Enables AI assistants to analyze documents larger than their context window by loading files into RAM and querying them via search, navigation, and Python execution tools. Supports recursive reasoning to process massive datasets in chunks using sub-agents.
Enables AI agents to access a codebase context, select relevant files, and route queries to the appropriate AI model based on complexity, all through an MCP interface.
Enables analysis of documents far larger than an LLM's context window by having the model emit small symbolic commands that execute server-side, returning compact handles instead of bulk data. It also provides a persistent memo pad so agents can stash context and expand it only when needed.
Enables AI agents to consult with multiple AI models (GPT, Gemini, Grok, etc.) through OpenRouter with intelligent auto-selection, conversation history, and caching. Allows your AI assistant to seek expert opinions from specialized models for different tasks like coding, analysis, or general questions.