LangChain

Technical explanation

LangChain emerged as the go-to framework for building LLM applications. It provides abstractions for common patterns like chaining multiple prompts together, connecting to external data sources, and giving models memory of past conversations. If you're building anything more complex than a single API call, LangChain offers structure.

The core concept is chains. A chain combines a prompt template, a language model, and optional output parsing. You can link chains together, where the output of one becomes the input of the next. Agents take this further by letting the model decide which tools to use. Need to search the web, query a database, or run code? Agents can pick the right tool based on the user's question.

LangChain supports basically every LLM provider. OpenAI, Anthropic, Google, Cohere, open-source models through Hugging Face or local inference. This abstraction lets you swap providers without rewriting your application logic. The library also includes integrations with vector databases, document loaders, and memory systems.

The criticism of LangChain is that it can be over-engineered for simple use cases. The abstraction layers add complexity that sometimes gets in the way. Debugging can be frustrating when you're deep in nested chains. Alternatives like LlamaIndex (for data-focused apps) or just raw API calls work better for simpler projects. But for complex agent systems, LangChain's structure helps manage the chaos.