Description
Explore the components of LLM-based assistants, from prompt design and tool use to knowledge retrieval and persistent memory. The video edition uses agent behaviour patterns to organise these ideas and discusses how applications coordinate users, models and external information.
The saved syllabus includes AutoGen, CrewAI, Semantic Kernel, LangChain and a Streamlit-based agent platform. It also covers prompt flows, grounding, rubrics, sequential planning and feedback. The material is aimed at Python developers who want to understand how agent capabilities are assembled and evaluated, rather than only call a model for a single response.
What you will learn
- Implement agent actions, tools, profiles and memory.
- Explore retrieval, semantic search and document indexing.
- Study multi-agent coordination and behaviour patterns.
- Evaluate grounded responses, planning and feedback workflows.
Course specifications
- Publisher
- Oreilly
- Instructors
- Micheal Lanham
- Language
- English
- Level
- All Levels
- Duration
- 10 hours and minutes
- Subtitle
- English
- Quality
- 1080p
- Edition
- 2026-6
Course topics
View course topics
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Prerequisites
- Intermediate Python programming knowledge is identified as the intended background.
Pictures
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Introduction video
How to open the files
Keep all parts of the same edition together before extracting the first archive. Open the included course media with a compatible player.
Download resources
Choose one edition and keep its parts together. Alternate providers are available when listed.
2026-6 · 1080p · 1
Direct downloads
2026-6 · 1080p
2026-6 · 1080p · 2
Rapidgator
2026-6 · 1080p
Size
2.44 GB
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