ANTIGRAVITY LABJP
Articles/Tips & Best Practices
Tips & Best Practices/2026-03-30Beginner

Complete Fix Guide for Antigravity Slow Performance and Freezing Issues

Diagnose and fix slow Antigravity performance. Master context optimization, MCP management, and model selection for lightning-fast responses.

performance12troubleshooting105slowoptimization3

As you work longer with Antigravity, you might notice "agent responses are getting slower" or "the UI freezes intermittently." This guide walks through the five main causes and practical fixes for each.

Five Main Causes of Antigravity Performance Degradation

1. Project Context Too Large

Antigravity sends all project files as context to the AI for accurate responses. As files accumulate, context size explodes, dramatically increasing API processing time.

Common culprits:

  • Projects with 1,000+ files
  • Complete node_modules folder included (typically 50,000+ files)
  • Build output folders (dist/, build/) in context
  • Cache files, logs, and temp directories

The fix:

Create an .antigravityignore file to exclude unnecessary folders:

node_modules/
.git/
dist/
build/
.next/
out/
*.log
.DS_Store
.env
__pycache__/
*.pyc
coverage/
.pytest_cache/

Or extend your .gitignore:

cp .gitignore .antigravityignore
echo "dist/" >> .antigravityignore
echo ".next/" >> .antigravityignore

This single change often cuts response time by 50% or more.

2. Too Many MCP Servers Enabled

Multiple MCP (Model Context Protocol) servers consume resources—Antigravity must maintain and verify connections to each, slowing everything down.

Signs:

  • New agent initialization takes 30+ seconds
  • Slow responses even for non-MCP queries
  • Crashes when running multiple agents simultaneously

Solution:

Disable MCPs you're not actively using:

  • Antigravity Settings → MCP Configuration
  • Toggle off MCPs you don't need
  • Or rotate: enable only the 3–5 you use most

Most workflows use just 3–5 MCPs (GitHub, Figma, custom APIs). More than that signals over-configuration.

3. Wrong AI Model Selection

Antigravity offers multiple models. Powerful models are slower and consume more credits.

Model breakdown:

  • Claude 3.5 Sonnet (Pro): High accuracy, slower (5–30s responses)
  • Claude 3.5 Haiku (Flash): Lightweight, fast (1–5s responses)
  • Claude Opus: Highest accuracy, slowest (10–60s responses)

Smart selection:

  • Initial research: Haiku
  • Code generation & debugging: Sonnet
  • Complex logic & strategy: Opus

Example setup:

Default Agent: Claude 3.5 Haiku
Complex Tasks: Claude 3.5 Sonnet

Switching to Haiku as default cuts average response time by 40%.

4. High Browser Memory Usage

Antigravity consumes increasing browser memory over time. UI becomes sluggish, especially with multiple tabs or windows open.

Check memory (Chrome):

  1. Menu → More tools → Task Manager
  2. Find Antigravity process
  3. Check Memory column (>1GB = warning sign)

Fix it:

  • Memory leak: Restart Antigravity
  • Extension conflicts: Temporarily disable other extensions
  • Multiple projects: Limit to one project at a time

Also try Antigravity Settings → Performance → lower "Cache Size."

5. Network Latency or VPN

High-latency connections naturally increase API response times.

Test latency:

ping api.antigravity.cloud
 
# Or measure with curl
curl -w "@curl-format.txt" -o /dev/null -s https://api.antigravity.cloud/health

Solutions:

  • VPN: Switch servers or temporarily disable
  • Corporate network: Request faster routing from IT
  • Region selection: Choose closest region in Antigravity settings

When Agents Stop Responding

If an agent gets stuck "thinking":

Diagnostic Checklist

  1. Network: Verify internet (ping 8.8.8.8)
  2. Browser console: F12 → Console for errors
  3. API status: Check Antigravity status page
  4. Credits: Account → Billing (zero credits = disabled)

Recovery

  • Stop agent: Escape or "Stop" button
  • Restart Antigravity
  • Clear cache: Ctrl+Shift+Delete (Chrome) or Settings → Clear cache
  • Reload project

When Code Output Gets Cut Off Mid-Way

Slow responses and truncated output look similar but have different causes. When a function ends without its closing }, or you get import statements with no body, the issue isn't speed—it's running out of output tokens. The fix is different.

Sort the symptom into one of three patterns first:

  • A: Code stops mid-function or mid-class — the model exhausted its output tokens and gave up.
  • B: Asking it to "continue" makes it forget earlier context — the chat history grew long and pushed older context out.
  • C: An explicit "file too long" error appears — the clearest case; your input is too large.

When it gets cut, start a fresh session instead of continuing

Rather than asking for more inside a long session, open a new one and paste only the part you need.

# Inside a long session (prone to truncation)
"Add authentication to the code from earlier"

# In a fresh session (recommended)
"Add JWT auth to the function below. Only auth.ts changes.
export function checkAuth(req: Request) { /* ... */ }"

For managing context, see Context Window management for large codebases.

"Return only the changed code" frees up output budget

Adding one line to your request cuts the preamble and full-file restatement, leaving more tokens for actual generation.

"Add error handling to fetchUserData.
 Return only the changed function—no other functions or explanation."

As an indie developer, I kept getting cut off trying to fix long files in one shot. Once I switched to extracting just the target function and asking for "that function only," truncation almost disappeared. Ask narrow, ask often—that is what works.

.gitignore vs .antigravityignore

Two separate tools, two separate purposes.

.gitignore

  • Excludes files from version control

.antigravityignore

  • Excludes files from Antigravity context
  • Can be more aggressive

Recommended template:

# Dependencies
node_modules/
.venv/
env/
vendor/

# Build output
dist/
build/
out/
.next/
.nuxt/

# Development tools
.git/
.vscode/
.idea/
*.log

# OS files
.DS_Store
Thumbs.db

# Caches
.cache/
.pytest_cache/
.eslintcache/

# Binaries
*.zip
*.tar.gz

Planning Mode vs Fast Mode

Two execution strategies:

Planning Mode (Thinking):

  • Complex logic, multi-step reasoning
  • 30–120 second response
  • High credit consumption
  • Highest accuracy

Fast Mode (Quick):

  • Simple questions, research
  • 5–15 second response
  • Low credit consumption
  • Good accuracy

When to use each:

Task                          | Mode
New project architecture      | Planning
Small bug fix                 | Fast
Multi-module design review    | Planning
Library documentation lookup  | Fast

Default to Fast Mode, switch to Planning when needed.

Memory and Storage Optimization

Clean Up Projects

Periodically remove unnecessary files:

# Find large files
find . -size +10M -type f | grep -v node_modules
 
# Remove old logs and caches
rm -rf logs/
rm -rf .cache/
 
# Check overall size
du -sh .

Exclude Generated Files

echo "generated/" >> .antigravityignore
echo "*.generated.js" >> .antigravityignore

Review Auto-continue Settings

Auto-continue lets agents continue tasks automatically. Convenient but risky—more errors, higher credit consumption.

Configuration:

Antigravity → Agent Settings → Auto-continue

  • Disabled (recommended): You verify each step
  • Enabled: Automatic progression (high credit use, possible surprises)

If performance suffers, disable it temporarily.

Save Credits While Keeping Speed

Optimize Model Selection

Routine work            | Haiku (¥0.5/1M tokens)
Design & complex logic  | Sonnet (¥3/1M tokens)
Research & analysis     | Opus (¥15/1M tokens)

80% Haiku, 20% Sonnet keeps quality high while reducing spend by 30%.

Compress Context

// Bad: broad scope
"Include all project files"

// Good: precise files
"Files: /src/main.ts, /api/routes.ts"

Explicit file selection reduces API payload by 50% in many cases.

Batch Tasks

Combine multiple small tasks into one prompt:

// Inefficient: 4 separate calls
1. Fix bug
2. Add tests
3. Update docs
4. Draft PR comment

// Efficient: 1 call
"Fix bug, add tests, update docs, draft PR comment"

Performance Improvement Checklist

Try in this order:

  • [ ] Exclude node_modules in .antigravityignore
  • [ ] Disable unused MCPs
  • [ ] Set default model to Haiku
  • [ ] Disable Auto-continue
  • [ ] Clear browser cache
  • [ ] Restart Antigravity
  • [ ] Measure network latency

Quick Wins Summary

Immediate (5 minutes):

  1. Create .antigravityignore with node_modules/
  2. Set Haiku as default model
  3. Disable Auto-continue

Short-term (30 minutes):

  1. Clear browser cache
  2. Disable unused MCPs
  3. Review and compress project size

Ongoing:

  1. Regularly clean old files
  2. Monitor browser memory
  3. Adjust model selection per task

Related Resources

10 Essential Antigravity Productivity Tips shares broader efficiency techniques beyond speed alone.

Wrapping up

Slow Antigravity performance almost always traces back to oversized context, too many MCPs, or wrong model selection. Excluding node_modules and defaulting to Haiku solves most cases. Paired with regular maintenance and task-appropriate mode selection, you'll enjoy fast, efficient workflows.

Share

Thank You for Reading

Antigravity Lab is ad-free, supported entirely by members like you. We publish practical guides daily with implementation code, benchmarks, and production-ready patterns. If you've found it useful, we'd love to have you on board.

  • Copy-paste ready implementation code
  • New advanced guides published daily
  • $5/mo or $10 for lifetime access
View Membership →

If you found this article helpful, a small tip ($1.50) would mean a lot to us. Your support helps keep this site ad-free and covers server and hosting costs.

Related Articles

Tips2026-04-30
Fixing Antigravity's Slow Performance and Broken File Watching on WSL2
If Antigravity feels sluggish on WSL2, ignores your saved files, or takes minutes to start, you're almost certainly hitting a WSL2 filesystem boundary issue. Here's how to diagnose and fix it for good.
Tips2026-04-21
Antigravity Uses Too Much CPU and Memory: A Practical Fix Guide
When your laptop fans spin up and Antigravity holds several gigabytes of RAM, the fix starts by identifying which subprocess is actually misbehaving. Here's a per-process playbook.
Tips2026-04-14
Maximizing Antigravity Performance with Gemma 4: A Practical Guide
Master Gemma 4 performance optimization in Antigravity with response speed tuning, cost reduction strategies, and complete implementation patterns for API and local execution.
📚RECOMMENDED BOOKS
Build a Large Language Model (From Scratch)
Sebastian Raschka
LLM Dev
Prompt Engineering for LLMs
Berryman & Ziegler
Prompting
AI Engineering
Chip Huyen
AI Eng
* Contains affiliate links
See all →