Microsoft Agent Framework：开源AI智能体与工作流平台，支持Python/C#跨平台开发

microsoft-agent-framework by rysweet/amplihack

195 周安装量

44 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/rysweet/amplihack --skill microsoft-agent-framework

AI/机器学习自动化 C#

🇨🇳中文介绍

Microsoft Agent Framework Skill

版本 : 0.1.0-preview | 最后更新 : 2025-11-15 | 框架版本 : 0.1.0-preview 语言 : Python 3.10+, C# (.NET 8.0+) | 许可证 : MIT

快速参考

Microsoft Agent Framework 是一个用于构建生产级 AI 智能体和工作流的开源平台，它融合了 AutoGen 的简洁性和 Semantic Kernel 的企业级特性。

核心能力 : AI 智能体（有状态对话、工具集成） | 工作流（基于图的编排、并行处理） | 企业级特性（遥测、中间件、MCP 支持）

安装 :

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

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

何时使用此技能

在以下场景中使用 Microsoft Agent Framework：

需要具备企业级特性（遥测、中间件、结构化输出）的生产级 AI 智能体
需要通过基于图的工作流进行多智能体编排，并支持条件路由
需要集成工具/函数，并包含审批工作流和错误处理

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4. 上下文与状态管理

跨智能体维护对话历史和共享状态。

from agents_framework import Thread, ContextProvider

# Thread 维护对话历史
thread = Thread()
await agent.run(thread=thread, message="Remember: My name is Alice")
await agent.run(thread=thread, message="What's my name?")  # "Alice"

# 自定义上下文提供者
class DatabaseContext(ContextProvider):
    async def get_context(self, thread_id: str):
        return await db.fetch_history(thread_id)

    async def save_context(self, thread_id: str, messages):
        await db.save_history(thread_id, messages)

agent = Agent(model=model, context_provider=DatabaseContext())

5. 中间件与遥测

添加日志记录、身份验证和监控等横切关注点。

from agents_framework import Middleware
from opentelemetry import trace

# 自定义中间件
class LoggingMiddleware(Middleware):
    async def process(self, message, next_handler):
        print(f"Processing: {message.content}")
        response = await next_handler(message)
        print(f"Response: {response.content}")
        return response

# OpenTelemetry 集成
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("agent-run"):
    response = await agent.run(message="Hello")

public class LoggingMiddleware : IMiddleware
{
    public async Task<Message> ProcessAsync(Message message, Func<Message, Task<Message>> next)
    {
        Console.WriteLine($"Processing: {message.Content}");
        var response = await next(message);
        Console.WriteLine($"Response: {response.Content}");
        return response;
    }
}

from agents_framework import HumanInTheLoop

@function_tool
def delete_file(path: str) -> str:
    """Delete a file (requires approval)."""
    return f"Deleted {path}"

# 添加审批包装器
delete_file_with_approval = HumanInTheLoop(
    tool=delete_file,
    approval_prompt="Approve deletion of {path}?"
)

agent = Agent(tools=[delete_file_with_approval])

并行智能体执行

workflow = GraphWorkflow()

# 添加多个智能体
workflow.add_node("analyst1", analyst_agent)
workflow.add_node("analyst2", analyst_agent)
workflow.add_node("synthesizer", synthesis_agent)

# 并行执行
workflow.add_edge("START", ["analyst1", "analyst2"])  # 两者并行运行
workflow.add_edge(["analyst1", "analyst2"], "synthesizer")  # 等待两者完成

result = await workflow.run(message="Analyze market trends")

结构化输出生成

from pydantic import BaseModel

class WeatherReport(BaseModel):
    location: str
    temperature: float
    conditions: str

agent = Agent(
    model=model,
    instructions="Generate weather reports",
    response_format=WeatherReport
)

response = await agent.run(message="Weather in Seattle")
report: WeatherReport = response.parsed
print(f"{report.location}: {report.temperature}°F, {report.conditions}")

错误处理与重试

from agents_framework import RetryPolicy

agent = Agent(
    model=model,
    retry_policy=RetryPolicy(
        max_retries=3,
        backoff_factor=2.0,
        exceptions=[TimeoutError, ConnectionError]
    )
)

try:
    response = await agent.run(message="Hello")
except Exception as e:
    print(f"Failed after retries: {e}")

与 amplihack 集成

在以下情况下使用 Microsoft Agent Framework :

构建有状态对话智能体（多轮对话）
需要企业级特性（遥测、中间件、身份验证）
具有条件路由的复杂多智能体编排
跨平台需求（Python + C#）
与微软生态系统（Azure, M365）集成

在以下情况下使用 amplihack 原生智能体 :

无状态任务委派（代码审查、分析）
简单的顺序/并行编排
基于文件的操作和本地工具
无需基础设施的快速原型设计
基于技能的高效令牌架构

# 使用 amplihack 进行编排
from claude import Agent as ClaudeAgent

orchestrator = ClaudeAgent("orchestrator.md")

# 将有状态智能体任务委派给 Agent Framework
from agents_framework import Agent, Thread

conversational_agent = Agent(
    model=ModelClient(model="gpt-4"),
    instructions="Maintain conversation context"
)

thread = Thread()
response1 = await conversational_agent.run(thread=thread, message="Start task")
response2 = await conversational_agent.run(thread=thread, message="Continue")

# 使用 amplihack 进行最终合成
result = orchestrator.process({"responses": [response1, response2]})

完整模式请参阅 @integration/amplihack-integration.md。

快速入门工作流

安装 : pip install agent-framework-core --pre (Python) 或 dotnet add package Microsoft.Agents.AI --prerelease (C#)

创建基础智能体 :

from agents_framework import Agent, ModelClient

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

response = await agent.run(message="Hello!")

添加工具 :

@function_tool
def calculate(expr: str) -> float:
    return eval(expr)

agent = Agent(model=model, tools=[calculate])

构建工作流 :

workflow = GraphWorkflow()
workflow.add_node("agent1", agent1)
workflow.add_node("agent2", agent2)
workflow.add_edge("agent1", "agent2")
result = await workflow.run(message="Task")

添加遥测 :

from opentelemetry import trace
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("agent-run"):
    response = await agent.run(message="Hello")

详细信息请参阅：

@reference/01-overview.md - 架构、组件、用例
@reference/02-agents.md - 智能体创建、生命周期、高级特性
@reference/03-workflows.md - 工作流模式、执行器、检查点
@reference/04-tools-functions.md - 工具定义、审批工作流、错误处理
@reference/05-context-middleware.md - 上下文提供者、中间件模式、身份验证
@reference/06-telemetry-monitoring.md - OpenTelemetry、日志记录、调试
@reference/07-advanced-patterns.md - 多智能体模式、流式传输、DevUI

@examples/01-basic-agent.py - 简单对话智能体
@examples/02-tool-integration.py - 具有函数调用功能的智能体
@examples/03-simple-workflow.py - 多智能体工作流
@examples/04-basic-agent.cs - C# 智能体实现
@examples/05-tool-integration.cs - C# 工具集成
@examples/06-simple-workflow.cs - C# 工作流示例

检查框架新鲜度：python @scripts/check-freshness.py

当前版本跟踪：@metadata/version-tracking.json

令牌计数 : ~4,200 tokens (低于 4,800 限制)

🇺🇸English

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:

Production AI Agents with enterprise features (telemetry, middleware, structured outputs)
Multi-Agent Orchestration via graph-based workflows with conditional routing
Tool/Function Integration with approval workflows and error handling
Cross-Platform Development requiring both Python and C# implementations
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 save_context(self, thread_id: str, messages):
        await db.save_history(thread_id, messages)

agent = Agent(model=model, context_provider=DatabaseContext())

5. Middleware & Telemetry

Add cross-cutting concerns like logging, auth, and monitoring.

Python :

from agents_framework import Middleware
from opentelemetry import trace

# Custom middleware
class LoggingMiddleware(Middleware):
    async def process(self, message, next_handler):
        print(f"Processing: {message.content}")
        response = await next_handler(message)
        print(f"Response: {response.content}")
        return response

# OpenTelemetry integration
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("agent-run"):
    response = await agent.run(message="Hello")

C# :

public class LoggingMiddleware : IMiddleware
{
    public async Task<Message> ProcessAsync(Message message, Func<Message, Task<Message>> next)
    {
        Console.WriteLine($"Processing: {message.Content}");
        var response = await next(message);
        Console.WriteLine($"Response: {response.Content}");
        return response;
    }
}

Common Patterns

Human-in-the-Loop Approval

from agents_framework import HumanInTheLoop

@function_tool
def delete_file(path: str) -> str:
    """Delete a file (requires approval)."""
    return f"Deleted {path}"

# Add approval wrapper
delete_file_with_approval = HumanInTheLoop(
    tool=delete_file,
    approval_prompt="Approve deletion of {path}?"
)

agent = Agent(tools=[delete_file_with_approval])

Parallel Agent Execution

workflow = GraphWorkflow()

# Add multiple agents
workflow.add_node("analyst1", analyst_agent)
workflow.add_node("analyst2", analyst_agent)
workflow.add_node("synthesizer", synthesis_agent)

# Parallel execution
workflow.add_edge("START", ["analyst1", "analyst2"])  # Both run in parallel
workflow.add_edge(["analyst1", "analyst2"], "synthesizer")  # Wait for both

result = await workflow.run(message="Analyze market trends")

Structured Output Generation

from pydantic import BaseModel

class WeatherReport(BaseModel):
    location: str
    temperature: float
    conditions: str

agent = Agent(
    model=model,
    instructions="Generate weather reports",
    response_format=WeatherReport
)

response = await agent.run(message="Weather in Seattle")
report: WeatherReport = response.parsed
print(f"{report.location}: {report.temperature}°F, {report.conditions}")

Error Handling & Retries

from agents_framework import RetryPolicy

agent = Agent(
    model=model,
    retry_policy=RetryPolicy(
        max_retries=3,
        backoff_factor=2.0,
        exceptions=[TimeoutError, ConnectionError]
    )
)

try:
    response = await agent.run(message="Hello")
except Exception as e:
    print(f"Failed after retries: {e}")

Integration with amplihack

Decision Framework

Use Microsoft Agent Framework when :

Building stateful conversational agents (multi-turn dialogue)
Need enterprise features (telemetry, middleware, auth)
Complex multi-agent orchestration with conditional routing
Cross-platform requirements (Python + C#)
Integration with Microsoft ecosystem (Azure, M365)

Use amplihack native agents when :

Stateless task delegation (code review, analysis)
Simple sequential/parallel orchestration
File-based operations and local tooling
Rapid prototyping without infrastructure
Token-efficient skill-based architecture

Hybrid Approach :

# Use amplihack for orchestration
from claude import Agent as ClaudeAgent

orchestrator = ClaudeAgent("orchestrator.md")

# Delegate to Agent Framework for stateful agents
from agents_framework import Agent, Thread

conversational_agent = Agent(
    model=ModelClient(model="gpt-4"),
    instructions="Maintain conversation context"
)

thread = Thread()
response1 = await conversational_agent.run(thread=thread, message="Start task")
response2 = await conversational_agent.run(thread=thread, message="Continue")

# Use amplihack for final synthesis
result = orchestrator.process({"responses": [response1, response2]})

See @integration/amplihack-integration.md for complete patterns.

Quick Start Workflow

Install : pip install agent-framework-core --pre (Python) or dotnet add package Microsoft.Agents.AI --prerelease (C#)

Create Basic Agent :

from agents_framework import Agent, ModelClient

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

response = await agent.run(message="Hello!")

Add Tools :

@function_tool
def calculate(expr: str) -> float:
    return eval(expr)

agent = Agent(model=model, tools=[calculate])

Build Workflow :

workflow = GraphWorkflow()
workflow.add_node("agent1", agent1)
workflow.add_node("agent2", agent2)
workflow.add_edge("agent1", "agent2")
result = await workflow.run(message="Task")

Reference Documentation

For detailed information, see:

@reference/01-overview.md - Architecture, components, use cases
@reference/02-agents.md - Agent creation, lifecycle, advanced features
@reference/03-workflows.md - Workflow patterns, executors, checkpointing
@reference/04-tools-functions.md - Tool definition, approval workflows, error handling
@reference/05-context-middleware.md - Context providers, middleware patterns, auth
@reference/06-telemetry-monitoring.md - OpenTelemetry, logging, debugging
@reference/07-advanced-patterns.md - Multi-agent patterns, streaming, DevUI

Working Examples

@examples/01-basic-agent.py - Simple conversational agent
@examples/02-tool-integration.py - Agent with function calling
@examples/03-simple-workflow.py - Multi-agent workflow
@examples/04-basic-agent.cs - C# agent implementation
@examples/05-tool-integration.cs - C# tool integration
@examples/06-simple-workflow.cs - C# workflow example

Maintenance

Check framework freshness: python @scripts/check-freshness.py

Current version tracking: @metadata/version-tracking.json

Token Count : ~4,200 tokens (under 4,800 limit)

Weekly Installs

143

Repository

rysweet/amplihack

GitHub Stars

First Seen

Jan 23, 2026

Security Audits

Gen Agent Trust HubPass SocketPass SnykWarn

Installed on

opencode123

github-copilot123

codex117

gemini-cli116

cursor113

kimi-cli109

AI Elements：基于shadcn/ui的AI原生应用组件库，快速构建对话界面

69,600 周安装

Add Telemetry :

from opentelemetry import trace
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("agent-run"):
    response = await agent.run(message="Hello")

Microsoft Agent Framework：开源AI智能体与工作流平台，支持Python/C#跨平台开发

🇨🇳中文介绍

Microsoft Agent Framework Skill

快速参考

何时使用此技能

相关 Skills

核心概念

1. AI 智能体

2. 工具与函数

3. 工作流

4. 上下文与状态管理

5. 中间件与遥测

常用模式

人工介入审批

并行智能体执行

结构化输出生成

错误处理与重试

与 amplihack 集成

决策框架

快速入门工作流

参考文档

工作示例

维护

🇺🇸English

Microsoft Agent Framework Skill

Quick Reference

When to Use This Skill

Core Concepts

1. AI Agents

2. Tools & Functions

3. Workflows

4. Context & State Management

5. Middleware & Telemetry

Common Patterns

Human-in-the-Loop Approval

Parallel Agent Execution

Structured Output Generation

Error Handling & Retries

Integration with amplihack

Decision Framework

Quick Start Workflow

Reference Documentation

Working Examples

Maintenance

最新 Skills