Is Jarvis Orb a local application or cloud-based?

Jarvis Orb is a compact, 3MB desktop application designed for local operation. It has zero cloud dependencies, ensuring privacy, speed, and full control over your AI's data directly on your machine.

Is Jarvis Orb compatible with other AI development tools?

Yes, it functions as an MCP (Model Context Protocol) server, integrating seamlessly with Claude Code, Cursor, and any other MCP-compatible tools to extend their context capabilities.

Jarvis Orb provides AI agents with persistent memory and visualizes their real-time thought processes as an interactive 3D desktop orb, ensuring your AI never loses context across sessions.

Jarvis Orb: AI Memory, Thought Visualization & MCP Server

Jarvis Orb solves the common issue of AI agents losing context with every session by giving them a persistent brain. It allows your AI to remember past decisions, project states, and user preferences, eliminating the need for repetitive explanations and preventing contradictory suggestions. Beyond just memory, Jarvis Orb offers a unique real-time visualization: it displays the AI's internal thought processes directly on your desktop as a dynamic 3D orb. This interactive orb shows events like memory saving, contradiction detection, and entity state changes, transforming your AI from a stateless tool into a continuous, observable presence.

Características Principales

01MCP Server Compatibility: Integrates seamlessly with Claude Code, Cursor, and other Model Context Protocol (MCP) compatible tools.

02Local & Lightweight Architecture: A compact 3MB desktop app with zero cloud dependencies, running entirely locally for privacy and speed.

03Real-time AI Brain Visualization: Displays AI thought processes, memory events, and state changes live on an interactive 3D desktop orb.

044-Tier Persistent Memory: Automatically classifies episodic, semantic, project, and procedural memories with temporal scoring and full-text search.

05Contradiction Detection & Entity Tracking: Identifies conflicting memories, marks superseded decisions, and tracks entities (projects, PRs) with state transition history.

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Casos de Uso

01Maintain consistent project context and architectural decisions across multiple AI sessions, eliminating repetitive explanations.

02Identify and address contradictions in AI suggestions, ensuring alignment with past project history and user preferences.

03Gain real-time insight into an AI's thought process during development, debugging, or complex problem-solving scenarios.