Microsoft AutoGen
An event-driven programming framework for building scalable multi-agent AI systems. Example scenarios: Deterministic and dynamic agentic workflows for business processes. Research on multi-agent collaboration. Distributed agents for multi-language applications.
Funding
OSS
Microsoft AutoGen
Overview
Microsoft AutoGen is an open‑source framework from [Microsoft Research](https://www.microsoft.com/en-us/research/project/autogen/) for building AI agents and multi‑agent applications. It orchestrates agents powered by large language models (LLMs), external tools, and human input to solve tasks through structured conversations and workflows. The project is actively maintained with comprehensive [documentation](https://microsoft.github.io/autogen), a lively [GitHub repository](https://github.com/microsoft/autogen), and an in‑depth [research blog introduction](https://www.microsoft.com/en-us/research/blog/autogen-enabling-next-generation-large-language-model-applications/).
The latest generation, **AutoGen 0.4**, is a major redesign that introduces an asynchronous, event‑driven runtime, modular packages, and stronger observability—aimed at reliability and scale for production agent systems. Microsoft has detailed the direction in a [forum talk](https://www.microsoft.com/en-us/research/video/autogen-v0-4-reimagining-the-foundation-of-agentic-ai-for-scale-and-more-microsoft-research-forum/) and tracks progress via [GitHub milestones](https://github.com/microsoft/autogen/milestones). Key packages include **autogen‑core**, **autogen‑agentchat**, and **autogen‑ext**, with flexible collaboration patterns and pluggable [model clients](https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/components/model-clients.html).
On top of the runtime, **AutoGen Studio** provides a low‑code, browser‑based interface to assemble agents, wire up tools, form teams, and test workflows before moving to code—ideal for rapid prototyping and stakeholder demos. See the [Studio overview](https://microsoft.github.io/autogen/dev//user-guide/autogenstudio-user-guide/index.html) and the [announcement](https://www.microsoft.com/en-us/research/blog/introducing-autogen-studio-a-low-code-interface-for-building-multi-agent-workflows/). The **Workbench** adds [Model Context Protocol (MCP)](https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/components/workbench.html) support, enabling agents to safely use tools exposed by MCP servers.
AutoGen has an active community of developers sharing examples, comparisons, and migration guidance as 0.4 evolves. Engagement spans a dedicated [Reddit community](https://www.reddit.com/r/AutoGenAI/)—including threads on [why teams choose AutoGen](https://www.reddit.com/r/AutoGenAI/comments/1ig33yz/why_are_people_using_microsoft_autogen_vs_other/) and [non‑OpenAI support in 0.4](https://www.reddit.com/r/AutoGenAI/comments/1hzh32h/non_oai_models_not_supported_in_v04/)—and robust [GitHub discussions](https://github.com/microsoft/autogen/discussions/7066) with ongoing [issues](https://github.com/microsoft/autogen/issues) and [releases](https://github.com/microsoft/autogen/releases).
---
Quick Facts
---
What Makes AutoGen Stand Out
---
Pros
Cons
---
Who It’s For
---
Common Use Cases
---
Integrations and Ecosystem
---
Versioning, Maintenance, and Community
---
Licensing and Cost
---
Getting Started
1. Explore the fundamentals and runtime architecture
2. Prototype in the browser with Studio
3. Wire up model providers and tools
4. Review examples and migrate patterns to code
5. Plan for 0.4 changes and production hardening
---
Company and Research Context
AutoGen is maintained by [Microsoft Research](https://www.microsoft.com/en-us/research/), with ongoing communications through its [project page](https://www.microsoft.com/en-us/research/project/autogen/) and community channels. For broader updates on the research organization, see the [Microsoft Research LinkedIn showcase](https://www.linkedin.com/showcase/microsoftresearch/).
---
Related Resources
Related Companies
AgentGPT
AgentGPT is an open‑source, browser‑run tool from Reworkd that lets you name an agent, set a goal, and have it autonomously plan, research, and execute tasks in iterative loops. It’s designed for fast experimentation and demos, with ready‑made templates for research, branding, and trip planning—no engineering setup required.
BabyAGI
BabyAGI is pioneering the future of autonomous AI through an experimental framework designed for self-building agents. Born from the insight that the most effective path to general autonomous agents is radical simplicity, BabyAGI focuses on creating the minimal viable system capable of building and evolving itself. At its core is functionz, an innovative function framework that revolutionizes how autonomous agents manage their capabilities. This database-driven system stores, manages, and executes functions through an intelligent graph-based architecture that tracks imports, dependencies, and authentication—all with automatic loading and comprehensive logging. BabyAGI provides developers with an intuitive dashboard for seamless function management, real-time updates, and detailed log analysis, making autonomous agent development accessible and transparent. By embracing a self-building philosophy, BabyAGI represents a fundamentally new approach to creating AI systems that can adapt, grow, and improve autonomously.
CrewAI
CrewAI is at the forefront of Agentic AI with its open source, multi-agent framework and cloud platform for building, managing and scaling agentic workflows across the entire organization.
Flowise AI
Flowise is an open source drag & drop tool to build your customized LLM flow. We provide a visual interface to let you build backends for LLM apps used for QnA, summarization and analysis on your documents.
LangChain
LangChain provides the agent engineering platform and open source frameworks developers need to ship reliable agents fast.
LlamaIndex
LlamaIndex empowers developers to build agents that extract insights and take action on complex enterprise documents. It combines industry-leading document parsing and extraction with a trusted framework for building intelligent agents that reason over documents, adapt to business logic, and scale to production. LlamaIndex is loved by developers and trusted by enterprises. Its open source framework is downloaded more than 4M+ every month and has processed more than 200 million documents on LlamaCloud.