# 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.
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> **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](https://docs.equilink.io)
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## Getting Started
```bash
# 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:
```python
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:
```python
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:
```python
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
```bash
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:
```bash
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](https://docs.equilink.io)
- 💡 Visit our [Examples Repository](https://github.com/equilink/examples)
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## 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](LICENSE) for more information.
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