AI Scientific Assistant Core (AISAC) is an integrated multi-agent system developed at Argonne National Laboratory for scientific and engineering workflows. AISAC builds on established technologies - LangGraph for orchestration, FAISS for vector search, and SQLite for persistence - and integrates them into a unified system prototype focused on transparency, provenance tracking, and scientific adaptability. The system implements a Router-Planner-Coordinator workflow and an optional Evaluator role, using prompt-engineered agents coordinated via LangGraph's StateGraph and supported by helper agents such as a Researcher. Each role is defined through custom system prompts that enforce structured JSON outputs. A hybrid memory approach (FAISS + SQLite) enables both semantic retrieval and structured conversation history. An incremental indexing strategy based on file hashing minimizes redundant re-embedding when scientific corpora evolve. A configuration-driven project bootstrap layer allows research teams to customize tools, prompts, and data sources without modifying core code. All agent decisions, tool invocations, and retrievals are logged and visualized through a custom Gradio interface, providing step-by-step transparency for each reasoning episode. The authors have applied AISAC to multiple research areas at Argonne, including specialized deployments for waste-to-products research and energy process safety, as well as general-purpose scientific assistance, demonstrating its cross-domain applicability.

翻译：AI科学助手核心（AISAC）是阿贡国家实验室开发的一种用于科学与工程工作流的集成多智能体系统。AISAC基于成熟技术构建——使用LangGraph进行编排、FAISS进行向量搜索、SQLite进行持久化——并将它们整合到一个专注于透明度、溯源追踪与科学适应性的统一系统原型中。该系统实现了路由器-规划器-协调器工作流及可选的评估器角色，通过LangGraph的StateGraph协调基于提示工程设计的智能体，并得到如研究员等辅助智能体的支持。每个角色均通过定制的系统提示定义，这些提示强制要求结构化的JSON输出。一种混合记忆方法（FAISS + SQLite）同时支持语义检索与结构化对话历史记录。基于文件哈希的增量索引策略在科学语料库更新时最大限度地减少了冗余的重复嵌入。一个配置驱动的项目引导层允许研究团队在不修改核心代码的情况下自定义工具、提示与数据源。所有智能体决策、工具调用及检索结果均通过定制的Gradio界面进行记录与可视化，为每个推理环节提供逐步的透明度。作者已将AISAC应用于阿贡实验室的多个研究领域，包括针对废物转化产品研究与能源过程安全的专门部署，以及通用科学辅助任务，展示了其跨领域的适用性。