Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur'an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) improves grounding, however, a single retrieve-then-generate pipeline is insufficient for diverse Islamic queries, including verbatim scripture, citation-grounded guidance, and rule-constrained computations such as zakat and inheritance. To address these challenges, we present Fanar-Sadiq, a bilingual Arabic-English Islamic QA system built on a multi-agent, tool-augmented architecture. It is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic queries to specialized modules within an agentic tool architecture. It supports intent-aware routing, retrieval-grounded fiqh answers with normalized citations and verification traces, exact verse lookup with quotation validation, and deterministic Sunni zakat and inheritance calculators with madhhab-sensitive branching. We evaluate the end-to-end system on public Islamic QA benchmarks and show strong effectiveness and efficiency. It is publicly accessible through an API and Web application and has received over 1.9M accesses in less than a year (https://api.fanar.qa/docs).

翻译：大型语言模型（LLMs）能够流畅回答宗教知识问题，但常出现幻觉和来源错配问题，这一问题在伊斯兰教语境下尤为严重——用户期望回答必须基于《古兰经》和《圣训》等经典文本以及教法（fiqh）细则。检索增强生成（RAG）虽能提升回答的可靠性，但单一的"检索-生成"流水线难以应对多样化的伊斯兰教义查询，包括逐字经文章节、需引文支撑的指导性回答，以及天课（zakat）和遗产继承等受规则约束的计算。为解决上述挑战，我们提出Fanar-Sadiq，一个基于多智能体、工具增强架构的双语（阿拉伯语-英语）伊斯兰教义问答系统。该系统是Fanar AI平台的核心组件。Fanar-Sadiq通过智能体工具架构将伊斯兰教义查询路由至专用模块：支持意图感知路由、带规范化引文及验证轨迹的检索增强型教法回答、带引用校验的精确经文章节检索，以及遵循教法学派（madhhab）分支的逊尼派天课与遗产确定性计算器。我们在公开的伊斯兰教义问答基准上进行了端到端系统评估，结果表明系统兼具高效性与准确性。该系统已通过API及Web应用公开访问，上线不到一年累计访问量超过190万次（https://api.fanar.qa/docs）。