gemini-api

Gemini API

Build applications with the Google Gemini API and SDKs.

When to Use

• Building applications that call the Gemini API
• Code imports google.generativeai (Python) or @google/generative-ai (TypeScript)
• User asks about Gemini API patterns, function calling, streaming, or vision
• Implementing agent workflows with the Gemini API
• Optimizing API costs, token usage, or latency

Model Selection

Model	ID	Best For
Pro 2.5	gemini-2.5-pro	Complex reasoning, architecture, research
Flash 2.5	gemini-2.5-flash	Balanced coding, most development tasks
Flash Lite 2.5	gemini-2.5-flash-lite	Fast responses, high-volume, cost-sensitive

Default to Flash 2.5 unless the task requires deep reasoning (Pro) or speed/cost optimization (Flash Lite). For production, prefer pinned snapshot IDs over aliases.

Python SDK

Installation

pip install google-generativeai

Basic Message

import os
import google.generativeai as genai

genai.configure(api_key=os.environ["GEMINI_API_KEY"])

model = genai.GenerativeModel("gemini-2.5-flash")
response = model.generate_content("Explain async/await in Python")
print(response.text)

Streaming

model = genai.GenerativeModel("gemini-2.5-flash")
response = model.generate_content(
    "Write a haiku about coding",
    stream=True,
)
for chunk in response:
    print(chunk.text, end="", flush=True)

System Instruction

model = genai.GenerativeModel(
    "gemini-2.5-flash",
    system_instruction="You are a senior Python developer. Be concise.",
)
response = model.generate_content("Review this function")

TypeScript SDK

Installation

npm install @google/generative-ai

Basic Message

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY!);
const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" });

const result = await model.generateContent("Explain async/await in TypeScript");
console.log(result.response.text());