Startseite Kurse durchsuchen AI & Machine Learning Building AI Agents with LangChain
Kostenloser Kurs English Verifiziertes Zertifikat Beginner #ai #machine learning #chatgpt #automation #building #agents #langchain #professionals

Building AI Agents with LangChain

In this course, you’ll learn how to build practical AI agents with LangChain, step by step. You’ll start with the basics of LLM apps, then move through prompts, tools, memory, retrieval, and agent workflows, ending with deployment and safety.

5.0 (3 Bewertungen)
106 eingeschriebene Lernende
561 Aufrufe
30 lessons
~15 min gesamt
Im eigenen Tempo, lebenslanger Zugriff

Worum geht es in diesem Kurs?

This beginner-friendly course helps working professionals learn how to design, build, and evaluate practical AI agents with LangChain. Starting from the fundamentals of LLM applications, you will progress through prompts, chains, tools, memory, retrieval, and agent workflows, then finish with deployment, safety, and best practices. Each module begins with a short introduction, uses real-world examples, and each lesson ends with practical exercises you can complete in 5-15 minutes. The course is hands-on, clear, and focused on building useful solutions step by step.

Was sind die wichtigsten Fakten zu diesem Kurs?

Preis Kostenlos
Zertifikat Verifiziertes Zertifikat
Dauer 15 min
Lektionen 30
Sprache English
Niveau Beginner
Prüfung Im eigenen Tempo, lebenslanger Zugriff

Lehrplan

6 modules · 30 lessons

1
Module 1: Getting Started with LangChain and AI Agents
5 lessons
  • What AI Agents Are and Where LangChain Fits
  • Core Concepts: Prompts, Models, Chains, and Tools
  • Setting Up Your Development Environment
  • Your First LangChain Application
  • How to Think Like a Builder: Problem, User, Output
2
Module 2: Prompting and Chain Design
5 lessons
  • Writing Clear Prompts for Reliable Results
  • Few-Shot Prompting and Examples
  • Structured Outputs and Formatting
  • Building Simple Chains
  • Testing and Improving Prompt Chains
3
Module 3: Memory and Conversation Management
5 lessons
  • Why Memory Matters in AI Agents
  • Types of Memory in LangChain
  • Designing Useful Conversation Flows
  • Preventing Memory Problems and Drift
  • Practical Memory Design for Business Agents
4
Module 4: Tools, Functions, and External Actions
5 lessons
  • What Tools Do in an AI Agent
  • Creating and Describing Tools Clearly
  • Calling Functions and Handling Results
  • Real-World Tool Examples
  • Safe Tool Use and Human Oversight
5
Module 5: Retrieval, Knowledge, and Grounded Answers
5 lessons
  • Why Retrieval Improves AI Answers
  • Working with Documents and Text Sources
  • Building a Simple Retrieval Workflow
  • Answering with Citations and Traceability
  • When Retrieval Fails and How to Improve It
6
Module 6: Building, Evaluating, and Deploying AI Agents
5 lessons
  • Planning a Complete Agent Solution
  • Testing Agent Behavior and Quality
  • Improving Reliability with Guardrails
  • Deploying for Real Users
  • Capstone: Design Your First Useful AI Agent

Abschlusszertifikat

Nach Abschluss des Kurses schaltest du ein verifiziertes Zertifikat frei — als PDF herunterladbar, mit eindeutiger Nummer und Verifizierungslink.

Jedes Zertifikat hat einen eindeutigen Verifizierungslink und QR-Code.

Häufig gestellte Fragen

Is this course free to start?

Yes. You can start learning this course for free.

Who is this course for?

This course is designed for learners who want practical, job-relevant skills and a structured path from basics to application.

Do I need prior experience?

No prior experience is required unless specific prerequisites are listed in the course requirements section.

How long does this course take?

The estimated total study time for this course is 15 min.

How does the certificate work?

Nach Abschluss des Kurses kannst du ein verifiziertes Zertifikat mit eindeutiger Nummer und öffentlichem Verifizierungslink herunterladen.

Bewertungen von Lernenden

5.0
3 Bewertungen
5
3
4
0
3
0
2
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1
0
J
James W. Verifiziert · 16 Mai 2026

Finally got chains vs agents.

J
Jessica L. Verifiziert · 6 Jan 2026

Tools made sense

The part on tools and memory clicked for me, especially how they fit into a LangChain agent workflow. It felt practical right away.

R
Rachel T. Verifiziert · 20 Aug 2025

Clear on retrieval

I liked the retrieval module most. The examples made it easy to see how an agent can use external context instead of just guessing.

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