DeskMind Studio – Modular AI Desktop Agent

DeskMind is a modular, extensible AI-powered desktop platform designed for automation, knowledge management, intelligent planning, plugin workflows, and secure execution.

This documentation repository includes an overview of DeskMind’s current features — each one shown with actual UI screenshots.

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🖼️ Screenshot Gallery

A complete visual overview of the DeskMind user interface.

Chat Interface

Docker Execution Example

Knowledge Base Explorer

Log View

Planner

Planner Runner

Docker Plugin

File Manager Plugin

Plugin Manager

Python Runner Plugin

Settings View

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⭐ Feature Overview (With Screenshot References)

1. AI Chat Interface

Screenshot: chat.jpg

The conversational hub of DeskMind — integrates LLM reasoning, plugin execution, contextual chat, and tool use.

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2. Plugin Architecture

Screenshots:

• plugin_manager.jpg — Plugin Manager
• plugin_pyrunner.jpg — Python Runner
• plugin_docker.jpg — Docker Plugin
• plugin_file.jpg — File Manager

Core capabilities:

• Secure plugin sandboxing
• Custom tools/functions
• Dynamic enable/disable
• Metadata + permission controls

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3. Knowledge Base / RAG Explorer

Screenshot: kb_explorer.jpg

Browse indexed documents, vector results, and knowledge sources.
(Upload & auto-chunking coming soon.)

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4. Planner View (Task & Phase Builder)

Screenshot: planner.jpg

Visual node-based planner to break goals into phases and tasks.
Supports LLM-based generation, refinement, and auto-layout.

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5. Planner Runner (Task Execution)

Screenshot: planner_runner.jpg

Execute tasks step-by-step with:

• Reasoning output
• Regeneration
• Explanation
• Plugin-driven task execution

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6. Docker Integration

Screenshots:

• plugin_docker.jpg — Docker management interface
• example_docker.jpg — LLM using Docker to perform a task

Features:

• List DeskMind containers
• Filter via prefix
• Start/stop/restart
• Inspect
• View logs
• LLM-controlled secure container actions

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7. File System Explorer Plugin

Screenshot: plugin_file.jpg

Browse hierarchical engineering files, XML-based data structures, and plugin workspaces.

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8. Log Viewer

Screenshot: log.jpg

Central hub for:

• LLM logs
• Plugin calls
• Docker events
• Errors
• Diagnostic traces

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9. Settings & Configuration

Screenshot: settings.jpg

Configure:

• LLM model providers (local/remote)
• RAG hosts & vector DB
• Plugin settings
• UI preferences
• Experimental features

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🚀 Roadmap

• Document upload + chunking
• R Language Plugin
• Multi-agent orchestration
• Decentralized RAG for organizations
• Avalonia UI port (FluentAvalonia)
• Local lightweight LLM bundling
• Node-based automation pipeline editor