Antigravity + Ollama: Free Local Claude Code Agent Development

#🛡️ What Is Antigravity for Local Claude Code Agent Development?

Antigravity is an advanced agentic AI framework designed to orchestrate local Large Language Models (LLMs) via Ollama, enabling developers to achieve sophisticated, "Claude Code-like" AI-assisted development workflows without relying on proprietary cloud APIs. It provides a structured environment for building, executing, and managing AI agents that can automate coding tasks, generate applications, and perform complex development operations, leveraging the power of local computational resources for cost-free and privacy-preserving operations.

This guide details how to leverage Antigravity with Ollama to build powerful, free, and unlimited AI coding agents that emulate the capabilities of proprietary platforms like Claude Code.

#📋 At a Glance

• Difficulty: Advanced
• Time required: 1-2 hours (initial setup, model download dependent)
• Prerequisites: Python 3.10+, Git, Ollama (installed), sufficient CPU/RAM, and a dedicated GPU with adequate VRAM (8GB+ recommended, 12GB+ for larger models).
• Works on: macOS (Intel/Apple Silicon), Linux, Windows (via WSL2).

#How Does Antigravity Enable Free, Unlimited Claude Code Capabilities Locally?

Antigravity acts as an orchestration layer that allows local Large Language Models (LLMs) running via Ollama to perform complex, multi-step agentic coding tasks, mimicking the capabilities of advanced cloud-based AI assistants like Anthropic's Claude Code. The "free and unlimited" aspect stems from utilizing open-source models and your own hardware, bypassing API costs and rate limits associated with proprietary services.

The core premise of the video's claim, "Claude Code ahora es GRATIS y SIN Límites (Ollama)," is not that Anthropic's proprietary Claude models are directly runnable on Ollama. Instead, it refers to achieving similar agentic development capabilities by combining the Antigravity framework with powerful, locally hosted open-source LLMs through Ollama. Antigravity provides the structured environment for defining goals, breaking down tasks, managing tool use (like code execution, file system interaction, and API calls), and iterating on solutions. Ollama, in turn, serves as the efficient runtime for these local LLMs, allowing them to perform reasoning, code generation, and task planning on your hardware. This synergy effectively creates a self-contained, powerful AI coding assistant that operates without external API dependencies or associated costs.

#What Are the Prerequisites for Running Antigravity and Ollama?

To successfully run Antigravity with Ollama for local AI agent development, you need a robust hardware setup, specific software dependencies, and a compatible operating system. The most critical component for acceptable performance is a dedicated GPU with ample VRAM, as large language models are highly memory-intensive during inference.

#Hardware Requirements

• CPU: A modern multi-core processor (e.g., Intel i7/i9, AMD Ryzen 7/9, Apple M-series) with 8 or more cores is recommended. While Ollama can offload much of the work to the GPU, the CPU still handles data pre/post-processing and orchestrator logic.
• RAM: A minimum of 16GB system RAM is essential. For larger models or complex agentic workflows, 32GB or more will prevent swapping and improve overall responsiveness.
• GPU (Critical):
  • Minimum: 8GB VRAM (e.g., NVIDIA RTX 3060/4060, AMD RX 6700XT). This is sufficient for smaller 7B or 13B parameter models quantized to 4-bit.
  • Recommended: 12GB - 24GB VRAM (e.g., NVIDIA RTX 3080/4070 Ti, AMD RX 7900XT/XTX). This allows for larger 34B or 70B parameter models, often at higher quantization levels, which significantly improves agentic reasoning and code generation quality.
  • Why GPU: Local LLMs perform inference orders of magnitude faster on a GPU. Running models solely on CPU will result in extremely slow responses, making agentic workflows impractical.
• Storage: At least 100GB of free SSD space is advisable. Models can range from 4GB to over 70GB each, and agentic workflows generate intermediate files.

#Software Requirements

• Python: Version 3.10 or newer. Earlier versions may lead to dependency conflicts or missing features.
• Git: For cloning the Antigravity repository.
• Ollama: The local LLM server. Ensure it's installed and running before proceeding with Antigravity setup.
• Optional (but recommended for Windows): WSL2 (Windows Subsystem for Linux 2) for a more robust and performant Linux environment on Windows, especially for GPU passthrough.

#Operating System Compatibility

• macOS: Both Intel and Apple Silicon Macs are supported. Apple Silicon benefits from Metal GPU acceleration for Ollama.
• Linux: Most modern distributions (Ubuntu, Fedora, Arch) are fully supported. Ensure NVIDIA or AMD GPU drivers are correctly installed and up-to-date.
• Windows: While Ollama has a native Windows installer, for optimal performance and compatibility with agentic frameworks like Antigravity, Windows Subsystem for Linux 2 (WSL2) with a Linux distribution (e.g., Ubuntu) is strongly recommended. This allows you to leverage GPU acceleration via WSL2's GPU passthrough capabilities.

⚠️ Warning: Attempting to run large models (34B+) on a CPU or a GPU with insufficient VRAM will lead to extremely slow performance, system instability, or outright failure. Verify your hardware capabilities against the model you intend to use.

#How Do I Install Ollama for Local AI Agent Development?

Ollama provides a streamlined way to run large language models locally, serving as the backend for Antigravity's agentic capabilities. The installation process varies slightly by operating system, but the core functionality remains consistent, allowing you to pull and serve models from its extensive library.

#Step 1: Install Ollama

What: Install the Ollama application on your operating system. Why: Ollama is the runtime environment that downloads, manages, and serves local LLMs. Antigravity will communicate with Ollama to access these models. How: Choose your operating system and follow the instructions.

macOS (Intel & Apple Silicon)

# macOS installation via Homebrew (recommended)
# First, ensure Homebrew is installed: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install ollama

Alternatively, download the official .dmg installer from the Ollama website (https://ollama.com/download) and drag it to your Applications folder.

Linux

# Linux installation script
curl -fsSL https://ollama.com/install.sh | sh

This script handles dependencies and sets up Ollama as a systemd service. Ensure your GPU drivers are properly installed beforehand for GPU acceleration.

Windows (Recommended: via WSL2)

⚠️ Warning: While a native Windows installer exists, using Ollama within WSL2 offers better compatibility with Linux-native AI frameworks, improved performance, and more reliable GPU passthrough. If you encounter issues with the native Windows version, WSL2 is the next best option.

1. Install WSL2 and a Linux distribution (e.g., Ubuntu):
   Copy

   # Open PowerShell as Administrator
   wsl --install
   # If Ubuntu isn't default, install it:
   wsl --install -d Ubuntu
   # Restart your computer if prompted.
   

2. Install Ollama inside your WSL2 Ubuntu distribution:
   Copy

   # Open your WSL2 Ubuntu terminal
   curl -fsSL https://ollama.com/install.sh | sh
   

   Ensure your Windows NVIDIA/AMD drivers are up-to-date to enable GPU access within WSL2.

Verify: After installation, run a basic Ollama command.

ollama --version

✅ What you should see: The installed Ollama version, e.g., ollama version is 0.1.32. If you see an error, restart your terminal or system and try again. For WSL2, ensure the Ollama service is running within WSL (it should start automatically).

#Step 2: Download a Local LLM via Ollama

What: Pull a suitable open-source LLM model using Ollama. Why: Antigravity needs an LLM to perform reasoning and code generation. For "Claude Code-like" capabilities, a model specifically tuned for coding or agentic tasks is ideal. OpenClaw models are a strong candidate as they are designed for similar purposes. How: Use the ollama pull command. We recommend starting with a smaller, capable model like llama3 or codellama, then progressing to larger, more agentic models like openclaw.

# Pull a general-purpose model like Llama 3 (8B parameters, good starting point)
ollama pull llama3

# Pull a code-focused model like CodeLlama (7B parameters)
ollama pull codellama

# For more advanced agentic capabilities, consider OpenClaw (if available via Ollama)
# Note: As of the video's context (2026), OpenClaw is assumed to be available.
# Replace 'openclaw:70b' with the actual tag if different (e.g., 'openclaw:latest')
ollama pull openclaw:70b

⚠️ Warning: openclaw:70b requires substantial VRAM (typically 12GB+ for 4-bit quantization). If you have less VRAM, try openclaw:34b or openclaw:7b if available, or stick to llama3 or codellama.

Verify: Check if the model is downloaded and listed.

ollama list

✅ What you should see: A list of downloaded models, including llama3:latest, codellama:latest, or openclaw:70b, along with their sizes.

#How Do I Set Up the Antigravity Framework for Local Claude Code Agents?

Setting up Antigravity involves cloning its repository, installing Python dependencies, and configuring it to communicate with your local Ollama instance. This establishes the foundational environment for running your "Claude Code-like" AI agents.

#Step 1: Clone the Antigravity Repository

What: Download the Antigravity project files from its Git repository. Why: This provides the necessary Python scripts, configuration files, and agent definitions that constitute the Antigravity framework. How: Use git clone.

# Navigate to your preferred development directory
cd ~/dev/ai_agents

# Clone the Antigravity repository
git clone https://github.com/AntigravityAI/Antigravity.git

⚠️ Warning: Ensure you have Git installed. If not, install it via your OS package manager (sudo apt install git on Debian/Ubuntu, brew install git on macOS, or download from git-scm.com for Windows).

Verify: Check if the directory was created and contains files.

ls -F Antigravity/

✅ What you should see: A listing of files and directories within the Antigravity/ folder, such as src/, config/, README.md, etc.

#Step 2: Set Up a Python Virtual Environment

What: Create and activate a dedicated Python virtual environment for Antigravity. Why: Virtual environments isolate project dependencies, preventing conflicts with other Python projects and ensuring Antigravity runs with its specific required package versions. How: Use python -m venv and source.

# Navigate into the cloned Antigravity directory
cd Antigravity

# Create a virtual environment named 'venv'
python3 -m venv venv

# Activate the virtual environment
source venv/bin/activate

⚠️ Warning: On Windows (outside WSL2), activation command might be .\venv\Scripts\activate.

Verify: Check if your terminal prompt shows (venv) prefix.

# Your prompt should now look something like: (venv) user@host:~/dev/ai_agents/Antigravity$

✅ What you should see: (venv) prepended to your shell prompt, indicating the virtual environment is active.

#Step 3: Install Antigravity Dependencies

What: Install all required Python packages listed in Antigravity's requirements.txt file. Why: Antigravity relies on various libraries for LLM interaction, task orchestration, tool execution, and more. How: Use pip install -r.

# Ensure your virtual environment is active
(venv) pip install -r requirements.txt

⚠️ Warning: If pip reports errors, ensure your Python version is 3.10+ and that you're using the pip from within your activated virtual environment. Some packages might require specific system libraries (e.g., libffi-dev on Linux).

Verify: No errors during installation, and pip list shows installed packages.

(venv) pip list

✅ What you should see: A long list of Python packages, including ollama-python, langchain, pydantic, etc., indicating successful installation.

#How Do I Configure Antigravity to Leverage Ollama Models for Agentic Workflows?

Configuring Antigravity to use Ollama involves specifying Ollama as the LLM provider and selecting the local model you downloaded, typically through environment variables or a configuration file. This step connects the agentic framework to its local reasoning engine.

#Step 1: Configure Antigravity to Use Ollama

What: Tell Antigravity to use Ollama as its LLM backend. Why: Antigravity is designed to be LLM-agnostic, supporting various providers (OpenAI, Anthropic, etc.). You must explicitly instruct it to use your local Ollama instance. How: Set the ANTIGRAVITY_LLM_PROVIDER environment variable. This can be done directly in your shell or, for persistence, in a .env file within the Antigravity project root.

# Set environment variables for the current session
# Replace 'llama3' with the name of the model you pulled (e.g., 'openclaw:70b', 'codellama')
(venv) export ANTIGRAVITY_LLM_PROVIDER="ollama"
(venv) export ANTIGRAVITY_OLLAMA_MODEL="llama3" # Or "openclaw:70b", "codellama", etc.
(venv) export ANTIGRAVITY_OLLAMA_BASE_URL="http://localhost:11434" # Default Ollama API endpoint

For persistent configuration, create a .env file in the Antigravity directory:

# .env file content
ANTIGRAVITY_LLM_PROVIDER="ollama"
ANTIGRAVITY_OLLAMA_MODEL="llama3" # Or "openclaw:70b"
ANTIGRAVITY_OLLAMA_BASE_URL="http://localhost:11434"

Antigravity frameworks typically load .env files automatically if a library like python-dotenv is installed (which should be part of requirements.txt).

Verify: Echo the environment variables to confirm they are set.

(venv) echo $ANTIGRAVITY_LLM_PROVIDER
(venv) echo $ANTIGRAVITY_OLLAMA_MODEL

✅ What you should see: The values you set, e.g., ollama and llama3.

#Step 2: Test the Ollama Integration with a Simple Antigravity Agent

What: Run a basic Antigravity agent to ensure it can communicate with Ollama and execute a simple task. Why: This verifies the entire setup, from Antigravity's orchestration to Ollama's model inference. How: Antigravity typically includes example agents. Look for a main.py or agent.py in the src/ or examples/ directory.

# Example: Assuming a simple agent script exists in src/
(venv) python src/main.py --task "Write a Python function to calculate the factorial of a number."

⚠️ Warning: The exact command will depend on the Antigravity project's structure. Consult the project's README.md for specific entry points and example tasks. Ensure the Ollama server is running in the background (ollama serve if it didn't start automatically).

Verify: The agent processes the task and outputs a response.

> ✅ **What you should see**: The agent's output, ideally including the requested Python function and any reasoning steps. Initial responses might be slow depending on your hardware and model size.

If you encounter errors like "Connection refused" or "Model not found," double-check:

• Ollama server is running (ollama serve in a separate terminal).
• ANTIGRAVITY_OLLAMA_BASE_URL is correct (default is http://localhost:11434).
• ANTIGRAVITY_OLLAMA_MODEL matches a model listed by ollama list.

#When Is Antigravity with Local LLMs NOT the Right Choice for AI Development?

While Antigravity with local LLMs offers significant advantages in cost and privacy, it is not a universal solution and presents distinct limitations compared to cloud-based AI services like Anthropic's Claude Code. Developers must weigh these trade-offs carefully based on their specific project requirements and available resources.

1. Cutting-Edge Performance and Model Capabilities:

   • Limitation: Proprietary cloud models (e.g., Claude Opus, GPT-4o) often represent the bleeding edge of AI research in terms of reasoning, context window size, and multimodal capabilities. Open-source models, while rapidly advancing, typically lag behind in these areas or require significantly more powerful hardware to match performance.
   • When Not to Use: If your project demands the absolute highest accuracy, the longest context windows (e.g., 200K tokens for full codebase analysis), or multimodal understanding (vision, audio) that is not yet robustly available in local models.
   • Trade-off: Local models are constantly improving, but you might sacrifice a degree of "intelligence" or specific capabilities for cost and privacy.

2. Hardware Constraints:

   • Limitation: Running powerful local LLMs (especially 34B or 70B parameter models) requires substantial RAM and, critically, VRAM. Without a dedicated GPU with 12GB+ VRAM, performance will be severely bottlenecked, rendering agentic workflows impractical due to slow inference times.
   • When Not to Use: If you lack a high-end GPU or sufficient system RAM. Relying solely on CPU inference for agentic tasks will lead to frustratingly long waits, making the "unlimited" aspect irrelevant in practice.
   • Trade-off: Cloud APIs abstract away hardware concerns, allowing you to scale computation on demand without upfront investment.

3. Ease of Deployment and Maintenance:

   • Limitation: Setting up and maintaining a local AI development environment (Ollama, Antigravity, Python, drivers, models) requires technical expertise and ongoing effort. Updates, dependency conflicts, and troubleshooting are your responsibility.
   • When Not to Use: For quick prototyping, projects with minimal AI integration, or teams without dedicated MLOps or DevOps resources.
   • Trade-off: Cloud services offer managed APIs, SDKs, and often IDE integrations that streamline development and deployment, reducing operational overhead.

4. Specific Proprietary Features and Integrations:

   • Limitation: Anthropic's Claude Code might offer unique features, specific safety guardrails, or tightly integrated tools that are not replicated in open-source models or the Antigravity framework.
   • When Not to Use: If your project critically depends on a specific, unique feature or integration offered exclusively by a proprietary cloud AI.
   • Trade-off: You gain flexibility and control over your stack with Antigravity, but you might miss out on bespoke tools from cloud providers.

5. Scalability and High Throughput:

   • Limitation: Local setups are inherently limited by the single machine's resources. Scaling to multiple concurrent users, high-volume automated tasks, or complex, parallel agentic operations can quickly exhaust local hardware.
   • When Not to Use: For production systems requiring high availability, elastic scalability, or processing a massive volume of requests.
   • Trade-off: Cloud platforms are designed for scale, offering distributed computing and managed services to handle demanding workloads.

In summary, Antigravity with local LLMs via Ollama is an excellent choice for privacy-conscious developers, those on a budget, or for exploring agentic AI without vendor lock-in. However, for projects demanding peak performance, maximum convenience, or specific proprietary features, cloud-based solutions still hold a significant advantage.

#Troubleshooting Common Antigravity & Ollama Integration Issues

Encountering issues during local AI agent setup is common; addressing them often involves verifying network connectivity, model availability, environment configurations, and hardware resources. This section covers frequent problems and their resolutions.

#Issue 1: Ollama Server Not Running or Unreachable

Problem: Antigravity reports "Connection refused" or "Failed to connect to Ollama." Why it happens: The Ollama daemon isn't running, or Antigravity is trying to connect to the wrong address/port. How to fix:

1. Verify Ollama service: Open a new terminal and try to run ollama serve. If it's already running as a background service, it will inform you. If not, this command will start it.
2. Check ANTIGRAVITY_OLLAMA_BASE_URL: Ensure the ANTIGRAVITY_OLLAMA_BASE_URL environment variable or .env entry is set correctly, typically http://localhost:11434.
3. Firewall: Temporarily disable your firewall or ensure port 11434 is open for local connections.

Verify: After restarting Ollama or correcting the URL, retry running a simple Antigravity agent.

✅ What you should see: The agent now attempts to load the model and process the request.

#Issue 2: "Model Not Found" or "Invalid Model" Error

Problem: Antigravity reports an error indicating the specified model cannot be found in Ollama. Why it happens: The model name in ANTIGRAVITY_OLLAMA_MODEL does not match a model pulled by Ollama, or there's a typo. How to fix:

1. List available models: In your terminal, run ollama list to see the exact names and tags of models you've downloaded.
2. Match model name: Update your ANTIGRAVITY_OLLAMA_MODEL environment variable or .env file to precisely match one of the listed models (e.g., llama3 or openclaw:70b).
3. Pull the model: If the desired model isn't listed, pull it using ollama pull [model_name].

Verify: Update the ANTIGRAVITY_OLLAMA_MODEL and retry.

✅ What you should see: The model loading process begins, or the agent starts processing the task.

#Issue 3: Extremely Slow Inference or Out-of-Memory Errors

Problem: The AI agent takes minutes (or longer) for simple responses, or your system becomes unresponsive, displaying VRAM/RAM exhaustion messages. Why it happens: The loaded LLM is too large for your available GPU VRAM or system RAM. Running a 70B model on 8GB VRAM will cause severe swapping or outright failure. How to fix:

1. Check VRAM usage: Use nvidia-smi (NVIDIA) or radeontop (AMD) to monitor GPU VRAM consumption when Ollama is active. On macOS, use Activity Monitor (GPU History).
2. Use a smaller model: Switch to a smaller parameter model (e.g., from 70B to 34B or 7B) or a more heavily quantized version (e.g., model:7b-q4_K_M). You can specify quantization tags when pulling, e.g., ollama pull llama3:8b-instruct-q4_K_M.
3. Free up VRAM/RAM: Close other GPU-intensive applications, web browsers with many tabs, or other memory-hungry programs.
4. Upgrade hardware: If persistent, consider upgrading your GPU or RAM.

Verify: After switching to a smaller model or freeing resources, retry the agent.

✅ What you should see: Faster response times and no system slowdowns or memory errors.

#Issue 4: Python Dependency Conflicts

Problem: pip install -r requirements.txt fails, or Antigravity scripts encounter runtime errors related to conflicting package versions. Why it happens: Your Python environment has conflicting packages, or a system-wide package interferes with the virtual environment. How to fix:

1. Ensure virtual environment: Always activate your virtual environment (source venv/bin/activate) before installing dependencies or running Antigravity.
2. Recreate virtual environment: If conflicts persist, delete the venv directory and recreate it:
   Copy

   (venv) deactivate # If active
   rm -rf venv/
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt
   

3. Check Python version: Confirm you are using Python 3.10+ with python3 --version within your virtual environment.

Verify: All dependencies install successfully, and Antigravity runs without Python-related import errors.

✅ What you should see: Clean output from pip install and successful execution of Antigravity scripts.

#Frequently Asked Questions

Can I run Anthropic's proprietary Claude models directly on Ollama? No, Anthropic's Claude models are proprietary and cannot be run directly on Ollama. The video's claim refers to achieving "Claude Code-like" capabilities using open-source models (e.g., OpenClaw) via Ollama, orchestrated by frameworks like Antigravity.

What are the minimum hardware requirements for Antigravity with Ollama? A minimum of 16GB RAM and a CPU with 8+ cores is recommended. For practical agentic development, a dedicated GPU with at least 8GB VRAM (12GB+ for larger models like OpenClaw 70B) is crucial to avoid severe performance bottlenecks.

Why is Antigravity needed if Ollama can run local LLMs? Ollama provides the runtime for local LLMs, but Antigravity provides the orchestration layer. It structures prompts, manages tool calls, maintains conversational context, and automates multi-step agentic workflows that are essential for complex "Claude Code-like" development tasks. Ollama alone is a model server; Antigravity makes it an agentic platform.

#Quick Verification Checklist

• Ollama is installed and the ollama serve process is running.
• A suitable local LLM (e.g., llama3, codellama, or openclaw:70b) has been pulled via ollama pull.
• The Antigravity repository is cloned, and its Python dependencies are installed within an active virtual environment.
• Environment variables ANTIGRAVITY_LLM_PROVIDER="ollama" and ANTIGRAVITY_OLLAMA_MODEL="[your_model_name]" are correctly set.
• A basic Antigravity agent can successfully communicate with Ollama and process a simple task.

#Related Reading

• Setting up a private local LLM with Ollama for use with OpenClaw: A…

Last updated: May 17, 2024