Microsoft Agent 框架支持创建使用 Anthropic 的 Claude 模型的代理。
入门
将所需的 NuGet 包添加到项目。
dotnet add package Microsoft.Agents.AI.Anthropic --prerelease
如果使用 Azure Foundry，请添加：
dotnet add package Anthropic.Foundry --prerelease
dotnet add package Azure.Identity
配置
环境变量
为人类身份验证设置所需的环境变量：
# Required for Anthropic API access
$env:ANTHROPIC_API_KEY="your-anthropic-api-key"
$env:ANTHROPIC_DEPLOYMENT_NAME="claude-haiku-4-5" # or your preferred model
可以从 人类控制台获取 API 密钥。
对于包含 API 密钥的 Azure Foundry
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Subdomain before .services.ai.azure.com
$env:ANTHROPIC_API_KEY="your-anthropic-api-key"
$env:ANTHROPIC_DEPLOYMENT_NAME="claude-haiku-4-5"
对于使用 Azure CLI 的 Azure Foundry
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Subdomain before .services.ai.azure.com
$env:ANTHROPIC_DEPLOYMENT_NAME="claude-haiku-4-5"
注释
将 Azure Foundry 与 Azure CLI 配合使用时，请确保已登录 az login 并有权访问 Azure Foundry 资源。 有关详细信息，请参阅 Azure CLI 文档。
创建人类代理
基本代理创建（人类公共 API）
使用公共 API 创建人类代理的最简单方法：
var apiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY");
var deploymentName = Environment.GetEnvironmentVariable("ANTHROPIC_DEPLOYMENT_NAME") ?? "claude-haiku-4-5";
AnthropicClient client = new() { APIKey = apiKey };
AIAgent agent = client.AsAIAgent(
model: deploymentName,
name: "HelpfulAssistant",
instructions: "You are a helpful assistant.");
// Invoke the agent and output the text result.
Console.WriteLine(await agent.RunAsync("Hello, how can you help me?"));
在 Azure Foundry 上使用 Anthropic 和 API 密钥
在 Azure Foundry 上设置人类学后，可以将它与 API 密钥身份验证一起使用：
var resource = Environment.GetEnvironmentVariable("ANTHROPIC_RESOURCE");
var apiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY");
var deploymentName = Environment.GetEnvironmentVariable("ANTHROPIC_DEPLOYMENT_NAME") ?? "claude-haiku-4-5";
AnthropicClient client = new AnthropicFoundryClient(
new AnthropicFoundryApiKeyCredentials(apiKey, resource));
AIAgent agent = client.AsAIAgent(
model: deploymentName,
name: "FoundryAgent",
instructions: "You are a helpful assistant using Anthropic on Azure Foundry.");
Console.WriteLine(await agent.RunAsync("How do I use Anthropic on Foundry?"));
在 Azure Foundry 上使用 Anthropic 以及 Azure 凭据（Azure CLI 凭据示例）
在优先使用 Azure 凭据的环境中：
var resource = Environment.GetEnvironmentVariable("ANTHROPIC_RESOURCE");
var deploymentName = Environment.GetEnvironmentVariable("ANTHROPIC_DEPLOYMENT_NAME") ?? "claude-haiku-4-5";
AnthropicClient client = new AnthropicFoundryClient(
new AnthropicAzureTokenCredential(new DefaultAzureCredential(), resource));
AIAgent agent = client.AsAIAgent(
model: deploymentName,
name: "FoundryAgent",
instructions: "You are a helpful assistant using Anthropic on Azure Foundry.");
Console.WriteLine(await agent.RunAsync("How do I use Anthropic on Foundry?"));
/// <summary>
/// Provides methods for invoking the Azure hosted Anthropic models using <see cref="TokenCredential"/> types.
/// </summary>
public sealed class AnthropicAzureTokenCredential(TokenCredential tokenCredential, string resourceName) : IAnthropicFoundryCredentials
{
/// <inheritdoc/>
public string ResourceName { get; } = resourceName;
/// <inheritdoc/>
public void Apply(HttpRequestMessage requestMessage)
{
requestMessage.Headers.Authorization = new AuthenticationHeaderValue(
scheme: "bearer",
parameter: tokenCredential.GetToken(new TokenRequestContext(scopes: ["https://ai.azure.com/.default"]), CancellationToken.None)
.Token);
}
}
警告
DefaultAzureCredential 对于开发来说很方便，但在生产中需要仔细考虑。 在生产环境中，请考虑使用特定凭据（例如）， ManagedIdentityCredential以避免延迟问题、意外凭据探测以及回退机制的潜在安全风险。
小窍门
有关完整的可运行示例，请参阅 .NET 示例 。
使用代理
代理是标准 AIAgent 代理，支持所有标准代理操作。
有关如何运行和与代理交互的详细信息，请参阅 代理入门教程 。
先决条件
安装 Microsoft Agent Framework Anthropic 包。
pip install agent-framework-anthropic --pre
配置
环境变量
为人类身份验证设置所需的环境变量：
# Required for Anthropic API access
ANTHROPIC_API_KEY="your-anthropic-api-key"
ANTHROPIC_CHAT_MODEL_ID="claude-sonnet-4-5-20250929" # or your preferred model
或者，可以在项目根目录中使用 .env 文件：
ANTHROPIC_API_KEY=your-anthropic-api-key
ANTHROPIC_CHAT_MODEL_ID=claude-sonnet-4-5-20250929
可以从 人类控制台获取 API 密钥。
入门
从代理框架导入所需的类：
import asyncio
from agent_framework.anthropic import AnthropicClient
创建人类代理
基本代理创建
创建人类代理的最简单方法：
async def basic_example():
# Create an agent using Anthropic
agent = AnthropicClient().as_agent(
name="HelpfulAssistant",
instructions="You are a helpful assistant.",
)
result = await agent.run("Hello, how can you help me?")
print(result.text)
使用显式配置
可以提供显式配置，而不是依赖于环境变量：
async def explicit_config_example():
agent = AnthropicClient(
model_id="claude-sonnet-4-5-20250929",
api_key="your-api-key-here",
).as_agent(
name="HelpfulAssistant",
instructions="You are a helpful assistant.",
)
result = await agent.run("What can you do?")
print(result.text)
在 Foundry 上使用人类学
在 Foundry 上设置人类学后，请确保设置了以下环境变量：
ANTHROPIC_FOUNDRY_API_KEY="your-foundry-api-key"
ANTHROPIC_FOUNDRY_RESOURCE="your-foundry-resource-name"
然后创建代理，如下所示：
from agent_framework.anthropic import AnthropicClient
from anthropic import AsyncAnthropicFoundry
async def foundry_example():
agent = AnthropicClient(
anthropic_client=AsyncAnthropicFoundry()
).as_agent(
name="FoundryAgent",
instructions="You are a helpful assistant using Anthropic on Foundry.",
)
result = await agent.run("How do I use Anthropic on Foundry?")
print(result.text)
注意：需要安装
anthropic>=0.74.0。
代理功能
函数工具
为代理配备自定义功能：
from typing import Annotated
def get_weather(
location: Annotated[str, "The location to get the weather for."],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def tools_example():
agent = AnthropicClient().as_agent(
name="WeatherAgent",
instructions="You are a helpful weather assistant.",
tools=get_weather, # Add tools to the agent
)
result = await agent.run("What's the weather like in Seattle?")
print(result.text)
流式处理响应
对即时生成的响应进行获取，以提升用户体验。
async def streaming_example():
agent = AnthropicClient().as_agent(
name="WeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
)
query = "What's the weather like in Portland and in Paris?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
托管工具
人类代理支持托管的工具，例如 Web 搜索、MCP（模型上下文协议）和代码执行：
from agent_framework.anthropic import AnthropicClient
async def hosted_tools_example():
client = AnthropicClient()
agent = client.as_agent(
name="DocsAgent",
instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
tools=[
client.get_mcp_tool(
name="Microsoft Learn MCP",
url="https://learn.microsoft.com/api/mcp",
),
client.get_web_search_tool(),
],
max_tokens=20000,
)
result = await agent.run("Can you compare Python decorators with C# attributes?")
print(result.text)
扩展思维（推理）
Anthropic通过thinking功能支持扩展思维能力，使模型能够展示其推理过程。
from agent_framework import TextReasoningContent, UsageContent
from agent_framework.anthropic import AnthropicClient
async def thinking_example():
client = AnthropicClient()
agent = client.as_agent(
name="DocsAgent",
instructions="You are a helpful agent.",
tools=[client.get_web_search_tool()],
default_options={
"max_tokens": 20000,
"thinking": {"type": "enabled", "budget_tokens": 10000}
},
)
query = "Can you compare Python decorators with C# attributes?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
if isinstance(content, TextReasoningContent):
# Display thinking in a different color
print(f"\033[32m{content.text}\033[0m", end="", flush=True)
if isinstance(content, UsageContent):
print(f"\n\033[34m[Usage: {content.details}]\033[0m\n", end="", flush=True)
if chunk.text:
print(chunk.text, end="", flush=True)
print()
人类技能
Anthropic 提供了可扩展代理功能的托管技能，例如创建 PowerPoint 演示文稿。 技能需要代码解释器工具才能正常工作：
from agent_framework import HostedFileContent
from agent_framework.anthropic import AnthropicClient
async def skills_example():
# Create client with skills beta flag
client = AnthropicClient(additional_beta_flags=["skills-2025-10-02"])
# Create an agent with the pptx skill enabled
# Skills require the Code Interpreter tool
agent = client.as_agent(
name="PresentationAgent",
instructions="You are a helpful agent for creating PowerPoint presentations.",
tools=client.get_code_interpreter_tool(),
default_options={
"max_tokens": 20000,
"thinking": {"type": "enabled", "budget_tokens": 10000},
"container": {
"skills": [{"type": "anthropic", "skill_id": "pptx", "version": "latest"}]
},
},
)
query = "Create a presentation about renewable energy with 5 slides"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
files: list[HostedFileContent] = []
async for chunk in agent.run(query, stream=True):
for content in chunk.contents:
match content.type:
case "text":
print(content.text, end="", flush=True)
case "text_reasoning":
print(f"\033[32m{content.text}\033[0m", end="", flush=True)
case "hosted_file":
# Catch generated files
files.append(content)
print("\n")
# Download generated files
if files:
print("Generated files:")
for idx, file in enumerate(files):
file_content = await client.anthropic_client.beta.files.download(
file_id=file.file_id,
betas=["files-api-2025-04-14"]
)
filename = f"presentation-{idx}.pptx"
with open(filename, "wb") as f:
await file_content.write_to_file(f.name)
print(f"File {idx}: {filename} saved to disk.")
完整示例
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randint
from typing import Annotated
from agent_framework import tool
from agent_framework.anthropic import AnthropicClient
"""
Anthropic Chat Agent Example
This sample demonstrates using Anthropic with an agent and a single custom tool.
"""
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_weather(
location: Annotated[str, "The location to get the weather for."],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def non_streaming_example() -> None:
"""Example of non-streaming response (get the complete result at once)."""
print("=== Non-streaming Response Example ===")
agent = AnthropicClient(
).as_agent(
name="WeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
)
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Result: {result}\n")
async def streaming_example() -> None:
"""Example of streaming response (get results as they are generated)."""
print("=== Streaming Response Example ===")
agent = AnthropicClient(
).as_agent(
name="WeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
)
query = "What's the weather like in Portland and in Paris?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run(query, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
async def main() -> None:
print("=== Anthropic Example ===")
await streaming_example()
await non_streaming_example()
if __name__ == "__main__":
asyncio.run(main())
使用代理
代理是标准 Agent 代理，支持所有标准代理操作。
有关如何运行和与代理交互的详细信息，请参阅 代理入门教程 。