Learn to build real-world AI agents, multi-agent workflows, and autonomous apps with LangGraph and LangChain
InstructorKRISHAI Technologies Private LimitedComidoc has tracked 1 coupon for this course since 2026, last checked 4 months, 1 day ago.
| Coupon code | Discount | Added | Status | Lifetime |
|---|---|---|---|---|
| MARCH001 | 28% off | Expired | 2d 10h |
Instruction covers specific graph patterns such as Prompt Chaining, Parallelization, Routing, and Orchestrator-Worker workflows.
The curriculum includes building full applications with front-end integration using Streamlit and API development via FastAPI.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This digital whiteboard slide introduces the topic 'AI Agents Vs Agentic AI' and features a diagram box labeled 'LLM' to illustrate foundational concepts.
The screen displays a Python script in VS Code importing essential LangChain components such as ChatOpenAI and StrOutputParser for building AI applications.
The Google Colab development environment is shown with the Secrets panel for managing credentials alongside terminal logs confirming the installation of required Python packages.
This screenshot displays the LangGraph platform interface, highlighting key features such as fault-tolerant scalability and integrated developer experience.
One signal suggests the content may rely on older LangChain and LangGraph versions, potentially missing significant API changes in newer releases. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Some segments are described as repetitive or scattered, which can disrupt the learning flow.
There are reports of code dependencies, such as specific models, no longer being available or functional. These signals predated the displayed update, and while a correction may have occurred, it cannot be confirmed by the update label alone.
The curriculum aligns with the target of developers learning agentic workflows, though it includes significant prerequisite material.