Google Antigravity is an agent-first IDE built by Google DeepMind's Advanced Agentic Coding team. It approaches context management differently from other AI coding tools because it is designed from the ground up around agentic workflows, where the AI is not just an assistant responding to prompts, but an autonomous agent that plans, executes, tracks progress, and retains knowledge across sessions. Its context management system centers on three pillars: Skills for reusable capability, Knowledge Items for persistent memory, and Artifacts for transparent documentation of its work.

This guide covers how to structure and manage context in Antigravity to get the most from its agentic capabilities.

How Antigravity Manages Context

Antigravity assembles its working context from multiple sources, layered by persistence:

1. Knowledge Items (KIs) - persistent, distilled knowledge from past conversations
2. Skills (SKILL.md files) - reusable instruction sets for specific capabilities
3. Workflows - step-by-step guides in the .agents/workflows/ directory
4. Conversation history - the current and past interactions
5. The codebase - files, directories, and project structure
6. MCP servers - external tools and data sources
7. Task artifacts - implementation plans, walkthroughs, and checklists the AI creates

What makes Antigravity distinctive is that it actively generates and maintains its own context artifacts. The AI creates task checklists, implementation plans, and walkthroughs as it works, and these become part of the persistent context for future sessions.

Skills: Reusable Capability Packages

Skills are Antigravity's primary mechanism for defining reusable capabilities. Each Skill is a folder containing a SKILL.md file with YAML frontmatter and detailed Markdown instructions.

Skill Structure

.agents/skills/
  my-skill/
    SKILL.md          # Required: instructions with YAML frontmatter
    scripts/          # Optional: helper scripts
    examples/         # Optional: reference implementations
    resources/        # Optional: templates or assets

SKILL.md Format

---
name: deploy-to-staging
description: Deploy the application to the staging environment
---

## Prerequisites
- Docker must be installed and running
- AWS CLI must be configured with staging credentials
- The current branch must have passing CI

## Steps
1. Build the Docker image with the staging configuration
2. Push the image to ECR
3. Update the ECS task definition
4. Trigger the deployment
5. Verify the health check endpoint responds

## Verification
- Check that the /health endpoint returns 200
- Verify the deployed version matches the expected Git SHA
- Run the smoke test suite against staging

When to Create Skills

Create a Skill when you have a workflow that:

• You perform more than once
• Requires specific steps in a specific order
• Benefits from consistent execution across team members
• Involves domain knowledge that is not obvious from the codebase alone

Skills vs. Other Context Mechanisms

Skills are for procedural knowledge ("how to do X"). They differ from:

• Knowledge Items which store factual knowledge ("what is X")
• GEMINI.md or CLAUDE.md style files which provide ambient project context
• Artifacts which document specific work done in a specific session

Knowledge Items: Persistent Memory Across Conversations

Knowledge Items (KIs) are Antigravity's mechanism for retaining knowledge across conversations. Unlike conversation history (which is session-bound), KIs are distilled, curated facts that persist indefinitely.

How KIs Work

At the end of each conversation, a separate Knowledge Subagent analyzes the conversation and extracts key information into KIs. Each KI has:

• metadata.json: summary, timestamps, references to original conversations
• artifacts/: related files, documentation, and analysis

KIs are stored in the Knowledge directory and are automatically loaded when starting new conversations. Antigravity checks KI summaries at the beginning of every session to avoid redundant work.

What Gets Stored as KIs

• Architecture decisions and their rationale
• Troubleshooting discoveries and resolutions
• Implementation patterns specific to your project
• Configuration details and their implications
• Integration specifics for external services
• Performance characteristics and optimization strategies

Using KIs Effectively

The most important rule for KIs is: always check them before starting research. If you are about to analyze a codebase module, check whether a KI already covers that analysis. This prevents redundant work and ensures continuity across sessions.

You can also reference specific KIs in conversations by pointing Antigravity at the KI's artifact files. This is especially useful when building on previous work or when onboarding new team members who can review the accumulated KIs.

Artifacts: The Transparency System

Antigravity creates artifacts as structured Markdown documents that make the agent's work transparent and reviewable. Key artifact types include:

task.md

A checklist that tracks progress on the current task. Antigravity creates this at the start of complex work and updates it as it progresses:

# Feature: User Authentication

- [x] Research existing auth patterns
- [x] Create implementation plan
- [/] Implement JWT token generation
- [ ] Add refresh token support
- [ ] Write integration tests
- [ ] Update API documentation

implementation_plan.md

Created during the PLANNING phase, this documents the proposed changes, file modifications, and verification strategy before any code is written. You review and approve (or modify) this plan before Antigravity proceeds to execution.

walkthrough.md

Created after completing work, this documents what was accomplished, what was tested, and the results. It serves as a record of the work and can be reviewed by team members.

Why Artifacts Matter for Context

Artifacts create a structured record that Antigravity can reference in future sessions. When you return to a project, the agent can read the previous implementation plan and walkthrough to understand what was done and why. This is far more efficient than re-analyzing the codebase from scratch.

Thinking About Context Levels in Antigravity

Minimal Context (Quick Tasks)

For simple questions or small fixes, just ask. Antigravity can explore the codebase, read relevant files, and provide answers without additional setup. Its file exploration tools are fast and respect .gitignore patterns.

Moderate Context (Feature Work)

For typical feature development, let Antigravity's Planning phase do the heavy lifting. It will:

1. Analyze the codebase to understand the current architecture
2. Create an implementation plan for your review
3. Execute the plan once approved
4. Verify the changes

The PLANNING > EXECUTION > VERIFICATION workflow is built into Antigravity's DNA, and each phase generates artifacts that carry context forward.

Comprehensive Context (Ongoing Projects)

For sustained work across multiple sessions, invest in Skills and ensure KIs are accumulating properly. Over time, Antigravity builds a rich knowledge base about your project that makes each subsequent session more productive.

Multi-Model Support and Context Routing

Antigravity supports multiple AI models and can use different models for different subtasks. This means context management extends to model selection: some tasks benefit from larger context windows, while others benefit from faster inference.

The agent handles this transparently, but being aware of it helps you understand why some responses might take longer (larger model processing more context) while others are faster (smaller model handling a focused subtask).

Browser Recording and Visual Context

Antigravity includes a built-in browser interaction system that records all browser actions as WebP videos. This creates a unique form of context: visual proof of work that can be reviewed later.

For frontend development, this means Antigravity can:

• Navigate to web applications and interact with UI elements
• Take screenshots to verify visual changes
• Record step-by-step interactions for documentation

These recordings become part of the walkthrough artifact, providing visual evidence that changes work as intended.

Conversation History and Context Summaries

Antigravity maintains conversation logs and summaries that persist across sessions. When you start a new conversation, the system provides:

• Summaries of recent conversations
• KI summaries with artifact paths
• Information about previously edited and viewed files

This means Antigravity starts each session with awareness of what happened in recent sessions, reducing the need to re-explain context that was covered before.

MCP Server Support

Antigravity supports MCP servers for connecting to external tools and data sources. Configuration follows the standard MCP pattern familiar from other tools.

Practical Use Cases

• Database access: Let Antigravity query your development database to understand schema and data
• Browser automation: Verify frontend changes visually
• Git hosting: Interact with GitHub or GitLab for PR management
• Documentation systems: Access internal wikis or knowledge bases

When to Use MCP

Use MCP when the task requires information from outside the codebase. For code-only work, Antigravity's built-in file system tools are sufficient. MCP adds the most value for tasks that span multiple systems (for example, updating both code and documentation, or verifying a code change against a running application).

External Documents: PDFs vs. Markdown

Markdown Is the Native Format

Skills, KIs, and artifacts are all Markdown. If you are creating context documents for Antigravity, use Markdown.

For External References

PDF documents can be provided as context through conversation uploads. However, for persistent reference material, converting to Markdown and placing it in a project directory (or as a Skill resource) provides better integration with Antigravity's context system.

Advanced Patterns

The Skill-Driven Development Pattern

Create Skills for every major workflow in your development process:

• deploy-staging for deployment
• create-api-endpoint for new endpoints
• database-migration for schema changes
• security-audit for security reviews

When you need to perform one of these tasks, point Antigravity at the relevant Skill. This ensures consistent execution regardless of which team member is working.

The Knowledge Accumulation Pattern

Treat KIs as a growing knowledge base about your project:

1. First session: Antigravity learns the basic architecture
2. Subsequent sessions: KIs accumulate details about specific modules, patterns, and decisions
3. Over time: Antigravity starts with a deep understanding of your project every session

This compounds over weeks and months, making the AI increasingly effective.

The Paired Review Pattern

Use Antigravity's PLANNING phase as a design review:

1. Describe the feature or change you want
2. Review the implementation plan Antigravity creates
3. Provide feedback and iterate on the plan
4. Only approve execution once the plan meets your standards

This catches design issues before code is written, saving significant time.

The Task Decomposition Pattern

For large features, let Antigravity break the work into multiple task boundary segments:

1. Tell Antigravity the overall goal
2. It creates a task.md with subtasks
3. Each subtask gets its own PLANNING > EXECUTION > VERIFICATION cycle
4. The walkthrough artifact captures the full story for future reference

Common Mistakes

1. Ignoring KI summaries. Antigravity provides KI summaries at the start of each conversation. Skipping them leads to redundant work and missed context.

2. Not creating Skills for repeatable work. If you find yourself explaining the same workflow multiple times, it should be a Skill.

3. Skipping the PLANNING phase. Jumping straight to execution means no implementation plan to review. The PLANNING phase is where Antigravity aligns with your intent.

4. Not reviewing artifacts. Implementation plans and walkthroughs are designed for human review. Skipping them defeats the purpose of Antigravity's transparency system.

5. Over-relying on conversation context. Conversation history is ephemeral. For information that should persist, ensure it gets captured in Skills or KIs.

6. Not building Workflows for common tasks. The .agents/workflows/ directory supports step-by-step guides that Antigravity follows precisely. These are particularly useful for onboarding, deployment, and maintenance tasks.

Go Deeper

To learn more about working effectively with AI coding agents and managing context across development workflows, check out these resources by Alex Merced:

• The 2026 Guide to AI-Assisted Development covers AI-assisted development workflows, prompt engineering, and context strategies for software engineers.

• The 2026 Guide to Lakehouses, Apache Iceberg and Agentic AI explores how AI agents are reshaping data architecture and how to build systems that support agentic workflows.

And for a fictional take on where AI is heading:

• The Emperors of A.I. Valley: A Novel of Power, Code, and the War for the Future is a novel about the power struggles and ethical dilemmas behind the companies building the most powerful AI systems in the world.