Autogen is a framework to build multi-conversational agents, This repo contains some of the code I have experimented with. This code might have some bugs. But the idea is to explore the framework rather than writing sophisticated code. For more examples explore the official Autogen repo

Investigating the usefulness of "AutoGen" by Microsoft. AutoGen is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. This repository contains experiments testing multi-agent coordination, task delegation, and performance benchmarks.

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

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.

Experiments using autogen

Experiments with Microsoft AutoGen multi-agent framework

A comparative study of four AI-assisted multi-agent orchestration workflows, AutoGen Predefined, AutoGen Round Robin, LangChain Nested Sequential, and LangChain Round Robin: used to design, build, test, and document an Electric Appliances E-Commerce Platform. Includes full notebooks, artifacts, and reproducible environment setup.

🤖 Sandbox for experimenting with Microsoft AutoGen multi-agent framework 🧪

Experiments on AutoGen framework to building stateful, multi-agent applications using Large Language Models (LLMs)

A hands-on journey into Microsoft Autogen — exploring multi-agent conversations, agent architecture, and LLM integrations. Includes daily notes, code experiments, and insights from the official Autogen documentation and tutorials.

Exploration of Microsoft AutoGen: multi-agent orchestration, multimodal features, message handling, distributed runtime, and autonomous agents. Includes hands-on experiments with agent-to-agent messaging, gRPC, LangChain integration, and async collaboration.

A comprehensive collection of hands-on experiments, learning notes, and projects exploring Microsoft AutoGen – from basic prompt flow orchestration to advanced multi-agent use cases.

This repo contains a few simple experiments and notes from my exploration of Microsoft's AutoGen framework, which is designed for building multi-agent systems using LLMs.

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.

EXPERIMENT: Multi-agent AI system powered by AutoGen that provides contextual AWS support through specialized researchers and solution providers. Features expert validation, structured solutions, and satisfaction tracking.