End-to-End-Agentic-Ai-Automation-Lab

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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.

★ 10040 forksJupyter NotebookMITMDalamin5

A large hands-on lab of Jupyter notebooks and Python projects covering LangGraph and AutoGen agent systems, MCP servers, production RAG with reranking, mem0 memory, n8n automation, plus fine-tuning and vLLM deployment. Best for developers learning end-to-end agentic AI by example.

Use Cases

Building LangGraph stateful agent workflowsAutoGen multi-agent teams with code executionMCP tool servers and client integrationsProduction RAG with hybrid search, BM25 and rerankingLong/short-term agent memory with mem0AI interviewer and ATS full-stack appsPersistent chatbot backend with FastAPI and Postgresn8n low-code agent automationBrowser automation agents with PlaywrightLLM guardrails with NeMoFine-tuning with LoRA/UnslothServing LLMs via vLLMSpeech-to-text and text-to-speech pipelinesMulti-agent research and blog writingAmbient background agentsAgentic RAG with corrective retrieval

Built With

Language: Jupyter Notebook
Frameworks: LangChain · LangGraph · AutoGen · CrewAI · MCP · FastAPI · n8n · vLLM · BentoML · Docker · mem0 · FAISS · Pinecone · Playwright · Unsloth · Pydantic

End-to-End-Agentic-Ai-Automation-Lab

Use Cases

Built With

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