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💡 Pro tip: Start with the free tier to test if AutoGen Studio fits your workflow before upgrading to a paid plan.
This guide does not establish that AutoGen Studio has an active coupon, education discount, or seasonal promotion. Confirm current offers on the vendor's website before paying.
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Yes. AutoGen Studio is part of Microsoft's open-source AutoGen project on GitHub and is released under the MIT license. There are no paid tiers, usage limits, or commercial restrictions. Your only costs are the LLM API keys you bring (e.g., OpenAI or Azure OpenAI usage fees) and the compute resources to run the Studio server. You can use it for personal projects, research, or commercial applications without licensing concerns.
The AutoGen SDK is a code-first Python (and .NET) library for building agent applications programmatically. AutoGen Studio is a visual web interface built on top of that SDK. Studio provides form-based configuration, a testing playground, and a gallery of reusable components so users can design multi-agent workflows without writing code. The key bridge is that Studio workflows can be exported as Python code compatible with the SDK, enabling teams to prototype visually and then move to code for production deployment.
Microsoft positions AutoGen Studio as a research prototype and prototyping tool rather than a production-ready platform. While you can technically run it in a production environment, it lacks built-in features like authentication, role-based access control, horizontal scaling, and enterprise secrets management. Teams using it in production should plan to add these layers themselves. The recommended workflow is to prototype in Studio, export to Python code via the SDK, and then deploy the exported code within your own production infrastructure.
Studio supports any model that implements the AutoGen ChatCompletionClient interface, including OpenAI (GPT-4o, GPT-4, GPT-3.5), Azure OpenAI, Anthropic Claude, Google Gemini, and local models via Ollama and LM Studio. For tools, it supports Python function tools (custom code), MCP protocol servers for standardized tool integration, and built-in capabilities like web search and sandboxed code execution. You can mix different models across agents in the same team — for example, using GPT-4o for a planning agent and a local model for a data-processing agent.
AutoGen Studio is distributed as a Python package. You install it with `pip install -U autogenstudio` (Python 3.10+ required), then launch the web UI by running `autogenstudio ui` in your terminal. This starts a local server, typically at http://localhost:8080. From there, configure your LLM provider keys in the Models section, explore Gallery templates, and start building agent teams. For isolated code execution, Docker is recommended. The entire setup process takes under 5 minutes if you already have Python and pip configured.
Start with the free tier and upgrade when you need more features
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