AutoGen

Can't wait to see what you all build!

🚀🚀 Earlier this summer our work on @pyautogen graduated to product & now @Microsoft has combined AutoGen and Semantic Kernel. What this means for agent developers is one single SDK that combines the best of both worlds and learnings. Sp congrats to @ekzhu and @vykthur!

The Semantic Kernel and @pyautogen team have worked hard over the last few months to bring together the best of both frameworks for a much stronger developer experience (and to simplify the choice!). Today we are excited to release Agent Framework in public preview and get feedback. Agent Framework brings the production ready focus of Semantic Kernel with full C#/Python support, as well as the nimble intuitive interface of AutoGen for rapid experimentation. Some of the things that I think we have done really well: - Middleware support - write hooks to control agent behaviour great for RAI, security, context engineering etc - Support for Agents backed by various services - Azure Foundry Agent, OpenAI Responses, Google A2A agents, ofcourse with full MCP support - Bring your own model, integrate with any agent - Workflow API that lets you express business or application logic as a computation graph (this is something that was a weak spot for AutoGen) Ofcourse, frameworks are only there to simplify aspects of your work - what matters is what you build! To illustrate this, I’d try to do a “demo a day” over the next week to showcase capabilities of the framework. The video below shows an sample app (DevUI) that ships with Agent Framework for running and debugging your Agent Framework tools. If you are starting a new AI Agent project - try it out!

Interested in the technical details behind Magentic-UI? Our report is now available on arXiv: arxiv.org/abs/2507.22358

🚀 AutoGen v0.6.4 is out! Shout-out to @github Copilot for helping author these new features! 🧠 GraphFlow now retains execution state after termination, just like other group chats. Resets only when the graph fully completes. ⚙️ New parameter_override in Workbenches for smarter client-side tool optimization. 🔗github.com/microsoft/auto…

One of the best decision we've made early on was to NOT use layers of abstraction and go native clients route instead. The second best decision was to use @pyautogen to build all of our workflows on top of. It hits all the right pain points running in distributed environments without adding too much unnecessary bloat that makes most other solutions practically unusable. The code has never been nicer!

Check out how to configure an MCP Agents in Magentic-UI here: #mcp-server-configuration" target="_blank" rel="nofollow noopener">github.com/microsoft/mage…

🚀 Introducing MCP Agents in Magentic-UI! Spin up custom agents that wrap one (or many) MCP tools, and let the Orchestrator pick the best agent for every step of the plan. Check out the demo below to see them in action 👇 #MCP #MagenticUI #AIagents

Curious how to build agents that can control a browser? I just wrote up a full tutorial on how to do it completely from scratch and with Magentic-UI. My goal is to demystify browser-use and CUA agents, it's fun to follow along! Link: husseinmozannar.github.io/#/blog/web_age… Jupyter notebook: github.com/microsoft/mage… 1/4

My recent talk on challenges in developing human-centered agents is now available online! It provides an HCI perspective of our learning from developing @pyautogen youtube.com/watch?v=O5jSX8…

👀New Magentic-UI tutorial by @MayaMurad0 Learn how to make the most of MAGUI's features and automate complex tasks.

The AI landscape is buzzing with "super agents"! 🤖 But how do we stay in the driver's seat? I went hands-on with Microsoft's research prototype, Magentic-UI from @pyautogen, to see how it helps orchestrate these powerful tools while keeping humans in control. Check out the demo! 👇

Magentic-UI is fully open-source and built to support human–agent collaboration research and exploration. 👾 GitHub: github.com/microsoft/mage…

🧠 Plan Learning helps Magentic-UI learn from you. After you complete a task once, it can: 📋 Infer a reusable step-by-step plan ⚡ Replay it for repeat tasks such as ordering lunch or sending weekly reports.

✨ Human-in-the-loop, by design. Magentic-UI introduces four core HCI features to make human–agent collaboration more transparent, controllable, and adaptive: 🧑‍🤝‍🧑 Co-Planning 🤝 Co-Tasking 🛡️ Action Guards 🧠 Plan Learning Let’s dive into each 👇