Learn Advanced Features
Streaming Responses
Get real-time responses as they’re generated
Function Calling
Let AI interact with external tools and APIs
Vision Support
Process images with multimodal models
API Documentation
Complete API reference and guides
Explore Documentation
API Reference
Complete API documentation
OpenAI API
OpenAI-compatible endpoints
Anthropic API
Anthropic Claude-compatible endpoints
Models Catalog
Browse all 70+ available models
Build Real Applications
1. Chatbot
Build an intelligent chatbot:2. Content Generator
Generate blog posts, emails, or social media content:3. Code Assistant
Build a coding helper:4. Data Analyzer
Analyze data and generate insights:Best Practices
Choose the Right Model
Choose the Right Model
- GPT-4: Best for complex reasoning
- GPT-3.5 Turbo: Fast and cost-effective
- Claude Opus: Excellent for analysis and long context
- Claude Sonnet: Balanced performance
- Gemini Pro: Strong multimodal capabilities
Optimize Costs
Optimize Costs
- Start with cheaper models for testing
- Use
max_tokensto limit response length - Cache responses when possible
- Use streaming to improve perceived performance
- Monitor usage in your dashboard
Handle Errors Gracefully
Handle Errors Gracefully
Optimize Prompts
Optimize Prompts
- Be specific and clear
- Provide examples when needed
- Use system messages to set context
- Break complex tasks into steps
- Test different temperature settings
Manage Context
Manage Context
- Keep track of conversation history
- Limit history to avoid token limits
- Summarize old messages if needed
- Use prompt caching for repeated content
Production Considerations
Security
- Store API keys in environment variables
- Never commit keys to version control
- Use different keys for dev/staging/production
- Rotate keys regularly
- Monitor usage for anomalies
Performance
- Use streaming for better UX
- Implement caching where appropriate
- Add retry logic with exponential backoff
- Monitor response times
- Consider using webhooks for async operations
Monitoring
- Track token usage
- Monitor error rates
- Log API requests (without sensitive data)
- Set up alerts for quota limits
- Review costs regularly
Scaling
- Implement rate limiting
- Use queues for high-volume requests
- Cache common responses
- Consider batching requests
- Plan for failover strategies
Join the Community
Discord
Chat with other developers
GitHub
View examples and contribute
Twitter/X
Follow for updates
YouTube
Watch tutorials
Get Help
Check the FAQ
Check the FAQ
Most common questions are answered in our FAQ.
Read the Docs
Read the Docs
Comprehensive guides available in Developer Docs.
Contact Support
Contact Support
Email us at support@megallm.io for technical assistance.
Report Issues
Report Issues
Found a bug? Report it on GitHub.
Useful Resources
- API Reference - Complete API documentation
- Models Catalog - All 70+ models with pricing
- CLI Tool - Set up AI coding assistants
- FAQ - Common questions and answers
- Changelog - Latest updates

