We scaled our AI assistant to use virtually unlimited number of tools (gaia-fork-k7yngvswe-gaias-projects-2dead09b.vercel.app)

A new breakthrough in AI tool management has emerged with the introduction of a system capable of scaling to thousands of integrations without overwhelming the model's context. Initially facing limitations at around 200 tools, the team discovered that traditional methods, like a manual tool search, were inefficient and led to hallucinations and bloated response times. The solution came through the innovative use of LangGraph Big Tools, which utilizes semantic search for tool retrieval. This allows the model to issue natural language queries that dynamically bind to relevant tools, effectively managing context and significantly improving response latency. The newly structured architecture features three distinct layers: a Communications Agent focused solely on conversational interactions, an Executor Agent that orchestrates tasks across various tools, and specialized Provider Subagents for specific integrations. This separation of responsibilities allows the model to better understand user context while executing tasks efficiently without confusion. Additionally, the architecture supports custom integrations with any Model Context Protocol-compliant tool, enhancing extensibility. Collectively, these improvements lead to a more responsive, contextually aware, and user-friendly AI assistant that continuously learns from interactions, optimizing its performance over time.