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Functions, Tools and Agents with LangChainvsLangChain 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

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.

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

DimensionFunctions, Tools and Agents with LangChainLangChain for LLM Application Development
ProviderDeepLearning.AIDeepLearning.AI
Editorial tierHands-on reviewedHands-on reviewed
LevelIntermediateIntermediate
Formatself pacedself paced
Duration~1.5 hours (5 lessons)~1.5 hours (6 lessons)
PricingFreeFree
InstructorHarrison Chase — Founder, LangChainHarrison Chase & Andrew Ng — Founder LangChain; Founder DeepLearning.AI
RatingNo public ratingNo public rating
Topicslangchain, build ai agents, prompt engineeringlangchain, llm fundamentals
Last verified2026-05-242026-05-23

Pros & cons

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
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?

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
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

Both cover the same topic at the same level; pick by format and pricing. Functions, Tools and Agents with LangChain (self paced, Free) vs LangChain for LLM Application Development (self paced, Free). If price-sensitive, take the cheaper; if commitment-sensitive, take the cohort or paid option for the accountability.

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Functions, Tools and Agents with LangChain vs LangChain for LLM Application Development (2026): which course wins? · AI Agent Rank