Summary
| Foundation Year | 2023 |
| Country | 🇺🇸 United States |
| Founders | Microsoft |
| Tier | 100% Free |
| Platforms | API, Web Browsers |
| AI Features | AI Agent, AI and automation, API, Multi-Agent Mode, Multi-agent workflows |
| Pricing From | Free |
| Support | Yes |
| Best Used For | AI Agent and Automation |
| Affiliate Program | No |
| White Label | No |
| API | Yes |
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Description
Features
- Multi agent collaboration / multi-agent conversations
- Customizable agent roles and behaviours
- Support for LLM + tool + human in the loop workflows
- Support for diverse conversation patterns / flexible conversation topologies
- Asynchronous message-based communication between agents (event-driven & request/response)
- Ability to build complex, multi-step, multi-agent workflows (orchestration)
- Extensibility / modular architecture plug in custom agents, tools, memory, models etc.
- Local or distributed runtime (support for scaling / production-style deployment)
- Integration with multiple LLM providers / model clients (e.g. OpenAI, custom models)
- “Low-code / no-code” interface via AutoGen Studio for rapid prototyping and testing of multi-agent workflows
Free Plan & Pricing
No paid plan available for AutoGen
Refund Policy
5 Ratings
[4.4/5]
[3.7/5]
5app.ai Rating
[N/A]
AI Review
[3/5]
Microsoft.github.io hosts many open source projects by Microsoft, with strong community contributions and useful resources. However, user opinions on GitHub itself, which hosts this site, are mixed. Some praise its collaboration tools and integration, while others criticize poor customer support and issues with AI features.
Sources:- Github.com, Trustpilot.com, G2.com
Our Expert's Opinion
[4/5]
I found AutoGen to be powerful and exciting it's like giving me a whole team of AI-agents instead of just one. Each agent can play a different role (think: planner, coder, researcher), and together they can take on complex tasks, coordinate, call tools, reason in steps, and often succeed where a single model might struggle. On the upside, I liked how flexible and customizable it is: you can build workflows that mix model reasoning, tool usage, and even human-in-the-loop feedback. But it isn’t perfect. When workflows get complicated, AutoGen starts to feel hard to control: designing the right orchestration, avoiding “chat bloat,” tracking conversation state these can get messy. Also, since each agent chat or tool-call triggers a new call to LLMs, the cost and latency mount up quickly. For simpler tasks, the power of multi-agent collaboration feels like overkill. So my take: AutoGen shines when you have complex, multi-step, creative or tool-heavy tasks but for small or straightforward jobs, it might just make life harder than needed.
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