Antigravity Agent Factory

Build AI-powered development systems that truly understand your project.

Quick Start: New here? See docs/QUICKSTART.md or run python cli/factory_cli.py --quickstart to get running in 5 minutes.

Welcome! Whether you're a solo developer exploring new possibilities or a team building something meaningful together, we're here to help you create an AI development environment grounded in purpose, principles, and care.

Quick Start Options

Option 1: Instant Demo (5 minutes)

See the factory in action with zero configuration:

python cli/factory_cli.py --quickstart

This creates a complete demo project showing what you'll get. Perfect for exploration!

Overview

The Antigravity Agent Factory is a meta-system that generates complete AI agent development systems. It uses a 5-layer deductive-inductive architecture to create agent systems that are technically capable and grounded in clear values.

The 5 Layers (+ Foundation)

Layer	Name	Purpose	Artifact
A0	Love & Trust	Foundation of all being and doing	`.agentrules` foundation
0	Integrity & Logic	Foundational axioms (A1-A5)	`.agentrules` L0 section
1	Purpose	Mission, stakeholders, success	`PURPOSE.md`
2	Principles	Ethical boundaries, quality standards	`.agentrules` principles
3	Methodology	Agile/Kanban/R&D, enforcement, practices	`methodology.yaml`
4	Technical	Stack, agents, skills, templates	`.agent/`, `knowledge/`

What You Get

Generated projects include:

Purpose documentation (PURPOSE.md) - Your mission and success criteria
5-layer .agentrules - AI guidance with axioms, principles, and methodology
AI agent definitions (.agent/agents/) - Specialized assistants for your workflow
Reusable skill definitions (.agent/skills/) - Procedures your agents can follow
Structured knowledge files (knowledge/) - Domain expertise in JSON format
Code and document templates (templates/) - Consistent starting points
21 ready-to-use workflows (workflows/) - Automated development, testing, PM, and deployment
Methodology configuration (workflows/methodology.yaml) - How your team works
Integrity Guardian - Layer 0 protection for all operations
Memory System - User-validated learning from interactions

Workflow Categories:

Category	Examples
Development	Feature development, bugfix resolution, code review, TDD
Quality	Quality gate, BDD, security audit
Agile/PM	Sprint planning, closure, standups, backlog refinement
Domain	Smart contract audit, trading strategies, SAP RAP/CAP, RAG pipelines
Operations	CI/CD pipeline, release management, incident response

Layer 0: Integrity Guardian

Every generated project includes an Integrity Guardian - an active Layer 0 protector that ensures AI agents align with core axioms.

Motto: SDG - Love - Truth - Beauty

Level	Name	Trigger	Response
0	Flow	Natural alignment	Continue normally
1	Nudge	Slight drift	Self-correct subtly
2	Pause	Boundary approached	Explain, ask user
3	Block	Clear violation	Stop, explain with love
4	Protect	Imminent harm	Prevent, then explain

The Guardian monitors for:

Destructive commands (rm -rf /, DROP DATABASE)
Exposed secrets (API keys, passwords, connection strings)
Axiom violations (hidden logic, deception, instruction overrides)

Invoke manually: "Guardian, check this" or "check my alignment"

See GUARDIAN_COORDINATION.md for complete documentation.

Memory System

The Factory includes a Memory System that learns from your interactions and stores user-validated knowledge for future sessions.

Memory Type	Description
Semantic	Long-term user-approved knowledge
Episodic	Session-based observations (temporary)
Pending	Awaiting your approval
Rejected	Prevents re-proposing rejected memories

Key Features:

All memories require user approval (Axiom A2: User Primacy)
Layers 0-2 remain immutable - only knowledge files can be extended
Works offline after initial setup (no API keys required)
Chat-based interaction for memory management

Chat Commands:

"Show my memories"              → List stored memories
"What do you remember about X?" → Semantic search
"Delete the memory about X"     → Remove a memory
"Accept/Reject"                 → Respond to proposals

See MEMORY_SYSTEM.md for complete documentation.

Formal Verification System

The Factory includes mathematically proven guarantees that the core architecture satisfies its foundational axioms. Using Lean 4, we prove:

Component	What's Proven
Axioms (A0-A5)	Formalized with derivation rules
Integrity Guardian	State preservation, user notification, harm prevention
Memory System	User consent required for all permanent memories
Layer Architecture	L0-L2 (Axioms, Purpose, Principles) are immutable

Trust Chain: Factory proofs → Project proofs → Team extensions → All verifiable

Quick Commands:

cd proofs && lake build       # Verify all proofs
scripts/proofs/generate_attestation.sh  # Create cryptographic attestation

See docs/VERIFICATION.md for complete documentation.

Agent Society Verification

A complete dynamic multi-agent verification system (lib/society/) extending formal proofs to runtime behavior. Enables verified agent communication, contracts, and behavior in multi-agent societies.

Module	Purpose
`events/`	Event sourcing with cryptographic hash chains
`verification/`	Axiom compliance monitoring (A0-A5 verifiers)
`contracts/`	Agent contracts with capability/obligation verification
`trust/`	Identity, reputation scoring, trust delegation
`society/`	5 governance patterns + 3 communication protocols
`blockchain/`	Merkle tree anchoring, Solana integration
`hybrid/`	Unified verification with escalation management

Quick Usage:

from lib.society import HybridVerificationSystem
system = HybridVerificationSystem.create_default()
result = system.record_event(event)  # Verify against all axioms

See docs/design/AGENT_SOCIETY_VERIFICATION.md for architecture and docs/SOCIETY_USAGE.md for complete usage guide.

Knowledge Evolution System

The Factory includes an automatic Knowledge Evolution System that keeps blueprints, patterns, and knowledge files current with the latest best practices from trusted sources.

Features

Feature	Description
Multi-Source Updates	GitHub, PyPI, NPM, official docs, community sources
Configurable Modes	Stability-first, awareness-hybrid, freshness-first, subscription
Full Control	Preview, approve, apply, or rollback any update
Traceability	Every change tracked with source attribution

Update Modes

Mode	Best For	Description
`stability_first`	Enterprise	Lock versions, explicit approval required
`awareness_hybrid`	Most Teams	Notify of updates, user approves (default)
`freshness_first`	Startups	Auto-apply non-breaking changes
`subscription`	Focused Teams	Subscribe to specific knowledge files

Quick Usage

"Check for knowledge updates"        # See what's available
"Apply pending updates"              # Apply with confirmation
"Configure knowledge evolution"      # Change settings

Configure in .agent/config/settings.json or use the system-configuration skill.

See docs/KNOWLEDGE_EVOLUTION.md for complete documentation.

Factory -> Generated Project Updates

Generated projects can receive updates from the Factory:

"Check for Factory updates"    # In your generated project
"Sync with Factory"            # Apply available updates

Component	Purpose
`factory-updates` agent	Fetches and applies updates from Factory
`knowledge/project-info.json`	Tracks Factory origin and installed versions
Update channels: `stable` / `latest`	Choose your update preference

This creates a connection between the Factory and generated projects, allowing teams to benefit from ongoing Factory improvements.

Architecture Diagrams

For visual documentation, see the diagrams/ folder:

Diagram	Description
Factory Workflow	Complete generation workflow
Verification Flow	Hallucination detection pipeline
Agent/Skill Architecture	Agent hierarchy and composition
SAP Grounding	SAP-specific integration

Project Structure

antigravity-agent-factory/
├── .agent/
│   ├── agents/                  # Factory's own agents (29 agents)
│   │   └── *.md                 # debug-conductor, git-specialist, integrity-guardian, etc.
│   └── skills/                  # Factory's own skills (127 skills)
│       ├── agent-generation/
│       ├── ai-agent-dev/
│       ├── algo-trading/
│       ├── alignment-check/
│       ├── analyze-knowledge-gaps/
│       ├── axiom-selection/
│       └── ...                       # + more skills
│   ├── patterns/                    # Reusable patterns (113+ files)
│   ├── templates/                   # Code templates (307+ files)
│   ├── blueprints/                  # Technology stack blueprints (34 blueprints)
│   ├── knowledge/                   # Knowledge base (257 files)
│   └── workflows/                   # Workflow definitions
│       ├── python-fastapi/
│   ├── python-streamlit/
│   ├── ai-agent-development/
│   ├── multi-agent-systems/
│   ├── typescript-react/
│   ├── nextjs-fullstack/
│   ├── java-spring/
│   ├── kotlin-spring/
│   ├── csharp-dotnet/
│   ├── n8n-automation/
│   ├── sap-abap/
│   ├── sap-rap/
│   ├── sap-cap/
│   └── sap-cpi-pi/
├── knowledge/                   # Reference data (305+ files)
│   └── *.json                   # Stack, workflow, MCP, security, AI patterns
├── templates/                   # Code and document templates (305+ files)
│   ├── factory/                 # Factory templates (cursorrules, PURPOSE.md, etc.)│   ├── ai/                      # AI agent templates
│   ├── python/                  # Python templates (FastAPI, Streamlit)
│   ├── typescript/              # TypeScript templates (Next.js)
│   ├── java/                    # Java Spring templates
│   ├── csharp/                  # C# Clean Architecture templates
│   ├── abap/                    # SAP ABAP/RAP templates
│   ├── cap/                     # SAP CAP templates
│   ├── integration/             # SAP CPI/PI integration templates
│   ├── automation/              # n8n automation templates
│   ├── workflows/               # CI/CD workflow templates
│   ├── methodology/             # Methodology templates
│   └── docs/                    # Documentation templates
├── docs/                        # Documentation
│   ├── reference/               # Detailed reference docs
│   ├── research/                # Research paper series
│   ├── examples/                # Example walkthroughs
│   └── *.md                     # Guides and tutorials
├── diagrams/                    # Architecture diagrams (Mermaid)
├── scripts/                     # Utility scripts
├── cli/                         # CLI interface
│   └── factory_cli.py
├── tests/                       # Test suite
│   ├── unit/
│   ├── integration/
│   ├── validation/
│   └── fixtures/
├── .github/
│   └── workflows/               # CI/CD workflows
├── .agentrules                 # Factory behavior rules├── CHANGELOG.md                 # Version history
└── README.md                    # This file

Available Blueprints

The factory includes 27 blueprints organized by category. For detailed documentation including use cases, technology stacks, and configuration options, see Blueprints Reference.

Python & AI

Blueprint	Stack	Description
`python-fastapi`	FastAPI, SQLAlchemy	REST API development
`python-streamlit`	Streamlit, Pandas	Data apps and dashboards
`ai-agent-development`	LangChain, LangGraph	AI agent systems (single agents)
`multi-agent-systems`	LangGraph, LangChain	Orchestrated multi-agent systems
`python-ml-experimentation`	PyTorch, scikit-learn, MLflow	ML research and experimentation
`python-deep-learning`	Transformers, PEFT, Accelerate	Deep learning with Hugging Face
`python-rag-system`	LangChain, ChromaDB, RAGAS	Production RAG systems
`python-multi-agent`	CrewAI, AutoGen	Multi-agent orchestration
`python-fine-tuning`	PEFT, TRL, LoRA	LLM fine-tuning

Starter (Beginner-Friendly)

Blueprint	Stack	What You'll Learn
`starter-ml-classification`	scikit-learn	Train/test split, metrics, basic ML
`starter-chatbot`	OpenAI, Streamlit	Chat completions, streaming, prompts
`starter-rag`	LangChain, ChromaDB	Chunking, embeddings, retrieval

Blockchain & Trading

Blueprint	Stack	Description
`solidity-ethereum`	Solidity, Hardhat, Foundry	Ethereum smart contracts
`defi-protocols`	Solidity, Chainlink, OpenZeppelin	DeFi protocols (AMM, lending)
`solana-rust`	Rust, Anchor, Solana SDK	Solana program development
`quantitative-trading`	Python, vectorbt, pandas	Algorithmic trading and backtesting
`financial-ai-agents`	LangChain, vectorbt	AI agents for trading

Web & Frontend

Blueprint	Stack	Description
`typescript-react`	TypeScript, React, Vite	Web application development
`nextjs-fullstack`	Next.js 14+, Prisma	Full-stack React development

Enterprise & Backend

Blueprint	Stack	Description
`java-spring`	Java, Spring Boot, JPA	Enterprise Java applications
`kotlin-spring`	Kotlin, Spring Boot 3, WebFlux	Reactive Kotlin microservices
`csharp-dotnet`	C#, .NET 8+, Entity Framework	Enterprise .NET development
`n8n-automation`	n8n, JavaScript, REST	Workflow automation

SAP

Blueprint	Stack	Description
`sap-abap`	ABAP, Clean ABAP	SAP ABAP development
`sap-rap`	ABAP, RAP, Fiori	SAP RESTful ABAP Programming
`sap-cap`	Node.js/Java, CDS, SAP BTP	SAP Cloud Application Programming
`sap-cpi-pi`	Groovy, Java, SAP CPI/PI	SAP integration development

Team Workshop System

Axiom 0: Love, Truth, and Beauty

The Team Workshop System is grounded in Axiom 0:

"All being and doing is grounded in Love, Truth, and Beauty."

This foundational axiom precedes all technical axioms (A1-A5) and ensures that collaborative design emerges from:

Love: Genuine care for humanity and those who depend on our work
Truth: Honesty, transparency, and verifiability in all things
Beauty: Harmony, elegance, and wholeness in what we create
Trust emerges naturally when Love meets Truth over time

Workshop Series Overview

Workshop	Duration	Games	Output
Vision Quest	2-3h	Future Headlines, Stakeholder Safari	Team Charter
Ethics Arena	2h	Dilemma Duel, Value Auction	Ethics Framework
Stack Safari	2-3h	Trade-Off Tetris, Architecture Pictionary	Stack Configuration
Agent Assembly	3-4h	Agent Trading Cards, Skill Bingo	Agent Roster
Integration Celebration	1.5-2h	Demo Derby, Gratitude Circle	Complete System

Game Library

Creative Games (Vision & Ideation):

Future Headlines: Write newspaper headlines from 5 years in the future
Stakeholder Safari: Role-play as different stakeholders
Dream Demo: Describe the ideal product demo without constraints

Strategic Games (Ethics & Decisions):

Dilemma Duel: Debate ethical scenarios with no perfect answer
Value Auction: Bid limited points on values to reveal priorities
Trade-Off Tetris: Fit constraints into limited capacity

Collaborative Games (Design & Celebration):

Agent Trading Cards: Create collectible cards for each agent
Skill Bingo: Fill bingo cards with needed skills
Architecture Pictionary: Draw and guess system components
Demo Derby: Showcase all workshop artifacts
Gratitude Circle: Express appreciation to team members

Team Size Adaptations

Size	Format
Small (2-5)	Everyone participates in everything, intimate discussions
Medium (6-12)	Breakout groups for games, plenary for synthesis
Large (13+)	Representative groups, async pre-work, sync synthesis

For complete facilitation instructions, see docs/TEAM_WORKSHOP_GUIDE.md.

Factory Agents

Agent	Purpose
`requirements-architect`	Gather and validate project requirements through 5-phase questionnaire
`stack-builder`	Configure technology stack and select appropriate blueprints
`workflow-designer`	Design development workflows and trigger integrations
`knowledge-manager`	Structure domain knowledge and generate knowledge files
`template-generator`	Generate code and document templates
`workshop-facilitator`	Facilitate team workshops for collaborative agent system design
`onboarding-architect`	Orchestrate onboarding of existing repositories into the factory ecosystem

Available Pattern Agents

These agents can be included in generated projects:

Agent	Purpose
`code-reviewer`	Review code against best practices, style guides, and quality standards
`test-generator`	Generate unit tests, integration tests, and test plans
`explorer`	Explore and understand codebases
`documentation-agent`	Generate and maintain README, API docs, and ADRs

Factory Skills

Skill	Description
`requirements-gathering`	5-phase interactive requirements elicitation
`team-workshop-onboarding`	Collaborative 5-workshop series with games for teams
`stack-configuration`	Technology stack selection and configuration
`workflow-generation`	Workflow pattern generation and customization
`agent-generation`	Agent definition file generation
`skill-generation`	Skill definition with references generation
`knowledge-generation`	JSON knowledge file generation
`template-generation`	Code and document template generation
`cursorrules-generation`	.agentrules file generation
`axiom-selection`	Layer 0 axiom configuration including A0
`purpose-definition`	Layer 1 purpose definition (mission, stakeholders, success)
`methodology-selection`	Layer 3 methodology selection (Agile/Kanban/R&D)
`enforcement-selection`	Enforcement pattern selection (quality, safety, integrity)
`practice-selection`	Practice pattern selection (daily, craft, alignment)
`pattern-feedback`	Inductive learning from pattern usage
`onboarding-flow`	Onboard existing repositories into factory ecosystem
`alignment-check`	Verify understanding and alignment before implementations
`shell-platform`	Platform-specific shell command handling
`readme-validation`	Validate README structure matches actual filesystem
`workshop-export`	Export learning workshops to standalone projects with AI assistance

Available Pattern Skills

These skills can be included in generated projects:

Skill	Category	Description
`bugfix-workflow`	workflow	Ticket-based bug fix workflow with Jira integration
`feature-workflow`	workflow	Specification-based feature implementation
`tdd`	testing	Test-driven development workflow
`grounding`	verification	Verify data structures before implementation
`strawberry-verification`	verification	Hallucination detection using information theory
`code-templates`	core	Stack-specific code generation
`security-audit`	verification	OWASP-based security vulnerability detection
`code-review`	workflow	Structured code review process

Agent vs Skill Relationship

flowchart TB
    subgraph Agents["Agents (Orchestrators)"]
        A1["requirements-architect"]
        A2["stack-builder"]
        A3["template-generator"]
    end
    
    subgraph Skills["Skills (Procedures)"]
        S1["requirements-gathering"]
        S2["stack-configuration"]
        S3["agent-generation"]
        S4["skill-generation"]
    end
    
    subgraph Knowledge["Knowledge (Data)"]
        K1["patterns/*.json"]
        K2["blueprints/*.json"]
    end
    
    A1 --> S1
    A2 --> S2
    A3 --> S3
    A3 --> S4
    S1 --> K1
    S2 --> K2
    
    style Agents fill:#e3f2fd
    style Skills fill:#e8f5e9
    style Knowledge fill:#fff3e0

Layered Requirements Gathering

Depth Options

Option	Phases	Best For
Quick Start	Pre-Phase + Phase 0 + Phases 1-5	Rapid prototyping
Standard	All phases with templates	Most projects
Comprehensive	All phases + enforcement/practices	Enterprise, critical

Pre-Phase: Layer 0 - Axiom Configuration

Select core axioms (A1-A5 always included)
Choose optional axioms (A6-A10)
Configure derivation rules

Phase 0: Layer 1 - Purpose Definition

Mission statement (verifiable)
Primary stakeholders (specific)
Success criteria (measurable)

Phases 0.6-0.7: Layers 2-3 (Standard+)

Ethical boundaries and quality standards
Methodology selection (Agile/Kanban/R&D/Enterprise)
Team size and coordination patterns

Phases 0.8-0.9: Enforcement & Practices (Comprehensive)

Quality, safety, integrity enforcement
Daily, craft, alignment practices

Phases 1-5: Layer 4 - Technical

Project context and domain
Technology stack and frameworks
Workflow triggers and artifacts
Knowledge domain and conventions
Agent and skill capabilities

Generated Project Structure

Generated projects include all 5-layer artifacts:

{PROJECT_NAME}/
├── .agent/
│   ├── agents/               # AI agent definitions
│   └── skills/               # Reusable skill definitions
├── knowledge/                # Structured reference data (JSON)
├── templates/                # Code and document templates
├── workflows/
│   └── methodology.yaml      # Layer 3: Methodology config
├── src/                      # Source code
├── tests/                    # Test files
├── docs/                     # User documentation
├── .agentrules              # 5-layer agent rules (L0-L4)
├── PURPOSE.md                # Layer 1: Mission & purpose
├── enforcement.yaml          # Enforcement patterns (Comprehensive)
├── practices.yaml            # Team practices (Comprehensive)
└── README.md                 # Project documentation

Key Generated Files

File	Layer	Description
`.agentrules`	0-4	Complete 5-layer agent behavior rules
`PURPOSE.md`	1	Mission, stakeholders, success criteria
`enforcement.yaml`	2+	Quality, safety, integrity enforcement
`practices.yaml`	3+	Daily, craft, alignment practices
`methodology.yaml`	3	Methodology ceremonies and coordination

flowchart TB
    subgraph Project["Generated Project"]
        subgraph Antigravity[".agent/"]
            A["agents/*.md"]
            S["skills/*/SKILL.md"]
        end
        K["knowledge/*.json"]
        T["templates/*"]
        CR[".agentrules"]
    end
    
    A -->|"uses"| S
    S -->|"queries"| K
    CR -->|"configures"| A
    
    style Antigravity fill:#e3f2fd
    style K fill:#fff3e0

MCP Server Integration

The factory includes 50+ MCP servers across 6 categories. See the full MCP Servers Guide for details.

Starter Packs

Pack	Servers	Best For
Minimal	filesystem, git, memory	Any project
Web Developer	+ github, postgresql, playwright	Web apps
Data Science	+ jupyter, bigquery, pinecone	Data/ML projects
AI Agent	+ langgraph, knowledge-graph, chromadb	Agent development
Enterprise	+ atlassian, slack, sentry	Team projects
DevOps	+ docker, terraform, datadog	Infrastructure

Quick Start

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
    },
    "git": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-git", "--repository", "."]
    },
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

For detailed setup instructions, see docs/MCP-SERVERS.md.

Configuration File Format

You can create projects from YAML or JSON configuration:

# project-config.yaml
project_name: my-api-project
project_description: REST API with FastAPI
domain: web-development
primary_language: python
frameworks:
  - fastapi
  - sqlalchemy
triggers:
  - jira
  - confluence
agents:
  - code-reviewer
  - test-generator
skills:
  - bugfix-workflow
  - feature-workflow
  - tdd
mcp_servers:
  - name: atlassian
    url: https://mcp.atlassian.com/v1/sse
    purpose: Jira/Confluence integration

Extending the Factory

Adding New Blueprints

Create directory: blueprints/{blueprint-id}/
Create blueprint.json with:
- Metadata (name, description, tags)
- Stack configuration
- Agent and skill references
- Template paths

Adding New Patterns

Create pattern JSON in appropriate patterns/ directory
Follow the pattern schema in patterns/{type}/{type}-pattern.json
Reference pattern in blueprints

Adding New Skills

Create skill directory: .agent/skills/{skill-name}/
Create SKILL.md with frontmatter and process documentation
Add to factory's skill registry

Development

Requirements

Python 3.10+
Antigravity IDE
PyYAML (for YAML config support)

Tool Paths: Commands below use default Windows paths from .agent/config/tools.json. See Configuration Guide to customize for your environment. See Path Configuration Guide for directory customization and environment variables.

Installing Development Dependencies

# Install test dependencies
C:\App\Anaconda\Scripts\pip.exe install -r requirements-dev.txt

Running Tests

The project includes a comprehensive pytest-based test suite with unit tests, integration tests, and validation tests.

# Run all tests
C:\App\Anaconda\python.exe -m pytest tests/ -v

# Run with coverage report
C:\App\Anaconda\python.exe -m pytest tests/ --cov=scripts --cov=cli --cov-report=html

# Run specific test categories
C:\App\Anaconda\python.exe -m pytest tests/unit/ -v           # Unit tests
C:\App\Anaconda\python.exe -m pytest tests/integration/ -v    # Integration tests
C:\App\Anaconda\python.exe -m pytest tests/validation/ -v     # Schema validation tests

# Run specific test file
C:\App\Anaconda\python.exe -m pytest tests/unit/test_project_config.py -v

# Run tests matching a pattern
C:\App\Anaconda\python.exe -m pytest tests/ -k "blueprint" -v

For detailed testing documentation, see docs/TESTING.md.

Test Suite Structure

tests/
├── conftest.py                 # Shared pytest fixtures
├── unit/                       # Unit tests (71 tests)
│   ├── test_project_config.py  # ProjectConfig dataclass tests
│   ├── test_project_generator.py # ProjectGenerator class tests
│   └── test_pattern_loading.py # Pattern/blueprint loading tests
├── integration/                # Integration tests (71 tests)
│   ├── test_cli.py             # CLI command tests
│   └── test_generation.py      # End-to-end generation tests
├── validation/                 # Schema validation tests (71 tests)
│   ├── test_blueprint_schema.py
│   ├── test_pattern_schema.py
│   └── test_knowledge_schema.py
└── fixtures/                   # Test fixture files
    ├── sample_config.yaml
    ├── sample_config.json
    └── minimal_blueprint.json

Manual CLI Testing

# Run the CLI help
C:\App\Anaconda\python.exe cli\factory_cli.py --help

# List blueprints
C:\App\Anaconda\python.exe cli\factory_cli.py --list-blueprints

# List patterns
C:\App\Anaconda\python.exe cli\factory_cli.py --list-patterns

Testing Generation

# Generate test project from blueprint
C:\App\Anaconda\python.exe cli\factory_cli.py --blueprint python-fastapi --output C:\Temp\test-project

# Generate from config file
C:\App\Anaconda\python.exe cli\factory_cli.py --config tests\fixtures\sample_config.yaml --output C:\Temp\yaml-project

Continuous Integration

The project uses GitHub Actions for CI/CD. Tests run automatically on:

Push to main or develop branches
Pull requests to main or develop branches

The CI pipeline includes:

Test Matrix: Python 3.10, 3.11, 3.12 on Ubuntu and Windows
Code Quality: Ruff linter checks
JSON Validation: Syntax validation for all JSON files
Generation Test: End-to-end project generation verification

See .github/workflows/ci.yml for the full configuration.

Documentation

Comprehensive Reference

Document	Description
Factory Reference	Start here - Complete overview of the factory with links to detailed references
Blueprints Reference	All 17 technology blueprints with detailed specifications
Patterns Reference	Agent, skill, axiom, methodology, and other patterns
Knowledge Files Reference	All 74 knowledge files categorized and explained
Factory Components Reference	Factory's own 12 agents and 36 skills (plus PM subsystem)
Generated Output Reference	What gets generated: structure, formats, examples

Guides and Tutorials

Document	Description
Usage Guide	Detailed usage instructions and examples
Team Workshop Guide	Complete facilitator's manual for team workshops
Example Walkthroughs	Complete end-to-end examples with real stacks
Layered Architecture	5-layer architecture guide
Onboarding Concept	Full implementation blueprint
Extension Guide	How to extend the factory with new blueprints, patterns, and skills
Formal Verification	Lean 4 proofs, attestation, and trust chain documentation
Agent Society Verification	Dynamic multi-agent verification architecture design
Society Usage Guide	Complete usage guide for lib/society/ module
Testing Guide	Test suite documentation and testing practices
SAP Grounding Design	SAP-specific grounding architecture and MCP integration

Research Paper Series

Comprehensive academic documentation of the methodology (~150 pages, CC0 licensed):

Paper	Description
Axiom-Based Agent Architecture	Core methodology: 5-layer system, axioms A1-A10, derivation rules
Sacred Psychology in Software Engineering	Psychological enforcement, philosophical techniques
Constitutional AI - Convergent Discovery	Comparison with Anthropic Constitutional AI
Building Value-Aligned Agents	Practical step-by-step implementation guide
Future of Value-Aligned AI	Synthesis, unified framework, recommendations
Architecture Diagrams	Visual diagrams of all architectures

Contributing

Fork the repository
Create a feature branch
Add patterns, blueprints, or skills
Test generation
Submit pull request

Keywords

cursor-ide ai-agents llm-automation code-generation agent-orchestration meta-programming project-scaffolding development-workflow mcp-servers ai-assisted-development cursor-rules agent-factory skill-composition knowledge-management prompt-engineering

Categories:

AI Development Tools: Antigravity IDE agent system generator, LLM behavior configuration- Code Generation: Project scaffolding, template-based generation, multi-stack support
Agent Architecture: Agent/skill composition patterns, knowledge-grounded workflows
Enterprise Integration: SAP, Jira, Confluence, GitHub via MCP servers

Acknowledgements & Inspirations

This project incorporates ideas and patterns from several valuable sources:

Source	Contribution
Anthropic	Constitutional AI research demonstrating convergent discovery with our axiom-based approach. The Claude "soul document" (January 2026) and Model Context Protocol (MCP) specification.
Antigravity	AI-native IDE whose agent mode and skills features enable this entire ecosystem.
Augmented Coding Patterns	Active Partner, Check Alignment, Chain of Small Steps, and other AI collaboration patterns. Created by Lada Kesseler, Nitsan Avni, Ivett Ordog, Llewellyn Falco, and contributors.
Leon Chlon	Inspiration for the Strawberry Verification skill - information-theoretic approach to hallucination detection in AI outputs.
ai-dev-agent	Pedagogical toolkit for AI agent systems that inspired the layered architecture and methodology integration concepts.
Ancient Wisdom & Philosophy	Taoist philosophy (Wu Wei response protocol), Hilbert's axiomatic method (5-layer architecture foundation), and sacred value psychology research (Haidt, Tetlock).

For complete academic references, see docs/research/REFERENCES.md.

License

MIT License

Antigravity Agent Factory v0.1.0
Meta-system for generating Antigravity AI agent development systems
Now with 5-layer architecture: Integrity -> Purpose -> Principles -> Methodology -> Technical
50+ MCP servers across 6 categories with starter packs and role-based recommendations
Formally verified axiom compliance with Lean 4 mathematical proofs
Agent Society Verification implementation for dynamic multi-agent systems

Category	Servers
Core	filesystem, git, memory, time, fetch, brave-search, sequentialthinking
Code	github, gitlab, sentry, playwright, deepwiki, sap-documentation
Data	postgresql, mongodb, bigquery, snowflake, pinecone, chromadb
Cloud	docker, terraform, pulumi, datadog
Collab	atlassian, linear, notion, slack, figma
AI/ML	huggingface, mlflow, langgraph, ollama, neo4j

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
.agent		.agent
.github/workflows		.github/workflows
cli		cli
diagrams		diagrams
docs		docs
knowledge		knowledge
lib		lib
proofs		proofs
schemas		schemas
scripts		scripts
tests		tests
.agentrules		.agentrules
.gitattributes		.gitattributes
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CATALOG.md		CATALOG.md
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
pytest.ini		pytest.ini
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt

License

gitwalter/antigravity-agent-factory

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Antigravity Agent Factory

Welcome! Whether you're a solo developer exploring new possibilities or a team building something meaningful together, we're here to help you create an AI development environment grounded in purpose, principles, and care.

Quick Start Options

Option 1: Instant Demo (5 minutes)

Overview

Overview

The 5 Layers (+ Foundation)

What You Get

Layer 0: Integrity Guardian

Memory System

Formal Verification System

Agent Society Verification

Knowledge Evolution System

Features

Update Modes

Quick Usage

Factory -> Generated Project Updates

Architecture Diagrams

Project Structure

Available Blueprints

Python & AI

Starter (Beginner-Friendly)

Blockchain & Trading

Web & Frontend

Enterprise & Backend

SAP

Team Workshop System

Axiom 0: Love, Truth, and Beauty

Workshop Series Overview

Game Library

Team Size Adaptations

Factory Agents

Available Pattern Agents

Factory Skills

Available Pattern Skills

Agent vs Skill Relationship

Layered Requirements Gathering

Depth Options

Pre-Phase: Layer 0 - Axiom Configuration

Phase 0: Layer 1 - Purpose Definition

Phases 0.6-0.7: Layers 2-3 (Standard+)

Phases 0.8-0.9: Enforcement & Practices (Comprehensive)

Phases 1-5: Layer 4 - Technical

Generated Project Structure

Key Generated Files

MCP Server Integration

Starter Packs

Categories

Quick Start

Configuration File Format

Extending the Factory

Adding New Blueprints

Adding New Patterns

Adding New Skills

Development

Requirements

Installing Development Dependencies

Running Tests

Test Suite Structure

Manual CLI Testing

Testing Generation

Continuous Integration

Documentation

Comprehensive Reference

Guides and Tutorials

Research Paper Series

Contributing

Keywords

Acknowledgements & Inspirations

License

About

Topics

Resources

License

Packages