MDalamin5/End-to-End-Agentic-Ai-Automation-Lab — This repository contains hands-on projects, code examples, and deployment workflows.

Description and use

A hands‑on collection of examples and deployment pipelines for building and scaling multi‑agent AI systems. It showcases end‑to‑end workflows with LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, and n8n automation, and provides ready‑to‑run Docker, AWS, and BentoML configurations for robust backend orchestration.

Problem

MDalamin5/End-to-End-Agentic-Ai-Automation-Lab helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

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.

Source