QUESTPOND ONLINE COURSE

Agentic AI in Python Crash Course

What you'll learn

This 20 hour crash course teaches how to build agentic AI systems in Python using OpenAI (GPT-4/4o), LangChain, and related tooling. The course is hands-on with notebooks for each module and a multi-agent chatroom capstone implemented both as a console app and a Streamlit web UI.

Course Curriculum

Explore the topics covered in this course.

+
Module 1: Introduction to Agentic AI (2 hours) Click to view syllabus
Module 1
  • Concepts, environment setup, first OpenAI API call
  • Hands-on tasks:
  • See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 2: LLM Fundamentals & Prompt Engineering (2 hours) Click to view syllabus
Module 2
  • Prompts, templates, few-shot, structured outputs.
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 3: Building Simple Agents (2 hours) Click to view syllabus
Module 3
  • Agent loops, LangChain basics
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 4: Tools, Memory & Reasoning (3 hours) Click to view syllabus
Module 4
  • Tools, memory stores, ReAct
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 5: Multi-Agent Systems (2 hours) Click to view syllabus
Module 5
  • Agent collaboration patterns
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 6: Integrating APIs & External Tools (3 hours) Click to view syllabus
Module 6
  • Connect to APIs, file handling
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 7: Custom Agent Workflows & Planning (3 hours) Click to view syllabus
Module 7
  • Task decomposition, vector stores
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Module 8: Capstone — Multi-Agent Chatroom (3 hours) Click to view syllabus
Module 8
  • Build multi-agent chatroom (console + Streamlit)
  • Hands-on tasks:
    See corresponding Jupyter notebook for step-by-step code and examples.
+
Capstone: Multi-Agent Chatroom Click to view syllabus
Module 9
  • Two implementations are included:
  1. Console-based: a lightweight terminal app that runs locally.
  2. Streamlit-based: provides a web UI suitable for demos and classrooms.
  3. Both use OpenAI as the LLM backend and LangChain patterns for agents, memory, and tool invocation.
+
Setup & Requirements Click to view syllabus
Module 10
  • Run:
    pip install -r requirements.txt

Set your OpenAI API key in environment variable OPENAI_API_KEY or use a .env file.

Example: export OPENAI_API_KEY='sk-...'