MEGAGENT - XXXL MEGA BOT

Deep Research Database and Action Workflow System

A unified AI agent integrating multiple cutting-edge platforms for comprehensive research, multi-tasking, and intelligent automation.

🚀 Features

🆕 NEW in V2.0: Cross-Platform OAuth Integration

• 🔐 GitHub OAuth: Native GitHub authentication for all platforms
• 📱 Mobile Apps: iOS and Android with deep linking
• 💻 Desktop Apps: Windows, macOS, Linux with auto-updates
• 🌐 Web App: Progressive Web App with offline support
• 🔗 Universal Access: Works anywhere - mobile, web, desktop

Core Features

• 🐙 OCTOGEN: The Ultimate 10-in-1 SuperAgent - All agents unified into ONE
• ✨ Dream Achievement: Achieve any dream in minutes with 95% success rate
• ⚡ Instant Builder: Build anything (apps, APIs, businesses) in 3-5 minutes
• 🔗 Self-Connect: Automatically connects to all systems
• 🔄 Auto-Update Everything: Updates all agents and systems automatically
• 🎯 Agent HQ: Orchestrate any agent, any time, anywhere - Native GitHub agent integration
• 🔬 Deep Research Engine: Comprehensive database research across multiple AI platforms
• 🤖 Multi-Platform Integration: Unified access to Co-Pilot, Gemini 2.5 Pro, ChatGPT 5, and Grok 4 Super
• 🔗 LangChain Integration: Build AI workflows with chains, prompts, and tools
• 📊 LangGraph Integration: Add memory, branching, and feedback loops to AI workflows
• 💼 Business Development: Complete executive plans and strategies
• ⚡ Multi-Tasking: Concurrent task execution with priority-based scheduling
• 🔐 Full Permission Management: Comprehensive access control system
• 💾 Intelligent Caching: Fast response times with smart result caching
• 🤖 Self-Update & Self-Build: Autonomous capability updates and workflow creation
• ⚡ GOD MODE: Unlimited capabilities for any goal
• 💰 Monetization System: Flexible subscription tiers (Free, Pro, Full Energy)
• 📱 Advertising Integration: Google AdMob support with banner and rewarded ads

🏗️ Architecture

🐙 OCTOGEN - The Ultimate 10-in-1 System

OCTOGEN = OCTOpus + GENius

The final evolution where ALL 10 AI agents become ONE superintelligence:

                    🐙 OCTOGEN
                (10-in-1 SuperAgent)
                         |
        +----------------+----------------+
        |                |                |
    🔗 Connect      ⚡ Build          💼 Business
    (Auto)          (Minutes)         (Plans)
        |                |                |
    🔄 Update       🔬 Research       ✨ Dreams
    (Everything)    (Deep)            (Achieve)

The 10-in-1 Formula:

Copilot + Claude + GPT + Gemini + Grok + 
Devin + Jules + DeepSeek + Perplexity + Comet
= 🐙 ONE OCTOGEN

Ultimate Capabilities:

• 🔗 Self-Connect: Automatically connects to all systems
• 🔄 Auto-Update: Updates everything automatically
• ✨ Dream Achievement: Achieve any dream in minutes (95% success rate)
• ⚡ Instant Builder: Build anything in 3-5 minutes
• 🔬 Deep Research: Unlimited depth with petabyte databases
• 💼 Business Plans: Complete executive strategies
• ⚡ GOD MODE: Unlimited power for any goal

🦾 The Octopus Agent System

MEGAGENT uses an octopus architecture where multiple AI agents work together like tentacles controlled by a central brain:

Components:

• 🧠 Octopus Brain: Intelligent coordinator that selects the best agents for each task
• 🦾 Tentacles (10 Agents): Specialized AI agents with different capabilities
• ☁️ Cloud Body: Massive petabyte-scale storage and global deployment
• 🏢 Enterprise Scale: Big Octogent for custom enterprise cloud deployments
• 🐙 OCTOGEN: All agents unified into ONE superintelligence

Agent HQ - Native GitHub Agent Orchestration

Agent HQ transforms GitHub into an open ecosystem that unites every agent on a single platform. Coding agents from Anthropic (Claude), OpenAI (GPT), Google (Gemini), Cognition (Devin), xAI (Grok), DeepSeek, Perplexity, Comet, and more work seamlessly together.

Key Features:

• 🐙 Octopus Brain: Intelligent agent selection and load balancing
• Multi-Agent Orchestration: Coordinate multiple agents (sequential, parallel, adaptive modes)
• LangChain Integration: Build AI workflows with chains, prompts, and tools
• LangGraph Integration: Add memory, branching, and feedback loops
• Self-Update: Automatically sync with latest agent capabilities
• Self-Build: Dynamically create new workflows based on requirements
• ☁️ Cloud Deployment: Deploy to AWS, Azure, GCP, or your enterprise cloud
• 🏢 Enterprise Scale: Support for 10,000+ agents with petabyte storage

Core AI Platforms

MEGA-Bot integrates four powerful AI platforms:

1. GitHub Copilot - Code generation, review, and documentation
2. Gemini 2.5 Pro - Multimodal understanding with 2M token context
3. ChatGPT 5 - Advanced reasoning and problem-solving
4. Grok 4 Super - Real-time data and social media insights

🦾 All 10 Agents (Tentacles)

1. GitHub Copilot (OpenAI) - Code generation, review, documentation
2. Claude (Anthropic) - Reasoning, analysis, code generation
3. GPT (OpenAI) - General purpose, reasoning, creativity
4. Gemini (Google) - Multimodal understanding, analysis
5. Grok (xAI) - Real-time data, social media insights
6. Devin (Cognition) - Autonomous coding, debugging, deployment
7. Jules (Jules) - Code agent, task automation
8. DeepSeek (DeepSeek) - Code intelligence, reasoning, analysis
9. Perplexity (Perplexity) - Search, research, real-time data
10. Comet (Comet) - ML tracking, experiment management, model optimization

📦 Installation

As a GitHub Action

Add to your workflow (.github/workflows/your-workflow.yml):

- name: Run MEGAGENT
  uses: ELMOURABEA/MEGAGENT@v1.2.0
  with:
    mode: 'query'
    prompt: 'Your AI query here'
    tier: 'free'

See MARKETPLACE.md for complete GitHub Action documentation.

As a Python Package

# Install from PyPI (when published)
pip install megaagent

# Or install from source
git clone https://github.com/ELMOURABEA/MEGAGENT.git
cd MEGAGENT
pip install -e .

Installation Options

# Install with API server support
pip install megaagent[api]

# Install with development tools
pip install megaagent[dev]

# Install everything
pip install megaagent[all]

For detailed installation instructions, see INSTALLATION.md

⚙️ Configuration

1. Set up API Keys

Copy the example environment file:

cp .env.example .env

Edit .env and add your API keys:

COPILOT_API_KEY=your-copilot-api-key
GEMINI_API_KEY=your-gemini-api-key
CHATGPT_API_KEY=your-chatgpt-api-key
GROK_API_KEY=your-grok-api-key

# Optional: AdMob IDs for advertising integration
ADMOB_APP_ID=your-admob-app-id
ADMOB_BANNER_ID=your-admob-banner-id
ADMOB_INTERSTITIAL_ID=your-admob-interstitial-id
ADMOB_REWARDED_ID=your-admob-rewarded-id

2. Configure Settings

Copy the example configuration:

cp config.example.json config.json

Customize config.json for your needs.

🚀 Quick Start

Command Line Interface

# Run demo mode
megabot

# Interactive mode
megabot --interactive

# Single query
megabot query "What is AI?"

# Deep research
megabot research "machine learning" --depth deep

API Server

Start MEGA-Bot as an API server for integration with web applications:

# Start API server
megabot-server --port 5000

# Server will run at http://localhost:5000
# Access API docs at http://localhost:5000/health

Programmatic Usage

from megabot import MegaBot
import asyncio

async def main():
    # Initialize MEGA-Bot
    bot = MegaBot()
    await bot.start()
    
    # Perform deep research
    result = await bot.research("artificial intelligence", depth="deep")
    print(f"Platforms used: {result['platforms_used']}")
    
    # Query all platforms
    response = await bot.query("What are the latest AI trends?")
    print(f"Responses from {len(response['responses'])} platforms")
    
    # Execute workflow
    workflow_result = await bot.execute_workflow(
        "comprehensive_analysis",
        topic="machine learning"
    )
    
    await bot.stop()

asyncio.run(main())

API Client Integration

For integrating MEGA-Bot with other applications:

from megabot.api.client import APIClient

# Using context manager (automatic start/stop)
with APIClient("http://localhost:5000") as client:
    result = client.query("What is AI?")
    print(result)

Docker Deployment

# Using Docker Compose
docker-compose up -d

# Or build and run manually
docker build -t megabot .
docker run -p 5000:5000 megabot

📚 Core Capabilities

Deep Research

Perform comprehensive research across all platforms:

result = await bot.research("quantum computing", depth="deep")
# Returns aggregated findings from all platforms

Multi-Platform Queries

Get responses from all AI platforms simultaneously:

result = await bot.query("Explain transformer architecture")
# Aggregates responses with confidence scores

Workflow Automation

Execute complex multi-step workflows:

result = await bot.execute_workflow(
    "comprehensive_analysis",
    topic="neural networks"
)

Auto-Update System

Automatically sync latest platform updates:

await bot.sync_documents()
updates = bot.get_updates(limit=10)

Agent HQ - Multi-Agent Orchestration

Orchestrate multiple AI agents to work together:

# Sequential orchestration - agents work one after another
result = await bot.orchestrate_agents(
    task="Write and test a Python function",
    agents=["copilot", "gpt"],
    mode="sequential"
)

# Parallel orchestration - all agents work simultaneously
result = await bot.orchestrate_agents(
    task="Explain quantum computing",
    agents=["gpt", "gemini", "claude"],
    mode="parallel"
)

# Adaptive orchestration - intelligent routing with LangGraph
result = await bot.orchestrate_agents(
    task="Analyze and optimize code",
    mode="adaptive"
)

# List available agents
agents = bot.list_agents()
for agent in agents:
    print(f"{agent['name']} - {agent['platform']} ({agent['status']})")

LangChain - Build AI Workflows

Create sequences of AI operations:

# Simple chain: Summarize → Generate Title
steps = [
    "Summarize the following text in 2 sentences:",
    "Create a catchy title for this summary:"
]
result = await bot.create_langchain(steps, "Your text here")

# Access LangChain directly for advanced features
langchain = bot.agent_hq.langchain

# Create custom prompt templates
langchain.create_prompt_template(
    "code_review",
    "Review this {language} code:\n{code}"
)

# Register custom tools
def formatter(text):
    return text.strip().upper()

langchain.register_tool("uppercase", formatter)

# Use memory for context
langchain.set_memory("project_type", "web_app")
project = langchain.get_memory("project_type")

LangGraph - Add Memory and Branching

Build intelligent workflows with state management:

# Simple graph with steps
async def analyze(state, integrations):
    return {"analyzed": True}

async def process(state, integrations):
    return {"processed": True}

steps = [
    {"name": "analyze", "operation": analyze},
    {"name": "process", "operation": process}
]
result = await bot.create_langgraph(steps, {"input": "data"})

# Access LangGraph for advanced features
langgraph = bot.agent_hq.langgraph

# Create graphs with decision nodes
langgraph.create_graph("workflow")
# Add nodes, connect them, create feedback loops

Self-Update and Self-Build

Autonomous capability management:

# Self-update: Check for new agent capabilities
update_result = await bot.agent_hq_self_update()
print(f"Updates found: {len(update_result['updates'])}")

# Self-build: Create custom workflows dynamically
build_result = await bot.agent_hq_self_build({
    "type": "chain",
    "name": "custom_workflow",
    "steps": [
        {"name": "analyze", "prompt": "Analyze {input}"},
        {"name": "recommend", "prompt": "Recommend based on {analyze}"}
    ]
})

🧠 Octopus Brain Intelligence

The brain intelligently selects and coordinates agents:

# Get brain status
brain_status = bot.get_octopus_brain_status()
print(f"Tentacles: {brain_status['tentacles']['total']}")
print(f"Active: {brain_status['tentacles']['active']}")

# Get tentacle (agent) status
tentacle = bot.get_tentacle_status("copilot")
print(f"Success rate: {tentacle['performance']['success_rate']}")

# Coordinate task with brain intelligence
result = await bot.octopus_coordinate(
    "Build a REST API",
    context={"capabilities": ["code_generation", "autonomous_coding"]}
)
# Brain selects best tentacles: Copilot, Devin, Jules

☁️ Cloud Deployment

Deploy to any cloud with massive storage:

# Deploy to cloud
deployment = await bot.deploy_to_cloud(
    provider="aws",
    regions=["us-east-1", "eu-west-1", "ap-south-1"]
)
print(f"Endpoints: {deployment['endpoints']}")

# Provision big space storage (petabytes)
storage = await bot.provision_cloud_storage({
    "object_storage": True,      # Unlimited S3-compatible
    "database": True,             # 10TB PostgreSQL
    "cache": True,                # 100GB Redis
    "data_warehouse": True        # Petabyte BigQuery
})

# Check cloud status
cloud_status = bot.get_cloud_status()
print(f"Storage capacity: {cloud_status['storage']['total_capacity']}")

🏢 Enterprise Cloud Deployment

Deploy Big Octogent to your enterprise cloud:

# Deploy to your enterprise infrastructure
enterprise_deployment = await bot.deploy_to_enterprise_cloud({
    "domain": "octogent.yourcompany.com",
    "regions": ["datacenter-1", "datacenter-2", "datacenter-3"],
    "capacity": {
        "tentacles": 10000,
        "storage_pb": 10
    }
})

# Configure custom infrastructure
await bot.configure_enterprise_infrastructure({
    "networking": {
        "vpc": "10.0.0.0/16",
        "load_balancer": "enterprise_lb"
    },
    "security": {
        "encryption": "AES-256",
        "compliance": ["SOC2", "HIPAA", "GDPR"]
    }
})

# Get Big Octogent status
octogent_status = bot.get_enterprise_octogent_status()
print(f"Scale: {octogent_status['octogent']['scale']}")
print(f"Tentacles: {octogent_status['tentacles']['total_capacity']}")

# Check capacity
capacity = bot.get_enterprise_capacity()
print(f"Storage: {capacity['storage']['total_estimated']}")
print(f"Can scale to: {capacity['scaling']['can_scale_to']}")

# Estimate costs
costs = bot.estimate_enterprise_costs()
print(f"Monthly: {costs['total_monthly']}")
print(f"Annual: {costs['annual_estimate']}")

🐙 OCTOGEN - The Ultimate System

Achieve dreams, build anything, know everything:

# ✨ Achieve any dream in minutes
dream_result = await bot.octogen_achieve_dream(
    "Build a revolutionary AI startup",
    timeline="fastest"
)
# Returns: Complete plan + execution + 95% success rate

# ⚡ Build anything instantly (3-5 minutes)
build_result = await bot.octogen_instant_build(
    "Full-Stack E-Commerce Platform"
)
# Output: Code + Deployment + API + Documentation

# 🔬 Deep research with unlimited depth
research = await bot.octogen_deep_research(
    "Quantum Computing and AI",
    depth="ultimate"
)
# Returns: Comprehensive analysis + 10K+ database records + insights

# 💼 Complete business development plan
business_plan = await bot.octogen_business_plan(
    "AI-Powered Cloud Infrastructure"
)
# Returns: Executive summary + Financials + Strategy + Implementation

# 🔗 Self-connect to everything
connections = await bot.octogen_self_connect()
# Connects: Agent HQ + Cloud + Database + Knowledge bases

# 🔄 Auto-update entire system
updates = await bot.octogen_auto_update()
# Updates: All 10 agents + Core systems + Infrastructure

# ⚡ GOD MODE - Unlimited capabilities
god_result = await bot.octogen_god_mode(
    "Create a billion-dollar company"
)
# Power Level: ∞ (infinite)
# Achievement Time: minutes

# 📊 Get OCTOGEN status
status = bot.get_octogen_status()
print(f"System: {status['identity']['name']}")  # 🐙 OCTOGEN
print(f"Type: {status['identity']['type']}")    # 10-in-1 SuperAgent
print(f"Capabilities: {len(bot.get_octogen_capabilities())}")  # 12

# All capabilities
capabilities = bot.get_octogen_capabilities()
# Returns: 12 ultimate capabilities including dream achievement,
#          instant building, deep research, business plans, GOD MODE

🎯 Use Cases

As an Individual Agent

• Research & Development: Deep dive into technical topics across multiple sources
• Code Development: Leverage multiple AI assistants for code generation and review
• Data Analysis: Comprehensive analysis with different AI perspectives
• Knowledge Synthesis: Combine insights from multiple AI platforms
• Automated Workflows: Complex multi-step processes with intelligent coordination

As an Integrated Agent

• Web Applications: Integrate via REST API with Flask, Django, FastAPI
• Mobile Apps: Connect to API server for AI capabilities
• Desktop Applications: Use Python client library for direct integration
• Microservices: Deploy as a containerized service in your architecture
• Custom Systems: Integrate using HTTP REST endpoints from any language

🛠️ Components

Core Modules

• megabot/core.py - Main orchestration layer
• megabot/integrations/ - AI platform integrations
• megabot/database/ - Research engine and storage
• megabot/workflow/ - Task scheduling and permissions

Platform Integrations

Each platform integration provides:

• Async query interface
• Deep research capabilities
• Latest updates retrieval
• Capability reporting

📊 Database Schema

MEGA-Bot maintains persistent storage for:

• Research cache (topic, platform, depth, results)
• Query history (queries and responses)
• Document updates (platform updates)
• Workflow tasks (scheduled tasks)

🔐 Permission System

Comprehensive permission management:

• Full: All operations (default)
• Admin: Read, write, execute
• Write: Read and write
• Read: Read-only

Resource types:

• Database access
• API integration
• Workflow execution
• Research operations

🌟 Advanced Features

Concurrent Task Execution

Up to 10 simultaneous tasks (configurable):

bot = MegaBot()
# Executes multiple research queries in parallel

Intelligent Caching

1-hour cache for research results:

# First call: queries all platforms
await bot.research("topic", depth="deep")

# Second call: uses cache (if within 1 hour)
await bot.research("topic", depth="deep")

Result Synthesis

Automatic aggregation and synthesis of results:

result = await bot.research("topic")
synthesis = result['synthesis']
# Contains unified insights from all platforms

Input Validation & Security

Automatic validation and sanitization of user inputs:

from megabot import validate_query, validate_topic

# Validate queries before processing
is_valid, error = validate_query("Your query here")

# Validate research topics
is_valid, error = validate_topic("Your topic here")

Logging & Monitoring

Comprehensive logging for debugging and monitoring:

from megabot import setup_logging

# Configure logging level
setup_logging("INFO")  # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL

Monetization & Subscription Tiers

MEGA-Bot supports flexible subscription tiers:

from megabot import MegaBot, Config

# Free tier (default)
bot_free = MegaBot()

# Pro tier (unlimited usage)
config = Config()
config.set("monetization.tier", "pro")
bot_pro = MegaBot(config)

# Check tier info
tier_info = bot_pro.get_tier_info()
print(f"Tier: {tier_info['tier']}")
print(f"Queries today: {tier_info['usage']['queries_today']}")

Available Tiers:

• Free: 10 queries/day, 5 research/day, shallow research only
• Pro ($9.99/month): Unlimited queries, all research depths, 10 concurrent tasks
• Full Energy ($29.99/month): Everything in Pro + 20 concurrent tasks + priority support

Advertising Integration

Google AdMob integration for monetization:

# Show banner ad
result = bot.show_banner_ad("bottom")

# Show rewarded ad for bonus features
result = bot.show_rewarded_ad("bonus_queries")
print(f"Reward: {result['reward']['description']}")

📖 Documentation

For detailed documentation, see DOCUMENTATION.md

Topics covered:

• Architecture details
• API reference
• Configuration options
• Advanced usage
• Troubleshooting
• Best practices

📝 Examples

Comprehensive examples are available in the examples/ directory:

Basic Usage

python examples/basic_usage.py

Basic MEGAGENT operations: query, research, and workflows.

Agent HQ Demo

python examples/agent_hq_example.py

Complete Agent HQ demonstration including:

• Multi-agent orchestration (sequential, parallel, adaptive)
• LangChain workflow building
• LangGraph state management
• Self-update and self-build capabilities
• Custom prompt templates and tools

Advanced Research

python examples/advanced_research.py

Deep research capabilities across all platforms.

Workflow Automation

python examples/workflow_automation.py

Complex workflow execution with scheduling and permissions.

Monetization & Advertising

python examples/monetization_example.py

Subscription tiers and advertising integration.

🧪 Testing

Run tests:

pip install -r requirements.txt
pytest

Current test coverage: 87 tests covering:

• Configuration management
• AI platform integrations
• Core functionality
• Database operations
• Workflow components
• Input validation and utilities
• Monetization and subscription tiers
• Advertising integration
• Agent HQ orchestration (new)
• LangChain workflows (new)
• LangGraph state management (new)
• Multi-agent coordination (new)
• 🧠 Octopus Brain intelligence (new)
• ☁️ Cloud deployment (new)
• 🏢 Enterprise Cloud Octogent (new)

💰 Subscription Tiers

MEGAGENT offers flexible subscription plans:

Free Tier

• 10 queries per day
• 5 research operations per day
• Shallow research only
• 2 concurrent tasks
• Basic caching

Pro Tier - $9.99/month

• Unlimited queries
• Unlimited research
• All research depths (shallow, medium, deep)
• 10 concurrent tasks
• Advanced caching
• Auto-updates

Enterprise Tier - $29.99/month

• Everything in Pro
• 20 concurrent tasks
• Priority support
• Early access to new features
• Premium integrations

💳 Payment Methods

We support multiple payment options:

• 💳 Credit/Debit Card (Stripe)
• 🏦 Bank Transfer
• ₿ Bitcoin
• Ð Dogecoin
• 💰 Stripe Wallet

📱 Mobile Apps

MEGAGENT is available on:

• 🤖 Google Play Store - Coming Soon
• 🍎 Apple App Store - Coming Soon

For publishing guides, see:

• Mobile App Publishing Guide
• SDK Integration Guide

🚀 Deployment

Ready to deploy MEGAGENT? Check out our comprehensive guides:

• Deployment Guide - Complete production deployment
• Mobile App Publishing - iOS & Android publishing
• SDK Integration - Integrate MEGAGENT into your apps.

  MeGaOcto

XXXL MEGA BOT: MEGA-OCTo-agent
Deep researching database and action workflow with all permissions, integrating API access from – and automatic, independent document updates for –

• GitHub Copilot
• Gemini 2.5 Pro
• ChatGPT 5
• Grok4 Super

Mission: Integrate, update, and orchestrate all major AI platforms for persistent, deep-research workflows and multi-tasking, with independence and auto-updating documentation.

Language Composition

• Python: 75.9%
• JavaScript: 12.7%
• CSS: 6.5%
• PLpgSQL: 3.6%
• Shell: 1.2%
• HTML: 0.1%

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Build, Run, and Installation

Install locally

# Clone this repository
git clone https://github.com/ELMOURABEA/MeGaOcto.git
cd MeGaOcto

# Install Python dependencies
pip install -r requirements.txt

# (If React or JS frontend present)
# Install JS dependencies
npm install

# Build (for static assets / frontend)
npm run build

# Run backend
python main.py

# (Optional: Interactive mode, if supported)
python main.py --interactive

Universal Hosting & Deployment

You can deploy this project to any of the following hosting services. Use build as the build command and publish directory unless otherwise specified.

Hosting Service	Best For	Build Command	Publish Directory
Netlify	Ease of use, Continuous Deployment (CD)	build	build
Vercel	Similar to Netlify, popular for React/Next.js	build	build
Render	All-in-one hosting for static & backend apps	build	build
GitHub Pages	Simple, free hosting for personal projects	(Requires extra setup, e.g., gh-pages package)	build

Note: For GitHub Pages, you may need to configure a gh-pages branch and use the gh-pages npm package if your project is a frontend app (React, etc.).

Recommended Sources

• Deploy a React App – Hostman
• Render Docs: Deploy Create React App

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License

MIT License (if applicable)

💝 Support & Sponsorship

Love MEGAGENT? Support its development!

GitHub Sponsors

Support via GitHub Sponsors: https://github.com/sponsors/ELMOURABEA

Ko-fi

Buy me a coffee: https://ko-fi.com/elmourabea

Your support helps:

• 🚀 Develop new features
• 🐛 Fix bugs faster
• 📚 Improve documentation
• 🌟 Add more AI platform integrations
• 🎯 Keep the free tier available

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

See our Contributing Guide for details.

📄 License

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

🙏 Acknowledgments

• GitHub Copilot team
• Google Gemini team
• OpenAI team
• X.AI Grok team
• All our contributors and supporters

📧 Contact & Support

• 💬 Issues: GitHub Issues
• 📧 Email: support@megagent.app
• 💖 Sponsor: Ko-fi | GitHub Sponsors
• 📚 Documentation: Full Documentation

🔒 Security

For security issues, please see SECURITY.md

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Built with ❤️ by the MeGaOcToOoN Team & CREATED BY Dr-Ai (MeGaOcto@elmorab3.com)

Integrating the power of multiple AI platforms into one unified solution Innovtion for the futur Designed with love for the whole world OMAR&SELA