Description
Agentic AI in Practice: From LangGraph to OpenClaw is a course on building autonomous AI agents using modern agent frameworks and open-source tools published by Udemy Online Academy. Designed for AI engineers, Python developers, and automation professionals, this course teaches how to create intelligent systems that can reason, plan, use external tools, and execute complex multi-step workflows. This course, Agentic AI Engineering, is designed to help you understand and build these next-generation AI systems. You will learn how modern Agentic AI architectures work and how developers are building intelligent agents that can perform tasks autonomously, interact with external tools, retrieve knowledge, and execute multi-step workflows.
Students will learn how to design agent-oriented architectures with LangGraph, integrate large language models (LLMs), implement memory and state management, connect external APIs and services, orchestrate multi-agent workflows, and build AI-based applications with OpenClaw. Finally, the course focuses on production-level AI systems. You will learn how to design scalable architectures with observability, event logging, tracing, and metrics that help monitor agent performance. Topics such as latency optimization, cost optimization, reliability engineering, and distributed execution will help you build AI systems that can reliably run in real-world environments.
What you will learn in Agentic AI in Practice: From LangGraph to OpenClaw:
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Implement agent architectures such as ReAct, Planner-Executor, and Supervisor-Worker using modern AI frameworks.
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Develop operating systems with LangChain and LangGraph to create structured, stateful, and reliable AI workflows.
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Integrate tools, APIs, and external data sources using function calls and structured tool interfaces.
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Build AI memory systems using vector databases and Retrieval Augmentative Generation (RAG).
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Design and orchestrate multi-agent systems where multiple AI agents collaborate to solve complex problems.
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Implement the Model Context Protocol (MCP) to connect AI agents with external tools and services.
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And…
Course specifications
Publisher: Udemy
Instructors: Hadelin de Ponteves ,Kirill Eremenko ,SuperDataScience Team ,Luka Anicin and Ligency
Language: English
Level: Introductory to Advanced
Number of Lessons: 163
Duration: 17 hours and 22 minutes
Course topics
Agentic AI in Practice: From LangGraph to OpenClaw Prerequisites
Basic understanding of Python programming is recommended.
Familiarity with fundamental AI or machine learning concepts will be helpful but not required.
Basic knowledge of APIs and software development workflows is beneficial.
A computer capable of running Python, Jupyter Notebook, or a code editor such as VS Code.
Internet access to use AI APIs, cloud services, and development tools used throughout the course.
Curiosity about AI agents, autonomous systems, and modern AI frameworks.
Pictures
Agentic AI in Practice: From LangGraph to OpenClaw introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 720p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
2 GB