🤖 End-to-End Agentic AI & Automation Lab
A comprehensive, production-grade repository for building, deploying, and managing intelligent AI agents, RAG pipelines, and automated workflows.
Overview • Key Highlights • Project Architecture • Tech Stack • Getting Started
📖 Overview
Welcome to the End-to-End Agentic AI Automation Lab. This repository is a massive, hands-on engineering playbook demonstrating how to transition from basic LLM API calls to complex, multi-agent autonomous systems and production-ready AI products.
Whether you are looking to build highly reliable Agentic workflows using LangGraph, orchestrate multi-agent collaboration via AutoGen, implement cutting-edge Model Context Protocol (MCP), or serve fine-tuned local models using vLLM and Unsloth, this repository has you covered.
🚀 Key Highlights
- Advanced Agentic Frameworks: Deep dives into LangGraph (StateGraphs, subgraphs, memory, HITL) and AutoGen (RoundRobin, Swarm, custom tools).
- Model Context Protocol (MCP): Industry-grade implementations of Anthropic's MCP for tool execution, web search, and Notion integration.
- Production RAG Systems: Implementation of Hybrid Search, BM25, LlamaParse, Semantic Routing, and Long/Short-Term Memory (Mem0).
- AI Workflow Automation: Zero-code/low-code multi-agent orchestration using n8n and LangFlow.
- LLM Fine-Tuning & Serving: Hands-on pipelines for fine-tuning with LoRA/Unsloth and deploying high-throughput inference endpoints with vLLM.
- End-to-End Products: Complete full-stack implementations of an AI Interviewer, a Production ATS, and SynapseAI (a stateful, persistent chatbot).
📂 Repository Modules & Projects
The lab is structured progressively. Click to expand each module to see the underlying projects:
1️⃣ Foundations & Data Ingestion (Modules 01 - 02)
01-Pydantic-Data-Validation: Data structuring, field validation, and structured LLM outputs.02-LangChain-Basics: Embedding models, VectorDBs (FAISS, Pinecone), and basic Retrieval-Augmented Generation (RAG) scratchpads.
2️⃣ LangGraph & Workflow Orchestration (Modules 03 - 04, 13 - 14)
03-LangGraph-Introduction: StateGraphs, Agentic workstations, multi-tool calling.04-LangGraph-Agentic-Workflows: Agentic RAG, Multi-Agent Supervisors, Human-in-the-Loop (HITL), and Corrective RAG (CRAG).13-e2e-Deep-Agents: Observation, evaluation, and reliable LangGraph applications.14-e2e-Ambient-Agent: Building background-running autonomous agents.
3️⃣ AutoGen Multi-Agent Systems (Modules 05 - 09)
05-Autogen-Introduction: Async capabilities, tools, and basic teams.06-Autogen-HITL-and-Agentic-Orchestrator: Selector Group Chats, Docker code execution, and Graph-based AutoGen.07-End-To-End-Projects-Autogen: GPT Analyzer (Modular architecture), AI Interviewer.08-Advanced-Autogen-Team: Swarm logic and Society of Mind teams.09-Autogen-RAG-and-Memory: Integratingmem0for cross-session AutoGen memory.
4️⃣ Model Context Protocol (MCP) & n8n (Modules 10 - 12)
10-MCP-All-You-Need: Bridging AutoGen and LangChain with MCP. Lead collector, FireCrawl MCP, and Playwright MCP.11-MCP-based-End-to-End-Products: Building fast, robust API backends utilizing MCP architectures via ngrok and FastAPI.12-n8n: High-level automations. Chain of Agents, Social Media Content Generation, parallel agent logic, and Telegram bot integrations.
5️⃣ Production RAG & Guardrails (Modules 17, 19)
17-Guardrails-for-llm: Implementing NeMo Guardrails for secure and constrained LLM outputs.19-Productions-RAG: Industry-practice RAG includingLlamaParse, BM25/Hybrid Search, HyDE, chunking strategies, and Reranking pipelines.
6️⃣ LLM Fine-Tuning & Deployment (Modules 21 - 22)
21-LLM-Deployment-vLLM: Deploying models for high-throughput generation using vLLM and accessing via LangChain SDK.22-LLM-FineTune-Deployment: Model fine-tuning using Unsloth, LoRA, HuggingFace Pipelines, and quantization setups for edge devices.
7️⃣ End-to-End Full-Stack Projects (Modules 18, 20, 23, 24)
18-e2e-chatbot-mem0-tools-HITL-MCP-RAG: A massive implementation of a fully-featured chatbot with long/short-term memory, PostgreSQL persistence, and streaming UI.20-e2e-Productions-grade-ATS: End-to-end Applicant Tracking System backed by Alembic, SQLModel, and LangGraph.23-e2e-multi-agent-plan-research-write-blog: A multi-agent writer architecture with a beautiful web frontend.24-SynapseAI-parsitence-chatbot: A modern API-first chatbot backend via FastAPI with complex graph routing.
🛠️ Tech Stack & Tools
Core AI/ML:
Agentic & Orchestration:
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Backend & Data:
⚙️ Getting Started
1. Clone the Repository
git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.git
cd End-to-End-Agentic-Ai-Automation-Lab
2. Set Up Virtual Environment
It is recommended to use conda or venv to manage dependencies.
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
3. Install Dependencies
Dependencies may vary per module. Navigate to the specific project folder and install the requirements:
cd 18-e2e-chatbot-mem0-tools-HITL-MCP-RAG
pip install -r requirements.txt
4. Environment Variables
Copy the .env.example file (if available in the module) to .env and add your API keys (OpenAI, Anthropic, HuggingFace, etc.):
OPENAI_API_KEY="your_api_key_here"
ANTHROPIC_API_KEY="your_api_key_here"
TAVILY_API_KEY="your_api_key_here"
🤝 Contributing
This repository is continuously evolving! Contributions, bug reports, and feature requests are highly welcome.
- 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
📜 License & Connect
Distributed under the MIT License. See LICENSE for more information.
