This paper presents IronEngine, a general AI assistant platform organized around a unified orchestration core that connects a desktop user interface, REST and WebSocket APIs, Python clients, local and cloud model backends, persistent memory, task scheduling, reusable skills, 24-category tool execution, MCP-compatible extensibility, and hardware-facing integration. IronEngine introduces a three-phase pipeline -- Discussion (Planner--Reviewer collaboration), Model Switch (VRAM-aware transition), and Execution (tool-augmented action loop) -- that separates planning quality from execution capability. The system features a hierarchical memory architecture with multi-level consolidation, a vectorized skill repository backed by ChromaDB, an adaptive model management layer supporting 92 model profiles with VRAM-aware context budgeting, and an intelligent tool routing system with 130+ alias normalization and automatic error correction. We present experimental results on file operation benchmarks achieving 100\% task completion with a mean total time of 1541 seconds across four heterogeneous tasks, and provide detailed comparisons with representative AI assistant systems including ChatGPT, Claude Desktop, Cursor, Windsurf, and open-source agent frameworks. Without disclosing proprietary prompts or core algorithms, this paper analyzes the platform's architectural decomposition, subsystem design, experimental performance, safety boundaries, and comparative engineering advantages. The resulting study positions IronEngine as a system-oriented foundation for general-purpose personal assistants, automation frameworks, and future human-centered agent platforms.

翻译：本文提出IronEngine，一个围绕统一编排核心构建的通用人工智能助手平台。该平台集成了桌面用户界面、REST与WebSocket API、Python客户端、本地与云端模型后端、持久化记忆系统、任务调度模块、可复用技能库、24类工具执行器、MCP兼容扩展机制及面向硬件的集成接口。IronEngine引入三阶段流水线架构——讨论阶段（规划器-评审器协同）、模型切换阶段（显存感知过渡）与执行阶段（工具增强动作循环）——实现了规划质量与执行能力的解耦。系统采用具有多级聚合机制的分层记忆架构，基于ChromaDB的向量化技能存储库，支持92种模型配置且具备显存感知上下文预算的自适应模型管理层，以及包含130余种别名归一化与自动纠错功能的智能工具路由系统。我们在文件操作基准测试中实现了100%的任务完成率，四项异构任务平均总耗时1541秒，并与ChatGPT、Claude Desktop、Cursor、Windsurf等代表性AI助手系统及开源智能体框架进行了详细对比。在不披露专有提示词与核心算法的前提下，本文分析了平台架构分解、子系统设计、实验性能、安全边界及工程比较优势。本研究将IronEngine定位为面向通用个人助手、自动化框架及未来以人为本的智能体平台的系统级基础架构。