API Logic Server

Create instant microservices from your database or natural language prompt - working API + Admin App + Logic in 5 seconds, with 40X code reduction through declarative rules.

Create instant microservices (MCP-enabled API + Admin App + Business Logic) from your database or a natural language prompt:

ApiLogicServer create --project_name=my_app --db_url=mysql://...

⚡ Quick Start (2 minutes)

# Install (Python 3.10-3.13)
python -m venv venv                      # windows: python -m venv venv
source venv/bin/activate                 # windows: venv\Scripts\activate
python -m pip install ApiLogicServer

# Start the Manager (opens readme with instructions)
genai-logic start

# Create project from your database (or use sample)
genai-logic create --project_name=my_app --db_url=

# Run it
cd my_app
python api_logic_server_run.py

What you get in 5 seconds:

🚀 JSON:API - Endpoints for every table with filtering, pagination, related data
📱 Instant Admin App UI - Multi-page React app with automatic joins (runs immediately)
🎨 Custom Apps via API - Use the API with your favorite vibe tools (Cursor, Bolt, Lovable, etc.) OR leverage GenAI-Logic training for Copilot to build custom UIs (see this article)
🧠 Logic Engine - Spreadsheet-like rules (40X more concise than code)
🔐 Security - Row-level authorization, JWT authentication
🐳 Docker-ready - Pre-configured containers for deployment

Mix and match: Use the instant Admin App for backoffice tasks AND build custom UIs with vibe tools for advanced features like cards and maps - all powered by the same logic-enabled API.

📖 8-minute video demo | 📚 Full Documentation | 🏠 Home

🎯 Why API Logic Server?

Built on 40+ years of proven technology (Wang PACE → Versata → API Logic Server):

6,000+ production deployments validated the declarative approach
Fortune 500 companies relied on this architecture for mission-critical systems
Now open source and modernized for cloud-native Python/React stack

The Problem It Solves

Traditional Approach	API Logic Server
Weeks to create API endpoints	5 seconds (automated)
200+ lines of update logic code	5 rules (40X reduction - to see an A/B Comparision, click here
Manual screen painting	Automated from data model, plus vibe-enabled custom UIs
Hard to integrate systems	MCP-enabled APIs
Opaque procedural code	Declarative living documentation

💡 When to Use API Logic Server

✅ Ideal Use Cases

API Logic Server excels at data-centric business applications where the complexity is in multi-table calculations, constraints, and derivations:

🔌 Application Integration - Instant APIs for legacy databases (modernization without rewrite)
⚡ Rapid Prototyping - Working backend in minutes for validation
🤖 GenAI Backends - Natural language → working microservice
🏢 Backoffice Apps - Admin dashboards for data maintenance
🔗 Microservices - Decompose monoliths with instant services
📊 Business Rule Automation - Complex calculations, cascading updates, constraint checking
🔄 CRUD-Heavy Applications - Order management, inventory, customer systems
🏛️ Legacy Modernization - MCP-enable existing databases, create modern API layer while legacy apps continue running

Sweet Spot: Applications where business logic complexity >> UI complexity

⚠️ Not Recommended For

API Logic Server is optimized for data-centric business logic, but less suited for:

Real-time streaming systems - Use Kafka/Flink for high-throughput event processing
Complex UI/UX interactions - Works great as the backend, but not a UI framework
Machine learning pipelines - Use TensorFlow/PyTorch for ML workflows
Low-level system programming - Traditional languages better suited
Document/content management - Use specialized CMS platforms
Simple static websites - Overkill for basic content delivery

For these scenarios, traditional approaches are more appropriate. API Logic Server can still serve as the backend for hybrid architectures.

❓ Frequently Asked Questions

Q: How is this different from low-code platforms (Retool, OutSystems, Hasura)?

A: Unlike pure low-code platforms, API Logic Server generates standard Python projects you own, extend and deploy. Screen creation is by vibe tools rather than screen painting. Unlike API generators, it includes sophisticated multi-table logic automation (40X code reduction). Read detailed comparison →

Q: Isn't this just vendor lock-in?

A: It's free and open source. The declarative rules sit on top of standard (readable, version-controlled) Python — you can always drop down to procedural code. If you ever need to migrate away, you can either keep using the rules engine (it's just a library) or replace declarative rules with equivalent procedural code using standard SQLAlchemy events.

Q: Can I customize the generated app?

A: Absolutely. You can override the UI, extend APIs, and plug in your own logic — using standard Python, SQLAlchemy, and any Vibe tool. The generated project is a starting point, not a black box. See customization patterns →

Q: What happens when logic doesn't fit the declarative model?

A: The declarative engine handles over 95% of typical business logic (calculations, validations, cascading updates). For complex workflows, state machines, or external integrations, you write standard Python event handlers that coexist with declarative rules. The engine calls your code at the right time — no conflict, full extensibility.

Q: How long does it take developers to become productive?

A: Developers can start writing rules immediately using natural language, and the DSL syntax is intuitive. Understanding the engine's optimization strategies (pruning, chaining) takes a few days of practice. Most teams are fully productive within a week.

Q: What if I have questions or need help?

A: Join our Discord community for real-time help, check GitHub Discussions, or browse the comprehensive documentation.

→ More FAQs in detailed article

🏗️ Architecture

┌─────────────────────────────────────────────┐
│  Natural Language Prompt or Existing DB     │
└───────────────┬─────────────────────────────┘
                │ ApiLogicServer create
                ↓
┌─────────────────────────────────────────────┐
│         Generated Project                    │
├─────────────────────────────────────────────┤
│  📱 Admin App (React)                       │
│  🔌 JSON:API (SAFRS)                        │
│  🧠 Logic (Rules Engine)                    │
│  💾 ORM (SQLAlchemy)                        │
│  🔐 Security (JWT + Row-level)              │
└─────────────────────────────────────────────┘
                │
                ↓
        🐳 Docker Container

Technology Stack:

Python 3.10-3.13
Flask + SQLAlchemy
React + Material-UI
Docker + Docker Compose

📦 What Gets Created

my_app/
├── api/                    # JSON:API endpoints (for Admin App AND custom UIs)
│   ├── expose_api_models.py
│   └── customize_api.py    # Add custom endpoints
├── ui/admin/              # Instant Admin App (ready to run)
│   └── admin.yaml         # Declare UI behavior
├── logic/                 # Business Logic (enforced on ALL API calls)
│   └── declare_logic.py   # Spreadsheet-like rules
├── security/              # Authentication & Authorization
│   └── declare_security.py
├── database/              # SQLAlchemy models
│   └── models.py
├── tests/                 # BDD tests (Behave)
├── docs/
│   └── training/          # GenAI-Logic training for Copilot
└── devops/
    └── docker/            # Deployment configs

Two UI approaches (use both!):

Instant Admin App - Runs immediately for backoffice/internal users
Custom UIs - Build with vibe tools (Cursor, Bolt, etc.) OR use GenAI-Logic Copilot training to create React/Vue/Angular apps that consume the API

🚀 Key Features

1️⃣ Declarative Logic (40X More Concise)

Before (200+ lines of Python):

# Manual cascade updates, balance calculations, constraint checking...
# Complex dependency tracking, old_row comparisons...
# Missed corner cases: reassign order, change quantity, delete order...
# (Imagine 200 lines of procedural code here)

After (5 declarative rules):

Rule.constraint(derive=Customer.Balance <= Customer.CreditLimit)
Rule.sum(derive=Customer.Balance, as_sum_of=Order.AmountTotal, 
         where=lambda row: row.ShippedDate is None)
Rule.sum(derive=Order.AmountTotal, as_sum_of=OrderDetail.Amount)
Rule.formula(derive=OrderDetail.Amount, 
             as_expression=lambda row: row.Quantity * row.UnitPrice)
Rule.copy(derive=OrderDetail.UnitPrice, from_parent=Product.UnitPrice)

Rules automatically handle all dependencies:

✅ Chain up (OrderDetail → Order → Customer)
✅ Chain down (Product price change cascades)
✅ Old row comparisons (moved orders adjust both customers)
✅ All 9 use cases (add, update, delete, reassign...)

2️⃣ Self-Serve APIs

Consumers select their own attributes and related data - no custom API development required:

# Get customer with orders and order details
GET /api/Customer/ALFKI?include=OrderList,OrderList.OrderDetailList&fields[Customer]=CompanyName,Balance

# Filter and sort
GET /api/Customer?filter[Balance][$gt]=1000&sort=-Balance&page[size]=10

Features:

Filtering, sorting, pagination
Related data (automatic joins)
Optimistic locking
Swagger documentation
Logic enforcement on all updates

3️⃣ GenAI Integration

Create from Natural Language

genai-logic genai --using=prompt.txt

Example prompt:

Create a system for customers, orders, items and products.
Customer balance is sum of unshipped order totals.
Order total is sum of item amounts.
Item amount is quantity * unit price.
Copy product price to item unit price.
Constraint: customer balance cannot exceed credit limit.

Result: Working microservice with logic in 10 seconds!

AI-Assisted Development

Every created project includes:

🤖 Copilot instructions (.copilot-instructions.md)
📚 Training materials (docs/training/)
🔍 Logic from natural language (translate English → rules)

# Paste into Copilot: "Create logic for check credit"
# Copilot generates the 5 rules above!

4️⃣ Living Documentation

Automated test generation from your rules:

# Generate Behave tests from declared logic
genai-logic add-tests

# Run tests
behave

# Generate documentation
python behave_logic_report.py

Output: Complete traceability from requirements → tests → rules → execution trace.

🎬 Video Overview (8 min)

See how Microservice Automation creates and runs a microservice - a multi-page app and an API:

🤝 Contributing

We welcome contributions! Areas of interest:

Area	Current	Ideas for Contribution
API	JSON:API, Swagger	GraphQL, gRPC support
Deployment	Docker, Azure	AWS (ECS/Lambda), Kubernetes/Helm charts
Logic	Rules engine	Additional rule types, performance tuning
UI	Admin App (React)	Custom UI frameworks, mobile SDK
GenAI	Web version, CLI	Enhanced natural language understanding

See issues for current requests.

Development Setup

For development (extending API Logic Server itself):

# Clone and install dev version
git clone https://github.com/ApiLogicServer/ApiLogicServer-src.git
cd ApiLogicServer-src

# Install dependencies
python -m pip install -r requirements.txt

# Run build/test automation
python test/build_and_test.py

See dev installation docs for complete setup.

📚 Documentation

📖 Full Documentation - Comprehensive guides and reference
🚀 Quick Start Tutorial - 20-minute walkthrough
🧠 Logic Documentation - Rule types and patterns
🔐 Security Guide - Authentication and authorization
🐳 Deployment - Docker, Azure, containers
🔧 Architecture - How it works
❓ FAQs - Common questions
🐛 Issue Tracker - Bug reports and feature requests

📖 Key Documentation Pages

Getting Started

Express Install - Get running in 10 minutes
Manager Workspace - Project organization and readme
Tutorial - Complete walkthrough

Core Concepts

Logic: Why - The case for declarative logic
API Self-Serve - Consumer-driven APIs
Behave Testing - BDD test framework integration

Advanced Topics

Natural Language Logic - GenAI logic translation
Integration Patterns - Kafka, B2B, MCP
Database Changes - Schema evolution

🌟 Examples and Samples

See all sample projects

Sample	Description	Database
basic_demo	Check credit rules, Behave testing	SQLite (Northwind)
genai_demo	Created from natural language	SQLite
classicmodels	Sales, employees, offices	MySQL
chinook	Music store (artists, albums, tracks)	SQLite/PostgreSQL
postgres-nw	Northwind on PostgreSQL	PostgreSQL
sqlserver	Adventure Works	SQL Server

🏆 Technology Lineage

API Logic Server builds on proven technology with 40+ years of production validation:

Wang Labs PACE (1980s-1990s)

Inventor: Val Huber (Designer and Co-inventor with Ron Ross of declarative rules)
Scale: 6,000+ production deployments
Innovation: First commercial declarative business rules system
Platform: Minicomputers → Visual Basic

Versata (1990s-2010s)

Leadership: Val Huber (CTO)
Customers: Fortune 500 companies
Scale: $3.4 billion company (IPO: VSTA)
Innovation: Evolved rules for J2EE enterprise applications
Platform: Java Enterprise Edition

API Logic Server (2020-present)

Leadership: Val Huber (Architect and Lead Developer)
Innovation: Modern cloud-native Python/React implementation
Platform: Open source, Docker, microservices architecture
Unique: Combined with GenAI for natural language logic

The same declarative rules paradigm, proven at scale, now open source and AI-enhanced.

📊 Comparison: Frameworks vs Low Code vs API Logic Server

Aspect	Frameworks (Django, Flask)	Low Code (Retool, Appsmith)	API Logic Server
API Creation	Manual (weeks)	Limited/proprietary	✅ Automated (5 sec)
Logic Automation	❌ Manual code	❌ Limited	✅ Rules (40X)
App Automation	❌ Manual	✅ GUI builder	✅ From data model
Customization	✅ Full Python	⚠️ Proprietary	✅ Python + Rules
IDE	✅ Standard tools	❌ Proprietary	✅ VSCode, PyCharm
Deployment	✅ Any platform	⚠️ Vendor lock-in	✅ Docker, any cloud
GenAI Integration	❌ None	⚠️ Limited	✅ Full (DB + Logic)
Testing	Manual	Limited	✅ Auto-generated

💾 Supported Databases

SQLite (included samples)
PostgreSQL
MySQL / MariaDB
SQL Server
Oracle
Any SQLAlchemy-supported database

🔐 Security Features

Authentication: SQL-based, Keycloak, or custom providers
Authorization: Role-based grants with row-level filtering
Multi-tenant: Global filters based on user attributes
JWT Tokens: Standard authentication flow
Swagger Security: Integrated with API documentation

See Security Overview

📄 License

MIT License - see LICENSE file

🙏 Acknowledgments

Built on the shoulders of giants:

Technology Pioneers:

Wang Labs PACE (1980s) - Original declarative rules concept (Val Huber, designer/co-inventor)
Versata (1990s-2010s) - $3.4B company, Fortune 500 deployments (Val Huber, CTO)
Ron Ross - Co-inventor of declarative rules paradigm

Open Source Foundation:

Flask - Web framework
SQLAlchemy - ORM layer
SAFRS - JSON:API implementation
React - Admin App UI
Material-UI - Component library
Behave - BDD testing framework

Community:

All contributors who have submitted issues, PRs, and feedback
The Python and React ecosystems

📝 In-Depth Articles

These Medium articles provide comprehensive context on architecture, use cases, and the reasoning behind API Logic Server:

Article	Topic	Key Insights
Welcome to GenAI-Logic	Vision & Overview	Big picture: Does GenAI deliver business agility? Start here for context.
Declarative GenAI Architecture	Technical Deep Dive	NL → DSL → Engines pattern, FrankenCode problem, includes FAQ section
Declarative Logic: Living in a Procedural World	Architecture Philosophy	How declarative rules work in Python, the paradox resolved
Living With Logic in the Enterprise	Production Patterns	Debugging, testing, performance, customization in real deployments
Business User / IT Collaboration	Team Dynamics	How logic acts as a contract between business and IT
Vibe an MCP Server	MCP Integration	Creating MCP-enabled services with natural language
Probabilistic and Deterministic Logic	AI + Rules	Engineering reliability into agentic systems (AI at runtime + rules)
Enterprise Vibe Automation	GenAI Workflows	Full-stack automation from prompts

These articles represent significant research and real-world experience - they address common questions, architectural decisions, and lessons learned from 40+ years of declarative technology evolution.

📞 Connect

🏠 Home: genai-logic.com
💬 Discord: Join our community - Get help and discuss
🐛 Issues: GitHub Issues
� Discussions: GitHub Discussions
� Documentation: apilogicserver.github.io/Docs
📝 Blog: Medium Articles - Conceptual background and insights

📖 Recommended Reading

Start with the welcome article to understand the vision and architecture:

Welcome to GenAI Logic - Introduction and core concepts

⭐ Star this repo if you find it useful!

Made with ❤️ by the API Logic Server team

Name		Name	Last commit message	Last commit date
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.github		.github
.idea		.idea
.vscode		.vscode
api_logic_server_cli		api_logic_server_cli
bin		bin
docker		docker
examples		examples
gen_ai		gen_ai
internal_dev		internal_dev
nginx		nginx
tests		tests
.bashrc		.bashrc
.dockerignore		.dockerignore
.env		.env
.gitignore		.gitignore
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
__init__.py		__init__.py
docker-compose.yml		docker-compose.yml
license.json		license.json
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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API Logic Server

⚡ Quick Start (2 minutes)

🎯 Why API Logic Server?

The Problem It Solves

💡 When to Use API Logic Server

✅ Ideal Use Cases

⚠️ Not Recommended For

❓ Frequently Asked Questions

🏗️ Architecture

📦 What Gets Created

🚀 Key Features

1️⃣ Declarative Logic (40X More Concise)

2️⃣ Self-Serve APIs

3️⃣ GenAI Integration

Create from Natural Language

AI-Assisted Development

4️⃣ Living Documentation

🎬 Video Overview (8 min)

🤝 Contributing

Development Setup

📚 Documentation

📖 Key Documentation Pages

Getting Started

Core Concepts

Advanced Topics

🌟 Examples and Samples

🏆 Technology Lineage

Wang Labs PACE (1980s-1990s)

Versata (1990s-2010s)

API Logic Server (2020-present)

📊 Comparison: Frameworks vs Low Code vs API Logic Server

💾 Supported Databases

🔐 Security Features

📄 License

🙏 Acknowledgments

📝 In-Depth Articles

📞 Connect

📖 Recommended Reading

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