Agen Antropik

Microsoft Agent Framework mendukung pembuatan agen yang menggunakan model Claude Anthropic.

Memulai Langkah Awal

Tambahkan paket NuGet yang diperlukan ke proyek Anda.

dotnet add package Microsoft.Agents.AI.Anthropic --prerelease

Jika Anda menggunakan Azure Foundry, tambahkan juga:

dotnet add package Anthropic.Foundry --prerelease
dotnet add package Azure.Identity

Konfigurasi

Variabel Lingkungan

Siapkan variabel lingkungan yang diperlukan untuk autentikasi Antropik:

# 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

Anda bisa mendapatkan kunci API dari Konsol Antropik.

Untuk Azure Foundry dengan Kunci API

$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"

Untuk Azure Foundry dengan Azure CLI

$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Subdomain before .services.ai.azure.com
$env:ANTHROPIC_DEPLOYMENT_NAME="claude-haiku-4-5"

Nota

Saat menggunakan Azure Foundry dengan Azure CLI, pastikan Anda sudah masuk dengan az login dan memiliki akses ke sumber daya Azure Foundry. Untuk informasi selengkapnya, lihat dokumentasi Azure CLI.

Membuat Agen Antropik

Pembuatan Agen Dasar (API Publik Antropis)

Cara paling sederhana untuk membuat agen Antropis menggunakan API publik:

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?"));

Menggunakan Anthropic di Azure Foundry dengan KUNCI API

Setelah menyiapkan Anthropic di Azure Foundry, Anda dapat menggunakannya dengan autentikasi kunci API:

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?"));

Menggunakan Anthropic di Azure Foundry dengan Azure Credentials (contoh Kredensial Azure Cli)

Untuk lingkungan di mana Azure Credentials lebih disukai:

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);
    }
}

Peringatan

DefaultAzureCredential nyaman untuk pengembangan tetapi membutuhkan pertimbangan yang cermat dalam produksi. Dalam produksi, pertimbangkan untuk menggunakan kredensial tertentu (misalnya, ManagedIdentityCredential) untuk menghindari masalah latensi, pemeriksaan kredensial yang tidak diinginkan, dan potensi risiko keamanan dari mekanisme fallback.

Petunjuk / Saran

Lihat sampel .NET untuk contoh lengkap yang dapat dijalankan.

Menggunakan Agen

Agen ini adalah AIAgent standar dan mendukung semua operasi agen standar.

Lihat Tutorial pemula Agent untuk informasi lebih lanjut tentang cara menjalankan dan berinteraksi dengan para agen.

Prasyarat

Instal paket Microsoft Agent Framework Anthropic.

pip install agent-framework-anthropic --pre

Konfigurasi

Variabel Lingkungan

Siapkan variabel lingkungan yang diperlukan untuk autentikasi Antropik:

# 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

Atau, Anda dapat menggunakan .env file di akar proyek Anda:

ANTHROPIC_API_KEY=your-anthropic-api-key
ANTHROPIC_CHAT_MODEL_ID=claude-sonnet-4-5-20250929

Anda bisa mendapatkan kunci API dari Konsol Antropik.

Memulai Langkah Awal

Impor kelas yang diperlukan dari Kerangka Kerja Agen:

import asyncio
from agent_framework.anthropic import AnthropicClient

Membuat Agen Antropik

Pembuatan Agen Dasar

Cara paling sederhana untuk membuat agen Antropis:

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)

Menggunakan Konfigurasi Eksplisit

Anda dapat menyediakan konfigurasi eksplisit alih-alih mengandalkan variabel lingkungan:

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)

Menggunakan Anthropic di Foundry

Setelah Anda menyiapkan Anthropic di Foundry, pastikan Anda memiliki variabel lingkungan berikut yang ditetapkan:

ANTHROPIC_FOUNDRY_API_KEY="your-foundry-api-key"
ANTHROPIC_FOUNDRY_RESOURCE="your-foundry-resource-name"

Kemudian buat agen sebagai berikut:

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)

Catatan: Ini memerlukan anthropic>=0.74.0 diinstal.

Fitur Agen

Perangkat Fungsional

Lengkapi agen Anda dengan fungsi kustom:

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)

Respons yang Mengalir

Dapatkan respons saat dihasilkan untuk pengalaman pengguna yang lebih baik:

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()

Alat yang Dihosting

Agen antropis mendukung alat yang dihosting seperti pencarian web, MCP (Protokol Konteks Model), dan eksekusi kode:

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)

Pemikiran yang Diperluas (Penalaran)

Antropis mendukung kemampuan berpikir yang diperluas melalui thinking fitur , yang memungkinkan model untuk menunjukkan proses penalarannya:

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()

Keterampilan Antropik

Antropis menyediakan keterampilan terkelola yang memperluas kemampuan agen, seperti membuat presentasi PowerPoint. Keterampilan mengharuskan alat Penerjemah Kode berfungsi:

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.")

Contoh lengkap

# 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())

Menggunakan Agen

Agen ini adalah Agent standar dan mendukung semua operasi agen standar.

Lihat Tutorial pemula Agent untuk informasi lebih lanjut tentang cara menjalankan dan berinteraksi dengan para agen.

Langkah selanjutnya

Agen Azure AI