Google Gemini CLI¶

Command-line interface for Google's Gemini AI models. Multimodal capabilities (text, images, code). Free tier with generous limits. Good for quick queries and prototyping. Not as integrated into development workflows as Claude Code or Copilot, but powerful for general-purpose AI tasks.

2026 Update

Gemini 2.0 Flash is default model (fast, multimodal). Gemini Ultra 2.0 available for complex tasks. CLI supports image uploads, code execution, function calling. Free tier: 60 queries/minute. Paid tier (Google AI Premium) for higher limits and Gemini Ultra access.

Quick Hits¶

Essential Commands Common Patterns Pro Tips & Gotchas

# Installation (Python)
pip install google-generativeai

# Or use official CLI wrapper
npm install -g @google/generative-ai-cli

# Authentication
export GOOGLE_API_KEY="your-api-key"  # Get from https://makersuite.google.com/app/apikey

# Or configure via CLI
gai config set api_key YOUR_API_KEY

# Basic usage (text generation)
gai "explain quantum computing in simple terms"

gai "write a Python function to validate email addresses"

# Multimodal (image + text)
gai "what's in this image?" --image photo.jpg

gai "generate alt text for accessibility" --image screenshot.png

# Code generation
gai "create a React component for user profile card with avatar and bio"

# Model selection
gai --model gemini-2.0-flash "fast query"       # Default, fastest
gai --model gemini-2.0-pro "complex analysis"   # Balanced
gai --model gemini-ultra-2.0 "hard problem"     # Most capable (requires paid tier)

# Streaming output (like ChatGPT)
gai --stream "write a long essay on AI ethics"

# JSON output mode
gai --json "list 5 programming languages with their use cases"

# Temperature control (creativity)
gai --temperature 0.1 "precise code generation"  # Low = deterministic
gai --temperature 0.9 "creative story ideas"     # High = creative

Real talk:

Free tier is generous (60 RPM, 1500 RPD)
API key required (get from Google AI Studio)
Multimodal support is killer feature (analyze images)
Not as context-aware as Claude Code (no file system access)
Good for one-off queries, not autonomous workflows

# Code generation workflows
gai "create FastAPI endpoint for user authentication with JWT"

gai "write SQL query to find top 10 customers by purchase value"

gai "generate Dockerfile for Node.js app with multi-stage build"

# Code review and analysis
cat src/api.js | gai "review this code for security issues"

gai "explain what this regex does: ^(?=.*[A-Z])(?=.*[0-9])(?=.*[!@#$]).{8,}$"

# Documentation generation
gai "generate JSDoc comments for this function" < utils/helpers.js

gai "create README for this project" --context "$(ls -R)"

# Data analysis
gai "analyze this CSV and suggest insights" --file data.csv

gai "convert this JSON to YAML" < config.json

# Image analysis (multimodal)
gai "describe this architecture diagram" --image diagram.png

gai "extract text from this screenshot (OCR)" --image screenshot.png

gai "what's wrong with this UI?" --image mockup.png

# Script generation
gai "bash script to backup PostgreSQL database to S3"

gai "Python script to scrape website and save to CSV"

# Testing
gai "generate unit tests for this function" < src/calculate.py

gai "create test data for user registration form (JSON)"

# Debugging
gai "why does this code throw TypeError?" --code "$(cat app.js)"

gai "suggest fix for this error: $(npm run build 2>&1)"

Why this works:

Gemini 2.0 Flash is very fast (sub-second responses)
Multimodal support enables image analysis workflows
Free tier sufficient for most personal use
Good at code generation and explanation
JSON mode useful for scripting and automation

Best Use Cases

Quick code snippets - Faster than searching Stack Overflow
Image analysis - Describe diagrams, extract text (OCR), analyze UI
Data transformation - JSON to YAML, CSV analysis, format conversion
Learning - Explain complex concepts, code review, debugging help
Prototyping - Generate boilerplate code quickly
Content generation - Documentation, commit messages, test data

Limitations

No file system access - Can't read/write files autonomously like Claude Code
No bash execution - Can't run commands or test generated code
Limited context - Must provide all context in prompt (no codebase awareness)
Not workflow-integrated - No git integration, no project understanding
Stateless - Each query is independent (no conversation memory)

Optimization Techniques

Use piping - cat file.js | gai "review this" for file input
JSON mode - Structured output for parsing in scripts
Temperature tuning - Lower for code (0.1-0.3), higher for ideas (0.7-0.9)
Model selection - Flash for speed, Pro for accuracy, Ultra for hard problems
Streaming - Use --stream for long responses (better UX)
Caching prompts - Save common prompts as shell aliases/functions

Common Gotchas

Rate limits - Free tier: 60 RPM (requests per minute), 1500 RPD (per day)
API key security - Don't commit to git, use environment variable
Hallucinations - Verify code/facts, especially for critical tasks
Token limits - Very long inputs may be truncated
No vision in free tier - Image analysis requires API key upgrade (check latest pricing)
Context window - 1M tokens for Gemini 2.0, but practical limit lower

Gemini vs Claude Code vs Copilot CLI

Use Gemini for:
- Multimodal tasks (image analysis, OCR)
- Quick one-off queries
- Data transformation and analysis
- Learning and explanation
- Budget-conscious usage (free tier is generous)
Use Claude Code for:
- Autonomous coding workflows
- Multi-file refactoring
- Codebase-aware development
- Git workflows and commits
Use Copilot CLI for:
- Shell command discovery
- Git operations
- GitHub integration
- Terminal-focused workflows

Practical Examples¶

Shell Integration¶

# ~/.zshrc or ~/.bashrc

# Quick Gemini query
function ask() {
    gai "$*"
}

# Code review from clipboard
function review-clip() {
    pbpaste | gai "review this code for bugs and improvements"
}

# Image description
function describe-image() {
    gai "describe this image in detail" --image "$1"
}

# Generate commit message from diff
function ai-commit() {
    local diff=$(git diff --staged)
    local message=$(gai "generate concise git commit message for: $diff")
    echo "$message"
    read -p "Use this message? (y/n) " -n 1 -r
    [[ $REPLY =~ ^[Yy]$ ]] && git commit -m "$message"
}

# Explain command
function explain-cmd() {
    gai "explain this shell command: $*"
}

Python Integration¶

# Use Gemini API directly in Python scripts
import google.generativeai as genai
import os

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

# Text generation
model = genai.GenerativeModel("gemini-2.0-flash")
response = model.generate_content("Explain async/await in JavaScript")
print(response.text)

# Multimodal (image + text)
import PIL.Image
img = PIL.Image.open("diagram.png")
response = model.generate_content(["Explain this architecture diagram", img])
print(response.text)

# Streaming
response = model.generate_content("Write a long essay", stream=True)
for chunk in response:
    print(chunk.text, end="")

# Function calling (structured output)
tools = [{
    "function_declarations": [{
        "name": "get_weather",
        "description": "Get weather for location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            }
        }
    }]
}]

model = genai.GenerativeModel("gemini-2.0-flash", tools=tools)
response = model.generate_content("What's the weather in London?")

Automation Scripts¶

#!/bin/bash
# auto-document.sh - Generate documentation for all functions in a file

for file in src/**/*.js; do
    echo "Documenting $file..."
    gai "add JSDoc comments to all functions" < "$file" > "$file.documented"
    diff "$file" "$file.documented"
    read -p "Apply changes? (y/n) " -n 1 -r
    [[ $REPLY =~ ^[Yy]$ ]] && mv "$file.documented" "$file"
done

Learning Resources¶

Official Resources¶

Google AI Studio - Web interface and API key management
Gemini API Documentation - Official API docs
Gemini Quickstart - Python quickstart guide
Gemini Models - Model capabilities and pricing

Example Use Cases¶

Development

Code generation - "Create Express middleware for rate limiting"
Refactoring - "Convert this class to functional component"
Testing - "Generate edge case tests for password validation"
Documentation - "Write API docs for these endpoints"

Data & Analysis

CSV analysis - "Summarize trends in this sales data"
JSON transformation - "Convert this OpenAPI spec to Postman collection"
Regex - "Create regex to validate phone numbers (international)"
SQL - "Write query to find duplicate records"

Multimodal Tasks

OCR - "Extract text from this receipt"
Diagram analysis - "Explain this system architecture diagram"
UI feedback - "Suggest improvements to this landing page design"
Accessibility - "Generate alt text for these product images"

Worth Checking¶

Official Docs

Google AI Studio

Gemini API Docs

Python SDK

Gemini Models
Tools & SDKs

Python Client

Node.js SDK

REST API
Community

Google AI Community

r/googlecloud

Stack Overflow [gemini-api]
Learning

Prompt Engineering

Code Examples

Gemini Cookbook

Last Updated: 2026-02-02 | Vibe Check: Solid Alternative - Gemini CLI is good for quick queries and multimodal tasks. Free tier is generous. Not as workflow-integrated as Claude Code or Copilot, but powerful for general-purpose AI. Best for one-off tasks, image analysis, and learning. Tags: gemini, google, ai, cli, multimodal