Top Free Antigravity Alternatives

Google Antigravity promised to revolutionize AI-assisted coding, but the reality for many developers has been frustrating. Time limits kick in when you're mid-flow. Quota burns through faster than expected. You might notice model tokens depleting even when you haven't actively switched models. And when you're trying to evaluate whether a tool actually fits your workflow, paying with credits before proper testing feels backward.

If you're looking for a free Antigravity alternative that gives you actual control—whether that's running models locally, self-hosting, or just avoiding arbitrary usage caps—this guide covers the tools that matter.

Why Developers Are Moving Away From Antigravity

The practical problems are clear:

Time-based restrictions interrupt your workflow. You don't code on a timer. When a session expires mid-task, you lose context and momentum.

Quota depletion happens faster than most users expect. What seems like a generous allowance can disappear in a few intensive coding sessions, especially when working with complex codebases or debugging.

Token consumption concerns are real. Some users report feeling like their quota is being consumed in ways that don't match their actual usage patterns. While the exact mechanics may vary by configuration, the frustration is consistent: you want to understand where your credits are going.

The testing problem is straightforward. To know if a tool fits your workflow, you need real usage time. When that evaluation period comes with usage pressure, you're making decisions with incomplete information.

For developers who prioritize privacy, cost control, and workflow flexibility, these issues push them toward tools they can run locally, audit completely, and use without arbitrary limits.

Best Free Antigravity Alternatives

Continue

What it is: Continue is an open-source AI coding assistant that integrates directly into VS Code and JetBrains IDEs. It's designed for flexible, IDE-native workflows with strong local model support.

Why it's a strong alternative:

  • Fully open source (Apache 2.0)
  • Works with local models via Ollama, LM Studio, or any OpenAI-compatible endpoint
  • No usage limits when running locally
  • Supports custom model configurations
  • Active development and community

Best use case: Developers who want IDE-integrated AI assistance with complete control over which models they use and where those models run.

Drawbacks: Requires initial setup for local models. Performance depends on your local hardware or chosen API endpoint.

Pricing model: Free and open source. API costs only if you choose to use remote providers.

How to run locally: Continue works seamlessly with Ollama. Install Ollama, pull a model like codellama or deepseek-coder, then configure Continue to use http://localhost:11434 as your endpoint. Full guidance: https://docs.continue.dev/guides/how-to-self-host-a-model

Official links:

OpenHands

What it is: OpenHands (formerly OpenDevin) is an open-source AI software engineer that acts as an autonomous agent. It can write code, execute commands, browse documentation, and interact with your development environment.

Why it's a strong alternative:

  • Designed for agent-style workflows where the AI takes initiative
  • Runs in Docker for isolation and reproducibility
  • Supports multiple LLM backends including local models
  • Built for complex, multi-step tasks
  • Strong community and active development

Best use case: Developers who want an AI agent that can handle end-to-end tasks like debugging, implementing features, or setting up infrastructure—not just code completion.

Drawbacks: Heavier resource footprint due to containerization. More complex setup than IDE extensions. Agent workflows require trust and verification.

Pricing model: Free and open source. Bring your own model or API key.

How to run locally: OpenHands runs via Docker and can connect to any OpenAI-compatible endpoint. For local operation, point it to Ollama or similar local inference servers. Setup involves Docker installation and environment configuration.

Official links:

Aider

What it is: Aider is a terminal-based AI pair programmer with deep Git integration. It's built for developers who live in the command line and want AI assistance that respects their existing workflow.

Why it's a strong alternative:

  • Terminal-native interface
  • Excellent Git integration—automatically commits changes with clear messages
  • Works with multiple LLM providers including local options
  • Focuses on actual code edits, not just suggestions
  • Minimal, focused feature set

Best use case: Developers who prefer terminal workflows and want AI assistance that integrates naturally with version control. Ideal for focused refactoring and implementation tasks.

Drawbacks: Terminal interface isn't for everyone. Less visual than IDE extensions. Requires comfort with command-line tools.

Pricing model: Free and open source (Apache 2.0). API costs depend on your chosen provider.

How to run locally: Aider supports Ollama and other local providers. Install via pip, configure your local model endpoint, and run commands like aider --model ollama/codellama. Check compatibility for your specific model.

Official links:

Roo Code

What it is: Roo Code is a VS Code extension that brings AI coding assistance directly into your editor with strong support for local model providers.

Why it's a strong alternative:

  • Native VS Code integration
  • Explicit support for local providers like Ollama and LM Studio
  • Privacy-focused design
  • Straightforward configuration
  • Active development with community input

Best use case: VS Code users who want editor-native AI assistance without sending code to external services.

Drawbacks: Limited to VS Code. Feature set may be lighter than some alternatives. Relatively newer project.

Pricing model: Free and open source.

How to run locally: Roo Code connects directly to Ollama, LM Studio, or other local inference servers. Configure your local endpoint in the extension settings and select your model.

Official links:

Practical Recommendation Matrix

Best for VS Code users: Continue or Roo Code. Continue offers more flexibility with model selection and configuration. Roo Code provides a more streamlined, privacy-first experience.

Best for terminal users: Aider. Purpose-built for command-line workflows with excellent Git integration.

Best for self-hosting: Continue. Mature self-hosting documentation, works with any OpenAI-compatible endpoint, and has extensive community knowledge around local deployments.

Best for local privacy: Roo Code or Aider. Both are designed with privacy in mind and work seamlessly with local-only model providers.

Best for agent-style workflows: OpenHands. Built specifically for autonomous, multi-step task execution.

Best for beginners: Continue. Most approachable setup, works in familiar IDE environment, and has extensive documentation for getting started with local models.

Local/Self-Host Setup Guide

Running Continue Locally

  1. Install Ollama from https://ollama.ai
  2. Pull a coding model: ollama pull deepseek-coder or ollama pull codellama
  3. Install Continue in VS Code or JetBrains
  4. Open Continue settings and add a model configuration:
  5. Test with a simple coding question

Alternative: Use LM Studio, LocalAI, or any OpenAI-compatible local inference server.

Full guide: https://docs.continue.dev/guides/how-to-self-host-a-model

Running OpenHands Locally

  1. Install Docker
  2. Clone the OpenHands repository
  3. Set up environment variables for your model provider
  4. For local models, point to Ollama or similar: OPENAI_API_BASE=http://localhost:11434/v1
  5. Launch with docker compose up
  6. Access the web interface and configure your agent

Setup specifics vary by version. Consult https://github.com/OpenHands/OpenHands for current instructions.

Running Aider Locally

  1. Install Aider: pip install aider-chat
  2. Install Ollama and pull a model
  3. Run Aider with your local model: aider --model ollama/deepseek-coder
  4. Aider will connect to Ollama automatically on localhost

For specific model compatibility and advanced configuration, check https://aider.chat/docs/

Running Roo Code Locally

  1. Install Roo Code from the VS Code marketplace
  2. Install Ollama or LM Studio
  3. Open Roo Code settings in VS Code
  4. Configure your local provider:
    • Provider: Ollama (or LM Studio)
    • Endpoint: http://localhost:11434 (or LM Studio's port)
    • Model: Select from available local models
  5. Test with a coding task

Documentation: https://docs.roocode.com/

Final Verdict

For most developers: Continue offers the best balance of flexibility, IDE integration, and local model support. It works where you already code, supports any model you want to run, and has no usage restrictions when running locally.

For privacy-first workflows: Roo Code or Aider. Both are designed from the ground up for local-only operation without external dependencies.

For power users: Aider if you're terminal-focused, OpenHands if you want autonomous agent capabilities. These tools assume comfort with command-line workflows and provide deeper control over the development process.

The common thread: all of these tools are free, open source, and designed to work without arbitrary limits. You control the models, the data never leaves your machine unless you choose otherwise, and your workflow isn't interrupted by quota pressure or time restrictions.

If Google Antigravity's usage model doesn't fit your development style, these alternatives offer real control without compromise.

FAQ

What is the best free alternative to Google Antigravity?

Continue is the strongest all-around choice for most developers. It integrates into VS Code and JetBrains, works with any model provider including local options, and has no usage limits when running locally. For terminal-focused developers, Aider is the better option.

Can I run an AI coding assistant locally?

Yes. All four tools covered here—Continue, OpenHands, Aider, and Roo Code—support local model operation. Using Ollama, LM Studio, or similar inference servers, you can run coding models entirely on your own hardware without sending code to external services.

Which alternative is best for privacy?

Roo Code and Aider are both designed with privacy as a core principle. When configured with local models, your code never leaves your machine. Continue also supports full local operation and gives you complete control over data flow.

Which one is easiest to self-host?

Continue has the most mature self-hosting documentation and the largest community knowledge base around local deployments. The setup process is straightforward: install Ollama, pull a model, point Continue at localhost, and start coding.

Do these tools actually replace Antigravity's capabilities?

For code completion, refactoring, and general coding assistance, yes. These tools handle the core use cases that most developers need. Agent-style task execution requires OpenHands. The tradeoff is setup effort and potentially lower performance with smaller local models, but you gain control, privacy, and unlimited usage.

What hardware do I need to run models locally?

For basic code completion: 16GB RAM and a recent CPU can run smaller models like CodeLlama 7B or DeepSeek-Coder 6.7B adequately. For better performance: 32GB+ RAM and a GPU with 8GB+ VRAM. Cloud-hosted models via API remain an option if local hardware is limiting, but you lose the privacy and cost benefits.

Are these actually free or is there a catch?

The software is free and open source. The catch: you either provide your own compute (local models) or pay for API access if using remote providers. With local operation, your only cost is electricity and hardware you already own. There are no subscription fees, usage quotas, or time limits on the tools themselves.