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SwiftNet

Welcome

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Implementation

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Security

SwiftNet implements a multi-layered security approach to ensure safe and controlled agent operations. Our security framework is built on five core pillars: containerization, environment control, monitoring, access management, and data protection.

1. Containerization Security

Docker Implementation

  • Individual container isolation for each agent instance

  • Read-only root filesystem

  • Custom security profiles using AppArmor/SELinux

  • Resource limitations (CPU, memory, network)

Container Hardening

Network Isolation

  • Internal container networks

  • Controlled external access

  • Port exposure limitations

  • Network policy enforcement

2. Environment Control

Virtual Environment Management

Access Control Implementation

  • Role-based access control (RBAC)

  • Principle of least privilege

  • Environment variable protection

  • Filesystem access restrictions

Configuration Security

3. Monitoring Systems

Activity Tracking

  • Real-time agent activity monitoring

  • Operation audit trails

  • Resource usage tracking

  • Performance metrics

Logging Implementation

Alert System

  • Anomaly detection

  • Critical event notifications

  • Threshold-based alerts

  • Incident response triggers

4. Access Management

Resource Control

  • API rate limiting

  • Bandwidth restrictions

  • Storage quotas

  • Computation limits

Authentication System

Permission Framework

  • Granular permission settings

  • Token-based authentication

  • Session management

  • API access control

5. Data Protection

Data Encryption

  • At-rest encryption

  • In-transit encryption (TLS 1.3)

  • Key management

  • Secure key rotation

Security Audit Implementation

Data Handling Policies

  • Data retention policies

  • Secure data deletion

  • Privacy compliance

  • Data classification

Security Best Practices

Installation Security

Configuration Checklist

Emergency Response

  1. Immediate containment

  2. Incident logging

  3. Impact assessment

  4. System recovery

Regular Security Maintenance

  • Weekly security updates

  • Monthly configuration reviews

  • Quarterly penetration testing

  • Annual security audit


Using SwiftNet involves interacting with digital systems designed for humans, which carries inherent risks.


Post-incident analysis
Continuous vulnerability scanning
yamlCopy# Example Docker security configuration
security_opts:
  - no-new-privileges:true
  - seccomp:security-profile.json
  - apparmor:security-profile
bashCopy# Secure environment setup
python -m venv .venv --system-site-packages false
source .venv/bin/activate
pip install --require-hashes -r requirements.txt
pythonCopy# Example secure configuration
config = {
    'allowed_domains': ['trusted-domain.com'],
    'max_file_size': 10485760,  # 10MB
    'allowed_file_types': ['.txt', '.pdf', '.md'],
    'timeout': 300  # 5 minutes
}
pythonCopy# Example logging configuration
import logging

logging.config.dictConfig({
    'version': 1,
    'handlers': {
        'secure_file': {
            'class': 'logging.handlers.RotatingFileHandler',
            'filename': 'secure.log',
            'maxBytes': 10485760,
            'backupCount': 5,
            'formatter': 'detailed'
        }
    },
    'formatters': {
        'detailed': {
            'format': '%(asctime)s %(levelname)s [%(name)s] %(message)s'
        }
    }
})
pythonCopy# Example authentication implementation
from swiftnet.security import AuthManager

auth_manager = AuthManager(
    token_expiration=3600,  # 1 hour
    max_failed_attempts=3,
    require_2fa=True
)
pythonCopy# Example audit implementation
class SecurityAuditor:
    def __init__(self):
        self.audit_interval = 86400  # 24 hours
        
    def audit_security_configs(self):
        # Check security configurations
        pass
        
    def audit_access_logs(self):
        # Analyze access patterns
        pass
        
    def generate_audit_report(self):
        # Generate comprehensive report
        pass
bashCopy# Secure installation process
# 1. Verify package integrity
sha256sum swiftnet-package.tar.gz
# 2. Install in isolated environment
python -m venv .venv
source .venv/bin/activate
# 3. Install with security flags
pip install --require-hashes swiftnet

Tokenomics

  • Team Allocation

    2% vested linearly over a 2-year period from launch date

  • Marketing Operations 1% allocated for marketing initiatives and community growth

  • Treasury and Ecosystem

    2% dedicated to ecosystem development and treasury reserves

  • Circulating Supply 95% available for public trading, ensuring high liquidity and fair distribution

Architecture

Comprehensive breakdown of SwiftNet's components and their interactions


implements a sophisticated multi-agent architecture centered around a lead Task Master agent that coordinates specialized agents for specific tasks.

Architecture Overview


Quick Start Guide

Complete guide to getting started with SwiftNet


Prerequisites

About SwiftNet

Complete guide to SwiftNet's capabilities, features, and implementation


is an advanced generalist multi-agent system designed for solving complex web and file-based tasks across various domains. It represents a significant step towards developing agents that can complete tasks that people encounter in their work and personal lives.


Core Features

Multi-agent architecture with specialized components

  • Dynamic task planning and execution

  • Autonomous adaptation to changing environments

  • Web and file system operation support

  • Flexible model integration capabilities


  • Use Cases

    • Complex web-based tasks

    • File system operations

    • Data analysis and processing

    • Automated testing and validation

    • System integration tasks


    SwiftNet
    Core Components

    1. Planning System

    • Task Ledger

      • Fact gathering and assumption tracking

      • Knowledge base maintenance

      • Real-time validation and updates

    • Progress Ledger

      • Task completion monitoring

      • Status updates and metrics

      • Performance tracking


    2. Agent Communication


    3. Execution Pipeline

    1. Task Analysis

      • Requirement assessment

      • Resource allocation

      • Agent selection

    2. Task Execution

      • Dynamic task distribution

      • Progress monitoring

      • Result validation


    4. Technical Infrastructure

    Model Integration

    • Default: GPT-4o for all agents

    • Support for varied LLM configurations

    • Standardized communication interface

    System Management

    • Centralized state control

    • Inter-agent synchronization

    • Resource optimization

    • Security protocols


    View implementation details

    Explore agent capabilities


    SwiftNet
    mermaidCopygraph TD
        A[Orchestrator] -->|Tasks| B[Specialized Agents]
        B -->|Updates| A
        B -->|Results| A
        A -->|Plans| B

  • Installation Steps

    • Clone Repository

    • Using UV package installer (recommended)

    • Install from source

    • Install Playwright



    Running Examples

    • Standard setup

    • With human oversight

    • With screenshot saving


    Docker

    Configuration

    Model Configuration


    SwiftNet uses GPT-4o as the default multimodal LLM but supports various configurations:

    • Model-agnostic architecture

    • Heterogeneous model support

    • Cost optimization options


    Azure OpenAI Configuration


    OpenAI Configuration


    Additional API Setup

    Required for web-search functionality


    Technical Reference

    Development Tools


    SwiftNet API


    git clone https://github.com/wibi16/swiftnet2.git
    cd swiftnet/python
    uv sync --all-extras
    source .venv/bin/activate  # For Unix
    .venv\Scripts\activate     # For Windows
    cd packages/swiftnet
    pip install -e .
    playwright install --with-deps chromium
    python examples/example.py --logs_dir ./logs
    python examples/example.py --logs_dir ./logs --hil_mode
    python examples/example.py --logs_dir ./logs --save_screenshots
    jsonCopy{
        "provider": "OpenAIChatCompletionClient",
        "config": {
            "model": "gpt-4o-2024-05-13",
            "api_key": "YOUR_API_KEY"
        }
    }
    Integration Examples

    Logging System


    Event Types

    • OrchestrationEvent: Task management logging

    • WebSurferEvent: Web interaction tracking

    • SystemEvent: Core operations monitoring


    Log Management


    jsonCopy{
        "provider": "AzureOpenAIChatCompletionClient",
        "config": {
            "model": "gpt-4o-2024-05-13",
            "azure_endpoint": "https://{endpoint}.openai.azure.com/",
            "azure_deployment": "{deployment-name}",
            "api_version": "2024-06-01",
            "azure_ad_token_provider": {
                "provider": "autogen_ext.auth.azure.AzureTokenProvider",
                "config": {
                    "provider_kind": "DefaultAzureCredential",
                    "scopes": ["https://cognitiveservices.azure.com/.default"]
                }
            }
        }
    }
    export BING_API_KEY=your_key_here
    pythonCopyfrom swiftnet import ChatCompletionClient
    
    # Basic client setup
    config = {}
    client = ChatCompletionClient.load_component(config)
    
    # Using SwiftNetHelper (Preview)
    from swiftnet import SwiftNetHelper
    helper = SwiftNetHelper()
    pythonCopy# Agent creation
    agent = SwiftNetHelper.create_agent(config)
    
    # Task execution
    result = await agent.execute_task(task_description)
    
    # Event handling
    @helper.on_event
    def handle_event(event):
        print(f"Event received: {event}")
    pythonCopy# Example log handler configuration
    from swiftnet.utils import LogHandler
    
    handler = LogHandler(log_file="log.jsonl")
    handler.start()

    Agent Ecosystem

    Agent Types


    The orchestrator of the SwiftNet ecosystem, responsible for managing and coordinating complex workflows.

    Key Capabilities

    • Breaks down complex tasks into manageable subtasks

    • Coordinates other agents to execute the plan

    • Monitors progress and adjusts strategies in real-time

    • Maintains execution context across the agent network

    Usage Example

    BrowserBot Agent

    A specialized agent for web interactions and browser automation.

    Key Capabilities

    • Automated web navigation and interaction

    • Content extraction and processing

    • Form handling and data submission

    • Session state management

    Usage Example

    File Navigator Agent

    Manages file system operations and content organization with a focus on maintaining structured data.

    Key Capabilities

    • Directory traversal and management

    • Multi-format file parsing

    • Content preview generation

    • File structure optimization

    Usage Example

    CodeSmith Agent

    Specialized in code generation, analysis, and integration tasks.

    Key Capabilities

    • Smart code generation based on requirements

    • Code analysis and optimization

    • Integration pattern implementation

    • Documentation generation

    Usage Example

    Shell Commander Agent

    Handles system-level operations and environment management.

    Key Capabilities

    • Command execution management

    • Environment configuration

    • Package and dependency management

    • System resource monitoring

    Usage Example

    Security Considerations

    • All agents operate within isolated Docker containers

    • Access controls implemented at the agent level

    • Comprehensive logging for all operations

    • Human supervision requirements for critical operations

    Getting Started


    Future Development

    Upcoming Features

    Data encryption for sensitive information

    Enhanced Agent Collaboration: Improved inter-agent communication and task sharing

  • Smart Caching System: Optimized performance for repeated operations

  • Advanced Error Recovery: Self-healing capabilities for failed operations

  • Custom Agent Creation: Framework for developing specialized agents

  • Performance Roadmap

    • Real-time task processing optimization

    • Reduced memory footprint

    • Improved concurrent operation handling

    • Enhanced resource management

    New Agent Types

    • DataScientist: Specialized in data analysis and visualization

    • NetworkManager: Advanced network operations and monitoring

    • SecurityGuard: Dedicated security monitoring and protection

    API Enhancements

    Next Steps

    Quick Start Guide

    1. Basic Setup

    2. First Project

    Development Resources

    • Documentation: docs.swiftnet.ai

    • API Reference: api.swiftnet.ai

    • Community Forum: community.swiftnet.ai

    • GitHub Repository: github.com/Swaraj9/swiftnet2

    Contributing Guidelines

    1. Fork the repository

    2. Create a feature branch

    3. Submit a pull request

    4. Follow our code style guide

    5. Include tests for new features

    Community Engagement

    • Join our Discord server

    • Participate in weekly dev calls

    • Share your SwiftNet projects

    • Help improve documentation

    Remember: SwiftNet's growth depends on community feedback and contributions. We encourage active participation in shaping its future development!


    Future Development
    pythonCopyfrom swiftnet.agents import TaskMaster
    
    task_master = TaskMaster()
    task_master.plan_execution("research_and_summarize", target="AI trends 2024")
    pythonCopyfrom swiftnet.agents import BrowserBot
    
    browser = BrowserBot()
    content = browser.navigate_and_extract("https://example.com")
    pythonCopyfrom swiftnet.agents import FileNavigator
    
    navigator = FileNavigator()
    files = navigator.scan_directory("./project")
    pythonCopyfrom swiftnet.agents import CodeSmith
    
    smith = CodeSmith()
    code = smith.generate_component("login_form", framework="react")
    pythonCopyfrom swiftnet.agents import ShellCommander
    
    commander = ShellCommander()
    result = commander.execute_command("pip install -r requirements.txt")
    bashCopy# Clone the repository
    git clone https://github.com/Swaraj9/swiftnet2.git
    cd swiftnet2/python
    
    # Set up virtual environment
    uv sync --all-extras
    source .venv/bin/activate  # Windows: .venv\Scripts\activate
    
    # Install SwiftNet
    cd packages/swiftnet
    pip install -e .
    
    # Install required tools
    playwright install --with-deps chromium
    bashCopygit clone https://github.com/Swaraj9/swiftnet2.git
    cd swiftnet2
    python -m venv .venv
    source .venv/bin/activate  # Windows: .venv\Scripts\activate
    pip install -e .[dev]
    pythonCopyfrom swiftnet import SwiftNet
    
    # Initialize SwiftNet
    swift = SwiftNet()
    
    # Run your first automated task
    result = swift.run_task("hello_world")
    pythonCopy# Example of upcoming API features
    from swiftnet.next import EnhancedTaskMaster
    
    # Advanced task orchestration
    task_master = EnhancedTaskMaster(
        auto_recovery=True,
        performance_optimization=True,
        smart_caching=True
    )
    
    # Enhanced agent collaboration
    result = await task_master.collaborate([
        "research_task",
        "data_analysis",
        "report_generation"
    ])