Jan 16
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Read the full guide: https://binaryverseai.com/langchain-vs-langgraph-decision-guide-framework/ Choosing the right framework decides how far your agent gets. In this 14:24 guide, we break down LangChain vs LangGraph so you can ship with confidence. Learn when a linear LCEL chain is enough, and when a stateful graph with loops, branching, and persistence wins. What you will learn: The simple difference in one line, chains for straight lines, graphs for agent brains A practical decision checklist for real projects How to move from a quick LCEL pipeline to a robust LangGraph state machine Where LangChain agents fit, and when to upgrade the runtime How LangGraph and LangSmith work together for observability and debugging Who this is for: Engineers building AI agents Python that must plan, call tools, and verify Teams choosing an AI application framework for production Builders who want fewer surprises and faster iteration Key takeaways: Use LangChain for stateless RAG, summarization, and simple tool use Use LangGraph when your app must remember context, retry, and coordinate multiple tools or roles Keep LCEL for the parts, use the graph for the brain, add LangSmith for the eyes If this saved you time, like and subscribe. Drop your stack in the comments so others can learn from it. Chapters: 00:00 Welcome back to the deep dive 00:48 LangChain (LC), your base framework 01:07 LangGraph (LG) 01:56 LC is built on linear chains 02:20 The biggest differentiator: state 02:43 Checkpointable state 03:45 Control flow 04:51 LangGraph makes HITL first class 05:27 Observability 06:43 Prototype RAG for a document set 07:02 Simple tool use 07:28 Vendor swapping 08:06 Multi-turn support workflow 08:34 Research assistant that plans, searches, and verifies 09:46 Multi-agent system 11:10 Shared blackboard 11:42 Guardrails 13:04 Manage state and complex control flow 13:55 Measure it 14:21 A crucial decision point #LangChain #LangGraph #RAG #LLM #AI #Python