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Master the Future of AI: From Fundamentals to Autonomous Multi-Agent Systems, RAG, and Enterprise Cloud Deployment.
- 🛠️ Tech Stack Used
- 🚀 Overview
- 📚 Key Breakdown
- 🎯 Key Objective
- 📂 Files & Resources
- ⚙️ Getting Started
- 🤝 Contributing
- 📄 License
- 📞 Contact & Networking
Welcome to the Agentic AI & Generative AI Bootcamp. This comprehensive repository encapsulates a cutting-edge curriculum designed to transform beginners into industry-ready AI Engineers.
We move beyond simple scripts to build robust, autonomous, and scalable AI systems. The journey spans from understanding the core principles of Agentic AI to mastering complex multi-agent orchestrations, implementing advanced Retrieval-Augmented Generation (RAG) strategies, and deploying production-grade applications on AWS and Google Cloud Platform (GCP).
- End-to-End Pipeline: From local development to cloud deployment.
- Multi-Agent Orchestration: Deep dive into LangGraph, CrewAI, Agno, and AutoGen.
- Advanced RAG: Implementation of Adaptive RAG, Self-RAG, and C-RAG.
- Production Ops: Full CI/CD pipelines, containerization (Docker), and monitoring (LangSmith, Langfuse).
- Low-Code & No-Code: Integration with LangFlow and n8n for rapid prototyping and workflow automation.
We utilize a state-of-the-art technology stack to build resilient AI applications.
| Category | Technologies |
|---|---|
| Languages & Core |    |
| LLMs & Foundations |      |
| Agent Frameworks | LangChain, LangGraph, CrewAI, Agno, AutoGen, LCEL, Model Context Protocol (MCP) |
| Vector Databases |   Pinecone |
| Orchestration | LangFlow, n8n, Airflow |
| Backend & Serving |  LangServe, BentoML, Streamlit, Gradio |
| DevOps & Cloud |     |
| Monitoring | LangSmith, Langfuse, Opik, ClearML |
Our structured learning path ensures a logical progression of skills.
- Module 1: Introduction to Agentic AI
- Agents vs. GenAI, Single vs. Multi-Agent Systems.
- Module 2: Data Validation & Foundations
- Structured data with Pydantic, JSON schemas, and safe inputs.
- Module 3: LangChain Core
- Document loaders, Splitting strategies, Embeddings, and basic Retrieval.
- Module 4: LangChain Expression Language (LCEL)
- Building efficient, pipelined LLM workflows and prompt chains.
- Module 5: LangServe Model Deployment
- Turning chains into production-ready APIs with simple endpoints.
- Module 6: LangGraph - Agentic Workflows
- Graph-based orchestration, routers, and state management.
- Module 7: State, Memory & Human-in-the-Loop
- Managing state schemas, memory persistence, and human feedback loops.
- Module 8: Advanced Agentic RAG
- Implement Adaptive RAG, Self-RAG, and Corrective RAG (C-RAG).
- Module 9: Multi-Agent System Design
- Defining roles, communication protocols, and scalable architectures.
- Module 10: CrewAI - AI Teams
- Managing role-playing agents, task delegation, and tool sharing.
- Module 11: LangFlow Integration
- Visual drag-and-drop workflow building and rapid prototyping.
- Module 12: Third-Party Integrations
- Extending agents with SQL, APIs, and external tools.
- Module 13: Observability & Monitoring
- Tracking costs, latency, and traces with LangSmith and Langfuse.
- Module 14: Agno Framework
- Building lightweight, high-performance financial and web-search agents.
- Module 15: AutoGen
- Creating autonomous, conversing agent teams with feedback loops.
- Module 16: Workflow Automation (n8n)
- Real-world automation: WhatsApp bots, RAG chatbots, and content pipelines.
- Module 17: Model Context Protocol (MCP)
- Standardizing data access and tool orchestration for Enterprise LLMs.
- Module 18: AWS Cloud for GenAI
- Amazon Bedrock, SageMaker, Lambda, and S3 integration.
- Module 19: GCP & Vertex AI
- Gemini Pro, Model Garden, and RAG on Google Cloud.
- Module 20: CI/CD & Final Deployment
- Containerizing with Docker, deploying via GitHub Actions, and serving with BentoML.
By the completion of this bootcamp, you will be able to:
- Architect complex multi-agent systems that solve real-world problems.
- Deploy scalable AI models using serverless and containerized infrastructure.
- Implement state-of-the-art RAG techniques to reduce hallucinations and improve accuracy.
- Automate business processes using intelligent agents and workflow tools like n8n.
- Monitor and optimize AI performance using industry-standard observability tools.
Access the detailed curriculum summaries, tech stack breakdowns, and full PDF guides directly below.
- Version 1: Modules Summary • Tech Stack
- Version 2: Modules Summary • Tech Stack
- Combined: Overview Summary • Full Tech Stack
- Course Textbooks:
- V1 PDF Guide
- V2 PDF Guide
- Basic knowledge of Python programming.
- Familiarity with foundational ML/AI concepts is helpful but not required.
- An OpenAI API key (or access to local models via Ollama).
-
Clone the Repository
git clone https://github.com/yourusername/agentic-ai-bootcamp.git cd agentic-ai-bootcamp -
Create a Virtual Environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install Dependencies
pip install -r requirements.txt
-
Set Up Environment Variables Create a
.envfile in the root directory:OPENAI_API_KEY=sk-... GROQ_API_KEY=gsk-... LANGCHAIN_API_KEY=lsv2-...
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
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