HalluciGuard – open-source middleware to detect and mitigate LLM hallucinations (github.com)

HalluciGuard has been launched as an open-source middleware designed to detect and mitigate hallucinations in large language models (LLMs), addressing a critical issue in AI reliability. With the rise of LLM usage in various sectors, AI hallucinations—where models generate misleading or false information—have emerged as a major barrier to trust. HalluciGuard enhances LLM applications by wrapping calls to these models in a reliability layer that extracts claims, scores their confidence, and verifies them against external sources like the web. Significantly, HalluciGuard offers a suite of features, including claim extraction, real-time risk flagging, and cost-saving cache mechanisms that can reduce API bills by over 80%. It supports multiple LLM providers such as OpenAI and Anthropic, making it versatile for diverse applications. With demonstrated reductions in hallucination rates—from 12.3% to just 1.8% for GPT-4o—and high detection accuracy, HalluciGuard promises to bolster the trustworthiness of AI in critical fields, enhancing its adoption by organizations that require reliable outputs. Contributions to its ongoing development are encouraged, reflecting a community-driven effort to raise the standards of AI safety.