Master Agentic Ai With Langgraph, Streamlit And Openai
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 30m | Size: 1.54 GB
Agentic AI applications with Python
What you'll learn
Understand agentic ai workflows and systems
Implement agentic ai systems with LangGraph, OpenAI and Streamlit
Automate complex business workflows with agentic ai
Human in the loop interactions with mission critical work flows
LangGraph Event Streams
LLM tool calling workflow agents with Structured Output
Agentic Design Patterns
Implement fast, robust and reliable streaming AI applications
Requirements
Interest in Generative AI and Agentic AI
Any programming language knowledge(Python preferred)
Description
Agentic AI has become more disruptive than Generative AI these days.Organisations are rushing to transform their business models to implement agentic ai applications to unlock business value to stay ahead of the curve. As organisations transform their readily available workflows to leverage agentic AI they will come across new business workflows that might exponentially add value to their revenue streams. So agentic ai has already impacted profoundly across sectors and verticles. As technology and solution providers we have to stay ahead of the curve on disruptive modern technologies such as in order to be relavant to our customers and guide them in the Gen Ai and Agentic AI adoption journey.LangGraph, LangChain, Streamlit, OpenAI, Python is an ideal blend of technologies to implement most of the agentic ai business work flows robust, reliable and secure manner in highly agile product and development environments.Features such as data streaming, LLM tool calling, LLM structured output, short term context windows, long term stateful knowledge graphs, Time Travel etc are invaluable features to implement highly scalable, fast, robust, reliable and trustworthy agentic applications. Human in the loop is vital when agentic ai is infused into mission critical workflows to safeguard data layer.LangGraph interrupts, event systems and state preservation mechanism foundationally enable LangGraph to be equipped with reliable human in the loop implementations.Primary technologies used in this course are as below.Agentic AIGenerative AILangGraphLangChainStreamlitPythonOpenAIVsCode
Who this course is for
Generative AI practitioners interested in Agentic AI
Python developers interested in Agentic AI
Business Consultants interested in transforming applicaions to use Agentic AI
Anyone passionate on cutting edge technologies
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