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AI Agents in LangGraphvsLangChain for LLM Application Development

Side-by-side comparison on level, duration, pricing, instructor, tier. Editor verdict on which course wins for which buyer.

DL.AI
DeepLearning.AI

AI Agents in LangGraph

The shortest path from 'I read about agents' to 'I built one that works.' Harrison Chase walks through LangGraph's state machine model end-to-end — agentic loop, tool use, persistent state, human-in-the-loop. Free, ~90 minutes, and the only short course we recommend ahead of a longer specialization. Limitation: it assumes Python comfort and skips over LLM fundamentals. Pair it with the LLM Fundamentals course below if you're new to the field.

DL.AI
DeepLearning.AI

LangChain for LLM Application Development

Companion to the AI Agents in LangGraph course — this one covers the LangChain layer underneath: prompts, chains, output parsers, memory primitives, document loaders. Free, fast, and the right next step if you finished the prompt engineering course and want to compose multi-step LLM workflows. Limitation: LangChain has moved fast; some helper APIs shown have been renamed or replaced with LangChain Expression Language. Patterns are still correct; idioms have aged.

Side-by-side

DimensionAI Agents in LangGraphLangChain for LLM Application Development
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelIntermediateIntermediate
Formatself pacedself paced
Duration~1.5 hours (4 lessons)~1.5 hours (6 lessons)
PricingFreeFree
InstructorHarrison Chase — Founder, LangChainHarrison Chase & Andrew Ng — Founder LangChain; Founder DeepLearning.AI
RatingNo public ratingNo public rating
Topicsbuild ai agents, langchain, langgraphlangchain, llm fundamentals
Last verified2026-05-232026-05-23

Pros & cons

AI Agents in LangGraph
Pros
  • +Built and taught by LangChain founder — most authoritative source possible
  • +Free, ~90 minutes, immediate hands-on labs
  • +Covers state, tools, HITL, persistence — the actual building blocks
Cons
  • −Skips LLM basics — assumes you know what a token / context window is
  • −No certificate — bad for resume-padding, fine for actual learning
LangChain for LLM Application Development
Pros
  • +Free, fast, built by LangChain's founder
  • +Right level of abstraction — above raw API calls, below full agents
  • +Pairs naturally with the LangGraph short course
Cons
  • −Some shown APIs have been renamed since release (LCEL is the new way)
  • −No coverage of LangSmith / evaluation — that's a separate course

Which course is for whom?

AI Agents in LangGraph
Best for
  • · Engineers who have used the OpenAI API but never built an agent loop
  • · PMs who want to understand how agents actually work under the hood
Not ideal for
  • · Complete beginners — start with LLM Fundamentals first
  • · People who need a certificate — this course doesn't issue one
LangChain for LLM Application Development
Best for
  • · Engineers building structured LLM apps but not yet full agents
  • · Anyone evaluating LangChain vs LlamaIndex vs raw API calls
Not ideal for
  • · Beginners — assumes you understand LLM API calls
  • · People who want production-ops focus (evals, tracing)

Editor's short verdict

These cover different primary topics — AI Agents in LangGraph focuses on build ai agents while LangChain for LLM Application Development focuses on langchain. Take the one matching your current goal first; the other can come later if your interests expand.

DL.AIHands-on reviewedEditor's pick
DeepLearning.AI

AI Agents in LangGraph

For: Engineers who have used the OpenAI API but never built an agent loop

Intermediate · ~1.5 hours (4 lessons) · Free
ReviewOpen free →

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