Browser-Based AI Assistants

Browser-based AI assistants have emerged as a new paradigm in human–computer interaction, enabling intelligent automation and natural language-driven control within web environments. This survey paper presents a comprehensive review of recent developments in intelligent web assistants powered by Large Language Models (LLMs), reasoning frameworks such as LangGraph, and the Model Context Protocol (MCP) for structured model-to-system communication. Existing studies demonAstrate significant advancements in natural language understanding, web task automation, and user-adaptive learning through feedback loops. However, challenges remain in privacy protection, computational efficiency, and multi-domain scalability. Through a detailed analysis of current research trends, this paper identifies existing limitations, compares architectural approaches, and highlights open research gaps—particularly in the secure integration of MCP within browsers and the optimization of multi-turn contextual reasoning. The findings suggest that browser-based LLM assistants represent a key direction for real-time, personalized we intelligence