Error Handling

This document covers error handling strategies in NeurosLink AI.

Error Types

Provider Errors

• Connection failures

• Rate limiting

• Authentication issues

Configuration Errors

• Invalid settings

• Missing environment variables

• Malformed configuration files

Runtime Errors

• Tool execution failures

• Memory allocation issues

• Timeout errors

Error Recovery

Automatic Retry

NeurosLink AI includes automatic retry mechanisms for transient failures.

Fallback Providers

Configure fallback providers to handle primary provider failures.

Graceful Degradation

System continues to operate with reduced functionality when errors occur.

Monitoring and Logging

Error Logging

All errors are logged with appropriate severity levels.

Metrics Collection

Error rates and patterns are tracked for analysis.

Alerting

Configure alerts for critical error conditions.

Best Practices

1. Always configure fallback providers

2. Set appropriate timeout values

3. Monitor error rates and patterns

4. Test error scenarios in development

5. Implement proper error boundaries

For more detailed information, see the Troubleshooting Guide.