🔥From LLMs to AI Agents: Complete Practical Path

🚀 Build Real AI Agents in 2026 (Not Just Demos)

From RAG → Multi-Agent Systems → LangGraph → OpenAI Agents → LLM Fine-tuning

👉 This is a complete Agentic AI journey.

🤖 1. 2026 Deep Agent – Multi-Agent RAG with Gemini & LangChain

Build advanced, production-ready multi-agent AI systems.

What you’ll learn:

• LangChain v1 AI Agents
• Multi-Agent RAG architectures
• Google Gemini 3 integration
• Multimodal RAG (PDFs, tables, images)
• Qdrant vector DB, hybrid search
• MCP tools, memory & cost optimization
• Docker-based deployments

🎯 Best for: Advanced GenAI & RAG developers
💡 Outcome: Enterprise-grade multi-agent AI systems

🔐 2. Agentic AI – Private Agentic RAG with LangGraph & Ollama

Build private, offline, secure RAG systems.

What you’ll learn:

• LangGraph v1 from scratch
• Private & local RAG using Ollama
• Corrective RAG (CRAG), Self-RAG, Adaptive RAG
• Page-level PDF ingestion (Docling)
• Metadata filtering & re-ranking
• MySQL-based AI agents

🎯 Best for: Privacy-focused & enterprise AI use cases
💡 Outcome: Fully private Agentic RAG systems

🧠 3. Master LangChain v1 & Ollama – Chatbots, RAG & AI Agents

Your foundation course for LangChain v1.

What you’ll learn:

• LangChain v1 core concepts
• Ollama with local LLMs
• Qwen3, Gemma3, DeepSeek, LLaMA
• Build chatbots with memory & streaming
• RAG pipelines
• Deploy AI apps on AWS
• Text-to-MySQL agents

🎯 Best for: Beginners to intermediate GenAI learners
💡 Outcome: Strong LangChain + RAG fundamentals

🕸️ 4. Master LangGraph v1 & Ollama – Build GenAI Agents

Design complex agent workflows using graphs.

What you’ll learn:

• LangGraph states, nodes & edges
• Conditional routing & reducers
• Nested graphs & orchestration
• Tool-using agents (ReAct)
• Local & cloud LLM agents
• End-to-end GenAI agent systems

🎯 Best for: Developers moving from chains to agents
💡 Outcome: Scalable, controllable AI agents

🧩 5. Master OpenAI Agent Builder – Deploy Chatbots to Your Website

Build and deploy AI agents with low-code tools.

What you’ll learn:

• OpenAI Agent Builder (no-code / low-code)
• ChatKit & MCP connectors
• RAG-powered AI assistants
• MySQL, AWS & RDS integrations
• Production deployment with guardrails
• Real business use cases (support, ecommerce)

🎯 Best for: Rapid AI product builders
💡 Outcome: Deployed AI assistants on websites

🧬 6. Fine-Tuning LLMs with Hugging Face Transformers

Train and fine-tune your own LLMs.

What you’ll learn:

• Transformers architecture
• BERT, Phi-2, LLaMA variants
• Dataset preparation & evaluation
• Fine-tuning for classification & QA
• Text summarization & inference
• Model distillation techniques

🎯 Best for: NLP & model-level AI engineers
💡 Outcome: Custom fine-tuned LLMs

🔗 7. MCP Mastery – Build AI Apps with Claude, LangChain & Ollama

Learn Model Context Protocol (MCP) and build real AI apps that connect tools, data, and LLMs.

What you’ll learn:

• MCP fundamentals (servers & clients)
• Build custom MCP servers in Python
• Connect real-world tools, APIs & resources
• Integrate MCP with Claude Desktop
• Use MCP with LangChain v1 & LangGraph v1
• Build RAG systems using vector databases
• ChromaDB + Ollama integration
• Secure, test & deploy production-ready MCP servers
• Build Streamlit-based AI apps

🎯 Best for:
GenAI developers, LangChain/LangGraph users, AI engineers

💡 Outcome:
You’ll be able to build tool-connected AI apps using MCP — the same protocol used by modern AI agents.