Microsoft Agent Framework: Open-Source SDK for .Net & Python

Hello Everyone Microsoft Agent Framework 1.0 is officially LIVE! 🎊 The wait is over. Microsoft has consolidated its best agentic research into a single, stable, 100% open-source framework. What’s new? Stable APIs for .NET & Python. Production-ready multi-agent orchestration. Full support for Model Context Protocol (MCP). The bridge between "cool demo" and "enterprise reality" just got a lot shorter. 🌉 https://lnkd.in/g7Fk9maX

We’re excited to announce the production-ready release of Microsoft Agent Framework 1.0 for .NET and Python, bringing stable APIs, long‑term support, and enterprise‑grade foundations for building agentic and multi‑agent systems.

Microsoft has announced the production-ready release of Agent Framework 1.0 on April 3, 2026. This marks a significant milestone in the development of agentic AI. Key features include: - A stable, production-ready SDK for building agentic AI applications in both .NET and Python. - The unification of Semantic Kernel and AutoGen into one open-source framework. - Enterprise-grade multi-agent orchestration, cross-runtime interoperability, and long-term support. For more details, visit the link: https://lnkd.in/d35B5Awn

Production-ready multi-agent orchestration is finally here. With Agent Framework v1.0 introducing some exciting new features. My personal favourites: 1. Agent workflows 2. Middleware hooks 3. Integration with managed agents in Foundry Agent Service 4. Multi-agent orchestration with HITL 5. Skills 6. Foundry Tools, Memory, Observability and Evaluations and many more. Check out the amazing blog post from Shawn Henry detailing the various features with concrete examples in Python and .NET https://lnkd.in/dKBRDujY

Today, we’re thrilled to announce that Microsoft Agent Framework has reached version 1.0 for both .NET and Python. This is the production-ready release: stable APIs, and a commitment to long-term support. Whether you’re building a single assistant or orchestrating a fleet of specialized agents, Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP.

A lot of backend discussions today revolve around performance. One framework that impressed me recently while building APIs is FastAPI. What stands out is how quickly you can build clean, high-performance APIs without adding too much complexity. A few things I personally like while working with it: • Automatic API documentation without extra setup • Type hints that make code easier to maintain • Great performance for async workloads • Very simple to connect with existing Python services For projects that are API-first — microservices, integrations, or mobile backends — it feels very efficient. Sometimes the right tool isn’t the biggest framework… it’s the one that keeps things simple and fast. Curious to hear from other developers — Are you using FastAPI, or sticking with Django or Flask for APIs? #FastAPI #Python #BackendDevelopment #APIDevelopment #SoftwareEngineering #Developers

💡Python – Simple to Learn, Powerful to Build Python is one of the most beginner-friendly and powerful programming languages. Its clean syntax makes coding easy to read, write, and maintain, while its vast ecosystem allows developers to build anything from automation scripts to scalable web applications. To build strong Python skills for backend development with Django, Flask, and FastAPI, mastering key modules is essential. 🔹 Core Modules: os, sys, datetime, json, re, collections📐 🔹 Backend Utilities: logging, pathlib, functools, argparse 🔹 Web/API Modules: requests, hashlib, uuid, secrets🌐 🔹 Async Programming (FastAPI): asyncio, concurrent.futures🎯 🔹 Database Modules: sqlite3, sqlalchemy, psycopg2♟️🧩 With a solid understanding of these modules, developers can easily build REST APIs, automate tasks, manage databases, and develop scalable backend systems.🖥️🖲️ #Python #Django #Flask #FastAPI #BackendDevelopment #PythonDeveloper #APIDevelopment #SoftwareEngineering

I built a local-first visual debugger for Python agents. If you’ve ever had an agent return a “fine” answer while the middle of the run was slow, expensive, wrong, or just impossible to inspect, this is for you. `flow-xray` lets you: - add `@trace` - run once - open one local HTML file You can inspect: - LLM calls - tool calls - branches and nested steps - errors - tokens and cost No cloud dashboard. No account. Just a local trace you can inspect in your browser. GitHub: https://lnkd.in/dR6vE5Ar PyPI: https://lnkd.in/dAA-cbRe pip install flow-xray #Python #LLM #AIAgents #OpenAI #DevTools #OpenSource #Debugging