aider-jac-osp 2.0.1

Aider: AI-powered coding assistant rebuilt with Jac Object-Spatial Programming and Genius Mode

Rebuilding Aider with Jac

Autonomous Code Editor Powered by Jac Object-Spatial Programming

Developed by Team ByteBrains

🎥 Live Demonstration

Watch the system in action: View Complete Demo

See real-time autonomous code editing, multi-file coordination, and spatial programming capabilities demonstrated on production codebases.

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Overview

An autonomous code editing system that demonstrates Agentic AI capabilities through intelligent task planning, multi-file coordination, and spatial code analysis. Built with Python-Jac integration for professional development workflows.

Key Achievements:

• 25.8% token cost reduction on production codebases
• Multi-file autonomous editing with coordinated changes
• Spatial code analysis using Object-Spatial Programming algorithms
• Professional CLI interface with comprehensive operation tracking
• Multi-LLM provider support including cost-effective models

Installation

git clone https://github.com/ThiruvarankanM/Rebuilding-Aider-with-Jac-OSP.git
cd Rebuilding-Aider-with-Jac-OSP
python -m venv .venv
source .venv/bin/activate
pip install -e .

Configuration

Set up the system:

aider-genius setup

Configure API settings in ~/.aider-genius/config.json:

{
  "llm_provider": "openrouter",
  "model": "google/gemma-2-9b-it:free",
  "api_key": "your-openrouter-key",
  "max_tokens": 4000,
  "temperature": 0.2
}

Usage

Project Analysis

aider-genius analyze                    # Analyze entire project structure
aider-genius analyze --dir src/         # Directory-specific analysis
aider-genius analyze --files main.py utils.py --verbose

Cost Optimization

aider-genius optimize main.py          # Single file optimization
aider-genius optimize --files *.py     # Batch optimization

Autonomous Editing

aider-genius edit "add error handling"
aider-genius edit "improve logging" --files app.py utils.py
aider-genius edit "optimize performance" --dry-run

Architecture

Core System Components

The system implements autonomous intelligence through:

• Task Planning: Independent decomposition of high-level objectives
• Spatial Analysis: Multi-dimensional code relationship understanding
• Coordinated Execution: Synchronized multi-file modification strategies
• Adaptive Learning: Pattern recognition for improved decision making

Technology Stack

• Python: Core system implementation and LLM integration
• Jac: Object-Spatial Programming for advanced code analysis
• Rich: Professional terminal interface with visual formatting
• Multi-LLM: OpenAI, Anthropic, OpenRouter provider support

Project Structure

aider/
├── cli.py                     # Command-line interface
├── integration/
│   ├── jac_bridge.py         # Python-Jac integration layer
│   ├── file_editor.py        # Autonomous editing engine
│   ├── llm_client.py         # Multi-provider LLM client
│   └── osp_interface.py      # Spatial programming interface
└── jac/                      # Spatial programming modules
    ├── repomap_osp.jac       # File ranking algorithms
    ├── token_optimizer.jac   # Cost optimization
    ├── planning_walker.jac   # Task decomposition
    └── context_gatherer.jac  # Context optimization

Key Features

Autonomous Code Understanding

• Real-time analysis of project structure and dependencies
• Intelligent file relevance scoring using spatial algorithms
• Cross-component relationship mapping for coordinated changes
• Pattern recognition for consistent code style maintenance

Professional Development Integration

• Comprehensive backup system with version control
• Dry-run mode for safe change preview
• Git integration for collaborative workflows
• Enterprise-grade error handling and logging

Cost-Effective Operation

• Proven 25.8% token reduction on large codebases
• Support for free-tier LLM models
• Intelligent prompt optimization for minimal API usage
• Configurable resource limits and usage tracking

Performance Metrics

Feature	Result	Impact
Token Optimization	25.8% reduction	Significant cost savings
File Analysis	23+ files processed	Comprehensive coverage
Multi-file Coordination	Multiple simultaneous edits	Synchronized changes
Processing Speed	Sub-3 second response	Real-time workflow

Object-Spatial Programming Integration

Aider-Genius utilizes Object-Spatial Programming (OSP) for advanced code analysis:

• Spatial code graphs for relationship visualization
• Multi-dimensional dependency analysis
• Context-aware code selection and modification
• Predictive impact assessment across file boundaries

Supported LLM Providers

• OpenAI: Complete GPT model support
• Anthropic: Claude integration
• OpenRouter: Multi-model access with free tiers
• Custom: Extensible provider system

Command Reference

Command	Description
aider-genius setup	Initialize system configuration
aider-genius analyze	Perform spatial code analysis
aider-genius optimize	Optimize token usage and costs
aider-genius edit <task>	Execute autonomous editing tasks
aider-genius --help	Display comprehensive help

Testing

# Verify system functionality
python system_test.py

# Test autonomous capabilities
aider-genius edit "comprehensive code improvement" --dry-run

Contributing

1. Fork the repository
2. Create feature branches for enhancements
3. Submit pull requests with comprehensive testing
4. Follow established code quality standards

Future Enhancements

• Advanced LLM integration (GPT-4, Claude-3)
• Web-based interface for visual spatial programming
• IDE plugins for native development environment integration
• Enhanced pattern recognition with AST-based analysis
• Team collaboration features with multi-developer coordination

License

MIT License - Open source autonomous AI innovation

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Professional autonomous coding solution powered by Agentic AI and Object-Spatial Programming | Team ByteBrains