Synergy between Google Antigravity, OpenCode, and Spec-Kit.

 This technical blog post explores the powerful synergy between Google Antigravity, OpenCode, and Spec-Kit. Together, these tools form a robust stack that moves AI development beyond “vibe coding” into the realm of structured, verifiable, and scalable engineering.

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 Beyond Vibe Coding: Mastering AI Development with Google Antigravity, OpenCode, and Spec-Kit

In the rapidly evolving world of AI-assisted programming, we often face the “Vibe Coding” dilemma: AI generates code that looks correct at first glance but fails to meet requirements or follow project standards.

To solve this, we are looking at a “Power Trio” of tools: Google Antigravity, OpenCode, and Spec-Kit. 

 The Power Trio: How They Complement Each Other

When combined, these three tools cover the entire development lifecycle—from defining requirements to multi-agent orchestration.

Feature	Google Antigravity	OpenCode	Spec-Kit
Primary Role	Agent-first IDE & Orchestration	Open-source coding agent	Spec-driven requirements
Strength	Managing “Missions” at scale	Multi-model support & Local CLI	Verifiable, executable specs
Environment	Enterprise-grade, Proprietary	Flexible, Community-driven	Open-source validation

Why This Combination Works

• Spec-Kit is the “Brain” : It uses commands like /specify and /plan to create a single source of truth.
• Antigravity is the “Hands” : It takes the plan and spawns specialized agents (Frontend, API, QA) to execute it.
• OpenCode is the “Freedom” : It provides an escape hatch from “walled gardens,” allowing you to use local models or specialized terminal-based agents for sensitive tasks.

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 The Recommended Workflow

To get the most out of this stack, follow this four-phase approach:

1. Phase 1: Governance (Spec-Kit)  – Use /speckit.constitution to define your project rules (e.g., “Use Java 21,” “No try-catch in handlers”).
2. Phase 2: The Blueprint (Spec-Kit)  – Run /speckit.specify to define the feature, followed by /speckit.plan.
3. Phase 3: Execution (Antigravity)  – Use Antigravity’s Agent Manager to assign tasks to different agents and monitor them via the Mission Control dashboard.
4. Phase 4: Specialization (OpenCode)  – Use OpenCode in your terminal for local security audits or complex refactoring using specific models like Claude or Llama.

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 Step-by-Step POC: Building a Spring Boot Calculator

Let’s put this into practice by building a simple Spring Boot application on a Windows environment.

1. Installation & Setup

First, ensure your environment is ready:

• Spec-Kit: Install via Python:

pip install specifyplus
specifyplus --version

• Google Antigravity: Download the IDE from antigravity.google.
• OpenCode: Download the installer from opencode.ai. Ensure opencode-cli is added to your PATH.

2. Initializing the Project

Create your project folder and initialize Spec-Kit:

mkdir SimpleSpringbootProject
cd SimpleSpringbootProject
specifyplus init SimpleSpringbootProjectGAOTSK

During initialization, select OpenCode as your LLM provider.

3. Integrating with Antigravity

Open the Google Antigravity IDE. To connect OpenCode, open the terminal inside Antigravity and run the connection command.

Pro Tip: You can connect Antigravity’s LLMs (like Gemini) to OpenCode by using the /connect command within the OpenCode interface.

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Now as we have selected opencode for llm and not the gemini we need to now install open code. This will do inside the terminal of Google Antigravity. To open the opencode inside google antigravity

Lets move the terminal to right side for better looks

Now let connect to open code

Note:- If you want to connect the LLM of antigravity you can do through opencode by using this command /connect and then select google and you will be able to see the LLM of antigravity inside opencode.

 The Spec-Driven Development Process

Now, we use the Spec-Kit commands to build our application logic.

Step 1: /constitution (Setting the Rules) 

Define your tech stack principles so the AI doesn’t have to guess.

Prompt: /constitution Use Java 21, Spring Boot 3.4, Maven, and follow clean code principles. Ensure all methods have unit tests.

Step 2: /specify (The “What”) 

Describe the calculator logic.

Prompt: /specify I want to build a simple Calculator service with 'add' and 'subtract' methods.

Step 3: /plan (The “How”) 

Define the architecture.

Prompt: /plan Create a REST API with a Controller named CalculatorController. Use GET endpoints: /add and /subtract. Organize into 'web' and 'service' layers.

Step 4: /tasks (The Backlog) 

Generate a tasks.md file that breaks the plan into executable steps.

Step 5: /implement (The Code) 

Watch as the AI writes the Java files, pom.xml, and unit tests automatically.

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 Running the Application

To execute the project, we can use natural language through OpenCode:

Command: "execute backend pom project"

Lets upgrade the JDK to higher version and execute the same command.

If the execution fails (e.g., due to an incorrect JDK version), simply upgrade your environment and re-run. Once successful, you can package your app:

Prompt: "create deployment jar using mvn clean install command"

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Now lets creat the jar for the same.we are using this prompt “create deployement jar using mvn clean install command”

 Wrap Up

By combining Spec-Kit’s precision, Antigravity’s orchestration, and OpenCode’s flexibility, you create a development environment that is both fast and disciplined.

🔗 Source Code: GitHub Repository