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End-to-End Agentic AI Automation Lab MCP Server

@MDalamin5

RepositoryHomepage

Basic information

Category

Reasoning

License

MIT

Transports

stdio

Author

MDalamin5

About End-to-End Agentic AI Automation Lab MCP Server

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.

#docker#aws#cicd#autogen#bentoml#a2a#agno#langchain#langsmith#crewai#langgraph#agentic-rag#agentic-ai#mcp-server

End-to-End Agentic AI Automation Lab MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "End-to-End-Agentic-Ai-Automation-Lab": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

End-to-End Agentic AI Automation Lab MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is 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. It covers frameworks like LangGraph, AutoGen, MCP, n8n, and LangFlow, and is designed for developers transitioning from basic LLM calls to complex multi-agent autonomous systems.

How to use End-to-End Agentic AI Automation Lab?

Clone the repository, set up a Python 3.10+ virtual environment, navigate to a specific module folder, install its dependencies from requirements.txt, and configure a .env file with required API keys (e.g., OpenAI, Anthropic). Each module is self-contained and can be run independently.

Key features of End-to-End Agentic AI Automation Lab

  • 24 modules spanning foundations, agentic frameworks, and full-stack products.
  • Advanced agentic implementations with LangGraph, AutoGen, and MCP.
  • Production RAG Pipelines including hybrid search, BM25, and LlamaParse.
  • Zero-code orchestration using n8n and LangFlow.
  • LLM fine-tuning with LoRA/Unsloth and deployment via vLLM.
  • End-to-end projects: AI Interviewer, Applicant Tracking System, SynapseAI chatbot.

Use cases of End-to-End Agentic AI Automation Lab

  • Build multi-agent LangGraph workflows with memory and human-in-the-loop.
  • Implement production-grade RAG systems with hybrid search and re-ranking.
  • Fine-tune and deploy local LLMs with high-throughput inference endpoints.
  • Create full-stack AI applications like persistent chatbots and ATS systems.

Frequently asked questions

1

What agentic frameworks does the lab cover?

It covers LangGraph (state graphs, subgraphs, memory), AutoGen (round-robin, swarm, custom tools), and Anthropic's Model Context Protocol (MCP) for tool execution and web search.

2

What are the runtime requirements?

Python 3.10 or higher is required. Dependencies vary per module; each module contains its own `requirements.txt`. API keys for services like OpenAI, Anthropic, and Tavily are needed.

3

Is the code production-ready?

Yes, the lab emphasizes production-grade implementations, including full-stack projects, Alembic-backed databases, and high-throughput model serving via vLLM.

4

Can I run the examples in Google Colab?

Yes, a Colab badge is provided in the repository, indicating compatibility with Google Colab for certain modules.

5

What license applies?

The repository is distributed under the MIT License.

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