Getting Started with Antigravity: Build Your First AI Agent

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:

bash

mkdir my-first-agent
cd my-first-agent

Create a Python virtual environment to isolate dependencies:

bash

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Antigravity and required dependencies:

bash

pip install antigravity-sdk openai python-dotenv requests

Create a .env file for your API keys:

bash

touch .env

Edit .env and add your API key:

OPENAI_API_KEY=sk-your-key-here
ANTIGRAVITY_WORKSPACE=your-workspace-id

Checkpoint: Verify installation with:

bash

python -c "import antigravity; print(antigravity.__version__)"

You should see a version number, not an error.

Step 2: Creating Your First Agent (5 minutes)

Create a new file agent.py:

bash

touch agent.py

Add this basic agent scaffold:

python

import os
from dotenv import load_dotenv
from antigravity import Agent, Tool, AgentConfig

# Load environment variables
load_dotenv()

# Initialize agent configuration
config = AgentConfig(
    name="ResearchAgent",
    description="An autonomous research agent that gathers and synthesizes information",
    llm_model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY"),
    max_iterations=5,
    temperature=0.7
)

# Create the agent
agent = Agent(config=config)

# Test basic agent
if __name__ == "__main__":
    response = agent.run("What are the latest developments in AI agents?")
    print(response)

Run your first agent:

bash

python agent.py

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:

python

import os
from dotenv import load_dotenv
from antigravity import Agent, Tool, AgentConfig
import requests
from datetime import datetime

load_dotenv()

# Define custom tools
class WebSearchTool(Tool):
    """Searches the web for information"""

    name = "web_search"
    description = "Search the web for current information and news"
    input_schema = {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query"
            }
        },
        "required": ["query"]
    }

    def execute(self, query: str) -> str:
        """Execute web search"""
        try:
            # In production, use a proper search API (SerpAPI, Google Search, etc.)
            # This is a simplified example
            results = f"Search results for '{query}': [Mock data - replace with real API]"
            return results
        except Exception as e:
            return f"Search failed: {str(e)}"

class DataAnalysisTool(Tool):
    """Analyzes and summarizes data"""

    name = "data_analysis"
    description = "Analyze and extract insights from structured data"
    input_schema = {
        "type": "object",
        "properties": {
            "data": {
                "type": "string",
                "description": "Data to analyze (JSON or CSV)"
            },
            "analysis_type": {
                "type": "string",
                "description": "Type of analysis (summary, trends, outliers)"
            }
        },
        "required": ["data", "analysis_type"]
    }

    def execute(self, data: str, analysis_type: str) -> str:
        """Execute data analysis"""
        try:
            result = f"Analysis ({analysis_type}) of provided data: [Results]"
            return result
        except Exception as e:
            return f"Analysis failed: {str(e)}"

# Configure agent with tools
config = AgentConfig(
    name="ResearchAgent",
    description="An autonomous research agent with web and data analysis capabilities",
    llm_model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY"),
    max_iterations=5,
    temperature=0.7,
    tools=[
        WebSearchTool(),
        DataAnalysisTool()
    ]
)

agent = Agent(config=config)

if __name__ == "__main__":
    # Agent now has access to search and analysis tools
    response = agent.run(
        "Research the current state of AI agents in 2026. "
        "Search for recent news, synthesize findings, and provide a summary."
    )
    print(response)

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:

python

import os
from dotenv import load_dotenv
from antigravity import Agent, AgentConfig, MemoryConfig, MemoryType

load_dotenv()

# Configure memory systems
memory_config = MemoryConfig(
    # Short-term memory for current conversation
    short_term=MemoryType.CONVERSATION,
    short_term_capacity=10,  # Keep last 10 messages

    # Long-term memory for learning patterns
    long_term=MemoryType.SEMANTIC,
    long_term_capacity=1000,

    # Episodic memory for decision logs
    episodic=MemoryType.EVENT_LOG,
    episodic_capacity=100,

    # Enable memory persistence to database
    persist=True,
    persistence_backend="sqlite://./agent_memory.db"
)

# Agent configuration with memory
config = AgentConfig(
    name="ResearchAgent",
    description="Research agent with learning capability",
    llm_model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY"),
    memory=memory_config,
    max_iterations=5,
    temperature=0.7
)

agent = Agent(config=config)

if __name__ == "__main__":
    # First interaction - agent learns
    response1 = agent.run("What's your understanding of AI agents?")
    print("First response:", response1)

    # Second interaction - agent remembers and builds on previous knowledge
    response2 = agent.run("Based on what we discussed earlier, what are the key challenges?")
    print("Second response:", response2)

    # Agent's memory persists between sessions
    print("Agent memory utilization:", agent.memory.get_stats())

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:

python

from antigravity import Workflow, WorkflowStep, Agent, AgentConfig
import os
from dotenv import load_dotenv

load_dotenv()

# Define workflow steps
class ResearchStep(WorkflowStep):
    """Step 1: Gather research from multiple sources"""

    async def execute(self, context):
        agent = context.agent
        result = await agent.run(
            "Search for recent developments in autonomous AI agents. "
            "Focus on enterprise applications and real-world deployments."
        )
        context.research_findings = result
        return result

class SynthesisStep(WorkflowStep):
    """Step 2: Synthesize findings into coherent narrative"""

    async def execute(self, context):
        agent = context.agent
        result = await agent.run(
            f"Based on these research findings: {context.research_findings}

"
            "Create a coherent summary identifying key trends, "
            "emerging best practices, and future directions."
        )
        context.synthesis = result
        return result

class ReportStep(WorkflowStep):
    """Step 3: Generate professional report"""

    async def execute(self, context):
        agent = context.agent
        result = await agent.run(
            f"Based on this synthesis: {context.synthesis}

"
            "Generate a professional report with executive summary, "
            "detailed findings, recommendations, and conclusion. "
            "Format for presentation to stakeholders."
        )
        context.report = result
        return result

# Build workflow
workflow = Workflow(
    name="AIAgentResearchWorkflow",
    steps=[
        ResearchStep(name="research"),
        SynthesisStep(name="synthesis"),
        ReportStep(name="report")
    ]
)

# Execute workflow
if __name__ == "__main__":
    config = AgentConfig(
        name="WorkflowAgent",
        llm_model="gpt-4",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    agent = Agent(config=config)
    result = workflow.run(agent)
    print("Workflow complete!")
    print("Final report:", result.report)

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:

python

import unittest
from agent import agent
import os
from dotenv import load_dotenv

load_dotenv()

class TestResearchAgent(unittest.TestCase):

    def setUp(self):
        """Initialize test fixtures"""
        self.agent = agent

    def test_agent_initialization(self):
        """Test agent initializes correctly"""
        self.assertIsNotNone(self.agent)
        self.assertEqual(self.agent.name, "ResearchAgent")

    def test_basic_execution(self):
        """Test agent can execute basic task"""
        response = self.agent.run("What is an AI agent?")
        self.assertIsNotNone(response)
        self.assertGreater(len(response), 0)

    def test_tool_availability(self):
        """Test all configured tools are available"""
        available_tools = self.agent.list_tools()
        self.assertIn("web_search", [t.name for t in available_tools])
        self.assertIn("data_analysis", [t.name for t in available_tools])

    def test_memory_persistence(self):
        """Test memory system works correctly"""
        initial_memory = self.agent.memory.count()
        self.agent.run("Test query for memory")
        final_memory = self.agent.memory.count()
        self.assertGreater(final_memory, initial_memory)

    def test_error_handling(self):
        """Test agent handles errors gracefully"""
        response = self.agent.run("Generate an error with invalid tool: xyz_tool")
        self.assertIsNotNone(response)
        # Agent should gracefully handle tool invocation failures

if __name__ == "__main__":
    unittest.main()

Run tests:

bash

python -m unittest test_agent.py -v

Enable debug logging:

python

import logging
logging.basicConfig(level=logging.DEBUG)

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:

dockerfile

FROM python:3.10-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

ENV OPENAI_API_KEY=${OPENAI_API_KEY}
ENV ANTIGRAVITY_WORKSPACE=${ANTIGRAVITY_WORKSPACE}

CMD ["python", "agent.py"]

Create requirements.txt:

antigravity-sdk==0.2.0
openai==1.3.0
python-dotenv==1.0.0
requests==2.31.0
gunicorn==21.2.0

Build and run:

bash

docker build -t my-research-agent .
docker run -e OPENAI_API_KEY=$OPENAI_API_KEY my-research-agent

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

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

Introduction

Prerequisites

Step 1: Installation (3 minutes)

Step 2: Creating Your First Agent (5 minutes)

Step 3: Adding Tools (8 minutes)

Step 4: Configuring Memory Systems (6 minutes)

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

Step 6: Testing and Debugging (4 minutes)

Step 7: Deploying Your Agent (3 minutes)

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

Common Pitfalls and How to Avoid Them

Key Takeaways

Build Your First AI Agent Now

Written by Keyur Patel

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