LangChain vs. LangGraph Explained with One RAG Chatbot

Bashiri Smith · Facebook reel · 2026-09-29 · 1:13 · 23,175 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), AI Agents, Tool Use & MCP · Level: beginner

Summary

The video uses one RAG chatbot to show how LangChain and LangGraph differ. LangChain is the integration layer: it gives ready-made connectors with a shared interface, so the embedding model, vector database and LLM work together with less custom code. LangGraph is the workflow layer: it handles branching, loops and human-in-the-loop pauses, such as rewriting the query when retrieval goes badly. The two are often used together.

Key points

• Basic RAG flow: embed the question, search a vector database, return relevant data, pass it to an LLM, and get an answer.
• Without LangChain, you use each service's own SDK and write glue code so that each step's output matches the format the next step expects.
• LangChain provides ready-made integrations with common interfaces, so connecting the embedding model, vector DB and LLM takes much less custom code.
• LangGraph adds decisions to the workflow. If retrieval results are good, the LLM generates an answer. If they are bad, the question is rewritten and the search runs again. After too many failed attempts, it pauses for a human.
• With LangGraph, you write the functions as steps (nodes) and define the rules that connect them. It manages the branching, loops and pauses for you.
• Rule of thumb: use LangChain to connect models and tools, and use LangGraph when you need control over branching, multi-step loops or pausing for a human.

Resources mentioned

• LangChain · tool · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Open-source framework with ready-made integrations and common interfaces for connecting LLMs, embedding models, vector stores and tools.
  Also in: SWE-to-AI Engineer Plan for 2027: LLMs, RAG, Agents, Evals, Job Search (Bashiri Smith on Facebook · notes), Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Relearn LLMs & RAG in 2026: A 7-Step Roadmap with Free Resources (Bashiri Smith on Facebook · notes) and 3 more
• LangGraph · tool · langchain.com · free
  Open-source library for building stateful LLM workflows as graphs, with branching, loops and human-in-the-loop pauses.
  Also in: Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), Wannabe vs $200K+ AI Engineer: RAG, Agents, Context and Fine-Tuning Mistakes (Bashiri Smith on Facebook · notes), 7 Habits to Become an AI Engineer: Books, Tooling, Research & Shipping (Bashiri Smith on Facebook · notes), 8-Week Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes) and 2 more
• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Use LangChain to connect the embedding model, vector database and LLM in a RAG pipeline.
• Use LangGraph when your workflow needs branching, retries or human-in-the-loop pauses.
• Comment 'Lang' on the video, or use the caption link, to join the BASWE.Ai Engineer community.
• Build a RAG chatbot with LangChain (embedding model, vector DB and LLM), then add a LangGraph workflow. It should check retrieval quality, answer if results are good, rewrite the question and search again if they are bad, and pause for human input after too many failed attempts.