AI Agents & LLMs with RAG: n8n, LangChain, LangGraph, Flowise, MCP & more – with ChatGPT, Gemini, Claude, DeepSeek & Co.
InstructorArnold OberleiterEditorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A visual comparison diagram illustrating the workflows of Traditional RAG versus Graph RAG, featuring numbered steps and icons for LLMs and databases.
A split-screen slide explaining the Model Context Protocol, featuring an architecture diagram on the left and a hub-and-spoke connection map on the right.
A dark-themed n8n workflow canvas displaying a complex network of connected nodes, with a small webcam overlay of a presenter in the top right corner.
A dark-themed FlowiseAI interface displaying a grid of agent templates such as Ollama Agent and Prompting machine alongside a sidebar menu.
The curriculum covers a wide array of technologies including local LLM management with Ollama, automation via n8n, and advanced retrieval techniques like GraphRAG and Contextual Retrieval.
Instruction provides a broad overview of the RAG landscape, spanning from basic API interactions to hosting and deployment strategies.
Multiple signals from late 2025 indicate that the Flowise content reflects an older version of the tool. While the course has a 2026 update label, these earlier reviews suggest the update may not have addressed the mismatch with current platform iterations.
Multiple signals from late 2025 indicate that the Flowise content reflects an older version of the tool. While the course has a 2026 update label, these earlier reviews suggest the update may not have addressed the mismatch with current platform iterations.
The curriculum aligns with the stated goals of building RAG agents and automation.
