LangChain Orchestration Modules

A chain follows a defined sequence of prompt, model, parser, or tool steps, while an agent chooses actions dynamically based on the task. Memory supplies conversation or task state, although modern LangChain applications often implement that state through message history, checkpoints, or LangGraph rather than assuming one universal memory class.

Yes, a LangChain workflow can connect an LLM to retrievers, Python or JavaScript functions, HTTP APIs, databases, and other tools. The important design choice is whether the workflow should follow a fixed sequence or let an agent decide which tool to call at runtime.

LangChain Expression Language lets developers compose runnable components such as prompts, chat models, parsers, and retrievers into pipelines. Common patterns include sequential composition and parallel branches, which are useful when an application needs predictable execution instead of unrestricted agent behavior.

LangGraph is useful when the application needs durable state, branching, retries, human approval, or a multi-step agent loop. It complements LangChain components by providing a graph-based runtime and checkpointing model for workflows that are more complex than a simple chain.