langchain-oss-primer

To choose between LangChain and LangGraph, evaluate your project needs against framework decision guidance. LangChain suits standard chains, while LangGraph fits complex stateful agent architectures requiring structured control flow.

The best way to structure a LangChain project is following a stepwise setup blueprint. This enforces a structured onboarding workflow covering environment configuration, dependency management, and initial project scaffolding.

Map your use case to a LangChain agent archetype like QA/Chatbot, SQL Agent, or Research Assistant using archetype mapping. This process aligns your project requirements with recommended next skills tailored to the chosen path.

Yes, starting a LangChain project requires specific Python or Node versions. The onboarding workflow outlines necessary prerequisites and environment tools to ensure correct dependency management and framework compatibility.

Use Deep Agents instead of LangChain when your project requires advanced deep-agents architecture. Framework selection guidance helps determine if Deep Agents better suits your specific AI workflow over standard LangChain setups.

Initial LangChain agent setup involves selecting a framework, choosing an archetype, and installing dependencies. You then configure the environment and load recommended next skills to accelerate project initialization.