A multi-agent event planning workflow built with Microsoft Agent Framework - combining Semantic Kernel's enterprise orchestration with AutoGen's multi-agent patterns.

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.

Autonomous AI Agent using DeepSeek automates business tasks like email summarization, meeting scheduling, and customer support. Powered by DeepSeek AI, AutoGen, and LangGraph, it streamlines workflows, integrates with APIs, and enhances productivity with adaptive multi-agent collaboration. 🚀

Interactive WebSocket UI with FastAPI backend for orchestrating multi-agent workflows using AG2AI Autogen. Features real-time streaming, JSON formatting, and agent alignment.

Hands-on demos showcasing Agentic AI workflows using AutoGen Framework with local LLMs via Ollama — featuring single, multi-agent, human-in-the-loop, and multimodal agents.

Experiments with AutoGen: Multi-agent LLM workflows using OpenAI, Azure, Gemini, Ollama & more.

Experimenting with the Microsoft's Autogen library, a multi-agent conversation framework to build LLM workflows

Hands-on tutorial for building AI agents with Microsoft AutoGen - covering single agents, tools, multi-agent teams, and human-in-the-loop workflows

Comprehensive research report on AI Agents & Multi-Agent Systems with Nano Banana visual workflows, framework comparisons (LangGraph, AutoGen, CrewAI), implementation guides, and benchmarking methodologies.

Production-ready multi-agent reasoning framework powered by LLMs. Integrates LangChain, CrewAI, and AutoGen for collaborative problem-solving with real-time data workflows.

AutoGen Multi-Agent Technology automates workflows with AI agents, enabling data analysis, real-time visualization, and simulations. It integrates with machine learning, APIs, and predictive analytics for applications in trading, gaming, and decision-making.

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.

An agentic medical RAG application for automated clinical discharge summary generation. Powered by Endee Vector DB for high-performance sub-millisecond similarity search and AutoGen for autonomous multi-agent clinical workflows. Built with FastAPI, Streamlit, and Bio-ClinicalBERT embeddings

Agent-Creator is a framework for building and experimenting with multi-agent systems powered by Microsoft’s AutoGen. The project demonstrates how autonomous AI agents can collaborate, communicate, and solve tasks in a simulated environment. The main file world.py acts as the orchestrator, defining agent behaviors, interactions, and workflows.

Agentic design pattern demos using AutoGen, featuring multi-agent systems for financial analysis, code generation, customer onboarding, blog writing, dialogue, and tool-assisted chess.

Multi-agent AI assistant for WeSign document signing workflows. Built with AutoGen, FastAPI, and MCP.

Configurable multi-agent personal assistant using AutoGen and OpenAI models, orchestrated via a JSON workflow compatible with AutoGen Studio.

Master Multi-Agent Systems with AutoGen: A step-by-step evolution from simple scripts to complex workflows

Multi-agent AI workflows with CrewAI, AutoGen, LangChain, and custom orchestrators powered by LLMs, memory, and vector stores

Multi-agent system using AutoGen 0.4.x for code development workflow with round-robin execution

Multi-agent AI system with role-based agents (planner, research, analyst, reviewer) using AutoGen, OpenAI, and external APIs to automate structured task workflows

Hands-on projects exploring AI agent patterns — tool use, multi-agent orchestration, and autonomous workflows — built with CrewAI, LangGraph, AutoGen, and MCP using local models via Ollama.

Comprehensive AutoGen examples showcasing multi-agent AI conversations with Google Gemini. Features group chats, function calling, vision analysis, nested conversations, and Reddit integration. Perfect for learning AutoGen patterns and building conversational AI workflows.

Personal lab for experimenting with AI agents and multi-agent systems. Mini-projects exploring different frameworks (CrewAI, LangGraph, AutoGen, OpenAI SDK) and patterns like RAG, tool-calling, and autonomous workflows.

A comprehensive multi-agent system built with Microsoft AutoGen (AG2) that automates the complete DevOps workflow from Jira ticket to merged pull request.

Multi-Agent Research & Review System — An open-source, AI-powered research automation platform using LangGraph and AutoGen to simulate enterprise-grade analyst workflows with collaborative agents, critique loops, and long-term memory.

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.

Engineered a multi-agent system using AutoGen & LangChain to simulate Scrum workflows across 5+ sprints, processing 50+ user stories with structured agent orchestration. Implemented automated effort estimation and comparative analysis (LangChain vs AutoGen), achieving ~95% accuracy and generating realistic project timelines (≈970–1100 hours).

A project demonstrating a multi-agent workflow with AutoGen, where Planner, Engineer, Executor, and Writer collaborate to generate a blog post about Nvidia's stock performance.

This repository contains an Agentic AI workflow 2 Agents - Primary Agent( Who answers the questions) and Evaluator Agent ( who evaluates the primary agent's response). These agents collaborate with each other to give the user a perfect response using Autogen's multi-agent framework.