LangGraph Framework is an intermediate-level course designed for developers and AI engineers who want to build production-ready, stateful AI systems that go beyond simple prompt-response interactions. In today's AI landscape, the most powerful applications aren't single agents working in isolation—they're coordinated systems that maintain context, make intelligent decisions, and collaborate to solve complex problems. This course teaches you to harness LangGraph's graph-based architecture to create AI workflows with persistent memory, conditional logic, and multi-agent coordination. Through hands-on labs, real-world case studies from companies like Klarna, CyberArk, and Replit, and practical projects, you'll learn to build systems that maintain context across interactions, handle failures gracefully, and coordinate multiple specialized agents to create emergent intelligence. Whether you're building customer service automation, research assistants, or complex business workflows, this course equips you with the skills to create AI systems that are not just intelligent, but reliable, maintainable, and production-ready.
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Empfohlene Erfahrung
Kompetenzen, die Sie erwerben
- Kategorie: Agentic Workflows
- Kategorie: MLOps (Machine Learning Operations)
- Kategorie: Software Design Patterns
- Kategorie: Context Management
- Kategorie: Distributed Computing
- Kategorie: AI Orchestration
- Kategorie: LangGraph
- Kategorie: Generative AI Agents
- Kategorie: LLM Application
- Kategorie: LangChain
Wichtige Details
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Dezember 2025
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In diesem Kurs gibt es 3 Module
In this introductory lesson, learners will explore the fundamental architecture of LangGraph and understand how it differs from traditional agent frameworks. They'll examine the core concepts of graph-based state management and learn why LangGraph provides superior control and reliability for AI applications compared to stateless approaches.
Das ist alles enthalten
4 Videos3 Lektüren1 Aufgabe
In this lesson, learners will master the practical implementation of LangGraph's state management system. They'll learn to design persistent workflows with memory, implement conditional logic for dynamic routing, and create robust error handling mechanisms. Through hands-on exercises, learners will build workflows that maintain context across complex multi-step processes and handle real-world edge cases effectively.
Das ist alles enthalten
3 Videos1 Lektüre1 Aufgabe
In this final lesson, learners will master the design and implementation of sophisticated multi-agent systems using LangGraph. They'll learn to coordinate autonomous AI agents through event-driven flows, implement inter-agent communication patterns, and create systems where specialized agents collaborate to solve complex problems. The lesson culminates with a comprehensive capstone project that demonstrates production-ready multi-agent coordination.
Das ist alles enthalten
4 Videos1 Lektüre3 Aufgaben
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Status: KostenlosDeepLearning.AI
Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
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