Google Gemini AI SDK

Overview

Google's official AI SDK provides first-class support for building AI agents with Gemini models. It's designed to help developers leverage Google's most capable AI models—including Gemini 3.1 Pro, Flash, and Nano—across multiple programming languages.

The SDK includes powerful agent-building capabilities such as the Deep Research Agent, which provides collaborative planning, visualization, and MCP support for complex research tasks. It also features Computer Use for GUI automation, multimodal processing, and the Live API for real-time voice agents.

Available in Python, JavaScript, Go, Java, and C#, the Google Gemini AI SDK enables rapid integration of Google's AI capabilities into any application.

Features

• Deep Research Agent: Advanced research capabilities with collaborative planning and visualization
• MCP Server Support: Native integration with Model Context Protocol for tool discovery
• Computer Use: GUI automation and desktop interaction capabilities
• Multimodal Processing: Handle text, images, audio, and video in a single model
• Long-Context Processing: Up to 2M token context windows for complex reasoning
• Live API: Real-time, low-latency voice and video interactions
• Multi-Language Support: Python, JavaScript, Go, Java, C#, and REST APIs
• Gemini Model Family: Access to Gemini 3.1 Pro, Flash, and Nano models

Installation

Python

pip install google-genai

JavaScript/TypeScript

npm install @google/genai

Go

go get google.golang.org/genai

Java

<dependency>
  <groupId>com.google.cloud</groupId>
  <artifactId>google-cloud-aiplatform</artifactId>
</dependency>

C#

dotnet add package Google.Cloud.AIPlatform.V1

Quick Start

Basic Text Generation

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="Explain quantum entanglement in simple terms."
)
print(response.text)

Tool Calling

from google import genai
from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is sunny, 25°C"

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="What's the weather like in Paris?",
    config=types.GenerateContentConfig(
        tools=[get_weather],
    )
)
print(response.text)

Deep Research Agent

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

research_result = client.agents.deep_research(
    prompt="Research the latest developments in AI agents and their applications in enterprise software.",
    config=types.DeepResearchConfig(
        max_iterations=10,
        include_visualizations=True,
        mcp_servers=["brave-search", "notion"],
    )
)
print(research_result.report)

Core Concepts

Gemini Models

The SDK provides access to the Gemini model family:

Model	Best For	Context Window
Gemini 3.1 Pro	Complex reasoning, coding, analysis	2M tokens
Gemini 3.1 Flash	High-volume, low-latency tasks	1M tokens
Gemini 3.1 Nano	Edge devices, mobile apps	128K tokens

Agents

The SDK includes several agent types:

• Deep Research Agent: For complex research tasks with multi-step reasoning
• Chat Agent: For conversational applications with memory
• Code Agent: For code generation, review, and debugging

MCP Integration

The SDK supports MCP (Model Context Protocol) servers for tool integration:

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents="Analyze my codebase and suggest improvements.",
    config=types.GenerateContentConfig(
        mcp_servers=[
            types.MCPServerConfig(name="github", config={"token": "ghp_xxx"}),
            types.MCPServerConfig(name="filesystem", config={"path": "/path/to/code"}),
        ],
    )
)

Advanced Features

Computer Use

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents=[
        types.Part.from_image(image="screenshot.png"),
        "Click the login button and fill in the form with username 'test' and password 'password123'.",
    ],
    config=types.GenerateContentConfig(
        tools=[types.Tool(computer_use=types.ComputerUse())],
    )
)

Live API for Voice Agents

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

with client.live.connect(model="gemini-3.1-flash") as session:
    session.config.set(system_instruction="You are a helpful customer support agent.")
    
    for response in session.start_stream():
        if response.text:
            print(response.text)

Long-Context Processing

# Process a 100-page document
with open("large_document.pdf", "rb") as f:
    document = f.read()

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents=[
        types.Part.from_bytes(document, mime_type="application/pdf"),
        "Summarize the key findings from this research paper.",
    ],
)

Examples

Enterprise Research Assistant

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

# Deep research with MCP tools
research = client.agents.deep_research(
    prompt="Analyze the competitive landscape for AI agent frameworks in 2026.",
    config=types.DeepResearchConfig(
        max_iterations=15,
        mcp_servers=["brave-search", "arxiv", "github"],
        include_visualizations=True,
        output_format="markdown",
    )
)

# Generate a report
report = f"""
# Competitive Analysis: AI Agent Frameworks

{research.report}

## Sources
{research.sources}
"""

Multi-Modal Data Analysis

from google.genai import types

client = genai.Client(api_key="YOUR_API_KEY")

response = client.models.generate_content(
    model="gemini-3.1-pro",
    contents=[
        types.Part.from_image("chart.png"),
        types.Part.from_bytes(csv_data, mime_type="text/csv"),
        "Analyze this chart and CSV data. Identify trends and anomalies.",
    ],
)

Real-Time Voice Assistant

from google.genai import types
import pyaudio

client = genai.Client(api_key="YOUR_API_KEY")

def audio_generator():
    while True:
        chunk = stream.read(1024)
        yield types.LiveClientRealtimeInput(data=chunk)

with client.live.connect(model="gemini-3.1-flash") as session:
    session.start_stream(audio_generator())
    for response in session:
        if response.text:
            speak(response.text)

Pros

• ✅ Official Google Support: Backed by Google with guaranteed compatibility
• ✅ Deep Research Agent: Powerful research capabilities out of the box
• ✅ Multimodal Native: Text, image, audio, video in a single model
• ✅ Long Context: Up to 2M tokens for complex reasoning
• ✅ Multi-Language: Python, JavaScript, Go, Java, C# support
• ✅ MCP Integration: Native support for Model Context Protocol
• ✅ Live API: Real-time voice and video interactions
• ✅ Computer Use: GUI automation capabilities

Cons

• ❌ Google Ecosystem Lock-in: Tied to Google Cloud and Gemini models
• ❌ Some Features in Preview: Deep Research and Computer Use still evolving
• ❌ Pricing: Can be expensive for heavy usage
• ❌ Smaller Community: Less community support than OpenAI/LangChain
• ❌ Complex Setup: Some features require Google Cloud project configuration

Use Cases

Use Case	Why Google Gemini AI SDK
Deep Research	Multi-step research with collaborative planning
Multimodal Apps	Combine text, image, audio, video seamlessly
Long-document Analysis	Process 2M token context windows
Real-time Voice	Live API for voice/video interactions
Enterprise AI	Google Cloud integration and support

Comparison with Alternatives

Feature	Google Gemini AI SDK	OpenAI Agents SDK	Anthropic SDK	Vercel AI SDK
Model Flexibility	Gemini only	OpenAI only	Claude only	Multi-provider
Multimodal Native	✅ Excellent	⚠️ Via tools	⚠️ Via tools	⚠️ Via providers
Long Context	✅ 2M tokens	⚠️ 128K max	⚠️ 200K max	⚠️ Varies
Deep Research	✅ Built-in	❌ No	❌ No	❌ No
Multi-Language	✅ 5 languages	✅ 2 languages	✅ 2 languages	⚠️ TS only
Learning Curve	Medium	Low	Low	Low
Best for	Gemini users	OpenAI users	Claude users	TS/Next.js

Best Practices

1. Use Deep Research for complex queries — Leverage built-in research capabilities
2. Leverage MCP for tool integration — Native MCP support for extensibility
3. Optimize context usage — 2M tokens is powerful but costly
4. Use Live API for real-time apps — Low-latency voice/video interactions
5. Test multimodal inputs — Verify image/audio/video handling
6. Monitor costs — Long context and multimodal can be expensive

Troubleshooting

Issue	Solution
Model not found	Verify model name matches Gemini model list
Tool calling fails	Check tool function signatures and descriptions
Deep Research timeout	Increase max_iterations or reduce scope
Live API latency	Use Flash model for lower latency
Multimodal errors	Verify image/audio format and size limits

Resources

• Official Documentation
• GitHub Repository
• Gemini Models Overview
• Deep Research Guide
• MCP Integration