Google Gemini

Google Gemini can back an Agent Framework agent through the Gemini Developer API or Vertex AI. The provider-specific client handles authentication and Gemini request options while Agent Framework owns the agent definition and orchestration.

Important

Google Gemini and Vertex AI are third-party systems. Review service terms, data handling, regional boundaries, model access, and usage costs before sending application data.

Install a Gemini IChatClient

The .NET sample demonstrates the official Google GenAI client and the community Mscc.GenerativeAI.Microsoft implementation.

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}");

Choose one IChatClient implementation and configure its Gemini Developer API or Vertex AI authentication.

Install the package

pip install agent-framework-gemini --pre

Configuration

Use either the Gemini Developer API:

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

Or configure Vertex AI:

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

GeminiChatClient supports streaming, function tools, structured output, extended thinking, and provider-hosted tools.

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")

The package includes factories for Google Search grounding, Google Maps grounding, code execution, file search, and MCP.

Google Search grounding

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())

The Go SDK provides geminiprovider for Gemini inference. Create a standard *agent.Agent through the provider-specific constructor.

See the Gemini provider package and examples.

Tools

Tool	C#	Python	Go	Notes
Function Tools	✅	✅	✅	Standard model function calling.
Tool Approval	✅	✅	✅	Applied by the framework tool loop.
Code Interpreter	❌	✅	❌	GeminiChatClient.get_code_interpreter_tool().
File Search	❌	✅	❌	GeminiChatClient.get_file_search_tool().
Web Search	❌	✅	❌	Google Search grounding through get_web_search_tool().
Google Maps grounding	❌	✅	❌	GeminiChatClient.get_maps_grounding_tool().
Hosted MCP Tools	❌	✅	❌	GeminiChatClient.get_mcp_tool().
Local MCP Tools	✅	✅	✅	Runs in the application process.

Next steps

ONNX