When integrating new LLM providers or AI services, prioritize OpenAI-compatible patterns and reuse existing utilities rather than creating separate implementations. This approach reduces code duplication, leverages battle-tested functionality, and maintains consistency across the codebase. Key practices: - Add new providers to `openai_compatible_providers` instead of creating separate handler blocks - Use `base_llm_http_handler` or `openai_like_chat_completion` for OpenAI-compatible APIs - Inherit from `OpenAILikeConfig` or `OpenAIGPTConfig` for transformation classes - Reuse existing utilities like `get_llm_provider`, `supports_function_calling`, and parameter mapping functions - Avoid hardcoding provider-specific logic in main completion functions Example of preferred approach: ```python # Instead of creating a separate provider block: elif custom_llm_provider == "new_provider": # Custom implementation... # Prefer adding to openai_compatible_providers: openai_compatible_providers = [..., "new_provider"] # And use existing handlers: response = base_llm_http_handler.completion( model=model, messages=messages, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key, # ... ) ``` This pattern ensures new AI integrations benefit from existing error handling, parameter validation, streaming support, and other features while minimizing maintenance overhead.