Complete Agentic AI Bootcamp With LangGraph and LangChain is a comprehensive, hands-on repository designed to teach modern Agentic AI engineering through real-world implementations, production-ready workflows, and enterprise-level AI system architectures.
This repository focuses on building intelligent AI systems using:
- LangGraph orchestration
- LangChain frameworks
- Multi-agent architectures
- Tool-augmented AI systems
- Retrieval-Augmented Generation (RAG)
- Stateful AI workflows
- AI memory systems
- Autonomous decision-making pipelines
The project is structured as a practical engineering bootcamp where modern AI concepts are implemented through real applications rather than theory-only tutorials.
It serves as a strong foundation for becoming an industry-grade Agentic AI Engineer capable of designing scalable, modular, production-ready AI systems.
- Understanding Agentic AI systems
- Building autonomous AI workflows
- Multi-agent system design
- AI planning and reasoning pipelines
- Intelligent task orchestration
- Stateful workflow orchestration
- Graph-based AI execution flows
- Conditional routing logic
- Agent communication systems
- Memory-aware workflow design
- Chains and pipelines
- Prompt templates
- Tool integrations
- LLM orchestration
- Output parsers
- Conversational AI systems
- Vector database integration
- Semantic search workflows
- Document ingestion pipelines
- Embedding generation
- Hybrid retrieval systems
- Context-aware AI responses
- Modular AI architecture
- API integration workflows
- Scalable AI backend systems
- AI deployment workflows
- Observability and monitoring concepts
- Enterprise AI engineering patterns
- Planner Agents
- Executor Agents
- Tool Router Agents
- Retriever Agents
- Memory Agents
- Decision-Making Agents
- Reflection & Evaluation Agents
- Conversational Agents
- Stateful graph execution
- Dynamic routing
- Conditional branching
- Multi-stage reasoning
- Agent collaboration
- Retry and fallback handling
- Persistent workflow states
- PDF ingestion systems
- URL content ingestion
- Vector embeddings
- Semantic retrieval
- Context compression
- Query understanding
- Knowledge-grounded AI responses
- Web Search Tools
- PDF Processing
- Database Integration
- API Tool Usage
- External Knowledge Access
- Dynamic Tool Selection
- Short-term memory
- Long-term memory
- Conversation history
- Context persistence
- AI memory orchestration
- FastAPI integration
- Streamlit applications
- Dockerized AI systems
- Cloud deployment concepts
- Scalable backend architecture
- LangGraph
- LangChain
- Hugging Face
- Transformers
- OpenAI APIs
- Groq APIs
- Ollama
- Python
- FastAPI
- Flask
- ChromaDB
- FAISS
- Pinecone (conceptual integration)
- Streamlit
- HTML
- CSS
- JavaScript
- Docker
- Cloud Deployment
- API-based AI Services
This repository helps learners understand how modern AI systems are built in production environments.
You will learn:
- How AI agents reason and act
- How tools are integrated into LLM systems
- How multi-agent workflows collaborate
- How RAG systems retrieve knowledge
- How AI memory systems work
- How LangGraph manages stateful orchestration
- How scalable AI systems are deployed
- Chains
- Prompts
- Memory
- Tools
- Agents
- Graph nodes
- Edges
- State management
- Conditional workflows
- Multi-agent orchestration
- Chunking
- Embeddings
- Retrieval
- Vector search
- Context fusion
- Autonomous workflows
- Task planning
- Reflection systems
- Tool routing
- Adaptive execution
from langgraph.graph import StateGraph
workflow = StateGraph()
workflow.add_node("planner", planner_agent)
workflow.add_node("executor", executor_agent)
workflow.add_node("retriever", retriever_agent)
workflow.set_entry_point("planner")
workflow.add_edge("planner", "retriever")
workflow.add_edge("retriever", "executor")git clone https://github.com/Md-Emon-Hasan/Complete-Agentic-AI-Bootcamp-With-LangGraph-and-Langchain.git
cd Complete-Agentic-AI-Bootcamp-With-LangGraph-and-Langchainpip install -r requirements.txtpython app.pyOr:
streamlit run app.py- Agentic AI System Design
- Multi-Agent Communication
- AI Workflow Orchestration
- Tool-Augmented Reasoning
- AI Memory Engineering
- Stateful AI Systems
- Enterprise AI Architecture
- Scalable LLM Applications
- AI Infrastructure Design
- AI Backend Engineering
- Autonomous AI Systems
This repository can be extended into:
- AI Research Assistants
- AI SaaS Platforms
- Autonomous AI Agents
- AI Customer Support Systems
- AI Coding Assistants
- AI Workflow Automation Systems
- Enterprise Knowledge Assistants
- AI Document Intelligence Systems
- AI Business Automation Platforms
- Multi-Agent Enterprise AI Systems
Modern AI engineering is rapidly shifting from simple prompt-based systems toward fully autonomous Agentic AI architectures.
Understanding LangGraph, LangChain, AI agents, RAG systems, memory orchestration, and multi-agent workflows is becoming essential for modern AI engineers.
This repository demonstrates:
- Real-world Agentic AI Engineering
- Production-oriented AI architecture
- Advanced workflow orchestration
- Enterprise AI system design
- Multi-agent collaboration systems
- Practical AI engineering workflows
It bridges the gap between learning isolated AI concepts and building complete, deployable AI systems.
- Advanced Multi-Agent Collaboration
- Distributed Agent Systems
- AI Observability & Monitoring
- LangSmith Integration
- Human-in-the-Loop Systems
- Advanced Reflection Agents
- Self-Healing AI Workflows
- Multi-Modal AI Agents
- AI Voice Agents
- AI Operating Systems
- Enterprise AgentOps Pipelines
- Kubernetes-based AI Deployment
- Real-Time Streaming Agents
- AI Security & Guardrails
- AI Evaluation Frameworks
Machine Learning Engineer
Focused on:
- Artificial Intelligence
- Machine Learning
- Generative AI
- AI Agents
- Agentic AI
- Multi-Agent Systems
- LangGraph Engineering
- AgentOps Architecture
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Email: [email protected]
- WhatsApp: WhatsApp
- Facebook: Md Emon Hasan
- Portfolio: Md Emon Hasan
If you found this repository helpful, feel free to:
- Star the repository
- Fork the project
- Connect for collaboration
- Discuss Agentic AI engineering
- Explore production-grade AI systems together
This project is open-source and available under the MIT License.
Complete-Agentic-AI-Bootcamp-With-LangGraph-and-Langchain Repository