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AI Agents in LangGraphvsFunctions, Tools and Agents with LangChain

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

Functions, Tools and Agents with LangChain

The middle-of-the-curriculum short course bridging from prompt patterns to full agent loops. Covers OpenAI function calling, LangChain tools, OutputParsers, and the conversational-agent loop. Free, 90 minutes, taught by Harrison Chase. Take this after the LangChain for LLM App Development short course and before the LangGraph one — they form the canonical 3-course LangChain sequence.

Side-by-side

DimensionAI Agents in LangGraphFunctions, Tools and Agents with LangChain
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelIntermediateIntermediate
Formatself pacedself paced
Duration~1.5 hours (4 lessons)~1.5 hours (5 lessons)
PricingFreeFree
InstructorHarrison Chase — Founder, LangChainHarrison Chase — Founder, LangChain
RatingNo public ratingNo public rating
Topicsbuild ai agents, langchain, langgraphlangchain, build ai agents, prompt engineering
Last verified2026-05-232026-05-24

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
Functions, Tools and Agents with LangChain
Pros
  • +By LangChain's founder — most authoritative source possible
  • +Bridges the gap between API-level prompting and full agentic loops
  • +Free, 90 minutes, fits in a single sitting
Cons
  • −Assumes prior LangChain basics — not a starting point
  • −Function-calling examples are OpenAI-flavored; patterns transfer to Anthropic

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
Functions, Tools and Agents with LangChain
Best for
  • · Engineers shipping their first LLM-with-tools app
  • · Anyone in the middle of the LangChain learning path
Not ideal for
  • · Complete beginners — start with LangChain for LLM App Development first

Editor's short verdict

These cover different primary topics — AI Agents in LangGraph focuses on build ai agents while Functions, Tools and Agents with LangChain 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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AI Agents in LangGraph vs Functions, Tools and Agents with LangChain (2026): which course wins? · AI Agent Rank