# End To End Agentic Ai Automation Lab > This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML. - **Type:** MCP server - **Install:** `agentstack add mcp-mdalamin5-end-to-end-agentic-ai-automation-lab` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [MDalamin5](https://agentstack.voostack.com/s/mdalamin5) - **Installs:** 0 - **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [MDalamin5](https://github.com/MDalamin5) - **Source:** https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab - **Website:** https://www.linkedin.com/in/mdalamin5/ ## Install ```sh agentstack add mcp-mdalamin5-end-to-end-agentic-ai-automation-lab ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 🤖 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.** [](https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab/stargazers) [](https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab/network/members) [](https://www.python.org/) [](https://opensource.org/licenses/MIT) [](https://colab.research.google.com/) [Overview](#-overview) • [Key Highlights](#-key-highlights) • [Project Architecture](#-repository-modules--projects) • [Tech Stack](#️-tech-stack--tools) • [Getting Started](#-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`: Integrating `mem0` for 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 including `LlamaParse`, 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:** -5B0000?style=for-the-badge) **Backend & Data:** --- ## ⚙️ Getting Started ### 1. Clone the Repository ```bash 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. ```bash 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: ```bash 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.): ```env 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. 1. Fork the Project 2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) 3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the Branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request --- ## 📜 License & Connect Distributed under the **MIT License**. See [`LICENSE`](LICENSE) for more information. **Developed with 💡 by [Md Al Amin](https://github.com/MDalamin5)** [](https://linkedin.com/in/mdalamin5/) [](https://github.com/MDalamin5) If you find this repository helpful, don't forget to ⭐ star it! ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [MDalamin5](https://github.com/MDalamin5) - **Source:** [MDalamin5/End-to-End-Agentic-Ai-Automation-Lab](https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab) - **License:** MIT - **Homepage:** https://www.linkedin.com/in/mdalamin5/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-mdalamin5-end-to-end-agentic-ai-automation-lab - Seller: https://agentstack.voostack.com/s/mdalamin5 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.