I choose Langchain because it provides these really helpful abstractions and modular tools that simplify building complex workflows with language models. Things like chaining API calls, working with vector databases (RAG), and managing conversation memory become much more straightforward. It just makes experimenting and prototyping LLM-based applications way faster than building everything from scratch.
LangChain handles orchestration between content retrieval, chunking, embedding, and generation. It lets us combine different tools (like vector search + OpenAI) into a clean pipeline without managing every integration manually.
LangGraph by LangChain made it possible to design the agentic workflow as state-graphs providing greater control and reliability. Also, LangSmith for observabilty of the Agentic actions.
