Orb
Summary
Orb is an AI personal assistant in the productivity space with no documented differentiator.
The tool assists with personal tasks and productivity activities through AI means. It addresses the need for automated assistance in daily workflows. No specific differentiator stands out from available information. No concrete pricing figure is provided. The main catch is the absence of documented details on performance or unique aspects, which leaves its actual utility unclear.
Bottom line: Consider Orb only after direct testing; skip when tools with published specs are available.
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Orb Backend is the open-source Python server that powers Orb, a personal AI assistant built around two ideas most tools skip: the assistant should initiate, and your data should stay on your hardware. The core workflow runs through a brain/mind architecture — brain.py handles model routing and tool dispatch, mind.py runs the planning and task-proposal loop — with a WebSocket and HTTP API surface that the iOS app connects to, and that custom clients can use directly. Voice input is handled by a local STT module; Apple Push Notification Service integration surfaces background job results to mobile.
The differentiating feature is the proactive engine. Rather than waiting for a prompt, proactive_engine.py monitors your task store and generates proposals — the model decides when to speak and what to surface. This is not a chatbot with a cron job bolted on; the intent router and agent layer are designed so the model can invoke tools and act on its own judgment within the session context.
The project is MIT-licensed, fully self-hostable via git clone and a supervisor.py process manager, and carries no paid tier or commercial API dependency. That makes it genuinely free to run at whatever scale your hardware allows, which is rare in this category. The trade-off is maturity: eight commits, a single-digit star count, and documentation that explicitly targets Windows server setups. Linux and macOS users will find the server_win.py entry point and supervisor assumptions are not abstracted away.
The API layer — documented in API.md — accepts WebSocket and HTTP connections, so integrating a custom client or a non-iOS front end is architecturally supported. MCP (model context protocol) integration is present via orb_mcp.py and mcp_http.py, giving the tool registry a path to external tool providers. Memory is handled by a local store, with a separate orb_memory.py module for longer-term context persistence.