Google Gemini

Google Gemini kan stödja en Agent Framework-agent via GEmini Developer API eller Vertex AI. Den providerspecifika klienten hanterar autentiserings- och Gemini-begärandealternativ medan Agent Framework äger agentdefinitionen och orkestreringen.

Important

Google Gemini och Vertex AI är tredjepartssystem. Granska tjänstvillkor, datahantering, regionala gränser, modellåtkomst och användningskostnader innan du skickar programdata.

Installera en Gemini IChatClient

Det .NET exemplet visar den officiella Google GenAI-klienten och communityimplementeringenMscc.GenerativeAI.Microsoft.

dotnet add package Google.GenAI
dotnet add package Mscc.GenerativeAI.Microsoft
dotnet add package Microsoft.Agents.AI --prerelease

Configuration

GOOGLE_GENAI_API_KEY="<google-ai-studio-api-key>"
GOOGLE_GENAI_MODEL="gemini-2.5-flash"

const string JokerInstructions = "You are good at telling jokes.";
const string JokerName = "JokerAgent";

string apiKey = Environment.GetEnvironmentVariable("GOOGLE_GENAI_API_KEY") ?? throw new InvalidOperationException("Please set the GOOGLE_GENAI_API_KEY environment variable.");
string model = Environment.GetEnvironmentVariable("GOOGLE_GENAI_MODEL") ?? "gemini-2.5-flash";

// Using a Google GenAI IChatClient implementation

ChatClientAgent agentGenAI = new(
    new Client(vertexAI: false, apiKey: apiKey).AsIChatClient(model),
    name: JokerName,
    instructions: JokerInstructions);

AgentResponse response = await agentGenAI.RunAsync("Tell me a joke about a pirate.");
Console.WriteLine($"Google GenAI client based agent response:\n{response}");

// Using a community driven Mscc.GenerativeAI.Microsoft package

ChatClientAgent agentCommunity = new(
    new GeminiChatClient(apiKey: apiKey, model: model),
    name: JokerName,
    instructions: JokerInstructions);

response = await agentCommunity.RunAsync("Tell me a joke about a pirate.");
Console.WriteLine($"Community client based agent response:\n{response}");

Välj en IChatClient implementering och konfigurera dess GEMINI Developer API eller Vertex AI-autentisering.

Installera paketet

pip install agent-framework-gemini --pre

Configuration

Använd antingen API:et Gemini Developer:

GEMINI_API_KEY="<api-key>"
GEMINI_MODEL="gemini-2.5-flash"
# GOOGLE_API_KEY and GOOGLE_MODEL are also supported.

Eller konfigurera Hörn-AI:

GOOGLE_GENAI_USE_VERTEXAI="true"
GOOGLE_CLOUD_PROJECT="<project-id>"
GOOGLE_CLOUD_LOCATION="us-central1"
GOOGLE_MODEL="gemini-2.5-flash"

GeminiChatClient har stöd för strömning, funktionsverktyg, strukturerade utdata, utökat tänkande och verktyg som hanteras av leverantören.

async def non_streaming_example() -> None:
    """Runs the agent and waits for the complete response before printing it."""
    print("=== Non-streaming ===")

    # 1. Create the agent with the Gemini chat client and local weather tool.
    agent = Agent(
        client=GeminiChatClient(),
        name="WeatherAgent",
        instructions="You are a helpful weather agent.",
        tools=[get_weather],
    )

    # 2. Ask the agent for a single weather lookup and print the final response.
    query = "What's the weather like in Karlsruhe, Germany?"
    print(f"User: {query}")
    result = await agent.run(query)
    print(f"Result: {result}\n")


async def streaming_example() -> None:
    """Runs the agent and prints each chunk as it is received."""
    print("=== Streaming ===")

    # 1. Create the same agent configuration for a streaming tool-call example.
    agent = Agent(
        client=GeminiChatClient(),
        name="WeatherAgent",
        instructions="You are a helpful weather agent.",
        tools=[get_weather],
    )

    # 2. Ask a multi-location question and stream the model output as it arrives.
    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")

Paketet innehåller fabriker för Grundkörning i Google Search, Grundkörning av Google Maps, kodkörning, filsökning och MCP.

Google Search-grund

import asyncio

from agent_framework import Agent
from agent_framework.gemini import GeminiChatClient
from dotenv import load_dotenv

load_dotenv()


async def main() -> None:
    """Run the Google Search grounding example."""
    print("=== Google Search grounding ===")

    # 1. Create the agent with Gemini and the built-in Google Search grounding tool.
    agent = Agent(
        client=GeminiChatClient(),
        name="SearchAgent",
        instructions="You are a helpful assistant. Use Google Search to provide accurate, up-to-date answers.",
        tools=[GeminiChatClient.get_web_search_tool()],
    )

    # 2. Ask a current-events style question and stream the grounded answer.
    query = "What is the latest stable release of the .NET SDK?"
    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")


if __name__ == "__main__":
    asyncio.run(main())

Go SDK tillhandahåller geminiprovider gemini-slutsatsdragning. Skapa en standard *agent.Agent via den providerspecifika konstruktorn.

Se geminiproviderpaketet och exemplen.

Tools

Verktyg	C#	Python	Go	Notes
Funktionsverktyg	✅	✅	✅	Standardmodellfunktionsanrop.
Godkännande av verktyg	✅	✅	✅	Används av ramverksverktygets loop.
Kodtolkare	❌	✅	❌	GeminiChatClient.get_code_interpreter_tool().
Filsökning	❌	✅	❌	GeminiChatClient.get_file_search_tool().
Webbsökning	❌	✅	❌	Google Search grundstötning genom get_web_search_tool().
Grundläggning av Google Maps	❌	✅	❌	GeminiChatClient.get_maps_grounding_tool().
Värdhanterade MCP-verktyg	❌	✅	❌	GeminiChatClient.get_mcp_tool().
Lokala MCP-verktyg	✅	✅	✅	Körs i programprocessen.

Nästa steg

ONNX