Why Antigravity and Cursor Are Better Together
AI development tools have been advancing quickly, but using Antigravity (Google's AI coding agent) alongside Cursor (an AI-first IDE) creates something more powerful than either can offer alone.
Their strengths are genuinely complementary:
What Cursor brings:
- Real-time inline code completion
- Context-aware multi-file editing
- Codebase-wide Q&A with
@codebase - Code generation accuracy that rivals and often surpasses GitHub Copilot
What Antigravity brings:
- Autonomous agent execution — browser control, terminal operations
- Automated handling of complex, multi-step tasks
- Fast inference backed by Google's infrastructure
- Holistic understanding and transformation of large codebases
Combine the two, and you get a genuine AI-first development environment where AI actively participates in every phase of the software lifecycle — writing, reviewing, refactoring, and testing.
Environment Setup: Getting the Most from Both Tools
Optimizing Cursor's Settings
Open Cursor's settings (Cmd + ,) and tune these options:
Model selection: For the best results when pairing with Antigravity:
Inline completion: claude-3-5-sonnet (balanced speed and accuracy)
Agent mode: gemini-2.0-flash (excels at long-context tasks)
Q&A: gpt-4o (broad knowledge base)
Setting up .cursorrules: Drop this file at your project root to give the AI project-specific context.
# .cursorrules
You are an expert in TypeScript and React.
## Coding conventions
- Use functional components with React Hooks
- Never use `any` — define proper types for everything
- Prefer the Result type pattern for error handling
- Write comments in clear English
## Project structure
- Components: src/components/
- Custom hooks: src/hooks/
- Type definitions: src/types/
## Testing
- Use Vitest + Testing Library
- Maintain at least 80% coverage
Connecting Antigravity via MCP
Add Antigravity to Cursor's MCP config at .cursor/mcp.json:
{
"mcpServers": {
"antigravity": {
"command": "npx",
"args": ["-y", "@antigravity/mcp-server"],
"env": {
"ANTIGRAVITY_API_KEY": "YOUR_ANTIGRAVITY_API_KEY"
}
}
}
}Once this is in place, Cursor's agent mode can invoke Antigravity's full tool suite directly.
Workflow 1: Supercharging Code Generation
Comment-Driven Development
Cursor's Tab completion doesn't just autocomplete — it understands context. Here's a technique that consistently produces excellent results: write a detailed comment first, then let Cursor fill in the implementation.
// Fetch a user's purchase history and sum the total
// spent in the last N days. Return a Result type.
// Cache results for 5 minutes.After writing that comment and pressing Tab, Cursor generates:
async function getUserPurchaseTotal(
userId: string,
days: number = 30
): Promise<Result<number, Error>> {
const cacheKey = `purchase_total:${userId}:${days}`;
const cached = await cache.get(cacheKey);
if (cached) return ok(JSON.parse(cached) as number);
try {
const cutoffDate = new Date();
cutoffDate.setDate(cutoffDate.getDate() - days);
const purchases = await db.query<{ total: number }>(
`SELECT SUM(amount) as total FROM purchases
WHERE user_id = ? AND created_at > ? AND deleted_at IS NULL`,
[userId, cutoffDate.toISOString()]
);
const total = purchases[0]?.total ?? 0;
await cache.set(cacheKey, JSON.stringify(total), 300);
return ok(total);
} catch (error) {
return err(new Error(`Failed to fetch purchase history: ${error}`));
}
}Agent Mode for Large-Scale Generation
Press Cmd + I to launch agent mode for bigger tasks:
@codebase Using the existing UserService as a reference,
create a new OrderService class with these features:
1. Create an order (with inventory check)
2. Update order status
3. Cancel an order (including refund logic)
4. Retrieve order history by user
Use the Result type pattern for error handling, and
generate Vitest unit tests alongside the implementation.
Workflow 2: AI-Powered Code Review
AI code review can significantly reduce the burden on human reviewers while improving consistency.
Automated Review Script
#!/bin/bash
# scripts/ai-review.sh
CHANGED_FILES=$(git diff --name-only HEAD~1 HEAD | grep -E '\.(ts|tsx|js|jsx)$')
if [ -z "$CHANGED_FILES" ]; then
echo "No changed files to review."
exit 0
fi
cat <<EOF > /tmp/review_request.txt
Review the following files:
$CHANGED_FILES
Check for:
1. Potential bugs
2. Security vulnerabilities
3. Performance issues
4. Code readability
5. Test coverage gaps
EOF
cat /tmp/review_request.txtInline Review with @git
Cursor's @git command lets you feed the diff directly into the chat:
@git diff HEAD~1 HEAD
Please review these changes. Focus on:
- Potential bugs or missed edge cases
- Security concerns (SQL injection, XSS, etc.)
- Suggestions for a better approach
Provide your feedback in clear, actionable points.
Workflow 3: Refactoring and Paying Down Technical Debt
AI excels at identifying and systematically addressing code quality issues.
Incremental Refactoring
Large-scale refactors are risky when done all at once. Here's a safe, AI-assisted approach:
// Before: problematic implementation
async function processOrder(orderId, userId) {
const order = await db.query(`SELECT * FROM orders WHERE id = ${orderId}`);
const user = await db.query(`SELECT * FROM users WHERE id = ${userId}`);
if (order.status == 'pending') {
await db.query(`UPDATE orders SET status = 'processing' WHERE id = ${orderId}`);
await sendEmail(user.email, 'Order confirmed');
return { success: true };
}
}Ask Cursor to fix it:
This code has these problems:
1. SQL injection vulnerability
2. No type definitions
3. No error handling
4. Email side effect makes testing difficult
Refactor it following the project's .cursorrules.
// After: clean, safe implementation
type ProcessOrderResult =
| { success: true; orderId: string }
| { success: false; error: string };
async function processOrder(
orderId: string,
userId: string
): Promise<ProcessOrderResult> {
try {
const [order] = await db.query<Order>(
"SELECT * FROM orders WHERE id = ? AND user_id = ?",
[orderId, userId]
);
if (!order) return { success: false, error: "Order not found" };
if (order.status !== "pending") {
return { success: false, error: `Invalid order status: ${order.status}` };
}
await db.transaction(async (trx) => {
await trx.query(
"UPDATE orders SET status = 'processing', updated_at = NOW() WHERE id = ?",
[orderId]
);
// Emit event instead of direct side effect (makes testing easy)
await eventBus.emit("order:confirmed", { orderId, userId });
});
return { success: true, orderId };
} catch (error) {
console.error("Order processing error:", error);
return { success: false, error: "An error occurred while processing the order" };
}
}Real Results: How Much Faster Is It?
After two months using this combined workflow on a production codebase, here's what we measured:
Time savings on new feature development: average 67% reduction (4 hours → 1.3 hours)
Time savings on test writing: average 72% reduction (2 hours → 33 minutes)
Code quality improvements:
- Bugs flagged in PR review: –58%
- Minor production bugs: –43%
Learning speed: Time to become productive with a new library or framework: –55%
The biggest wins came from test generation. AI-assisted testing surfaces edge cases that humans often miss, and it does so in a fraction of the time.
Explore More with Antigravity Lab Premium
This article walked through the core Antigravity × Cursor workflow. Antigravity Lab Premium members get access to deeper content:
- Integrating AI into CI/CD pipelines (GitHub Actions × Antigravity)
- Team-wide AI coding standards and governance practices
- AI-assisted migration strategies for large legacy codebases
- Building fully automated code review with Cursor Custom Agents
We're committed to making your daily development more enjoyable and more productive. If this guide helped, your support as a Premium member is what keeps this content coming — and we're truly grateful for it.