Choosing between LlamaIndex and LangChain for building AI agents involves understanding the unique strengths each framework offers, as both are well-regarded in production environments and supported by the open-source community. LangChain is noted for its modular architecture that excels in constructing complex AI workflows and offers lower initial learning curves with extensive documentation and resources, making it suitable for prototyping and end-to-end AI applications. Conversely, LlamaIndex is specialized for efficient data retrieval and indexing from over 160 data sources, making it advantageous for applications requiring Retrieval-Augmented Generation (RAG) and handling multi-modal data. While LangChain's flexibility allows for integration with various language models and external tools, LlamaIndex supports multi-modal applications with high-level abstractions for both text and image data. Despite the steeper learning curve associated with LlamaIndex, its power in specific RAG tasks makes it a valuable tool for certain use cases. Combining both frameworks can enhance the development of data-intensive applications with complex workflows, leveraging LlamaIndex's data handling capabilities alongside LangChain's management of complex reasoning and agent-based systems.