Developed an OpenRouter-backed backend provider for the NevaMind-AI/memU repository, enabling unified API access to multiple large language model providers with support for chat, embeddings, and vision features. Designed and implemented the integration end-to-end using Python, focusing on robust API integration and backend development practices. Provided detailed configuration guidance, comprehensive testing instructions, and an example script to demonstrate memory-aware conversation processing. This work addressed vendor lock-in by allowing dynamic provider selection and laid the foundation for cost and performance optimization across LLM services. The solution supports experimentation and evaluation, streamlining multi-provider workflows for teams working with advanced language models.