Google Gemini

Connect Integrations to
Google Gemini

Build advanced AI agents with Google Gemini. Connect 600+ integrations, automate workflows, and deploy with ease using Metorial.

Back to Google Gemini overview

Choosing the Right Gemini Model

Google offers several Gemini models, each optimized for different use cases. This guide helps you choose the right one for your Metorial-powered agent.

Available Models

Gemini 1.5 Pro

  • Model ID: gemini-1.5-pro-latest
  • Best for: Complex tasks, long context, function calling
  • Context window: Up to 2 million tokens
  • Recommended for: Most Metorial integrations
let response = await genAI.models.generateContent({
  model: 'gemini-1.5-pro-latest',
  contents: [/* ... */],
  config: { tools: session.tools }
});

Gemini 1.5 Flash

  • Model ID: gemini-1.5-flash-latest
  • Best for: Fast responses, high-volume applications
  • Context window: Up to 1 million tokens
  • Recommended for: Simple integration tasks, real-time applications
let response = await genAI.models.generateContent({
  model: 'gemini-1.5-flash-latest',
  contents: [/* ... */],
  config: { tools: session.tools }
});

Which Model Should You Use?

Use Gemini 1.5 Pro when:

  • You need reliable function calling with complex integrations
  • Your prompts require deep reasoning
  • Context length is important (large documents, long conversations)
  • Quality is more important than speed

Use Gemini 1.5 Flash when:

  • You need fast responses
  • You're handling high request volumes
  • Your integration tasks are straightforward
  • Cost efficiency is a priority

Testing Different Models

It's easy to test different models with Metorial:

const models = ['gemini-1.5-pro-latest', 'gemini-1.5-flash-latest'];

for (const model of models) {
  await metorial.withProviderSession(
    metorialGoogle,
    { serverDeployments: ['your-server-deployment-id'] },
    async session => {
      let response = await genAI.models.generateContent({
        model,
        contents: [{
          role: 'user',
          parts: [{ text: 'Test prompt' }]
        }],
        config: { tools: session.tools }
      });
      
      console.log(`Results from ${model}:`, response);
    }
  );
}

Recommendation

For most applications using Metorial integrations, start with Gemini 1.5 Pro. Its superior function calling capabilities work better with complex integration tasks. Switch to Flash only if you need faster responses and your integration tasks are simple.

Google Gemini on Metorial

Build exceptional AI applications with Google Gemini and Metorial's comprehensive integration platform. Connect Gemini's state-of-the-art multimodal AI models to over 600 integrations including Google Workspace, Slack, GitHub, Salesforce, and hundreds more through our MCP-powered SDKs. Metorial eliminates the complexity of building and maintaining integrations, allowing you to add powerful capabilities to your Gemini-based agents in just a couple of lines of Python or TypeScript code. Whether you're creating virtual assistants, data analysis tools, or intelligent workflow automation with Google's advanced AI, Metorial provides the integration infrastructure you need to ship faster. Our open-source platform handles authentication flows, API versioning, rate limiting, and error handling automatically, so you can focus on crafting intelligent behaviors and delightful user experiences. Stop reinventing the wheel for every integration—let Metorial manage the connections while you concentrate on building innovative AI solutions. With Metorial, your Gemini agents can seamlessly interact with the tools and platforms your users depend on daily.

Connect anything. Anywhere.

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About Metorial

Metorial provides developers with instant access to 600+ MCP servers for building AI agents that can interact with real-world tools and services. Built on MCP, Metorial simplifies agent tool integration by offering pre-configured connections to popular platforms like Google Drive, Slack, GitHub, Notion, and hundreds of other APIs. Our platform supports all major AI agent frameworks—including LangChain, AutoGen, CrewAI, and LangGraph—enabling developers to add tool calling capabilities to their agents in just a few lines of code. By eliminating the need for custom integration code, Metorial helps AI developers move from prototype to production faster while maintaining security and reliability. Whether you're building autonomous research agents, customer service bots, or workflow automation tools, Metorial's MCP server library provides the integrations you need to connect your agents to the real world.

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