LangChain Academy offers free courses built by the LangChain team — the creators of the most popular LLM framework. The flagship course, Introduction to LangGraph, teaches agent architectures, tool use, memory, and multi-agent orchestration. It's not a traditional "certification" (no proctored exam) — it's a structured learning path with a completion certificate. For AI engineers building with LangChain, it's the most efficient way to learn directly from the source.
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Quick Answers
Is there a LangChain certification?
LangChain Academy offers free courses with certificates of completion — not a formal, proctored certification like AWS or Azure. The certificate proves you completed the course material, including hands-on exercises. It's respected in the AI engineering community because it's built by the LangChain team.
Is LangChain Academy free?
Yes, completely free. All courses, materials, and the completion certificate cost nothing. No credit card, no trial. Created by the LangChain team to grow the developer ecosystem.
Is LangChain still relevant in 2026?
Yes. LangChain is the most used LLM framework and LangGraph (for agent architectures) is gaining rapid adoption. The framework evolves fast — LangGraph addresses earlier criticisms about LangChain's complexity. Learning LangChain patterns is valuable even if you eventually switch frameworks.
LangChain Academy vs DeepLearning.AI LangChain courses — which is better?
DeepLearning.AI courses are better for beginners (gentler introduction, broader context). LangChain Academy goes deeper on advanced topics (LangGraph, multi-agent systems, production patterns). Ideal path: DeepLearning.AI LangChain courses first, then LangChain Academy for depth.
LangChain is the most popular framework for building LLM applications — and LangChain Academy is where the team that builds it teaches AI engineers how to use it. It's free, practical, and goes deeper on agent architectures than any other learning resource. This guide covers what's available, how it compares to alternatives, and whether it's worth your time.
- LangChain Academy
A free learning platform created by the LangChain team (LangChain Inc.) that offers structured courses on building LLM applications with LangChain and LangGraph. Courses include video lessons, code exercises, and Jupyter notebooks. Completion certificates are available for LinkedIn.
LangChain Academy launched as a way to formalize the education around LangChain's growing ecosystem. Before Academy, learning LangChain meant reading documentation and watching community tutorials. Academy provides a structured, official path — built by the people who create the framework.
Key facts:
- Completely free — no paid tier, no upsell
- Built by LangChain's team — Harrison Chase and LangChain engineers
- Hands-on — Jupyter notebook exercises, not just videos
- Completion certificate — for LinkedIn, shared as a badge
- Focused on LangGraph — the newer, more powerful part of the LangChain ecosystem
LangChain Academy regularly adds new courses as the framework expands. Check the academy website for the latest catalog. The courses listed above reflect availability as of early 2026.
This is the primary course and the main reason to use LangChain Academy. LangGraph is LangChain's framework for building stateful, multi-step AI agents — and it addresses many criticisms of earlier LangChain versions.
What You'll Learn
- State graphs: building agent workflows as directed graphs (nodes = actions, edges = transitions)
- Tool use: giving agents the ability to call functions, APIs, and databases
- Memory: adding persistent memory to agent conversations
- Human-in-the-loop: patterns for requiring human approval at decision points
- Multi-agent systems: multiple agents collaborating on complex tasks
- Streaming: real-time output from agent workflows
- Error handling: recovery patterns when agents make mistakes
Why LangGraph Matters
LangGraph solves the main criticism of early LangChain: that chains were too rigid and agents too unpredictable. LangGraph provides controllable agent architectures — you define the graph of possible actions, and the LLM decides which path to take. It's the middle ground between hardcoded chains and fully autonomous agents.
LangGraph is where LangChain is heading. Learning LangGraph through Academy means learning the production-grade patterns for building AI agents — directly from the team that builds the framework.
Both platforms offer LangChain courses. They're complementary, not competitors.
The Recommended Path
- First: DeepLearning.AI short courses — "LangChain for LLM Application Development" and "Functions, Tools and Agents with LangChain"
- Then: LangChain Academy — "Introduction to LangGraph" for advanced agent architectures
- While learning: Build a portfolio project with LangChain/LangGraph
This order works because DeepLearning.AI provides the gentle introduction, and LangChain Academy provides the depth.
Take DeepLearning.AI LangChain courses first (beginner-friendly intro), then LangChain Academy (advanced depth). They're both free — the only cost is your time.
A common concern: "LangChain changes constantly, new frameworks keep appearing — is it worth investing time in LangChain specifically?"
The case for learning LangChain:
- Largest community and ecosystem of any LLM framework
- Most job listings mentioning LLM frameworks reference LangChain
- LangGraph addresses earlier complexity criticisms
- The patterns you learn (chains, agents, tools, memory, RAG) transfer to any framework
- LangSmith (observability) and LangServe (deployment) complete the production story
The honest caveat:
- The framework moves fast — code written 6 months ago may use deprecated patterns
- Some engineers prefer lighter alternatives (direct API calls, Vercel AI SDK)
- Learning the underlying patterns matters more than memorizing LangChain syntax
LangChain teaches the patterns of LLM application development: chains, agents, tool use, memory, retrieval. Even if you eventually switch to another framework, these patterns are universal. The syntax changes — the architecture doesn't.
Prerequisites: Basic LangChain + Python
Don't start Academy cold. Complete at least one DeepLearning.AI LangChain course and build a simple LangChain project. Academy assumes you know what chains, agents, and tools are.
Complete the LangGraph Course Actively
Don't just watch — run every notebook, modify the code, break things. The real learning happens when you change the graph structure and see how agent behavior changes.
Build a LangGraph Project Immediately
After finishing the course, build an agent with LangGraph for your portfolio. Ideas: a code review agent, a data analysis agent that writes SQL, or a multi-step research agent. For detailed project specs with Cursor prompts, see our GenAI Project Ideas for AI Engineers — projects 5 and 7 use LangGraph.
Add the Certificate to LinkedIn
The completion certificate has value in the AI engineering community. Add it to your LinkedIn certifications section. It signals that you've invested in learning the dominant LLM framework from its creators.
LangChain and LangGraph are the agent framework layer of the GenAI stack. For the complete path — from programming fundamentals to your first AI engineering role — see our How to Become an AI Engineer: The Only Free Guide You Need.
LangChain Academy ranks #2 in our complete certification ranking. For the full comparison of all GenAI certifications — see our Best GenAI & AI Certifications in 2026.
LangChain Academy: Key Takeaways
- 1LangChain Academy is free and built by the LangChain team — the most authoritative source for learning the framework
- 2The flagship LangGraph course teaches agent architectures: state graphs, tool use, memory, multi-agent systems
- 3Not a formal certification — it's a completion certificate, valuable for LinkedIn signaling in the AI community
- 4Best path: DeepLearning.AI LangChain courses first (intro), then Academy (depth)
- 5The patterns (agents, tools, memory, RAG) transfer across frameworks — even if you later switch from LangChain
- 6Always build a project after completing the course — the certificate alone is insufficient
Frequently Asked Questions
Is LangChain Academy enough to get a job?
No course alone is enough. LangChain Academy teaches the framework, but employers want proof you can build with it. Complete the course, then build 1-2 portfolio projects using LangChain/LangGraph and deploy them. The combination of Academy certificate + GitHub projects is what gets attention.
Do I need to know Python for LangChain Academy?
Yes. LangChain is Python-first (there's a JavaScript version, but Academy focuses on Python). You need comfortable Python skills — not expert level, but you should be able to write functions, handle async code, and work with APIs.
Is LangChain going to be replaced by something else?
LLM frameworks evolve rapidly, but LangChain has the largest ecosystem and community. LangGraph specifically is gaining momentum for agent architectures. More importantly, the patterns LangChain teaches (chains, agents, tools, retrieval) are universal — they'll transfer to any successor framework.
Can I use LangChain Academy courses for interview prep?
Yes — LangGraph knowledge is directly relevant to AI engineering interviews. Questions about agent architectures, tool use, memory management, and RAG pipelines are common in AI engineering interviews. The LangGraph course prepares you for these.
How does LangChain Academy compare to cloud certifications?
Different purposes. Cloud certifications (AWS, Azure, Google) prove platform-specific skills. LangChain Academy teaches framework-specific skills. Most AI engineers benefit from both: LangChain for the application layer, a cloud cert for the deployment layer.
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Sources & References
- LangChain Academy — LangChain Inc. (2025)
- LangGraph Documentation — LangChain Inc. (2025)
- LangChain Documentation — Introduction — LangChain Inc. (2025)