LangChain is only one way to build Python AI applications. Teams may choose a provider SDK, a graph-based workflow, a typed agent framework, or a general orchestration platform depending on the problem. The goal is not to avoid abstraction; it is to choose the smallest abstraction that improves reliability.
Compare frameworks by how they handle model calls, structured outputs, tools, retrieval, state, retries, and human approvals. Debugging matters because an AI workflow can fail at the prompt, tool, data, model, or infrastructure layer. A framework that hides these boundaries may speed up a demo while slowing down production diagnosis.
Prototype the riskiest feature instead of building a generic chatbot. Measure how much code is framework-specific, how traces are captured, and how easily tests can replace live model calls. Keep domain logic in ordinary Python modules where possible. This approach makes the application easier to understand and reduces the cost of future changes.