Overview
This course, “Agentic AI and Multi-Agent Systems using Python, LangChain, and OpenAI", is designed to introduce learners to the next generation of intelligent systems that can act autonomously, collaborate, and solve complex real-world problems. In this course, participants will explore how AI agents can be designed to think, plan, and execute tasks independently while interacting with other agents in a coordinated environment. Using Python as the foundation, learners will gain hands-on experience with modern frameworks like LangChain and OpenAI to build scalable and intelligent agent-based applications. The course emphasises practical implementation, enabling students to create multi-agent systems capable of communication, task delegation, and adaptive decision-making.
Our Trainers
MR. PRATEEK MISHRA
Mr Pratik Mishra
Sr GenAI Developer at Virtuesa Private Limited.
8+ years of experience in tech, 6000+ students trained, countless lessons learned.
Over the last 8 years, I have worn many hats as a developer, trainer, mentor and lifelong learner.
I have worked with a variety of technologies – Python, Java, Spring Boot, ReactJS, Next.js, AWS, data science, and machine learning – challenges along the way.
The real joy is training 6000+ students and speaking at a place like IIT Roorkee, VIT Bhopal, Chitkara University and MANIT Bhopal.
Deliver corporate sessions for companies like Breaking and CloudThat.
Modules
Module 1: Python for AI & LLM Fundamentals (6 Hours)
Topics:
- Python Recap: Functions, Classes, Decorators, Async/Await
- Introduction to Large Language Models (LLMs): GPT-4, Claude, Gemini, Llama
- Transformer Architecture & Attention Mechanism (Conceptual Overview)
- OpenAI API: Chat Completions, Embeddings, and Function Calling
- Prompt Engineering: Zero-shot, Few-shot, Chain-of-Thought, System Prompts
- Tokens, Temperature, Top-p, and Model Parameters
Hands-on: Build a simple Q&A chatbot using the OpenAI API with Python.
Outcome: Students understand LLM fundamentals, use OpenAI API, and apply prompt engineering techniques.
Module 2: LangChain Framework – Chains, Memory & RAG (9 Hours)
Topics:
- LangChain Architecture: LLMs, ChatModels, Prompts, Output Parsers
- LangChain Expression Language (LCEL) and Runnables
- Memory Modules: ConversationBufferMemory, SummaryMemory, VectorStoreMemory
- Document Loaders: PDF, Web, CSV, Notion, GitHub
- Text Splitters, Embeddings, and Vector Stores (FAISS, Chroma, Pinecone)
- Retrieval-Augmented Generation (RAG): Naive RAG, Advanced RAG patterns
- LangChain Tools: Search, Calculator, Python REPL, Custom Tools
Hands-on: Build a document Q&A chatbot with memory using LangChain RAG pipeline over a custom PDF knowledge base.
Outcome: Students can build conversational AI apps with memory and retrieval using LangChain.
Module 3: AI Agents & LangGraph (9 Hours)
Topics:
- What are AI Agents? Agent Loop: Observe, Think, Act, Reflect
- ReAct (Reasoning + Acting) Agent Pattern with Tool Integration
- OpenAI Tool Calling / Function Calling Agents
- Introduction to LangGraph: Nodes, Edges, State, and Graph Compilation
- LangGraph StateGraph: Typed State, Reducers, Conditional Edges, Loops
- Agent Memory in LangGraph: Short-term, Long-term, Episodic Memory
- Human-in-the-Loop: Interrupt, Resume, and Approval Workflows in LangGraph
- Debugging & Tracing Agents with LangSmith
Hands-on: Build a ReAct agent with web search, calculator, and code execution tools. Build a stateful customer support agent using LangGraph.
Outcome: Students can build and debug autonomous AI Agents and stateful graph-based agent workflows using LangGraph.
Module 4: Multi-Agent Systems & Capstone Project (6 Hours)
Topics:
- Multi-Agent Architecture: Supervisor, Worker, Specialist, and Orchestrator Patterns
- Building Multi-Agent Systems with LangGraph: Subgraphs, Inter-Agent Communication
- CrewAI Framework: Roles, Tasks, Tools, and Agent Crews
- Agent Coordination: Sequential, Parallel, and Hierarchical Execution
- Real-world Multi-Agent Use Cases: Research Automation, Code Review, Content Pipeline
- Deployment: FastAPI + LangServe, Docker, Streamlit UI for AI Agent
- Capstone Project Options:
- AI Research Assistant: Multi-agent system that searches, reads, and summarizes research papers
- Automated Code Reviewer: Agent that reads GitHub PRs and gives structured feedback
- Customer Support Bot: LangGraph-powered agent with RAG, escalation, and ticketing tool integration
Outcomes
- Students understand LLM fundamentals, use OpenAI API, and apply prompt engineering techniques.
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Students can build conversational AI apps with memory and retrieval using LangChain.
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Students can build and debug autonomous AI Agents and stateful graph-based agent workflows using LangGraph.
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Students architect and deploy multi-agent AI systems and present a complete working capstone project.
