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AI Agents in LangGraphvsMCP: Build Rich-Context AI Apps with Anthropic

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

MCP: Build Rich-Context AI Apps with Anthropic

MCP (Model Context Protocol) is the standard Anthropic introduced for connecting LLMs to external tools and data sources — and in 2026 it's becoming the lingua franca across Claude, Cursor, and most agent runtimes. This course is the canonical introduction, taught by Anthropic. Free, 90 minutes, hands-on building MCP servers and clients. The right course to take after the basic prompt engineering tutorials, before building production agents.

Side-by-side

DimensionAI Agents in LangGraphMCP: Build Rich-Context AI Apps with Anthropic
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelIntermediateIntermediate
Formatself pacedself paced
Duration~1.5 hours (4 lessons)~1.5 hours
PricingFreeFree
InstructorHarrison Chase — Founder, LangChainElie Schoppik — Anthropic Developer Education
RatingNo public ratingNo public rating
Topicsbuild ai agents, langchain, langgraphmcp, build ai agents
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
MCP: Build Rich-Context AI Apps with Anthropic
Pros
  • +Authored by Anthropic — MCP is their protocol
  • +Free, hands-on, fast
  • +MCP is becoming standard — investment compounds across Claude, Cursor, agent frameworks
Cons
  • −Newer course — feedback corpus is smaller than the older shorts
  • −Pre-req: comfortable with Python + API calls

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
MCP: Build Rich-Context AI Apps with Anthropic
Best for
  • · Developers building production AI agents in 2026
  • · Anyone integrating LLMs with proprietary tools/data
Not ideal for
  • · Beginners — assumes Python + LLM basics
  • · Non-developers — MCP is fundamentally a developer-protocol

Editor's short verdict

These cover different primary topics — AI Agents in LangGraph focuses on build ai agents while MCP: Build Rich-Context AI Apps with Anthropic focuses on mcp. 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 MCP: Build Rich-Context AI Apps with Anthropic (2026): which course wins? · AI Agent Rank