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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.
Individual container isolation for each agent instance
Read-only root filesystem
Custom security profiles using AppArmor/SELinux
Resource limitations (CPU, memory, network)
Internal container networks
Controlled external access
Port exposure limitations
Network policy enforcement
Role-based access control (RBAC)
Principle of least privilege
Environment variable protection
Filesystem access restrictions
Real-time agent activity monitoring
Operation audit trails
Resource usage tracking
Performance metrics
Anomaly detection
Critical event notifications
Threshold-based alerts
Incident response triggers
API rate limiting
Bandwidth restrictions
Storage quotas
Computation limits
Granular permission settings
Token-based authentication
Session management
API access control
At-rest encryption
In-transit encryption (TLS 1.3)
Key management
Secure key rotation
Data retention policies
Secure data deletion
Privacy compliance
Data classification
Immediate containment
Incident logging
Impact assessment
System recovery
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.
yamlCopy# Example Docker security configuration
security_opts:
- no-new-privileges:true
- seccomp:security-profile.json
- apparmor:security-profilebashCopy# Secure environment setup
python -m venv .venv --system-site-packages false
source .venv/bin/activate
pip install --require-hashes -r requirements.txtpythonCopy# 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
passbashCopy# 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 swiftnetMarketing Operations 1% allocated for marketing initiatives and community growth
Circulating Supply 95% available for public trading, ensuring high liquidity and fair distribution
Complete guide to SwiftNet's capabilities, features, and implementation

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
Complex web-based tasks
File system operations
Data analysis and processing
Automated testing and validation
System integration tasks

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
Task Analysis
Requirement assessment
Resource allocation
Agent selection
Task Execution
Dynamic task distribution
Progress monitoring
Result validation
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

mermaidCopygraph TD
A[Orchestrator] -->|Tasks| B[Specialized Agents]
B -->|Updates| A
B -->|Results| A
A -->|Plans| BClone Repository
Using UV package installer (recommended)
Install from source
Install Playwright
Standard setup
With human oversight
With screenshot saving
git clone https://github.com/wibi16/swiftnet2.git
cd swiftnet/pythonuv sync --all-extras
source .venv/bin/activate # For Unix
.venv\Scripts\activate # For Windowscd packages/swiftnet
pip install -e .playwright install --with-deps chromiumpython examples/example.py --logs_dir ./logspython examples/example.py --logs_dir ./logs --hil_modepython examples/example.py --logs_dir ./logs --save_screenshots
jsonCopy{
"provider": "OpenAIChatCompletionClient",
"config": {
"model": "gpt-4o-2024-05-13",
"api_key": "YOUR_API_KEY"
}
}OrchestrationEvent: Task management logging
WebSurferEvent: Web interaction tracking
SystemEvent: Core operations monitoring
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_herepythonCopyfrom 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()The orchestrator of the SwiftNet ecosystem, responsible for managing and coordinating complex workflows.
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
A specialized agent for web interactions and browser automation.
Automated web navigation and interaction
Content extraction and processing
Form handling and data submission
Session state management
Manages file system operations and content organization with a focus on maintaining structured data.
Directory traversal and management
Multi-format file parsing
Content preview generation
File structure optimization
Specialized in code generation, analysis, and integration tasks.
Smart code generation based on requirements
Code analysis and optimization
Integration pattern implementation
Documentation generation
Handles system-level operations and environment management.
Command execution management
Environment configuration
Package and dependency management
System resource monitoring
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
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
Real-time task processing optimization
Reduced memory footprint
Improved concurrent operation handling
Enhanced resource management
DataScientist: Specialized in data analysis and visualization
NetworkManager: Advanced network operations and monitoring
SecurityGuard: Dedicated security monitoring and protection
Basic Setup
First Project
Documentation: docs.swiftnet.ai
API Reference: api.swiftnet.ai
Community Forum: community.swiftnet.ai
GitHub Repository: github.com/Swaraj9/swiftnet2
Fork the repository
Create a feature branch
Submit a pull request
Follow our code style guide
Include tests for new features
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!
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 chromiumbashCopygit 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"
])