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Equilink Logo

Equilink

Transform your AI workflows with Equilink the intelligent orchestration platform that bridges the gap between different AI models and your applications. Built for developers who need seamless AI integration, Equilink provides a unified framework for managing AI interactions, custom workflows, and automated response systems.


Core Features
🔄 Unified AI Interface: Seamlessly switch between different AI providers without changing your code
🎯 Smart Routing: Automatically direct queries to the most suitable AI model based on task requirements
🔗 Workflow Builder: Create complex AI interaction patterns with our visual workflow designer
📈 Performance Analytics: Track and optimize your AI usage and response quality
🛠️ Developer-First: Extensive SDK support with detailed documentation and examples


Connect With Us


Equilink Workflow Demo

Getting Started

# Install Equilink using pip
pip install equilink

# Initialize a new project
equilink init my-project

# Start the development server
equilink serve

That's it! Visit http://localhost:3000 to access the Equilink Dashboard.


Key Features

AI Model Integration

Connect to any supported AI provider with a single line of code:

from equilink import AIManager

# Initialize with your preferred provider
ai = AIManager(provider="openai")  # or "anthropic", "google", etc.

# Send queries with automatic routing
response = ai.process("Analyze this market data", context_type="financial")

Workflow Builder

Create sophisticated AI workflows using our intuitive builder:

from equilink import Workflow

workflow = Workflow("data_analysis")
workflow.add_step("data_cleaning", model="gpt-4")
workflow.add_step("analysis", model="claude-2")
workflow.add_step("visualization", model="gemini-pro")

# Execute the workflow
results = workflow.run(input_data=your_data)

Smart Caching

Optimize performance and reduce costs with intelligent response caching:

from equilink import CacheManager

cache = CacheManager()
cache.enable(ttl="1h")  # Cache responses for 1 hour

# Automatically uses cached responses when available
response = ai.process("What's the weather?", use_cache=True)

Project Structure

your-project/
├─ workflows/          # Custom workflow definitions
├─ models/            # Model configurations and extensions
├─ cache/             # Cache storage and settings
├─ integrations/      # Third-party service integrations
├─ analytics/         # Performance tracking and reporting
├─ config.yaml        # Project configuration
└─ main.py           # Application entry point

Configuration

Create a .env file in your project root:

EQUILINK_API_KEY=your_api_key
AI_PROVIDER_KEYS={
    "openai": "sk-...",
    "anthropic": "sk-..."
}
CACHE_STRATEGY="redis"  # or "local", "memcached"

Use Cases

  • 🤖 Chatbots & Virtual Assistants: Create intelligent conversational agents
  • 📊 Data Analysis: Automate complex data processing workflows
  • 🔍 Content Moderation: Deploy AI-powered content filtering
  • 📝 Document Processing: Extract and analyze information from documents
  • 🎯 Personalization: Build adaptive user experiences

Getting Help


Contributing

Help make Equilink better! We welcome contributions of all sizes:

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Open a pull request

License

Equilink is available under the MIT License. See LICENSE for more information.


Ready to transform your AI workflows?
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