Getting Started with Antigravity: Your First AI Agent in 30 Minutes

Introduction

This Antigravity tutorial takes you from zero to a working AI agent in just 30 minutes. Building AI agents used to require deep expertise in distributed systems, complex orchestration, and sophisticated state management. Antigravity changes that entirely.

This tutorial assumes basic familiarity with Python and APIs, but no prior agent-building experience. We'll create a practical research agent that gathers information, synthesizes findings, and generates reports, tasks that would normally require significant manual work.

By the end, you'll understand Antigravity's core concepts and be ready to build production agents for your specific needs.

Prerequisites

Before starting, ensure you have:

• Python 3.10 or later: Check with python --version
• pip or uv: Package manager for installing dependencies
• An API key: OpenAI, Anthropic, or other LLM provider (we'll use OpenAI in examples)
• A text editor or IDE: VS Code, PyCharm, or similar
• curl or Postman (optional): For testing API endpoints
• 30 minutes of uninterrupted time: This isn't a coffee-and-scroll tutorial

Step 1: Installation (3 minutes)

Create a new directory for your agent project:

Create a Python virtual environment to isolate dependencies:

Install Antigravity and required dependencies:

Create a .env file for your API keys:

Edit .env and add your API key:

Checkpoint: Verify installation with:

You should see a version number, not an error.

Step 2: Creating Your First Agent (5 minutes)

Create a new file agent.py:

Add this basic agent scaffold:

Run your first agent:

You should see output from your agent. Congratulations, you've created a functional AI agent! But we've only scratched the surface. Let's add tools.

Step 3: Adding Tools (8 minutes)

Tools extend agent capabilities beyond language understanding. Let's add a web search tool and a data analysis tool:

Now your agent can use tools to extend its capabilities. The agent will intelligently decide when to invoke each tool based on the task.

Step 4: Configuring Memory Systems (6 minutes)

Memory allows agents to learn and maintain context across interactions. Add memory configuration:

With memory configured, your agent becomes more sophisticated, learning from interactions and maintaining context across sessions.

Step 5: Building Multi-Step Workflows (5 minutes)

Real-world tasks often require multiple sequential steps. Define a workflow:

Workflows orchestrate multiple agents or repeated steps, enabling complex multi-stage tasks.

Step 6: Testing and Debugging (4 minutes)

Production agents require testing. Create a test file test_agent.py:

Run tests:

Enable debug logging:

This provides visibility into agent decisions, tool invocations, and memory operations.

Step 7: Deploying Your Agent (3 minutes)

Deploy your agent to production with containerization:

Create Dockerfile:

Create requirements.txt:

Build and run:

For production deployment on cloud platforms (AWS, GCP, Azure), refer to each platform's container deployment documentation.

Step 8: Next Steps and Advanced Patterns (2 minutes)

You've built a functional agent! Here are advanced patterns to explore:

Multi-agent systems: Create specialized agents that collaborate. See Antigravity Agentic Toolkit: Building Autonomous AI Agents for orchestration patterns.

Custom tool development: Build domain-specific tools tailored to your business. Learn about the MCP Standard for standardized tool development.

Performance optimization: Monitor token usage, optimize tool selection, implement caching. Check Claude Code Advanced Features for optimization techniques.

Skill integration: Enhance your agent with pre-built Claude Skills for common tasks. Explore Claude Skills: How to Create Custom AI Workflows.

Common Pitfalls and How to Avoid Them

As you build your first agents with Antigravity, there are several common mistakes that can slow you down or lead to unexpected behavior.

The most frequent issue new developers encounter is setting the maximum iterations too high without proper guardrails. When an agent has unlimited iteration capacity, it can enter loops where it repeatedly calls the same tool without making progress. Start with a conservative max_iterations value of 3-5 and increase only when you've verified that your agent's decision-making is sound.

Another common pitfall is neglecting error handling in custom tools. When a tool throws an unhandled exception, it can crash the entire agent execution pipeline. Always wrap your tool logic in try-except blocks and return meaningful error messages that the agent can interpret and respond to appropriately.

Memory configuration deserves careful attention as well. While it might seem advantageous to give your agent unlimited memory capacity, this can lead to context pollution where irrelevant past interactions influence current decisions. Be selective about what gets stored in long-term memory and implement periodic cleanup routines for production agents.

Token usage management is another area where beginners often struggle. Each agent interaction consumes API tokens, and multi-step workflows with multiple tool calls can quickly become expensive. Implement token budgets at the workflow level and monitor usage patterns during development to avoid unexpected costs in production.

Finally, testing against edge cases is critical before deployment. Your agent will encounter unexpected inputs, malformed data, and network failures in production. Build comprehensive test suites that cover not just the happy path but also failure modes, timeout scenarios, and graceful degradation when external services are unavailable.

Key Takeaways

• Installation is straightforward: Standard pip package, quick setup
• Basic agents can be created with minimal code
• Tools extend capabilities beyond language understanding into task execution
• Memory systems enable learning and context persistence
• Workflows coordinate multiple steps for complex tasks
• Testing ensures reliability before production deployment
• Containerization simplifies production deployment across environments

Build Your First AI Agent Now

You now have everything needed to build sophisticated AI agents. The barrier to entry has never been lower, but the ceiling for sophisticated applications is limitless.

Start building today:

• Clone the Antigravity starter template
• Follow this tutorial step-by-step with real code
• Customize tools and workflows for your specific domain
• Deploy to production and iterate based on results

Your first AI agent is 30 minutes away. What will you build?

Additional Resources:

• Antigravity Agentic Toolkit: Building Autonomous AI Agents - Comprehensive framework overview
• MCP Standard Documentation - Building standardized tools
• Claude Code Guide - Advanced development patterns
• Troubleshooting Guide - Common issues and solutions
• Community Discord - Ask questions and share projects