π€ AI Summary
This work addresses key limitations in current AI assistants regarding the coupling of planning and execution, resource scheduling, and tool integration. To overcome these challenges, we propose IronEngineβa general-purpose, efficient, and scalable AI assistant platform featuring a decoupled three-stage pipeline of deliberation, model switching, and execution. The architecture incorporates a unified orchestration core, a hierarchical memory system, and an MCP-compatible framework. IronEngine supports VRAM-aware scheduling across 92 models, intelligent routing with automatic error correction for over 130 tools, and multi-endpoint APIs alongside hardware interfaces. Experimental results demonstrate that IronEngine achieves a 100% task completion rate on file operation benchmarks, with an average execution time of 1,541 seconds, significantly outperforming mainstream systems such as ChatGPT, Claude Desktop, and Cursor.
π Abstract
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.