Hi HN, We're excited to share GroundCite, an open-source, Gemini-based multi-agent library designed to solve one of the biggest headaches in using powerful LLMs for reliable, grounded research: invalid, irrelevant, or broken citations. The Problem While Google Gemini's grounding feature is powerful, relying on it for high-stakes, trustworthy content often results in sources that are: 1. Irrelevant: The source doesn't actually support the claim. 2. Unreliable: The model cites a site you know to be low-quality or out of date. 3. Broken/Invalid: The URL is dead or the cited snippet is missing. This fundamentally undermines the research integrity of LLM outputs. Our Solution: GroundCite GroundCite acts as a smart layer around the core Gemini model to enforce accuracy and provide developers with granular control over the grounding process. It utilizes a multi-agent architecture to validate and filter sources before the final answer is generated, turning Gemini into a much more reliable research assistant. Key Features: • Source Filtering & Control: You can explicitly define a list of trusted sites for the model to use, or, critically, exclude unreliable sources (like forums or outdated wikis) from the grounding pool. • Active Citation Validation: The library validates the generated source URLs and confirms that the content within them actually supports the model's claim. • Enhanced Reliability: By forcing smarter, controlled grounding and citation practices, GroundCite significantly boosts the trustworthiness and accuracy of the output. We built this out of necessity and decided to open-source it for the wider community struggling with reliable AI research. Links • Playground / Demo: See the difference controlled grounding makes and try it before integrating: https://groundcite.cennest.com/ • GitHub Repository: We welcome stars, issues, and contributions! https://github.com/cennest/ground-cite • Technical Articles / Full Story: https://www.cennest.com/category/groundcite/ We are looking for feedback on the multi-agent architecture, potential use cases, and how we can best extend this to support other LLMs and grounding services. Thanks!