LangChain Roadmap
LangChain gives reusable building blocks for model calls, prompts, parsers, retrievers, tools, agents, and workflows.
What it is
LangChain is a framework for building repeatable LLM workflows. LangGraph extends that into stateful graph-based agents.
Why it matters
As AI apps grow, you need named components for prompts, models, retrievers, tools, state, tracing, and evaluation.
How it works
Learn messages and prompts first, then chains and parsers, then retrievers and RAG, then agents, and finally LangGraph state and checkpoints.
Common mistakes
- Using frameworks before understanding raw API calls.
- Hiding the filled prompt from yourself.
- Adding agents before a simple chain works.
Best practices
- Build one raw version first.
- Inspect every retrieved document.
- Use LangGraph when workflow state and branching matter.
FAQ
Should beginners start with LangChain?
Build one raw API project first, then use LangChain when repeated workflow patterns appear.
When should I use LangGraph?
Use LangGraph for stateful, branching, resumable, or human-in-the-loop agent workflows.
Build It In The Roadmap
Turn this tutorial into a focused roadmap step with adjacent concepts and practice prompts.
Explore Roadmap