microsoft-agent-framework

Build and orchestrate AI agents with Microsoft Agent Framework.

1|Updated Jan 14, 2026

Search Tags:#tool integration #AI agents #multi-agent #yaml #Python #.NET #agent-framework

Ready to run

npx skills add https://github.com/EmeaAppGbb/spec2cloud-weather-agent --skill microsoft-agent-framework-emeaappgbb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: microsoft-agent-framework
Source: https://github.com/EmeaAppGbb/spec2cloud-weather-agent/tree/main/.github/skills/microsoft-agent-framework
Command: npx skills add https://github.com/EmeaAppGbb/spec2cloud-weather-agent --skill microsoft-agent-framework-emeaappgbb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Microsoft Agent Framework enables building AI agents and orchestrating complex workflows, including chat agents, tool integration, memory/state management, observability, and declarative YAML agents across Python and .NET.

Core Features & Use Cases

Single-agent and multi-agent workflows: Build scalable AI assistants and orchestrate tools in sequence or in parallel.
Tool integration and orchestration patterns: Use ChatAgent, tools (@ai_function), memory/state management, and various orchestration patterns (GroupChat, Sequential, Concurrent, Handoff).
Observability and declarative workflows: Monitor agent health and behaviors; define agents using YAML for repeatable deployments.

Quick Start

Use the framework to create a WeatherAgent example, connect a Python tool, and run a simple query.

Frequently Asked Questions about microsoft-agent-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I orchestrate multi-agent workflows in Python and .NET?▼

Multi-agent workflows are orchestrated using patterns like GroupChat, Sequential, Concurrent, and Handoff. The framework supports building scalable AI assistants and orchestrating tools in sequence or in parallel across Python and .NET.

What is declarative YAML agent deployment and when do I need it?▼

Declarative YAML agents define AI agent configurations for repeatable deployments. You need this approach when you want to standardize agent behaviors and tool integration across different environments without writing imperative code.

Can I manage memory and state for chat agents using this framework?▼

Yes, you can manage memory and state for chat agents. The framework provides ChatAgent capabilities with built-in memory and state management to maintain context across multi-agent workflows and tool integration scenarios.

How do I integrate external tools with AI agents using @ai_function?▼

You integrate external tools with AI agents using the @ai_function decorator. This allows your ChatAgent to call Python or .NET functions directly, enabling tool orchestration within single-agent and multi-agent workflows.

Does the framework provide observability for monitoring agent health?▼

Yes, the framework provides observability features to monitor agent health and behaviors. This allows you to track agent performance and operational status during tool orchestration and multi-agent workflows.

What is the best way to build a single-agent chat application with tool integration?▼

The best way to build a single-agent chat application is using the ChatAgent component with @ai_function decorators for tool integration. This setup supports memory management and observability for scalable AI assistants.