# Multi-Swarm Framework Documentation

Welcome to the Multi-Swarm Framework documentation. This framework enables the creation of sophisticated AI agent swarms that can collaborate effectively on complex tasks.

## Latest Updates (Version 1.0.5)

### Plugin System
- Extensible plugin architecture for custom tools
- Built-in plugin registry and management
- Example tools including Git integration
- Easy-to-implement plugin interface
- Automatic plugin discovery and loading
- Plugin health monitoring and lifecycle management

### Model Configuration System
- Centralized YAML-based configuration
- Enhanced model selection based on tasks
- Support for experimental models
- Improved version compatibility checks
- Better configuration management utilities

### Enhanced Knowledge Coordination
- Advanced coordination across multiple agents
- Improved temporal awareness in document retrieval
- Better knowledge gap detection and analysis
- Advanced knowledge synthesis capabilities
- Optimized collaborative search functionality

### Agency Management
- Improved communication flows between agents
- Enhanced state management and persistence
- Better error handling and recovery mechanisms
- Advanced logging and monitoring capabilities

### Core Components
- Updated base agent class with improved provider selection
- Enhanced thread management system
- Improved file storage and retrieval
- Better resource management and cleanup

### Model Selection Improvements
- Task-specific model configurations
- Automatic provider selection based on agent roles
- Support for latest Claude and Gemini models
- Optimized performance for different task types

### Framework Core Updates
- Enhanced agent communication flows
- Improved state management
- Better error handling and recovery
- Advanced logging and monitoring

## Quick Navigation

### Getting Started
- [Installation Guide](getting-started/installation.md)
- [Basic Usage](getting-started/basic-usage.md)
- [Configuration](getting-started/configuration.md)
- [First Agency](getting-started/first-agency.md)

### Core Concepts
- [Agencies](api/agency.md)
- [Agents](api/agent.md)
- [Tools](api/tools.md)
- [Communication Flows](api/communication.md)
- [Plugin System](api/plugins.md)

### Advanced Features
- [Plugin Development](api/plugins/development.md)
- [RAG System](api/rag.md)
- [Knowledge Management](api/knowledge.md)
- [State Persistence](api/state.md)
- [Docker Integration](api/docker.md)

### LLM Providers
- [Claude (Anthropic)](providers/claude.md)
- [Gemini (Google)](providers/gemini.md)
- [Provider Selection](providers/selection.md)
- [Custom Providers](providers/custom.md)

### Examples
- [Basic Examples](examples/basic.md)
- [Advanced Examples](examples/advanced.md)
- [Real-world Use Cases](examples/use-cases.md)
- [Performance Optimization](examples/optimization.md)

### API Reference
- [Agency API](api/agency-api.md)
- [Agent API](api/agent-api.md)
- [Tool API](api/tool-api.md)
- [Provider API](api/provider-api.md)

### Development
- [Contributing](development/contributing.md)
- [Testing](development/testing.md)
- [Code Style](development/code-style.md)
- [Release Process](development/releases.md)

## Key Features

### Multi-LLM Support
The framework supports multiple LLM providers with automatic model selection:

#### Claude Models
- claude-3-5-sonnet-latest: Balanced performance for general tasks
- claude-3-5-opus-latest: Advanced capabilities for research and analysis

#### Gemini Models
- gemini-2.0-flash-exp: High-performance model for various tasks

### RAG System
The framework includes a sophisticated RAG (Retrieval-Augmented Generation) system:

- Temporal awareness for document relevance
- Knowledge gap detection and analysis
- Cross-agent knowledge synthesis
- Collaborative search capabilities

### Agency Management
Advanced features for managing agent swarms:

- Flexible agent creation and configuration
- Sophisticated communication patterns
- State persistence and recovery
- Automated task distribution
- Knowledge coordination

### Built-in Tools
The framework provides several built-in tools:

- Code Interpreter for Python execution
- File Search with semantic capabilities
- Docker integration for isolated environments
- Customizable tool creation framework

## System Requirements

### Minimum Requirements
- Python 3.8+
- 4GB RAM
- 2 CPU cores

### Recommended Requirements
- Python 3.10+
- 8GB RAM
- 4 CPU cores
- NVIDIA GPU (for GPU acceleration)

## Support

- [GitHub Issues](https://github.com/bartvanspitaels99/multi-swarm/issues)
- [GitHub Discussions](https://github.com/bartvanspitaels99/multi-swarm/discussions)
- [Stack Overflow](https://stackoverflow.com/questions/tagged/multi-swarm)

## License

This project is licensed under the MIT License. See the [LICENSE](../LICENSE) file for details.

## Version History

### 1.0.5 (Latest)
- Added plugin system for extensible agent capabilities
- Introduced centralized YAML configuration for model management
- Enhanced experimental model support
- Improved version compatibility handling
- Added example tools including Git integration
- Enhanced plugin discovery and management

### 1.0.4
- Enhanced knowledge coordination across agents
- Improved temporal awareness in document retrieval
- Better knowledge gap detection and analysis
- Advanced knowledge synthesis capabilities
- Optimized collaborative search functionality
- Improved communication flows
- Enhanced state management
- Better error handling

### 1.0.3
- Enhanced RAG capabilities with temporal awareness
- Improved knowledge gap detection
- Task-specific model configurations
- Advanced provider selection

### 1.0.2
- Added multi-LLM support
- Improved state management
- Enhanced error handling

### 1.0.1
- Initial public release
- Basic agency and agent support
- Simple RAG capabilities 