Microsoft Agent Framework Skill

Version: 0.1.0-preview | Last Updated: 2025-11-15 | Framework Version: 0.1.0-preview Languages: Python 3.10+, C# (.NET 8.0+) | License: MIT

Quick Reference

Microsoft Agent Framework is an open-source platform for building production AI agents and workflows, unifying AutoGen's simplicity with Semantic Kernel's enterprise features.

Core Capabilities: AI Agents (stateful conversations, tool integration) | Workflows (graph-based orchestration, parallel processing) | Enterprise features (telemetry, middleware, MCP support)

Installation:

• Python: pip install agent-framework-core --pre
• C#: dotnet add package Microsoft.Agents.AI --prerelease

Repository: https://github.com/microsoft/agent-framework (5.1k stars)

When to Use This Skill

Use Microsoft Agent Framework when you need:

1. Production AI Agents with enterprise features (telemetry, middleware, structured outputs)
2. Multi-Agent Orchestration via graph-based workflows with conditional routing
3. Tool/Function Integration with approval workflows and error handling
4. Cross-Platform Development requiring both Python and C# implementations
5. Research-to-Production Pipeline leveraging AutoGen + Semantic Kernel convergence

Integration with amplihack: Use Agent Framework for stateful conversational agents and complex orchestration. Use amplihack's native agent system for stateless task delegation and simple orchestration. See @integration/decision-framework.md for detailed guidance.

Core Concepts

1. AI Agents

Stateful conversational entities that process messages, call tools, and maintain context.

Python Example:

from agents_framework import Agent, ModelClient

# Create agent with model
agent = Agent(
    name="assistant",
    model=ModelClient(model="gpt-4"),
    instructions="You are a helpful assistant"
)

# Single-turn conversation
response = await agent.run(message="Hello!")
print(response.content)

# Multi-turn with thread
from agents_framework import Thread
thread = Thread()
response = await agent.run(thread=thread, message="What's 2+2?")
response = await agent.run(thread=thread, message="Double that")

C# Example:

using Microsoft.Agents.AI;

var agent = new Agent(
    name: "assistant",
    model: new ModelClient(model: "gpt-4"),
    instructions: "You are a helpful assistant"
);

var response = await agent.RunAsync("Hello!");
Console.WriteLine(response.Content);

2. Tools & Functions

Extend agent capabilities by providing callable functions.

Python Example:

from agents_framework import function_tool

@function_tool
def get_weather(location: str) -> str:
    """Get weather for a location."""
    return f"Weather in {location}: Sunny, 72°F"

agent = Agent(
    name="assistant",
    model=ModelClient(model="gpt-4"),
    tools=[get_weather]
)

response = await agent.run(message="What's the weather in Seattle?")
# Agent automatically calls get_weather() and responds with result

C# Example:

[FunctionTool]
public static string GetWeather(string location)
{
    return $"Weather in {location}: Sunny, 72°F";
}

var agent = new Agent(
    name: "assistant",
    model: new ModelClient(model: "gpt-4"),
    tools: new[] { typeof(Tools).GetMethod("GetWeather") }
);

3. Workflows

Graph-based orchestration for multi-agent systems with conditional routing and parallel execution.

Python Example:

from agents_framework import Workflow, GraphWorkflow

# Define workflow graph
workflow = GraphWorkflow()

# Add agents as nodes
workflow.add_node("researcher", research_agent)
workflow.add_node("writer", writer_agent)
workflow.add_node("reviewer", review_agent)

# Define edges (control flow)
workflow.add_edge("researcher", "writer")  # Sequential
workflow.add_edge("writer", "reviewer")

# Conditional routing
def should_revise(state):
    return state.get("needs_revision", False)

workflow.add_conditional_edge(
    "reviewer",
    should_revise,
    {"revise": "writer", "done": "END"}
)

# Execute workflow
result = await workflow.run(initial_message="Research AI trends")

C# Example:

var workflow = new GraphWorkflow();

workflow.AddNode("researcher", researchAgent);
workflow.AddNode("writer", writerAgent);
workflow.AddNode("reviewer", reviewAgent);

workflow.AddEdge("researcher", "writer");
workflow.AddEdge("writer", "reviewer");

var result = await workflow.RunAsync("Research AI trends");

4. Context & State Management

Maintain conversation history and shared state across agents.

Python:

from agents_framework import Thread, ContextProvider

# Thread maintains conversation history
thread = Thread()
await agent.run(thread=thread, message="Remember: My name is Alice")
await agent.run(thread=thread, message="What's my name?")  # "Alice"

# Custom context provider
class DatabaseContext(ContextProvider):
    async def get_context(self, thread_id: str):
        return await db.fetch_history(thread_id)

    async def