RustyRAG

RustyRAG is a cloud-based Retrieval-Augmented Generation (RAG) API designed for AI agents and applications that require real-time, cited answers from user-provided documents. It addresses the need for fast, production-ready document retrieval and question answering, enabling users to turn their data into actionable, grounded responses suitable for integration with various AI workflows.

The platform offers a suite of features for document ingestion, retrieval, and answer generation. Users can upload documents directly or connect sources such as Google Drive, OneDrive, SharePoint, Gmail, Outlook, Slack, Notion, GitHub, Jira, Confluence, Salesforce, ServiceNow, and web crawls. RustyRAG supports hybrid semantic and keyword search across millions of document chunks, with streaming cited answers typically delivered in under 300 milliseconds. The system performs retrieval, reranking, and citation in parallel, and is optimized for low latency through an architecture built entirely in Rust, with inference running on Cerebras and Groq hardware. Additional capabilities include OCR for scanned documents, layout parsing for PDFs and tables, and auto-refreshing of indexed content when source documents change.

Documents can be organized into collections and shared with teammates or entire organizations, with role-based access controls and one-click revocation. The platform provides observability features such as query tracing, answer auditing, per-workspace usage metrics, cost attribution, and the ability to replay queries for debugging and compliance purposes.

RustyRAG is accessible via multiple integration options, including TypeScript and Python SDKs, a native MCP server for agent frameworks, and a REST API. This allows developers to incorporate the service into a wide range of applications, workflows, and AI agent environments. The system is designed to require minimal setup, with no infrastructure management or retrieval pipeline tuning necessary. Users obtain an API key, upload documents, and can immediately begin streaming cited answers into their applications.

The tool is positioned as a production-grade RAG solution, targeting developers and teams building AI-powered products that need reliable, fast, and cited document-based question answering.