---
name: gemini-api-integration-3043f8324fb7
description: "Use when integrating Google Gemini API into projects. Covers model selection, multimodal inputs, streaming, function calling, and production best practices."
risk: safe
source: community
date_added: "2026-03-04"
---

# Gemini API Integration

## Overview

This skill guides AI agents through integrating Google Gemini API into applications — from basic text generation to advanced multimodal, function calling, and streaming use cases. It covers the full Gemini SDK lifecycle with production-grade patterns.

## When to Use This Skill

- Use when setting up Gemini API for the first time in a Node.js, Python, or browser project
- Use when implementing multimodal inputs (text + image/audio/video)
- Use when adding streaming responses to improve perceived latency
- Use when implementing function calling / tool use with Gemini
- Use when optimizing model selection (Flash vs Pro vs Ultra) for cost and performance
- Use when debugging Gemini API errors, rate limits, or quota issues

## Step-by-Step Guide

### 1. Installation & Setup

**Node.js / TypeScript:**
```bash
npm install @google/generative-ai
```

**Python:**
```bash
pip install google-generativeai
```

Set your API key securely:
```bash
export GEMINI_API_KEY="your-api-key-here"
```

### 2. Basic Text Generation

**Node.js:**
```javascript
import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });

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

**Python:**
```python
import google.generativeai as genai
import os

genai.configure(api_key=os.environ["GEMINI_API_KEY"])
model = genai.GenerativeModel("gemini-1.5-flash")

response = model.generate_content("Explain async/await in JavaScript")
print(response.text)
```

### 3. Streaming Responses

```javascript
const result = await model.generateContentStream("Write a detailed blog post about AI");

for await (const chunk of result.stream) {
  process.stdout.write(chunk.text());
}
```

### 4. Multimodal Input (Text + Image)

```javascript
import fs from "fs";

const imageData = fs.readFileSync("screenshot.png");
const imagePart = {
  inlineData: {
    data: imageData.toString("base64"),
    mimeType: "image/png",
  },
};

const result = await model.generateContent(["Describe this image:", imagePart]);
console.log(result.response.text());
```

### 5. Function Calling / Tool Use

```javascript
const tools = [{
  functionDeclarations: [{
    name: "get_weather",
    description: "Get current weather for a city",
    parameters: {
      type: "OBJECT",
      properties: {
        city: { type: "STRING", description: "City name" },
      },
      required: ["city"],
    },
  }],
}];

const model = genAI.getGenerativeModel({ model: "gemini-1.5-pro", tools });
const result = await model.generateContent("What's the weather in Mumbai?");

const call = result.response.functionCalls()?.[0];
if (call) {
  // Execute the actual function
  const weatherData = await getWeather(call.args.city);
  // Send result back to model
}
```

### 6. Multi-turn Chat

```javascript
const chat = model.startChat({
  history: [
    { role: "user", parts: [{ text: "You are a helpful coding assistant." }] },
    { role: "model", parts: [{ text: "Sure! I'm ready to help with code." }] },
  ],
});

const response = await chat.sendMessage("How do I reverse a string in Python?");
console.log(response.response.text());
```

### 7. Model Selection Guide

| Model | Best For | Speed | Cost |
|-------|----------|-------|------|
| `gemini-1.5-flash` | High-throughput, cost-sensitive tasks | Fast | Low |
| `gemini-1.5-pro` | Complex reasoning, long context | Medium | Medium |
| `gemini-2.0-flash` | Latest fast model, multimodal | Very Fast | Low |
| `gemini-2.0-pro` | Most capable, advanced tasks | Slow | High |

## Best Practices

- ✅ **Do:** Use `gemini-1.5-flash` for most tasks — it's fast and cost-effective
- ✅ **Do:** Always stream responses for user-facing chat UIs to reduce perceived latency
- ✅ **Do:** Store API keys in environment variables, never hard-code them
- ✅ **Do:** Implement exponential backoff for rate limit (429) errors
- ✅ **Do:** Use `systemInstruction` to set persistent model behavior
- ❌ **Don't:** Use `gemini-pro` for simple tasks — Flash is cheaper and faster
- ❌ **Don't:** Send large base64 images inline for files > 20MB — use File API instead
- ❌ **Don't:** Ignore safety ratings in responses for production apps

## Error Handling

```javascript
try {
  const result = await model.generateContent(prompt);
  return result.response.text();
} catch (error) {
  if (error.status === 429) {
    // Rate limited — wait and retry with exponential backoff
    await new Promise(r => setTimeout(r, 2 ** retryCount * 1000));
  } else if (error.status === 400) {
    // Invalid request — check prompt or parameters
    console.error("Invalid request:", error.message);
  } else {
    throw error;
  }
}
```

## Troubleshooting

**Problem:** `API_KEY_INVALID` error
**Solution:** Ensure `GEMINI_API_KEY` environment variable is set and the key is active in Google AI Studio.

**Problem:** Response blocked by safety filters
**Solution:** Check `result.response.promptFeedback.blockReason` and adjust your prompt or safety settings.

**Problem:** Slow response times
**Solution:** Switch to `gemini-1.5-flash` and enable streaming. Consider caching repeated prompts.

**Problem:** `RESOURCE_EXHAUSTED` (quota exceeded)
**Solution:** Check your quota in Google Cloud Console. Implement request queuing and exponential backoff.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.


---
Source attribution: TJSNDHU
Original project: https://github.com/TJSNDHU/Aurem/blob/b58fa793bd971fa2139306d18e8b9e5e91854626/.agent/skills/plugins/antigravity-awesome-skills-claude/skills/gemini-api-integration/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Antigravity User

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: TJSNDHU
Original project: https://github.com/TJSNDHU/Aurem/blob/b58fa793bd971fa2139306d18e8b9e5e91854626/.agent/skills/plugins/antigravity-awesome-skills-claude/skills/gemini-api-integration/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering, Data & Analytics
Roles: ML / AI Engineer, Backend Developer, Data Scientist
Specialties: API, Backend
Tools: Google Gemini
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: 3d31f34359dfa43e6ab7b6520f50c72583e8f8204a7a66be90a5809ee66df572