AI Agent Ecosystem

A comprehensive AI agent management platform with specialized research categories, document processing pipeline, and integrated LLM backends.

🌟 Key Features

🎨 Research Categories Interface

• Unique Differentiator: Beautiful gradient-based category containers
• Three Specialized Workspaces:
  • 📝 Text Generation - Content creation, summaries, reports
  • 📊 Technical Analysis - Data analysis, comparisons, insights
  • 📄 Document Processing - Batch processing, organization, synthesis

📁 Document Processing Pipeline

• Multi-format Support: PDF, DOCX, TXT, MD, JSON, CSV, HTML
• Batch Processing: Extract text, summarize, analyze, categorize
• File Management: Upload, organize, retrieve, delete
• Storage Analytics: Usage statistics and optimization

🤖 LLM Integration

• Ollama Backend: Local LLM inference with privacy
• Model Management: Import and manage models from /mnt/llm/LLM-Models
• Multiple Models: Support for Llama, Dolphin, CodeLlama, and more
• Real-time Generation: Streaming and batch text generation

🔧 System Architecture

• FastAPI Backend: High-performance async API
• Agent Orchestrator: Task management and coordination
• WebSocket Support: Real-time updates and monitoring
• Modular Design: Pluggable components and backends

🚀 Quick Start

Prerequisites

• Python 3.12+
• Ollama installed
• Access to model files

Documentation

• docs/setup.md — VantaBlack + split UI (local dashboard, remote API), networking, Git
• docs/integration.md — Integration and architecture
• docs/README.md — Doc index
• docs/archive/ — Older troubleshooting notes

Installation

1. Clone the repository:

   git clone <repository-url>
   cd AI_Agent_Ecosystem

2. Create virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Start Ollama server:

   ollama serve --host 0.0.0.0

5. Start the API:

   python api/main.py

6. Access the web interface: Open http://localhost:8000 in your browser

📖 Usage

Research Categories

1. Navigate to the Research section
2. Choose from three specialized categories:
   • Text Generation: For content creation and writing assistance
   • Technical Analysis: For data analysis and insights
   • Document Processing: For batch document workflows

Document Upload

• Drag and drop files into any workspace
• Supported formats: PDF, DOCX, TXT, MD, JSON, CSV, HTML
• Files are automatically processed and analyzed

Model Management

• Discover available models: GET /models/discover
• Import GGUF models: POST /models/import
• List Ollama models: GET /models/ollama

🏗️ Architecture

AI_Agent_Ecosystem/
├── api/                 # FastAPI backend
├── agents/              # AI agent implementations
├── orchestrator/        # Task coordination
├── llm_backends/        # Ollama and other LLM adapters
├── features/            # Feature modules (file_management, document_analysis, …)
├── web/                 # Dashboard (static HTML/JS/CSS)
├── monitoring/          # Status tracking, realtime monitor
├── scripts/             # start/stop helpers, fix DB, remote API starter
├── tests/               # Ad-hoc / integration test scripts
├── docs/                # Setup, integration, archived notes
├── config/              # YAML/JSON settings
└── logs/                # Runtime logs (gitignored)

🔌 API Endpoints

Core Endpoints

• GET /health - System health check
• POST /tasks - Create new tasks
• GET /tasks/{task_id} - Get task status

File Management

• POST /files/upload - Upload files
• GET /files - List uploaded files
• POST /files/batch-process - Batch process files

Text Generation

• POST /generate-text - Generate text with LLM
• POST /analyze - Analyze content
• POST /process-batch - Process batch requests

Model Management

• GET /models/discover - Discover available models
• POST /models/import - Import model to Ollama
• GET /models/ollama - List Ollama models

🎯 Unique Differentiators

1. Research Category Interface: Beautiful, intuitive workspace organization
2. Local LLM Focus: Privacy-first with Ollama integration
3. Document Pipeline: Comprehensive file processing capabilities
4. Model Flexibility: Easy import and management of local models
5. Real-time Updates: WebSocket-based live system monitoring

🛠️ Development

Project Structure

• Modular Architecture: Each feature is a separate module
• Async Design: Built on FastAPI and asyncio
• Type Safety: Full type hints throughout
• Error Handling: Comprehensive error management
• Logging: Structured logging for debugging

Adding New Features

1. Create feature module in features/
2. Add API routes in api/
3. Update orchestrator if needed
4. Add frontend components in web/

📊 System Requirements

• RAM: 8GB minimum, 16GB recommended
• Storage: 50GB for models and data
• GPU: Optional but recommended for faster inference
• Network: For model downloads and updates

🔒 Security

• Local Processing: All LLM inference runs locally
• File Isolation: User files are properly sandboxed
• Input Validation: All inputs are validated and sanitized
• Error Handling: Secure error messages without data leakage

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📞 Support

For support and questions:

• Create an issue on GitHub
• See docs/setup.md and the docs/ folder
• With the API running, open http://localhost:8000/docs (Swagger) or /redoc

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