Mirage unifies roughly 50 data sources into a single virtual filesystem, exposing standard bash commands (cp, grep, wc, cat, find, etc.) for AI agents to operate across backends, with support for MCP, FUSE, and native framework adapters. Its value is eliminating the need to learn many separate SDKs/MCPs, but the general-purpose command execution surface means permissions and risk need careful scoping based on how sensitive the mounted backends are.
Mirage is a Unified Virtual File System for AI Agents: it mounts around 50 built-in backends—S3, Google Drive, Slack, Gmail, OneDrive/SharePoint, Redis, Postgres, and more—side-by-side as a single filesystem. Any LLM that already knows bash can read, grep, and pipe across every backend using familiar commands like cp, grep, wc, cat, and find, with no new vocabulary to learn. It ships Python and TypeScript SDKs plus a CLI, and integrates with agent frameworks (OpenAI Agents SDK, LangChain, Claude Code, and others) via native adapters, installable plugins, MCP, or FUSE. The included test case (office_server_copy_no_reenrich) copies an Office document across OneDrive/SharePoint targets and verifies the copy doesn't trigger unnecessary re-enrichment, illustrating consistent file-operation behavior across cloud storage backends.
Choose the install path for your stack: Python users can run uv add mirage-ai to get the mirage-ai package and the mirage CLI (requires Python ≥3.11); TypeScript/Node.js users can run npm install @struktoai/mirage-node (or mirage-browser / mirage-agents) (requires Node.js ≥20); a standalone CLI is also available via the official install script or npm install -g @struktoai/mirage-cli. After installing, declare the backends to mount (e.g., OneDrive, SharePoint) and their credentials in a workspace configuration, then connect through MCP, a native adapter, or FUSE depending on the target coding agent. The source material does not include a specific MCP client configuration JSON example.