Krok 1. Pierwszy agent

dotnet add package Azure.AI.Projects --prerelease
dotnet add package Azure.Identity
dotnet add package Microsoft.Agents.AI.Foundry --prerelease

Utwórz agenta:

using System;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;

var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
    ?? throw new InvalidOperationException("Set AZURE_OPENAI_ENDPOINT");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
    .AsAIAgent(
        model: deploymentName,
        instructions: "You are a friendly assistant. Keep your answers brief.",
        name: "HelloAgent");

Uruchom go:

Console.WriteLine(await agent.RunAsync("What is the largest city in France?"));

Możesz też przesłać strumieniowo odpowiedź:

await foreach (var update in agent.RunStreamingAsync("Tell me a one-sentence fun fact."))
{
    Console.Write(update);
}

pip install agent-framework

Tworzenie i uruchamianie agenta:

client = FoundryChatClient(
    project_endpoint="https://your-project.services.ai.azure.com",
    model="gpt-4o",
    credential=AzureCliCredential(),
)

agent = Agent(
    client=client,
    name="HelloAgent",
    instructions="You are a friendly assistant. Keep your answers brief.",
)

# Non-streaming: get the complete response at once
result = await agent.run("What is the capital of France?")
print(f"Agent: {result}")

Możesz też przesłać strumieniowo odpowiedź:

# Streaming: receive tokens as they are generated
print("Agent (streaming): ", end="", flush=True)
async for chunk in agent.run("Tell me a one-sentence fun fact.", stream=True):
    if chunk.text:
        print(chunk.text, end="", flush=True)
print()

from dotenv import load_dotenv
load_dotenv()