Welcome to the comprehensive Freddy platform documentation. Here you'll find guides, tutorials, best practices, and everything you need to get started with the Freddy AI platform.
Documentation
Comprehensive guides, tutorials, and best practices
- Platform Overview - Understanding the Freddy AI platform
- Quick Start Guide - Get up and running in minutes
- Authentication Methods - Security and API access
- API Reference - Complete endpoint documentation
- API Reference - Complete API endpoint documentation
- Authentication Guide - How to authenticate with the API
- Debugging Guide - Understanding API responses and errors
- Best Practices - Recommended patterns and practices
- Rate Limiting - Understanding API limits and quotas
- Performance Tips - Optimizing your API usage
- Security Guidelines - Keeping your integration secure
- Web Applications - Integrate with web applications
- Mobile Applications - Mobile app integration patterns
- Webhook Integration - Real-time notifications and events
- Custom Solutions - Building custom integrations
Step-by-Step Tutorials
Beginner Tutorials:
- Building Your First Chat Application - Create a simple AI chat interface
- User Authentication Setup - Implement secure user authentication
- Making Your First API Call - Get started with the API
Intermediate Tutorials:
- Building a Dashboard with Analytics - Create usage dashboards
- Implementing Real-time Features - Add live updates to your app
- Advanced Authentication Patterns - Complex auth scenarios
Advanced Tutorials:
- Custom AI Model Integration - Integrate custom AI models
- Scaling Your Application - Handle high-volume usage
- Performance Optimization - Optimize for speed and efficiency
MCP Server for AI Tools
Connect your AI coding assistant directly to our documentation. Claude, ChatGPT, Cursor, and VS Code can search and reference Freddy docs in real-time using the Model Context Protocol.
MCP Server URL:https://api.aitronos.com/mcp
- MCP Server for AI-powered documentation search (Claude, Cursor, VS Code)
- 196+ API Endpoints documented with interactive examples
- Auto-sync documentation with backend changes
- Enhanced tutorials with step-by-step guides
- Code examples in multiple programming languages
- Postman collections for easy API testing
Documentation automatically updated with platform changes
Freddy is a comprehensive AI-powered backend system that provides:
- ** AI Intelligence** - Multiple AI model support (OpenAI, Anthropic, Google)
- ** User Management** - Complete authentication and user lifecycle
- ** Organization Tools** - Multi-tenant architecture with role-based access
- ** Analytics & Monitoring** - Real-time usage tracking and insights
- ** Billing & Finance** - Automated usage-based billing system
The Freddy API uses API keys for authentication. All requests must include your API key in the header:
curl -H "X-API-Key: ak_your_api_key_here" https://api.aitronos.com/v1/endpointGetting Your API Key:
- Log in to Freddy
- Navigate to Settings → API Keys
- Click "Create New API Key"
- Copy and securely store your key
The API returns standard HTTP status codes and structured error responses:
{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "The request contains invalid parameters.",
"system_message": "Validation error",
"type": "client_error",
"status": 400,
"details": {},
"trace_id": "abc-123-def",
"timestamp": "2025-12-22T15:30:00Z"
}
}Common Status Codes:
200- Success400- Bad Request (validation error)401- Unauthorized (invalid API key)429- Too Many Requests (rate limited)500- Internal Server Error
API requests are rate limited based on your service tier:
- Standard: 100 requests/minute
- Premium: 1000 requests/minute
- Enterprise: Custom limits
Rate limit headers are included in every response:
X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1234567890** Security:**
- Never expose API keys in client-side code
- Use environment variables for API keys
- Implement proper error handling
- Validate all user inputs
** Performance:**
- Cache responses when appropriate
- Use pagination for large datasets
- Implement exponential backoff for retries
- Monitor your usage and rate limits
** Development:**
- Use the interactive API docs for testing
- Implement proper logging and monitoring
- Follow RESTful conventions
- Keep your API keys secure
Create a simple chat interface using the Freddy API:
- Set up authentication with your API key
- Create a chat session using the
/v1/threadsendpoint - Send messages using the
/v1/messagesendpoint - Get AI responses using the
/v1/model/responseendpoint
Implement secure user authentication:
- User Registration -
/v1/auth/register - Email Verification -
/v1/auth/verify-email - User Login -
/v1/auth/login - Session Management - Handle JWT tokens securely
Get started with a simple API request:
curl -X GET "https://api.aitronos.com/v1/models" \
-H "X-API-Key: ak_your_api_key_here"This returns a list of available AI models you can use.
Integrate Freddy into web applications:
- Use HTTPS for all API calls
- Implement proper CORS handling
- Store API keys securely on the server
- Use WebSockets for real-time features
Best practices for mobile apps:
- Store API keys in secure storage
- Implement offline functionality
- Handle network connectivity issues
- Optimize for mobile data usage
Set up real-time notifications:
- Configure webhook endpoints
- Verify webhook signatures
- Handle webhook retries
- Process events asynchronously
Build custom solutions:
- Generate client SDKs
- Implement custom authentication
- Build monitoring and alerting
Optimize your API usage:
- Use appropriate pagination
- Implement caching strategies
- Batch requests when possible
- Monitor response times
Handle high-volume usage:
- Implement connection pooling
- Use load balancing
- Monitor rate limits
- Plan for traffic spikes
Keep your integration secure:
- Use HTTPS everywhere
- Validate all inputs
- Implement proper authentication
- Monitor for suspicious activity
This documentation is automatically updated with platform changes