About LoOper

Overview

LoOper redefines desktop automation by separating AI decision-making from action execution:

Key Features

Agent Builder & Neuro-Symbolic Graphs

Build secure automation agents where LLMs make high-level decisions from pre-defined action chains. Create deterministic workflow graphs that combine neural (LLM) and symbolic (recorded chains) intelligence for reliable, auditable automation.

Minimal LLM Dependency

LLMs act as strategic selectors - choosing which pre-built action sequence to execute next. The actual work is done by recorded, validated chains that interact directly with applications, reducing LLM calls to decision points only.

Human-in-the-Loop Design

All action workflows are built from manually recorded, monitored sequences. LLMs never execute raw actions - they choose from human-validated behavioral knowledge base, ensuring safety and predictability.

Visual Template Matching & Validation

Recorded sequences use computer vision for reliable UI element detection with fallback mechanisms. Each action is visually validated during execution, creating a secure automation layer.

Strategic LLM Integration

Use LLMs for workflow planning and decision making, screen analysis and content understanding, natural language command interpretation, dynamic parameter selection for pre-built actions, and error recovery with adaptive workflow selection.

Scheduler & Secure Sandboxing

Schedule workflows and execute them in isolated environments. The agent builder approach ensures predictable behavior even in automated scenarios.

Extensible Knowledge Base

Grow your automation capabilities by recording new action sequences. Each recording adds to your agent's behavioral knowledge base that LLMs can leverage for decision making.

Use Cases

Secure Business Process Automation

Build auditable automation agents for finance, HR, and operations where LLMs make routing decisions from pre-validated action chains. Each action is a recorded, monitored sequence ensuring compliance and reliability.

Intelligent Application Testing

Create testing agents where LLMs analyze test results and select appropriate test sequences from your behavioral knowledge base. Combines AI-driven test planning with deterministic execution.

Adaptive IT Operations

Develop operations agents that monitor system states and execute predefined maintenance, deployment, or troubleshooting sequences based on LLM analysis - reducing manual intervention while maintaining control.

Cognitive Content Processing

Build content processing agents where LLMs understand and categorize content, then select appropriate processing workflows from your recorded action library for document handling, image processing, or data transformation.

Personal Productivity Agents

Create personal assistants that learn your habits through recorded sequences, then use LLMs to intelligently select and automate routines based on context, time, or priority - all while keeping actions human-verified.

Technical Highlights

Neuro-Symbolic Agent Architecture

• Symbolic Layer: Recorded, validated action chains that interact directly with applications
• Neural Layer: LLMs that make strategic decisions from pre-built action options
• Interface: Clean separation where LLMs never execute raw actions, only select from human-validated sequences

Deterministic Action Execution

• All application interactions happen through recorded, monitored sequences
• Zero LLM involvement in mouse/keyboard execution - pure recorded behavior
• Visual template matching with fallback mechanisms for reliable UI interaction
• Action validation and audit trail for every execution step

Strategic LLM Usage (Minimal Calls)

• LLMs activated only at decision points in workflow
• Typical usage: Analyze screen → Select next action sequence → Execute recorded chain
• Reduces LLM calls by 90%+ compared to traditional LLM-driven automation
• Local Ollama support ensures privacy and offline capability

Behavioral Knowledge Base

• Each recorded sequence adds to your agent's action library
• LLMs learn to associate screen states with appropriate action sequences
• Knowledge base grows organically through human demonstration
• Enables transfer learning across similar applications and workflows

Secure Workflow Design

• Human-in-the-loop recording ensures all actions are pre-validated
• Sandbox execution isolates workflows from host system
• Comprehensive logging and monitoring of all LLM decisions and action executions
• Fail-safe mechanisms: LLMs can only choose from pre-defined, safe action options

Advanced Recording Intelligence

• Smart grouping: Scroll bursts, text sequences, modifier combinations
• Automatic screenshot capture for visual validation during playback
• Drag-and-drop detection with configurable thresholds
• Context-aware recording (active application, window focus)

Visual Workflow Management

• Drag-and-drop node editor for building agent workflows
• Conditional routing based on LLM decisions or screen state
• Loop controls with exit conditions and iteration limits
• Sub-workflow composition for knowledge base organization

Screenshots & Demo

Visual node-based workflow editor

Complete workflow with chains library

Advanced conditional triggers

Chain import

Edit imported chain

LLM configuration

Sequence properties

Toolbar

Why Choose LoOper?

Support & Community

• Documentation: Comprehensive user guide and API reference
• Community: Join our forums and Discord for discussions
• Support: Visit our Contact page for assistance

Ready to Automate?

Transform desktop automation from simple macro recording to intelligent, visual, AI-powered workflow orchestration.