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

📘 Documentation: docs.equilink.io

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

📚 Check our Documentation
💡 Visit our Examples Repository

Contributing

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

Fork the repository
Create a feature branch
Commit your changes
Open a pull request

License

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

Ready to transform your AI workflows?
Get Started • Documentation • Community

Built with 💡 by developers, for developers

4.3 KiB Raw Permalink Blame History Unescape Escape