output. It supports proprietary and open-source LLMs and can be used from Python and JavaScript. The repository was created on January 29, 2023, and its GitHub star count is 7543.
That said, the release notes and changelog for v0.11.0 are not in the available material, so there is no way to know what this version changed. There are also no comparison figures for how well it detects risks. The repository description is a single sentence about adding guardrails to LLMs. The research record lists only a momentum score of 3.2, with no novelty, technical, or quality scores given. The Apache-2.0 license makes it easy to start a review, but until it is clear what this version changed, there is not enough basis to decide on adopting it.