Microsoft 代理程式架構支援建立使用 Anthropic 的 Claude 模型的代理程式。
使用者入門
將必要的 NuGet 套件新增至您的專案。
dotnet add package Microsoft.Agents.AI.Anthropic --prerelease
如果你正在使用 Azure Foundry，也請新增：
dotnet add package Anthropic.Foundry --prerelease
dotnet add package Azure.Identity
設定
環境變數
設定 Anthropic 驗證所需的環境變數：
# 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
您可以從 Anthropic 控制台取得 API 金鑰。
針對 Azure Foundry 使用 API Key
$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"
在 Azure CLI 中使用 Azure Foundry
$env:ANTHROPIC_RESOURCE="your-foundry-resource-name" # Subdomain before .services.ai.azure.com
$env:ANTHROPIC_DEPLOYMENT_NAME="claude-haiku-4-5"
備註
使用 Azure Foundry 搭配 Azure CLI 時，請確保你已使用 Azure CLI az login 登入，並能存取 Azure Foundry 資源。 如需詳細資訊，請參閱 Azure CLI 檔。
創建人類代理
基本代理建立（Anthropic Public API）
使用公開 API 建立 Anthropic 代理最簡單的方法：
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 API Key 的 Anthropic
在 Azure Foundry 上設定好 Anthropic 後，你可以搭配 API 金鑰驗證一起使用：
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 搭配 Azure 憑證（Azure CLI 憑證範例）
對於偏好使用 Azure 憑證的環境：
var resource = Environment.GetEnvironmentVariable("ANTHROPIC_RESOURCE");
var deploymentName = Environment.GetEnvironmentVariable("ANTHROPIC_DEPLOYMENT_NAME") ?? "claude-haiku-4-5";
AnthropicClient client = new AnthropicFoundryClient(
new AnthropicAzureTokenCredential(new AzureCliCredential(), 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);
}
}
使用代理程式
代理程式是標準 AIAgent ，支援所有標準代理程式作業。
請參閱 代理程式入門教學課程 ，以取得如何執行代理程式並與代理程式互動的詳細資訊。
先決條件
安裝 Microsoft Agent Framework Anthropic 套件。
pip install agent-framework-anthropic --pre
設定
環境變數
設定 Anthropic 驗證所需的環境變數：
# 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
您也可以在專案根目錄中使用一個 .env 檔案：
ANTHROPIC_API_KEY=your-anthropic-api-key
ANTHROPIC_CHAT_MODEL_ID=claude-sonnet-4-5-20250929
您可以從 Anthropic 控制台取得 API 金鑰。
使用者入門
從代理程式架構匯入所需的類別：
import asyncio
from agent_framework.anthropic import AnthropicClient
創建人類代理
基本代理程式創建
創建一個 Anthropic 智能體的最簡單方法：
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)
使用明確配置
您可以提供明確的設定，而不是依賴環境變數：
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 Anthropic
在 Foundry 上設定好 Anthropic 後，請確保你設定了以下環境變數：
ANTHROPIC_FOUNDRY_API_KEY="your-foundry-api-key"
ANTHROPIC_FOUNDRY_RESOURCE="your-foundry-resource-name"
接著建立代理人如下：
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：需要安裝
anthropic>=0.74.0。
代理功能
功能工具
為您的代理程式配備自訂功能：
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)
串流回應
在生成響應時獲取響應以獲得更好的用戶體驗：
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_stream(query):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
託管工具
Anthropic 代理程式支援託管工具，例如 Web 搜尋、MCP（模型上下文協定）和程式碼執行：
from agent_framework import HostedMCPTool, HostedWebSearchTool
async def hosted_tools_example():
agent = AnthropicClient().as_agent(
name="DocsAgent",
instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
tools=[
HostedMCPTool(
name="Microsoft Learn MCP",
url="https://learn.microsoft.com/api/mcp",
),
HostedWebSearchTool(),
],
max_tokens=20000,
)
result = await agent.run("Can you compare Python decorators with C# attributes?")
print(result.text)
延伸思考（推理）
Anthropic 透過該 thinking 功能支援擴展思考能力，該功能允許模型展示其推理過程：
from agent_framework import TextReasoningContent, UsageContent
from agent_framework.anthropic import AnthropicClient
async def thinking_example():
agent = AnthropicClient().as_agent(
name="DocsAgent",
instructions="You are a helpful agent.",
tools=[HostedWebSearchTool()],
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_stream(query):
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()
人類技能
Anthropic 提供可管理的技能，擴展客服人員的能力，例如製作 PowerPoint 簡報。 技能需要程式碼解譯工具才能運作：
from agent_framework import HostedCodeInterpreterTool, 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=HostedCodeInterpreterTool(),
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_stream(query):
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.")
使用代理程式
代理程式是標準 BaseAgent ，支援所有標準代理程式作業。
請參閱 代理程式入門教學課程 ，以取得如何執行代理程式並與代理程式互動的詳細資訊。